Online_PubMed_authorcountries00former_countries00author_name00world_map0map_affiliationscountries0former_countries0publications0world_map00net.sf.taverna.t2.activitiesdataflow-activity1.5net.sf.taverna.t2.activities.dataflow.DataflowActivitynet.sf.taverna.t2.coreworkflowmodel-impl1.5net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Parallelize 1 net.sf.taverna.t2.coreworkflowmodel-impl1.5net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.ErrorBouncenet.sf.taverna.t2.coreworkflowmodel-impl1.5net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Failovernet.sf.taverna.t2.coreworkflowmodel-impl1.5net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Retry 1.0 1000 5000 0 net.sf.taverna.t2.coreworkflowmodel-impl1.5net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Invokeget_publicationsauthor_name0publications00net.sf.taverna.t2.activitiesdataflow-activity1.5net.sf.taverna.t2.activities.dataflow.DataflowActivitynet.sf.taverna.t2.coreworkflowmodel-impl1.5net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Parallelize 1 net.sf.taverna.t2.coreworkflowmodel-impl1.5net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.ErrorBouncenet.sf.taverna.t2.coreworkflowmodel-impl1.5net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Failovernet.sf.taverna.t2.coreworkflowmodel-impl1.5net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Retry 1.0 1000 5000 0 net.sf.taverna.t2.coreworkflowmodel-impl1.5net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Invokemap_affiliationscountriescountriesmap_affiliationsformer_countriesformer_countriesmap_affiliationspublicationsget_publicationspublicationsget_publicationsauthor_nameauthor_nameworld_mapmap_affiliationsworld_map 2c3f0cf0-f2b8-45ed-bb1c-d4dc5c7821f7 2015-09-07 11:55:07.339 UTC 9b2a982c-77e4-495e-842c-19472f153c6c 2015-06-19 11:59:01.829 UTC fbf0ce58-9d39-445b-918c-61bdab1417d9 2015-06-19 12:19:21.654 UTC 136a1e11-4815-4b35-8847-06c9c7fb7260 2015-06-19 12:29:55.987 UTC ebfcf0a0-0d4a-42d3-870f-32c5b83584b8 2015-09-07 11:52:19.85 UTC 615eccd8-9a42-4654-9a3b-48b64afdada9 2015-06-19 12:25:17.454 UTC Online PubMed author search and geographic affiliation mapping 2015-08-19 09:00:30.360 UTC f7791444-71af-4176-9e34-c24839546ddf 2015-06-19 12:15:32.506 UTC 8dacad8c-2b32-4089-8781-4abddd13e527 2015-06-22 13:27:51.310 UTC 4c2a2e7e-7dd7-4206-b201-00a8bbd65320 2015-06-19 14:42:37.360 UTC 02b6f148-b0a4-401f-b0e7-7dbb4d05e49d 2015-06-19 12:31:22.727 UTC b1c00178-b829-4ac6-a0d8-568cc24d8b95 2015-06-19 12:18:19.581 UTC 0d997592-8583-4327-a0f4-e8373032f1ba 2015-06-22 13:24:32.143 UTC 06b5fb98-eb5f-43be-9248-84a39a0d7dfa 2015-08-19 08:59:44.666 UTC dde636be-e294-4b68-a5b6-5d313f0f504e 2015-06-22 13:21:21.421 UTC c8021247-19c5-4e31-a344-609eae205a78 2015-06-19 12:23:22.312 UTC 98d5b9d3-01e8-4688-9e47-10f211b8f89e 2015-08-19 09:00:31.598 UTC 29cc8027-52c6-4ea6-818d-12c862a87758 2015-06-26 09:27:21.121 UTC 81bbf471-b4d7-42f9-a484-4a39de81c664 2015-06-19 12:13:55.413 UTC 7e307d86-38eb-43c7-844b-c9e23c655020 2015-09-07 11:50:27.565 UTC a51334fb-8274-4458-959f-af9cbbf8bddd 2015-06-19 14:43:38.400 UTC Magnus Palmblad Arzu Tugce Guler 2015-08-19 08:56:31.749 UTC This workflow retrieves bibliographic data for a single author using the PMC Europe RESTful Web service and visualizes the geographic distribution of this author's and their co-authors' geographic distribution using the rworldmap package. This is version 2.0 of this workflow, incorporating changes to the Web service allowing up to 1,000 records to be retrieved at once, using the pageSize parameter. 2015-09-07 11:54:44.880 UTC 7db2bf92-f3fc-44b9-833c-cabf5192bf5e 2015-06-19 13:46:48.541 UTC 65a595fa-a93d-48fa-a4e0-0980091dae08 2015-09-07 11:54:46.459 UTC Workflow27countries00former_countries00publications00world_map0count_affiliations_by_countrycountry_code1country_name1affiliations1count_list11country_list11net.sf.taverna.t2.activitiesbeanshell-activity1.5net.sf.taverna.t2.activities.beanshell.BeanshellActivity affiliations 1 text/plain java.lang.String true country_name 1 text/plain java.lang.String true country_code 1 text/plain java.lang.String true country_list 1 1 count_list 1 1 workflow net.sf.taverna.t2.coreworkflowmodel-impl1.5net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Parallelize 1 net.sf.taverna.t2.coreworkflowmodel-impl1.5net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.ErrorBouncenet.sf.taverna.t2.coreworkflowmodel-impl1.5net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Failovernet.sf.taverna.t2.coreworkflowmodel-impl1.5net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Retry 1.0 1000 5000 0 net.sf.taverna.t2.coreworkflowmodel-impl1.5net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Invokeimport_country_listfileurl0A11B11net.sf.taverna.t2.activitiesspreadsheet-import-activity1.5net.sf.taverna.t2.activities.spreadsheet.SpreadsheetImportActivity 0 1 0 -1 true false false EMPTY_STRING PORT_PER_COLUMN , net.sf.taverna.t2.coreworkflowmodel-impl1.5net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Parallelize 1 net.sf.taverna.t2.coreworkflowmodel-impl1.5net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.ErrorBouncenet.sf.taverna.t2.coreworkflowmodel-impl1.5net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Failovernet.sf.taverna.t2.coreworkflowmodel-impl1.5net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Retry 1.0 1000 5000 0 net.sf.taverna.t2.coreworkflowmodel-impl1.5net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Invokemap_former_countriesmeta_count_list1meta_country_list1former_countries_list1current_countries_list1count_list11country_list11net.sf.taverna.t2.activitiesbeanshell-activity1.5net.sf.taverna.t2.activities.beanshell.BeanshellActivity meta_count_list 1 text/plain java.lang.String true meta_country_list 1 text/plain java.lang.String true former_countries_list 1 text/plain java.lang.String true current_countries_list 1 text/plain java.lang.String true count_list 1 1 country_list 1 1 workflow net.sf.taverna.t2.coreworkflowmodel-impl1.5net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Parallelize 1 net.sf.taverna.t2.coreworkflowmodel-impl1.5net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.ErrorBouncenet.sf.taverna.t2.coreworkflowmodel-impl1.5net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Failovernet.sf.taverna.t2.coreworkflowmodel-impl1.5net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Retry 1.0 1000 5000 0 net.sf.taverna.t2.coreworkflowmodel-impl1.5net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Invokeimport_former_countries_listfileurl0A11B11net.sf.taverna.t2.activitiesspreadsheet-import-activity1.5net.sf.taverna.t2.activities.spreadsheet.SpreadsheetImportActivity 0 1 0 -1 true false false EMPTY_STRING PORT_PER_COLUMN , net.sf.taverna.t2.coreworkflowmodel-impl1.5net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Parallelize 1 net.sf.taverna.t2.coreworkflowmodel-impl1.5net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.ErrorBouncenet.sf.taverna.t2.coreworkflowmodel-impl1.5net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Failovernet.sf.taverna.t2.coreworkflowmodel-impl1.5net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Retry 1.0 1000 5000 0 net.sf.taverna.t2.coreworkflowmodel-impl1.5net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Invokedraw_world_mapcounts1countries1output00net.sf.taverna.t2.activitiesrshell-activity1.5net.sf.taverna.t2.activities.rshell.RshellActivity countries 1 false counts 1 false output 0 0 false localhost 6311 false false countries STRING_LIST counts INTEGER_LIST output PNG_FILE net.sf.taverna.t2.coreworkflowmodel-impl1.5net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Parallelize 1 net.sf.taverna.t2.coreworkflowmodel-impl1.5net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.ErrorBouncenet.sf.taverna.t2.coreworkflowmodel-impl1.5net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Failovernet.sf.taverna.t2.coreworkflowmodel-impl1.5net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Retry 1.0 1000 5000 0 net.sf.taverna.t2.coreworkflowmodel-impl1.5net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Invokeget_authors_affiliationsxml_text0nodelist11net.sf.taverna.t2.activitiesxpath-activity1.5net.sf.taverna.t2.activities.xpath.XPathActivity <?xml version="1.0" encoding="UTF-8"?> <return><version>4.1</version><hitCount>54</hitCount><request><queryString>auth:"Magnus Palmblad" sort_date:y</queryString><resultType>CORE</resultType><offSet>0</offSet><synonym>false</synonym></request><resultList><result><id>25873627</id><source>MED</source><pmid>25873627</pmid><DOI>10.1093/nar/gkv281</DOI><title>Assessing the translational landscape of myogenic differentiation by ribosome profiling.</title><authorString>de Klerk E, Fokkema IF, Thiadens KA, Goeman JJ, Palmblad M, den Dunnen JT, von Lindern M, 't Hoen PA.</authorString><authorList><author><fullName>de Klerk E</fullName><firstName>Eleonora</firstName><lastName>de Klerk</lastName><initials>E</initials><affiliation>Department of Human Genetics, Leiden University Medical Center, Postzone S4-P, PO Box 9600, 2300 RC Leiden, The Netherlands.</affiliation></author><author><fullName>Fokkema IF</fullName><firstName>Ivo F A C</firstName><lastName>Fokkema</lastName><initials>IF</initials><affiliation>Department of Human Genetics, Leiden University Medical Center, Postzone S4-P, PO Box 9600, 2300 RC Leiden, The Netherlands.</affiliation></author><author><fullName>Thiadens KA</fullName><firstName>Klaske A M H</firstName><lastName>Thiadens</lastName><initials>KA</initials><affiliation>Department of Hematopoiesis, Sanquin Research and Landsteiner Laboratory, AMC/UvA, 1066CX 125 Amsterdam, The Netherlands.</affiliation></author><author><fullName>Goeman JJ</fullName><firstName>Jelle J</firstName><lastName>Goeman</lastName><initials>JJ</initials><affiliation>Biostatistics, Department for Health Evidence, Radboud University Medical Center, Postzone 133, P.O. Box 9101, 6500 HB Nijmegen, The Netherlands.</affiliation></author><author><fullName>Palmblad M</fullName><firstName>Magnus</firstName><lastName>Palmblad</lastName><initials>M</initials><affiliation>Center for Proteomics and Metabolomics, Leiden University Medical Center, 2300 RC Leiden, The Netherlands.</affiliation></author><author><fullName>den Dunnen JT</fullName><firstName>Johan T</firstName><lastName>den Dunnen</lastName><initials>JT</initials><affiliation>Department of Human Genetics, Leiden University Medical Center, Postzone S4-P, PO Box 9600, 2300 RC Leiden, The Netherlands.</affiliation></author><author><fullName>von Lindern M</fullName><firstName>Marieke</firstName><lastName>von Lindern</lastName><initials>M</initials><affiliation>Department of Hematopoiesis, Sanquin Research and Landsteiner Laboratory, AMC/UvA, 1066CX 125 Amsterdam, The Netherlands.</affiliation></author><author><fullName>'t Hoen PA</fullName><firstName>Peter A C</firstName><lastName>'t Hoen</lastName><initials>PA</initials><affiliation>Department of Human Genetics, Leiden University Medical Center, Postzone S4-P, PO Box 9600, 2300 RC Leiden, The Netherlands P.A.C._t_Hoen@lumc.nl.</affiliation></author></authorList><journalInfo><issue>9</issue><volume>43</volume><journalIssueId>2286225</journalIssueId><dateOfPublication>2015 May</dateOfPublication><monthOfPublication>5</monthOfPublication><yearOfPublication>2015</yearOfPublication><printPublicationDate>2015-05-01</printPublicationDate><journal><title>Nucleic acids research</title><ISOAbbreviation>Nucleic Acids Res.</ISOAbbreviation><medlineAbbreviation>Nucleic Acids Res</medlineAbbreviation><NLMid>0411011</NLMid><ISSN>0305-1048</ISSN><ESSN>1362-4962</ESSN></journal></journalInfo><pageInfo>4408-4428</pageInfo><abstractText>The formation of skeletal muscles is associated with drastic changes in protein requirements known to be safeguarded by tight control of gene transcription and mRNA processing. The contribution of regulation of mRNA translation during myogenesis has not been studied so far. We monitored translation during myogenic differentiation of C2C12 myoblasts, using a simplified protocol for ribosome footprint profiling. Comparison of ribosome footprints to total RNA showed that gene expression is mostly regulated at the transcriptional level. However, a subset of transcripts, enriched for mRNAs encoding for ribosomal proteins, was regulated at the level of translation. Enrichment was also found for specific pathways known to regulate muscle biology. We developed a dedicated pipeline to identify translation initiation sites (TISs) and discovered 5333 unannotated TISs, providing a catalog of upstream and alternative open reading frames used during myogenesis. We identified 298 transcripts with a significant switch in TIS usage during myogenesis, which was not explained by alternative promoter usage, as profiled by DeepCAGE. Also these transcripts were enriched for ribosomal protein genes. This study demonstrates that differential mRNA translation controls protein expression of specific subsets of genes during myogenesis. Experimental protocols, analytical workflows, tools and data are available through public repositories (http://lumc.github.io/ribosome-profiling-analysis-framework/).</abstractText><affiliation>Department of Human Genetics, Leiden University Medical Center, Postzone S4-P, PO Box 9600, 2300 RC Leiden, The Netherlands.</affiliation><language>eng</language><pubModel>Print-Electronic</pubModel><pubTypeList><pubType>Journal Article</pubType></pubTypeList><subsetList><subset><code>IM</code><name>Index Medicus</name></subset></subsetList><fullTextUrlList><fullTextUrl><availability>Subscription required</availability><availabilityCode>S</availabilityCode><documentStyle>doi</documentStyle><site>DOI</site><url>http://dx.doi.org/10.1093/nar/gkv281</url></fullTextUrl></fullTextUrlList><inEPMC>N</inEPMC><inPMC>N</inPMC><citedByCount>0</citedByCount><hasReferences>N</hasReferences><hasTextMinedTerms>N</hasTextMinedTerms><hasDbCrossReferences>N</hasDbCrossReferences><hasLabsLinks>N</hasLabsLinks><hasTMAccessionNumbers>N</hasTMAccessionNumbers><dateOfCreation>2015-05-20</dateOfCreation><electronicPublicationDate>2015-04-14</electronicPublicationDate><firstPublicationDate>2015-04-14</firstPublicationDate><luceneScore>709.1525</luceneScore></result><result><id>25745805</id><source>MED</source><pmid>25745805</pmid><DOI>10.1021/ac5040314</DOI><title>Bibliometric mapping: eight decades of analytical chemistry, with special focus on the use of mass spectrometry.</title><authorString>Waaijer CJ, Palmblad M.</authorString><authorList><author><fullName>Waaijer CJ</fullName><firstName>Cathelijn J F</firstName><lastName>Waaijer</lastName><initials>CJ</initials><affiliation>†Centre for Science and Technology Studies, Faculty of Social and Behavioural Sciences, Leiden University, P.O. Box 905, 2300 AX Leiden, The Netherlands.</affiliation></author><author><fullName>Palmblad M</fullName><firstName>Magnus</firstName><lastName>Palmblad</lastName><initials>M</initials><affiliation>‡Center for Proteomics and Metabolomics, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands.</affiliation></author></authorList><journalInfo><issue>9</issue><volume>87</volume><journalIssueId>2281065</journalIssueId><dateOfPublication>2015 May</dateOfPublication><monthOfPublication>5</monthOfPublication><yearOfPublication>2015</yearOfPublication><printPublicationDate>2015-05-01</printPublicationDate><journal><title>Analytical chemistry</title><ISOAbbreviation>Anal. Chem.</ISOAbbreviation><medlineAbbreviation>Anal Chem</medlineAbbreviation><NLMid>0370536</NLMid><ISSN>0003-2700</ISSN><ESSN>1520-6882</ESSN></journal></journalInfo><pageInfo>4588-4596</pageInfo><abstractText>In this Feature we use automatic bibliometric mapping tools to visualize the history of analytical chemistry from the 1920s until the present. In particular, we have focused on the application of mass spectrometry in different fields. The analysis shows major shifts in research focus and use of mass spectrometry. We conclude by discussing the application of bibliometric mapping and visualization tools in analytical chemists' research.</abstractText><affiliation>†Centre for Science and Technology Studies, Faculty of Social and Behavioural Sciences, Leiden University, P.O. Box 905, 2300 AX Leiden, The Netherlands.</affiliation><language>eng</language><pubModel>Print-Electronic</pubModel><pubTypeList><pubType>Journal Article</pubType></pubTypeList><subsetList><subset><code>IM</code><name>Index Medicus</name></subset></subsetList><fullTextUrlList><fullTextUrl><availability>Subscription required</availability><availabilityCode>S</availabilityCode><documentStyle>doi</documentStyle><site>DOI</site><url>http://dx.doi.org/10.1021/ac5040314</url></fullTextUrl></fullTextUrlList><inEPMC>N</inEPMC><inPMC>N</inPMC><citedByCount>0</citedByCount><hasReferences>N</hasReferences><hasTextMinedTerms>N</hasTextMinedTerms><hasDbCrossReferences>N</hasDbCrossReferences><hasLabsLinks>N</hasLabsLinks><hasTMAccessionNumbers>N</hasTMAccessionNumbers><dateOfCreation>2015-05-05</dateOfCreation><electronicPublicationDate>2015-03-26</electronicPublicationDate><firstPublicationDate>2015-03-26</firstPublicationDate><luceneScore>1326.7339</luceneScore></result><result><id>25719938</id><source>MED</source><pmid>25719938</pmid><DOI>10.1021/ac504708y</DOI><title>Top-down MALDI-in-source decay-FTICR mass spectrometry of isotopically resolved proteins.</title><authorString>Nicolardi S, Switzar L, Deelder AM, Palmblad M, van der Burgt YE.</authorString><authorList><author><fullName>Nicolardi S</fullName><firstName>Simone</firstName><lastName>Nicolardi</lastName><initials>S</initials><affiliation>Center for Proteomics and Metabolomics, Leiden University Medical Center (LUMC), PO Box 9600, 2300 RC, Leiden, The Netherlands.</affiliation></author><author><fullName>Switzar L</fullName><firstName>Linda</firstName><lastName>Switzar</lastName><initials>L</initials><affiliation>Center for Proteomics and Metabolomics, Leiden University Medical Center (LUMC), PO Box 9600, 2300 RC, Leiden, The Netherlands.</affiliation></author><author><fullName>Deelder AM</fullName><firstName>André M</firstName><lastName>Deelder</lastName><initials>AM</initials><affiliation>Center for Proteomics and Metabolomics, Leiden University Medical Center (LUMC), PO Box 9600, 2300 RC, Leiden, The Netherlands.</affiliation></author><author><fullName>Palmblad M</fullName><firstName>Magnus</firstName><lastName>Palmblad</lastName><initials>M</initials><affiliation>Center for Proteomics and Metabolomics, Leiden University Medical Center (LUMC), PO Box 9600, 2300 RC, Leiden, The Netherlands.</affiliation></author><author><fullName>van der Burgt YE</fullName><firstName>Yuri E M</firstName><lastName>van der Burgt</lastName><initials>YE</initials><affiliation>Center for Proteomics and Metabolomics, Leiden University Medical Center (LUMC), PO Box 9600, 2300 RC, Leiden, The Netherlands.</affiliation></author></authorList><journalInfo><issue>6</issue><volume>87</volume><journalIssueId>2267259</journalIssueId><dateOfPublication>2015 Mar</dateOfPublication><monthOfPublication>3</monthOfPublication><yearOfPublication>2015</yearOfPublication><printPublicationDate>2015-03-01</printPublicationDate><journal><title>Analytical chemistry</title><ISOAbbreviation>Anal. Chem.</ISOAbbreviation><medlineAbbreviation>Anal Chem</medlineAbbreviation><NLMid>0370536</NLMid><ISSN>0003-2700</ISSN><ESSN>1520-6882</ESSN></journal></journalInfo><pageInfo>3429-3437</pageInfo><abstractText>An accurate mass measurement of a known protein provides information on potential amino acid deletions and post-translational modifications. Although this field is dominated by strategies based on electrospray ionization, mass spectrometry (MS) methods using matrix-assisted laser desorption/ionization (MALDI) have the advantage of yielding predominantly singly charged precursor ions, thus avoiding peak overlap from different charge states of multiple species. Such MALDI-MS methods require mass measurement at ultrahigh resolution, which is provided by Fourier transform ion cyclotron resonance (FTICR) mass analyzers. Recently, using a MALDI-FTICR-MS platform equipped with a 15 T magnet, we reported on the mass analysis of intact human serum peptides and small proteins with isotopic resolution up to ∼15 kDa and identified new proteoforms from an accurate measurement of mass distances. In the current study, we have used this FTICR system after an upgrade with a novel dynamically harmonized ICR cell, i.e., ParaCell, for mapping isotopically resolved intact proteins up to about 17 kDa and performed top-down MALDI in-source decay (ISD) analysis. Standard proteins myoglobin (m/z-value 16,950) and ribonuclease B (m/z-value 14,900) were measured with resolving powers of 62,000 and 61,000, respectively. Furthermore, it will be shown that (singly charged) MALDI-ISD fragment ions can be measured at isotopic resolution up to m/z-value 12,000 (e.g., resolving power 39,000 at m/z-value 12,000) providing more reliable identifications. Moreover, examples are presented of pseudo-MS(3) experiments on ISD fragment ions from RNase B by collisional-induced dissociation (CID).</abstractText><affiliation>Center for Proteomics and Metabolomics, Leiden University Medical Center (LUMC), PO Box 9600, 2300 RC, Leiden, The Netherlands.</affiliation><language>eng</language><pubModel>Print-Electronic</pubModel><pubTypeList><pubType>Journal Article</pubType></pubTypeList><subsetList><subset><code>IM</code><name>Index Medicus</name></subset></subsetList><fullTextUrlList><fullTextUrl><availability>Subscription required</availability><availabilityCode>S</availabilityCode><documentStyle>doi</documentStyle><site>DOI</site><url>http://dx.doi.org/10.1021/ac504708y</url></fullTextUrl></fullTextUrlList><inEPMC>N</inEPMC><inPMC>N</inPMC><citedByCount>0</citedByCount><hasReferences>N</hasReferences><hasTextMinedTerms>N</hasTextMinedTerms><hasDbCrossReferences>N</hasDbCrossReferences><hasLabsLinks>N</hasLabsLinks><hasTMAccessionNumbers>N</hasTMAccessionNumbers><dateOfCreation>2015-03-18</dateOfCreation><electronicPublicationDate>2015-03-05</electronicPublicationDate><firstPublicationDate>2015-03-05</firstPublicationDate><luceneScore>863.5478</luceneScore></result><result><id>24780057</id><source>MED</source><pmid>24780057</pmid><DOI>10.1021/ac500383c</DOI><title>Structural analysis of an intact monoclonal antibody by online electrochemical reduction of disulfide bonds and Fourier transform ion cyclotron resonance mass spectrometry.</title><authorString>Nicolardi S, Deelder AM, Palmblad M, van der Burgt YE.</authorString><authorList><author><fullName>Nicolardi S</fullName><firstName>Simone</firstName><lastName>Nicolardi</lastName><initials>S</initials><affiliation>Center for Proteomics and Metabolomics, Leiden University Medical Center , Albinusdreef 2, 2300 RC Leiden, The Netherlands.</affiliation></author><author><fullName>Deelder AM</fullName><firstName>André M</firstName><lastName>Deelder</lastName><initials>AM</initials></author><author><fullName>Palmblad M</fullName><firstName>Magnus</firstName><lastName>Palmblad</lastName><initials>M</initials></author><author><fullName>van der Burgt YE</fullName><firstName>Yuri E M</firstName><lastName>van der Burgt</lastName><initials>YE</initials></author></authorList><journalInfo><issue>11</issue><volume>86</volume><journalIssueId>2165705</journalIssueId><dateOfPublication>2014 Jun</dateOfPublication><monthOfPublication>6</monthOfPublication><yearOfPublication>2014</yearOfPublication><printPublicationDate>2014-06-01</printPublicationDate><journal><title>Analytical chemistry</title><ISOAbbreviation>Anal. Chem.</ISOAbbreviation><medlineAbbreviation>Anal Chem</medlineAbbreviation><NLMid>0370536</NLMid><ISSN>0003-2700</ISSN><ESSN>1520-6882</ESSN></journal></journalInfo><pageInfo>5376-5382</pageInfo><abstractText>Structural confirmation and quality control of recombinant monoclonal antibodies (mAbs) by top-down mass spectrometry is still challenging due to the size of the proteins, disulfide content, and post-translational modifications such as glycosylation. In this study we have applied electrochemistry (EC) to overcome disulfide bridge complexity in top-down analysis of mAbs. To this end, an electrochemical cell was coupled directly to an electrospray ionization (ESI) source and a Fourier transform ion cyclotron resonance (FTICR) mass spectrometer (MS) equipped with a 15 T magnet. By performing online EC-assisted reduction of interchain disulfide bonds in an intact mAb, the released light chains could be selected for tandem mass spectrometry (MS/MS) analysis without interference from heavy-chain fragments. Moreover, the acquisition of full MS scans under denaturing conditions allowed profiling of all abundant mAb glycoforms. Ultrahigh-resolution FTICR-MS measurements provided fully resolved isotopic distributions of intact mAb and enabled the identification of the most abundant adducts and other interfering species. Furthermore, it was found that reduction of interchain disulfide bonds occurs in the ESI source dependent on capillary voltage and solvent composition. This phenomenon was systematically evaluated and compared with the results obtained from reduction in the electrochemical cell.</abstractText><affiliation>Center for Proteomics and Metabolomics, Leiden University Medical Center , Albinusdreef 2, 2300 RC Leiden, The Netherlands.</affiliation><language>eng</language><pubModel>Print-Electronic</pubModel><pubTypeList><pubType>Journal Article</pubType></pubTypeList><subsetList><subset><code>IM</code><name>Index Medicus</name></subset></subsetList><fullTextUrlList><fullTextUrl><availability>Subscription required</availability><availabilityCode>S</availabilityCode><documentStyle>doi</documentStyle><site>DOI</site><url>http://dx.doi.org/10.1021/ac500383c</url></fullTextUrl></fullTextUrlList><inEPMC>N</inEPMC><inPMC>N</inPMC><citedByCount>0</citedByCount><hasReferences>N</hasReferences><hasTextMinedTerms>N</hasTextMinedTerms><hasDbCrossReferences>N</hasDbCrossReferences><hasLabsLinks>N</hasLabsLinks><hasTMAccessionNumbers>N</hasTMAccessionNumbers><dateOfCreation>2014-06-03</dateOfCreation><electronicPublicationDate>2014-05-12</electronicPublicationDate><firstPublicationDate>2014-05-12</firstPublicationDate><luceneScore>930.33093</luceneScore></result><result><id>PMC4162283</id><source>PMC</source><pmcid>PMC4162283</pmcid><title>Automated Big Data Analysis in Bottom-up and Targeted Proteomics.</title><authorString>van der Plas-Duivesteijn S, Domański D, Smith D, Borchers C, Palmblad M, Mohamme Y.</authorString><authorList><author><fullName>van der Plas-Duivesteijn S</fullName><firstName>Suzanne</firstName><lastName>van der Plas-Duivesteijn</lastName><initials>S</initials><affiliation>1Center for Proteomics and Metabolomics, Leiden University Medical Center, Leiden, The Netherlands,</affiliation></author><author><fullName>Domański D</fullName><firstName>Dominik</firstName><lastName>Domański</lastName><initials>D</initials></author><author><fullName>Smith D</fullName><firstName>Derek</firstName><lastName>Smith</lastName><initials>D</initials></author><author><fullName>Borchers C</fullName><firstName>Christoph</firstName><lastName>Borchers</lastName><initials>C</initials></author><author><fullName>Palmblad M</fullName><firstName>Magnus</firstName><lastName>Palmblad</lastName><initials>M</initials></author><author><fullName>Mohamme Y</fullName><firstName>Yassene</firstName><lastName>Mohamme</lastName><initials>Y</initials></author></authorList><journalInfo><issue>Suppl</issue><volume>25</volume><journalIssueId>2210219</journalIssueId><dateOfPublication>2014 May</dateOfPublication><monthOfPublication>5</monthOfPublication><yearOfPublication>2014</yearOfPublication><printPublicationDate>2014-05-01</printPublicationDate><journal><title>Journal of biomolecular techniques : JBT</title><ISOAbbreviation>J Biomol Tech</ISOAbbreviation><medlineAbbreviation>J Biomol Tech</medlineAbbreviation><NLMid>100888641</NLMid><ISSN>1524-0215</ISSN><ESSN>1943-4731</ESSN></journal></journalInfo><pageInfo>S7-S7</pageInfo><abstractText>Similar to other data intensive sciences, analyzing mass spectrometry-based proteomics data involves multiple steps and diverse software using different algorithms and data formats and sizes. Besides that the distributed and evolving nature of the data in online repositories, another challenge is that a scientists have to deal with many steps of analysis pipelines. A documented data processing is also becoming an essential part for the overall reproducibility of the results. Thanks to different e-Science initiatives, scientific workflow engines have become a means for automated, sharable and reproducible data processing. While these are designed as general tools, they can be employed to solve different challenges that we are facing in handling our Big Data. Here we present three use cases: improving the performance of different spectral search engines by decomposing input data and recomposing the resulting files, building spectral libraries from more than 20 million spectra, and integrating information from multiple resources to select most appropriate peptides for targeted proteomics analyses. The three use cases demonstrate different challenges in exploiting proteomics data analysis. In the first we integrate local and cloud processing resources in order to obtain better performance resulting in more than 30-fold speed improvement. By considering search engines as legacy software our solution is applicable to multiple search algorithms. The second use case is an example of automated processing of many data files of different sizes and locations, starting with raw data and ending with the final, ready-to-use library. This demonstrates the robustness and fault tolerance when dealing with huge amount data stored in multiple files. The third use case demonstrates retrieval and integration of information and data from multiple online repositories. In addition to the diversity of data formats and Web interfaces, this use case also illustrates how to deal with incomplete data.</abstractText><affiliation>1Center for Proteomics and Metabolomics, Leiden University Medical Center, Leiden, The Netherlands,</affiliation><language>eng</language><pubModel>Undetermined</pubModel><pubTypeList><pubType>Abstract</pubType></pubTypeList><fullTextUrlList><fullTextUrl><availability>Free</availability><availabilityCode>F</availabilityCode><documentStyle>html</documentStyle><site>PubMedCentral</site><url>http://www.pubmedcentral.org/articles/PMC4162283/?tool=EBI</url></fullTextUrl><fullTextUrl><availability>Free</availability><availabilityCode>F</availabilityCode><documentStyle>html</documentStyle><site>Europe_PMC</site><url>http://europepmc.org/articles/PMC4162283</url></fullTextUrl></fullTextUrlList><isOpenAccess>N</isOpenAccess><inEPMC>Y</inEPMC><inPMC>Y</inPMC><citedByCount>0</citedByCount><hasReferences>N</hasReferences><hasTextMinedTerms>Y</hasTextMinedTerms><hasDbCrossReferences>N</hasDbCrossReferences><hasLabsLinks>N</hasLabsLinks><hasTMAccessionNumbers>N</hasTMAccessionNumbers><dateOfCreation>2014-10-11</dateOfCreation><dateOfRevision>2014-10-11</dateOfRevision><firstPublicationDate>2014-05-01</firstPublicationDate><luceneScore>568.6877</luceneScore></result><result><id>24769191</id><source>MED</source><pmid>24769191</pmid><DOI>10.1016/j.jprot.2014.04.018</DOI><title>PeptidePicker: a scientific workflow with web interface for selecting appropriate peptides for targeted proteomics experiments.</title><authorString>Mohammed Y, Domański D, Jackson AM, Smith DS, Deelder AM, Palmblad M, Borchers CH.</authorString><authorList><author><fullName>Mohammed Y</fullName><firstName>Yassene</firstName><lastName>Mohammed</lastName><initials>Y</initials><affiliation>University of Victoria - Genome British Columbia Proteomics Centre, University of Victoria, Victoria, BC V8Z7X8, Canada; Center for Proteomics and Metabolomics, Leiden University Medical Center, The Netherlands.</affiliation></author><author><fullName>Domański D</fullName><firstName>Dominik</firstName><lastName>Domański</lastName><initials>D</initials><affiliation>Mass Spectrometry Laboratory, Institute of Biochemistry and Biophysics, Polish Academy of Sciences, Warsaw, Poland.</affiliation></author><author><fullName>Jackson AM</fullName><firstName>Angela M</firstName><lastName>Jackson</lastName><initials>AM</initials><affiliation>University of Victoria - Genome British Columbia Proteomics Centre, University of Victoria, Victoria, BC V8Z7X8, Canada.</affiliation></author><author><fullName>Smith DS</fullName><firstName>Derek S</firstName><lastName>Smith</lastName><initials>DS</initials><affiliation>University of Victoria - Genome British Columbia Proteomics Centre, University of Victoria, Victoria, BC V8Z7X8, Canada.</affiliation></author><author><fullName>Deelder AM</fullName><firstName>André M</firstName><lastName>Deelder</lastName><initials>AM</initials><affiliation>Center for Proteomics and Metabolomics, Leiden University Medical Center, The Netherlands.</affiliation></author><author><fullName>Palmblad M</fullName><firstName>Magnus</firstName><lastName>Palmblad</lastName><initials>M</initials><affiliation>Center for Proteomics and Metabolomics, Leiden University Medical Center, The Netherlands.</affiliation></author><author><fullName>Borchers CH</fullName><firstName>Christoph H</firstName><lastName>Borchers</lastName><initials>CH</initials><affiliation>University of Victoria - Genome British Columbia Proteomics Centre, University of Victoria, Victoria, BC V8Z7X8, Canada; Department of Biochemistry &amp; Microbiology, University of Victoria, Victoria, BC V8P 5C2, Canada. Electronic address: christoph@proteincentre.com.</affiliation></author></authorList><journalInfo><volume>106</volume><journalIssueId>2173258</journalIssueId><dateOfPublication>2014 Jun</dateOfPublication><monthOfPublication>6</monthOfPublication><yearOfPublication>2014</yearOfPublication><printPublicationDate>2014-06-01</printPublicationDate><journal><title>Journal of proteomics</title><ISOAbbreviation>J Proteomics</ISOAbbreviation><medlineAbbreviation>J Proteomics</medlineAbbreviation><NLMid>101475056</NLMid><ISSN>1874-3919</ISSN><ESSN>1876-7737</ESSN></journal></journalInfo><pageInfo>151-161</pageInfo><abstractText>UNLABELLED: One challenge in Multiple Reaction Monitoring (MRM)-based proteomics is to select the most appropriate surrogate peptides to represent a target protein. We present here a software package to automatically generate these most appropriate surrogate peptides for an LC/MRM-MS analysis. Our method integrates information about the proteins, their tryptic peptides, and the suitability of these peptides for MRM which is available online in UniProtKB, NCBI's dbSNP, ExPASy, PeptideAtlas, PRIDE, and GPMDB. The scoring algorithm reflects our knowledge in choosing the best candidate peptides for MRM, based on the uniqueness of the peptide in the targeted proteome, its physiochemical properties, and whether it previously has been observed. The modularity of the workflow allows further extension and additional selection criteria to be incorporated. We have developed a simple Web interface where the researcher provides the protein accession number, the subject organism, and peptide-specific options. Currently, the software is designed for human and mouse proteomes, but additional species can be easily be added. Our software improved the peptide selection by eliminating human error, considering multiple data sources and all of the isoforms of the protein, and resulted in faster peptide selection - approximately 50 proteins per hour compared to 8 per day. BIOLOGICAL SIGNIFICANCE: Compiling a list of optimal surrogate peptides for target proteins to be analyzed by LC/MRM-MS has been a cumbersome process, in which expert researchers retrieved information from different online repositories and used their own reasoning to find the most appropriate peptides. Our scientific workflow automates this process by integrating information from different data sources including UniProt, Global Proteome Machine, NCBI's dbSNP, and PeptideAtlas, simulating the researchers' reasoning, and incorporating their knowledge of how to select the best proteotypic peptides for an MRM analysis. The developed software can help to standardize the selection of peptides, eliminate human error, and increase productivity.</abstractText><affiliation>University of Victoria - Genome British Columbia Proteomics Centre, University of Victoria, Victoria, BC V8Z7X8, Canada; Center for Proteomics and Metabolomics, Leiden University Medical Center, The Netherlands.</affiliation><language>eng</language><pubModel>Print-Electronic</pubModel><pubTypeList><pubType>Journal Article</pubType><pubType>Research Support, Non-U.S. Gov't</pubType></pubTypeList><meshHeadingList><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Animals</descriptorName></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Humans</descriptorName></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Mice</descriptorName></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Peptides</descriptorName><meshQualifierList><meshQualifier><abbreviation>CH</abbreviation><qualifierName>chemistry</qualifierName><majorTopic_YN>Y</majorTopic_YN></meshQualifier></meshQualifierList></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Trypsin</descriptorName><meshQualifierList><meshQualifier><abbreviation>CH</abbreviation><qualifierName>chemistry</qualifierName><majorTopic_YN>N</majorTopic_YN></meshQualifier></meshQualifierList></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Proteome</descriptorName></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Models, Statistical</descriptorName></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Reproducibility of Results</descriptorName></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Proteomics</descriptorName><meshQualifierList><meshQualifier><abbreviation>MT</abbreviation><qualifierName>methods</qualifierName><majorTopic_YN>Y</majorTopic_YN></meshQualifier></meshQualifierList></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Mass Spectrometry</descriptorName></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Software</descriptorName></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>User-Computer Interface</descriptorName></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Workflow</descriptorName></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Computational Biology</descriptorName><meshQualifierList><meshQualifier><abbreviation>MT</abbreviation><qualifierName>methods</qualifierName><majorTopic_YN>Y</majorTopic_YN></meshQualifier></meshQualifierList></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Programming Languages</descriptorName></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Algorithms</descriptorName></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Databases, Protein</descriptorName></meshHeading></meshHeadingList><keywordList><keyword>Data integration</keyword><keyword>Targeted Proteomics</keyword><keyword>Srm</keyword><keyword>Scientific Workflow</keyword><keyword>Mrm</keyword><keyword>Peptide Selection</keyword></keywordList><chemicalList><chemical><name>Trypsin</name><registryNumber>EC 3.4.21.4</registryNumber></chemical><chemical><name>Peptides</name><registryNumber>0</registryNumber></chemical><chemical><name>Proteome</name><registryNumber>0</registryNumber></chemical></chemicalList><subsetList><subset><code>IM</code><name>Index Medicus</name></subset></subsetList><fullTextUrlList><fullTextUrl><availability>Subscription required</availability><availabilityCode>S</availabilityCode><documentStyle>doi</documentStyle><site>DOI</site><url>http://dx.doi.org/10.1016/j.jprot.2014.04.018</url></fullTextUrl></fullTextUrlList><inEPMC>N</inEPMC><inPMC>N</inPMC><citedByCount>4</citedByCount><hasReferences>Y</hasReferences><hasTextMinedTerms>N</hasTextMinedTerms><hasDbCrossReferences>N</hasDbCrossReferences><hasLabsLinks>N</hasLabsLinks><hasTMAccessionNumbers>N</hasTMAccessionNumbers><dateOfCompletion>2015-02-20</dateOfCompletion><dateOfCreation>2014-06-23</dateOfCreation><electronicPublicationDate>2014-04-22</electronicPublicationDate><firstPublicationDate>2014-04-22</firstPublicationDate><luceneScore>702.41406</luceneScore></result><result><id>24458521</id><source>MED</source><pmid>24458521</pmid><DOI>10.1002/prca.201300072</DOI><title>Fibronectin is a serum biomarker for Duchenne muscular dystrophy.</title><authorString>Cynthia Martin F, Hiller M, Spitali P, Oonk S, Dalebout H, Palmblad M, Chaouch A, Guglieri M, Straub V, Lochmüller H, Niks EH, Verschuuren JJ, Aartsma-Rus A, Deelder AM, van der Burgt YE, 't Hoen PA.</authorString><authorList><author><fullName>Cynthia Martin F</fullName><firstName>F</firstName><lastName>Cynthia Martin</lastName><initials>F</initials><affiliation>Department of Human Genetics, Leiden University Medical Center (LUMC), RC, Leiden, The Netherlands.</affiliation></author><author><fullName>Hiller M</fullName><firstName>Monika</firstName><lastName>Hiller</lastName><initials>M</initials></author><author><fullName>Spitali P</fullName><firstName>Pietro</firstName><lastName>Spitali</lastName><initials>P</initials><authorId type="ORCID">0000-0003-2783-688X</authorId></author><author><fullName>Oonk S</fullName><firstName>Stijn</firstName><lastName>Oonk</lastName><initials>S</initials></author><author><fullName>Dalebout H</fullName><firstName>Hans</firstName><lastName>Dalebout</lastName><initials>H</initials></author><author><fullName>Palmblad M</fullName><firstName>Magnus</firstName><lastName>Palmblad</lastName><initials>M</initials></author><author><fullName>Chaouch A</fullName><firstName>Amina</firstName><lastName>Chaouch</lastName><initials>A</initials></author><author><fullName>Guglieri M</fullName><firstName>Michela</firstName><lastName>Guglieri</lastName><initials>M</initials></author><author><fullName>Straub V</fullName><firstName>Volker</firstName><lastName>Straub</lastName><initials>V</initials></author><author><fullName>Lochmüller H</fullName><firstName>Hanns</firstName><lastName>Lochmüller</lastName><initials>H</initials><authorId type="ORCID">0000-0003-2324-8001</authorId></author><author><fullName>Niks EH</fullName><firstName>Erik H</firstName><lastName>Niks</lastName><initials>EH</initials></author><author><fullName>Verschuuren JJ</fullName><firstName>Jan J G M</firstName><lastName>Verschuuren</lastName><initials>JJ</initials></author><author><fullName>Aartsma-Rus A</fullName><firstName>Annemieke</firstName><lastName>Aartsma-Rus</lastName><initials>A</initials></author><author><fullName>Deelder AM</fullName><firstName>André M</firstName><lastName>Deelder</lastName><initials>AM</initials></author><author><fullName>van der Burgt YE</fullName><firstName>Yuri E M</firstName><lastName>van der Burgt</lastName><initials>YE</initials></author><author><fullName>'t Hoen PA</fullName><firstName>Peter A C</firstName><lastName>'t Hoen</lastName><initials>PA</initials></author></authorList><authorIdList><authorId type="ORCID">0000-0003-2783-688X</authorId><authorId type="ORCID">0000-0003-2324-8001</authorId></authorIdList><journalInfo><issue>3-4</issue><volume>8</volume><journalIssueId>2149574</journalIssueId><dateOfPublication>2014 Apr</dateOfPublication><monthOfPublication>4</monthOfPublication><yearOfPublication>2014</yearOfPublication><printPublicationDate>2014-04-01</printPublicationDate><journal><title>Proteomics. Clinical applications</title><ISOAbbreviation>Proteomics Clin Appl</ISOAbbreviation><medlineAbbreviation>Proteomics Clin Appl</medlineAbbreviation><NLMid>101298608</NLMid><ISSN>1862-8346</ISSN><ESSN>1862-8354</ESSN></journal></journalInfo><pageInfo>269-278</pageInfo><abstractText>PURPOSE: To identify and validate serum biomarkers for the progression of Duchenne muscular dystrophy (DMD) using a MS-based bottom-up pipeline. EXPERIMENTAL DESIGN: We used a bottom-up proteomics approach, including a protein concentration equalization step, different proteolytic digestions, and MS detection schemes, to identify candidate biomarkers in serum samples from control subjects and DMD patients. Fibronectin was chosen for follow-up based on the differences in peptide spectral counts and sequence coverage observed between the DMD and control groups. Subsequently, fibronectin levels were determined with ELISA in 68 DMD patients, 38 milder Becker muscular dystrophy patients, 33 patients with other neuromuscular disorders, and 15 age-matched adult and child controls. RESULTS: There was a significant increase in fibronectin levels in DMD patients compared to age-matched controls. Fibronectin levels in patients with Becker muscular dystrophy, Bethlem myopathy, or myasthenia gravis were comparable to control levels. Progressive elevation in fibronectin levels was observed in longitudinal samples from 22 DMD patients followed up for a period of 6 months up to 4 years. CONCLUSION AND CLINICAL RELEVANCE: This study suggests that serum fibronectin levels may constitute a promising biomarker to monitor disease progression in DMD patients.</abstractText><affiliation>Department of Human Genetics, Leiden University Medical Center (LUMC), RC, Leiden, The Netherlands.</affiliation><language>eng</language><pubModel>Print-Electronic</pubModel><pubTypeList><pubType>Journal Article</pubType><pubType>Research Support, Non-U.S. Gov't</pubType></pubTypeList><grantsList><grant><grantId>MR/K000608/1</grantId><agency>Medical Research Council</agency><orderIn>0</orderIn></grant></grantsList><meshHeadingList><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Humans</descriptorName></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Muscle, Skeletal</descriptorName><meshQualifierList><meshQualifier><abbreviation>ME</abbreviation><qualifierName>metabolism</qualifierName><majorTopic_YN>Y</majorTopic_YN></meshQualifier><meshQualifier><abbreviation>PA</abbreviation><qualifierName>pathology</qualifierName><majorTopic_YN>N</majorTopic_YN></meshQualifier></meshQualifierList></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Contracture</descriptorName><meshQualifierList><meshQualifier><abbreviation>BL</abbreviation><qualifierName>blood</qualifierName><majorTopic_YN>N</majorTopic_YN></meshQualifier><meshQualifier><abbreviation>GE</abbreviation><qualifierName>genetics</qualifierName><majorTopic_YN>N</majorTopic_YN></meshQualifier><meshQualifier><abbreviation>PA</abbreviation><qualifierName>pathology</qualifierName><majorTopic_YN>N</majorTopic_YN></meshQualifier></meshQualifierList></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Muscular Dystrophies</descriptorName><meshQualifierList><meshQualifier><abbreviation>BL</abbreviation><qualifierName>blood</qualifierName><majorTopic_YN>N</majorTopic_YN></meshQualifier><meshQualifier><abbreviation>CN</abbreviation><qualifierName>congenital</qualifierName><majorTopic_YN>N</majorTopic_YN></meshQualifier><meshQualifier><abbreviation>GE</abbreviation><qualifierName>genetics</qualifierName><majorTopic_YN>N</majorTopic_YN></meshQualifier><meshQualifier><abbreviation>PA</abbreviation><qualifierName>pathology</qualifierName><majorTopic_YN>N</majorTopic_YN></meshQualifier></meshQualifierList></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Muscular Dystrophy, Duchenne</descriptorName><meshQualifierList><meshQualifier><abbreviation>BL</abbreviation><qualifierName>blood</qualifierName><majorTopic_YN>Y</majorTopic_YN></meshQualifier><meshQualifier><abbreviation>GE</abbreviation><qualifierName>genetics</qualifierName><majorTopic_YN>N</majorTopic_YN></meshQualifier><meshQualifier><abbreviation>PA</abbreviation><qualifierName>pathology</qualifierName><majorTopic_YN>N</majorTopic_YN></meshQualifier></meshQualifierList></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Myasthenia Gravis</descriptorName><meshQualifierList><meshQualifier><abbreviation>BL</abbreviation><qualifierName>blood</qualifierName><majorTopic_YN>N</majorTopic_YN></meshQualifier><meshQualifier><abbreviation>GE</abbreviation><qualifierName>genetics</qualifierName><majorTopic_YN>N</majorTopic_YN></meshQualifier><meshQualifier><abbreviation>PA</abbreviation><qualifierName>pathology</qualifierName><majorTopic_YN>N</majorTopic_YN></meshQualifier></meshQualifierList></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Fibronectins</descriptorName><meshQualifierList><meshQualifier><abbreviation>BL</abbreviation><qualifierName>blood</qualifierName><majorTopic_YN>Y</majorTopic_YN></meshQualifier><meshQualifier><abbreviation>GE</abbreviation><qualifierName>genetics</qualifierName><majorTopic_YN>N</majorTopic_YN></meshQualifier></meshQualifierList></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Biological Markers</descriptorName><meshQualifierList><meshQualifier><abbreviation>BL</abbreviation><qualifierName>blood</qualifierName><majorTopic_YN>Y</majorTopic_YN></meshQualifier></meshQualifierList></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Mass Spectrometry</descriptorName></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Adolescent</descriptorName></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Adult</descriptorName></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Child</descriptorName></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Child, Preschool</descriptorName></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Middle Aged</descriptorName></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Female</descriptorName></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Male</descriptorName></meshHeading></meshHeadingList><keywordList><keyword>Fibronectin</keyword><keyword>Biomarker</keyword><keyword>Duchenne muscular dystrophy</keyword><keyword>Spectral Counting</keyword></keywordList><chemicalList><chemical><name>Biological Markers</name><registryNumber>0</registryNumber></chemical><chemical><name>Fibronectins</name><registryNumber>0</registryNumber></chemical></chemicalList><subsetList><subset><code>IM</code><name>Index Medicus</name></subset></subsetList><fullTextUrlList><fullTextUrl><availability>Subscription required</availability><availabilityCode>S</availabilityCode><documentStyle>doi</documentStyle><site>DOI</site><url>http://dx.doi.org/10.1002/prca.201300072</url></fullTextUrl></fullTextUrlList><inEPMC>N</inEPMC><inPMC>N</inPMC><citedByCount>2</citedByCount><hasReferences>Y</hasReferences><hasTextMinedTerms>N</hasTextMinedTerms><hasDbCrossReferences>N</hasDbCrossReferences><hasLabsLinks>N</hasLabsLinks><hasTMAccessionNumbers>N</hasTMAccessionNumbers><dateOfCompletion>2015-04-02</dateOfCompletion><dateOfCreation>2014-04-14</dateOfCreation><dateOfRevision>2014-12-24</dateOfRevision><electronicPublicationDate>2014-03-11</electronicPublicationDate><firstPublicationDate>2014-03-11</firstPublicationDate><luceneScore>548.0187</luceneScore></result><result><id>24460240</id><source>MED</source><pmid>24460240</pmid><DOI>10.1021/pr4010585</DOI><title>Identifying proteins in zebrafish embryos using spectral libraries generated from dissected adult organs and tissues.</title><authorString>van der Plas-Duivesteijn SJ, Mohammed Y, Dalebout H, Meijer A, Botermans A, Hoogendijk JL, Henneman AA, Deelder AM, Spaink HP, Palmblad M.</authorString><authorList><author><fullName>van der Plas-Duivesteijn SJ</fullName><firstName>Suzanne J</firstName><lastName>van der Plas-Duivesteijn</lastName><initials>SJ</initials><affiliation>Center for Proteomics and Metabolomics, Leiden University Medical Center , Albinusdreef 2, Leiden 2300 RC, The Netherlands.</affiliation></author><author><fullName>Mohammed Y</fullName><firstName>Yassene</firstName><lastName>Mohammed</lastName><initials>Y</initials></author><author><fullName>Dalebout H</fullName><firstName>Hans</firstName><lastName>Dalebout</lastName><initials>H</initials></author><author><fullName>Meijer A</fullName><firstName>Annemarie</firstName><lastName>Meijer</lastName><initials>A</initials></author><author><fullName>Botermans A</fullName><firstName>Anouk</firstName><lastName>Botermans</lastName><initials>A</initials></author><author><fullName>Hoogendijk JL</fullName><firstName>Jordy L</firstName><lastName>Hoogendijk</lastName><initials>JL</initials></author><author><fullName>Henneman AA</fullName><firstName>Alex A</firstName><lastName>Henneman</lastName><initials>AA</initials></author><author><fullName>Deelder AM</fullName><firstName>André M</firstName><lastName>Deelder</lastName><initials>AM</initials></author><author><fullName>Spaink HP</fullName><firstName>Herman P</firstName><lastName>Spaink</lastName><initials>HP</initials></author><author><fullName>Palmblad M</fullName><firstName>Magnus</firstName><lastName>Palmblad</lastName><initials>M</initials></author></authorList><journalInfo><issue>3</issue><volume>13</volume><journalIssueId>2137242</journalIssueId><dateOfPublication>2014 Mar</dateOfPublication><monthOfPublication>3</monthOfPublication><yearOfPublication>2014</yearOfPublication><printPublicationDate>2014-03-01</printPublicationDate><journal><title>Journal of proteome research</title><ISOAbbreviation>J. Proteome Res.</ISOAbbreviation><medlineAbbreviation>J Proteome Res</medlineAbbreviation><NLMid>101128775</NLMid><ISSN>1535-3893</ISSN><ESSN>1535-3907</ESSN></journal></journalInfo><pageInfo>1537-1544</pageInfo><abstractText>Spectral libraries provide a sensitive and accurate method for identifying peptides from tandem mass spectra, complementary to searching genome-derived databases or sequencing de novo. Their application requires comprehensive libraries including peptides from low-abundant proteins. Here we describe a method for constructing such libraries using biological differentiation to "fractionate" the proteome by harvesting adult organs and tissues and build comprehensive libraries for identifying proteins in zebrafish (Danio rerio) embryos and larvae (an important and widely used model system). Hierarchical clustering using direct comparison of spectra was used to prioritize organ selection. The resulting and publicly available library covers 14,164 proteins, significantly improved the number of peptide-spectrum matches in zebrafish developmental stages, and can be used on data from different instruments and laboratories. The library contains information on tissue and organ expression of these proteins and is also applicable for adult experiments. The approach itself is not limited to zebrafish but would work for any model system.</abstractText><affiliation>Center for Proteomics and Metabolomics, Leiden University Medical Center , Albinusdreef 2, Leiden 2300 RC, The Netherlands.</affiliation><language>eng</language><pubModel>Print-Electronic</pubModel><pubTypeList><pubType>Journal Article</pubType><pubType>Research Support, Non-U.S. Gov't</pubType></pubTypeList><meshHeadingList><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Animals</descriptorName></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Zebrafish</descriptorName><meshQualifierList><meshQualifier><abbreviation>GD</abbreviation><qualifierName>growth &amp; development</qualifierName><majorTopic_YN>N</majorTopic_YN></meshQualifier><meshQualifier><abbreviation>ME</abbreviation><qualifierName>metabolism</qualifierName><majorTopic_YN>Y</majorTopic_YN></meshQualifier></meshQualifierList></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Fish Proteins</descriptorName><meshQualifierList><meshQualifier><abbreviation>AN</abbreviation><qualifierName>analysis</qualifierName><majorTopic_YN>Y</majorTopic_YN></meshQualifier></meshQualifierList></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Proteome</descriptorName><meshQualifierList><meshQualifier><abbreviation>AN</abbreviation><qualifierName>analysis</qualifierName><majorTopic_YN>Y</majorTopic_YN></meshQualifier></meshQualifierList></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Cluster Analysis</descriptorName></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Organogenesis</descriptorName></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Larva</descriptorName><meshQualifierList><meshQualifier><abbreviation>GD</abbreviation><qualifierName>growth &amp; development</qualifierName><majorTopic_YN>N</majorTopic_YN></meshQualifier><meshQualifier><abbreviation>ME</abbreviation><qualifierName>metabolism</qualifierName><majorTopic_YN>N</majorTopic_YN></meshQualifier><meshQualifier><abbreviation>CH</abbreviation><qualifierName>chemistry</qualifierName><majorTopic_YN>N</majorTopic_YN></meshQualifier></meshQualifierList></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Organ Specificity</descriptorName></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Proteomics</descriptorName><meshQualifierList><meshQualifier><abbreviation>MT</abbreviation><qualifierName>methods</qualifierName><majorTopic_YN>N</majorTopic_YN></meshQualifier></meshQualifierList></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Tandem Mass Spectrometry</descriptorName><meshQualifierList><meshQualifier><abbreviation>SN</abbreviation><qualifierName>statistics &amp; numerical data</qualifierName><majorTopic_YN>Y</majorTopic_YN></meshQualifier></meshQualifierList></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Peptide Library</descriptorName></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Algorithms</descriptorName></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Embryo, Nonmammalian</descriptorName></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Female</descriptorName></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Male</descriptorName></meshHeading></meshHeadingList><chemicalList><chemical><name>Fish Proteins</name><registryNumber>0</registryNumber></chemical><chemical><name>Peptide Library</name><registryNumber>0</registryNumber></chemical><chemical><name>Proteome</name><registryNumber>0</registryNumber></chemical></chemicalList><subsetList><subset><code>IM</code><name>Index Medicus</name></subset></subsetList><fullTextUrlList><fullTextUrl><availability>Subscription required</availability><availabilityCode>S</availabilityCode><documentStyle>doi</documentStyle><site>DOI</site><url>http://dx.doi.org/10.1021/pr4010585</url></fullTextUrl></fullTextUrlList><inEPMC>N</inEPMC><inPMC>N</inPMC><citedByCount>1</citedByCount><hasReferences>N</hasReferences><hasTextMinedTerms>N</hasTextMinedTerms><hasDbCrossReferences>N</hasDbCrossReferences><hasLabsLinks>N</hasLabsLinks><hasTMAccessionNumbers>N</hasTMAccessionNumbers><dateOfCompletion>2014-11-03</dateOfCompletion><dateOfCreation>2014-04-24</dateOfCreation><electronicPublicationDate>2014-02-05</electronicPublicationDate><firstPublicationDate>2014-02-05</firstPublicationDate><luceneScore>625.2164</luceneScore></result><result><id>24156766</id><source>MED</source><pmid>24156766</pmid><pmcid>PMC4016144</pmcid><DOI>10.1186/1756-0500-6-428</DOI><title>Parallel deep transcriptome and proteome analysis of zebrafish larvae.</title><authorString>Palmblad M, Henkel CV, Dirks RP, Meijer AH, Deelder AM, Spaink HP.</authorString><authorList><author><fullName>Palmblad M</fullName><firstName>Magnus</firstName><lastName>Palmblad</lastName><initials>M</initials><affiliation>Center for Proteomics and Metabolomics, Leiden University Medical Center, Zone L04-Q, P,O, Box 9600, 2300 RC, Leiden, The Netherlands. n.m.palmblad@lumc.nl.</affiliation></author><author><fullName>Henkel CV</fullName><firstName>Christiaan V</firstName><lastName>Henkel</lastName><initials>CV</initials></author><author><fullName>Dirks RP</fullName><firstName>Ron P</firstName><lastName>Dirks</lastName><initials>RP</initials></author><author><fullName>Meijer AH</fullName><firstName>Annemarie H</firstName><lastName>Meijer</lastName><initials>AH</initials></author><author><fullName>Deelder AM</fullName><firstName>André M</firstName><lastName>Deelder</lastName><initials>AM</initials></author><author><fullName>Spaink HP</fullName><firstName>Herman P</firstName><lastName>Spaink</lastName><initials>HP</initials></author></authorList><journalInfo><volume>6</volume><journalIssueId>2000656</journalIssueId><dateOfPublication>2013 </dateOfPublication><monthOfPublication>0</monthOfPublication><yearOfPublication>2013</yearOfPublication><printPublicationDate>2013-01-01</printPublicationDate><journal><title>BMC research notes</title><ISOAbbreviation>BMC Res Notes</ISOAbbreviation><medlineAbbreviation>BMC Res Notes</medlineAbbreviation><NLMid>101462768</NLMid><ESSN>1756-0500</ESSN></journal></journalInfo><pageInfo>428</pageInfo><abstractText>BACKGROUND: Sensitivity and throughput of transcriptomic and proteomic technologies have advanced tremendously in recent years. With the use of deep sequencing of RNA samples (RNA-seq) and mass spectrometry technology for protein identification and quantitation, it is now feasible to compare gene and protein expression on a massive scale and for any organism for which genomic data is available. Although these technologies are currently applied to many research questions in various model systems ranging from cell cultures to the entire organism level, there are few comparative studies of these technologies in the same system, let alone on the same samples. Here we present a comparison between gene and protein expression in embryos of zebrafish, which is an upcoming model in disease studies. RESULTS: We compared Agilent custom made expression microarrays with Illumina deep sequencing for RNA analysis, showing as expected a high degree of correlation of expression of a common set of 18,230 genes. Gene expression was also found to correlate with the abundance of 963 distinct proteins, with several categories of genes as exceptions. These exceptions include ribosomal proteins, histones and vitellogenins, for which biological and technical explanations are discussed. CONCLUSIONS: By comparing state of the art transcriptomic and proteomic technologies on samples derived from the same group of organisms we have for the first time benchmarked the differences in these technologies with regard to sensitivity and bias towards detection of particular gene categories in zebrafish. Our datasets submitted to public repositories are a good starting point for researchers interested in disease progression in zebrafish at a stage of development highly suited for high throughput screening technologies.</abstractText><affiliation>Center for Proteomics and Metabolomics, Leiden University Medical Center, Zone L04-Q, P,O, Box 9600, 2300 RC, Leiden, The Netherlands. n.m.palmblad@lumc.nl.</affiliation><language>eng</language><pubModel>Electronic</pubModel><pubTypeList><pubType>Journal Article</pubType><pubType>Research Support, Non-U.S. Gov't</pubType></pubTypeList><meshHeadingList><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Animals</descriptorName></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Zebrafish</descriptorName><meshQualifierList><meshQualifier><abbreviation>EM</abbreviation><qualifierName>embryology</qualifierName><majorTopic_YN>N</majorTopic_YN></meshQualifier><meshQualifier><abbreviation>GE</abbreviation><qualifierName>genetics</qualifierName><majorTopic_YN>Y</majorTopic_YN></meshQualifier></meshQualifierList></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Ribosomal Proteins</descriptorName><meshQualifierList><meshQualifier><abbreviation>GE</abbreviation><qualifierName>genetics</qualifierName><majorTopic_YN>N</majorTopic_YN></meshQualifier></meshQualifierList></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Zebrafish Proteins</descriptorName><meshQualifierList><meshQualifier><abbreviation>GE</abbreviation><qualifierName>genetics</qualifierName><majorTopic_YN>Y</majorTopic_YN></meshQualifier></meshQualifierList></meshHeading><meshHeading><majorTopic_YN>Y</majorTopic_YN><descriptorName>Proteome</descriptorName></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Histones</descriptorName><meshQualifierList><meshQualifier><abbreviation>GE</abbreviation><qualifierName>genetics</qualifierName><majorTopic_YN>N</majorTopic_YN></meshQualifier></meshQualifierList></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Oligonucleotide Array Sequence Analysis</descriptorName><meshQualifierList><meshQualifier><abbreviation>ST</abbreviation><qualifierName>standards</qualifierName><majorTopic_YN>Y</majorTopic_YN></meshQualifier></meshQualifierList></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Gene Expression Profiling</descriptorName></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Gene Expression Regulation, Developmental</descriptorName></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Proteomics</descriptorName><meshQualifierList><meshQualifier><abbreviation>IS</abbreviation><qualifierName>instrumentation</qualifierName><majorTopic_YN>N</majorTopic_YN></meshQualifier><meshQualifier><abbreviation>MT</abbreviation><qualifierName>methods</qualifierName><majorTopic_YN>N</majorTopic_YN></meshQualifier></meshQualifierList></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Vitellogenins</descriptorName><meshQualifierList><meshQualifier><abbreviation>GE</abbreviation><qualifierName>genetics</qualifierName><majorTopic_YN>N</majorTopic_YN></meshQualifier></meshQualifierList></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Benchmarking</descriptorName></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>High-Throughput Nucleotide Sequencing</descriptorName><meshQualifierList><meshQualifier><abbreviation>ST</abbreviation><qualifierName>standards</qualifierName><majorTopic_YN>Y</majorTopic_YN></meshQualifier></meshQualifierList></meshHeading><meshHeading><majorTopic_YN>Y</majorTopic_YN><descriptorName>Transcriptome</descriptorName></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Embryo, Nonmammalian</descriptorName></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Female</descriptorName></meshHeading></meshHeadingList><chemicalList><chemical><name>Histones</name><registryNumber>0</registryNumber></chemical><chemical><name>Ribosomal Proteins</name><registryNumber>0</registryNumber></chemical><chemical><name>Vitellogenins</name><registryNumber>0</registryNumber></chemical><chemical><name>Zebrafish Proteins</name><registryNumber>0</registryNumber></chemical><chemical><name>Proteome</name><registryNumber>0</registryNumber></chemical></chemicalList><subsetList><subset><code>IM</code><name>Index Medicus</name></subset></subsetList><fullTextUrlList><fullTextUrl><availability>Free</availability><availabilityCode>F</availabilityCode><documentStyle>html</documentStyle><site>PubMedCentral</site><url>http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=EBI&amp;pubmedid=24156766</url></fullTextUrl><fullTextUrl><availability>Free</availability><availabilityCode>F</availabilityCode><documentStyle>pdf</documentStyle><site>PubMedCentral</site><url>http://www.pubmedcentral.nih.gov/picrender.fcgi?tool=EBI&amp;pubmedid=24156766&amp;action=stream&amp;blobtype=pdf</url></fullTextUrl><fullTextUrl><availability>Subscription required</availability><availabilityCode>S</availabilityCode><documentStyle>doi</documentStyle><site>DOI</site><url>http://dx.doi.org/10.1186/1756-0500-6-428</url></fullTextUrl><fullTextUrl><availability>Open access</availability><availabilityCode>OA</availabilityCode><documentStyle>html</documentStyle><site>Europe_PMC</site><url>http://europepmc.org/articles/PMC4016144</url></fullTextUrl><fullTextUrl><availability>Open access</availability><availabilityCode>OA</availabilityCode><documentStyle>pdf</documentStyle><site>Europe_PMC</site><url>http://europepmc.org/articles/PMC4016144?pdf=render</url></fullTextUrl></fullTextUrlList><isOpenAccess>Y</isOpenAccess><inEPMC>Y</inEPMC><inPMC>Y</inPMC><citedByCount>1</citedByCount><hasReferences>Y</hasReferences><hasTextMinedTerms>Y</hasTextMinedTerms><hasDbCrossReferences>N</hasDbCrossReferences><hasLabsLinks>N</hasLabsLinks><hasTMAccessionNumbers>N</hasTMAccessionNumbers><dateOfCompletion>2014-09-02</dateOfCompletion><dateOfCreation>2014-01-17</dateOfCreation><electronicPublicationDate>2013-10-24</electronicPublicationDate><firstPublicationDate>2013-10-24</firstPublicationDate><luceneScore>674.4574</luceneScore></result><result><id>24032411</id><source>MED</source><pmid>24032411</pmid><DOI>10.1021/pr4006525</DOI><title>Authentication of fish products by large-scale comparison of tandem mass spectra.</title><authorString>Wulff T, Nielsen ME, Deelder AM, Jessen F, Palmblad M.</authorString><authorList><author><fullName>Wulff T</fullName><firstName>Tune</firstName><lastName>Wulff</lastName><initials>T</initials><affiliation>National Food Institute, Technical University of Denmark , Mørkhøj Bygade 19, Soborg 2860, Denmark.</affiliation></author><author><fullName>Nielsen ME</fullName><firstName>Michael Engelbrecht</firstName><lastName>Nielsen</lastName><initials>ME</initials><authorId type="ORCID">0000-0001-5044-4441</authorId></author><author><fullName>Deelder AM</fullName><firstName>André M</firstName><lastName>Deelder</lastName><initials>AM</initials></author><author><fullName>Jessen F</fullName><firstName>Flemming</firstName><lastName>Jessen</lastName><initials>F</initials></author><author><fullName>Palmblad M</fullName><firstName>Magnus</firstName><lastName>Palmblad</lastName><initials>M</initials></author></authorList><authorIdList><authorId type="ORCID">0000-0001-5044-4441</authorId></authorIdList><journalInfo><issue>11</issue><volume>12</volume><journalIssueId>2088841</journalIssueId><dateOfPublication>2013 Nov</dateOfPublication><monthOfPublication>11</monthOfPublication><yearOfPublication>2013</yearOfPublication><printPublicationDate>2013-11-01</printPublicationDate><journal><title>Journal of proteome research</title><ISOAbbreviation>J. Proteome Res.</ISOAbbreviation><medlineAbbreviation>J Proteome Res</medlineAbbreviation><NLMid>101128775</NLMid><ISSN>1535-3893</ISSN><ESSN>1535-3907</ESSN></journal></journalInfo><pageInfo>5253-5259</pageInfo><abstractText>Authentication of food is a major concern worldwide to ensure that food products are correctly labeled in terms of which animals are actually processed for consumption. Normally authentication is based on species recognition by comparison of selected sequences of DNA or protein. We here present a new robust, proteome-wide tandem mass spectrometry method for species recognition and food product authentication. The method does not use or require any genome sequences or selection of tandem mass spectra but uses all acquired data. The experimental steps were performed in a simple, standardized workflow including protein extraction, digestion, and data analysis. First, a set of reference spectral libraries was generated using unprocessed muscle tissue from 22 different fish species. Query tandem mass spectrometry data sets from "unknown" fresh muscle tissue samples were then searched against the reference libraries. The number of matching spectra could unambiguously identify the origin of all fresh samples. A number of processed samples were also analyzed to further test the robustness and applicability of the method. The results clearly show that the method is also able to correctly identify heavily processed samples.</abstractText><affiliation>National Food Institute, Technical University of Denmark , Mørkhøj Bygade 19, Soborg 2860, Denmark.</affiliation><language>eng</language><pubModel>Print-Electronic</pubModel><pubTypeList><pubType>Journal Article</pubType><pubType>Research Support, Non-U.S. Gov't</pubType></pubTypeList><meshHeadingList><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Animals</descriptorName></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Fishes</descriptorName><meshQualifierList><meshQualifier><abbreviation>GE</abbreviation><qualifierName>genetics</qualifierName><majorTopic_YN>Y</majorTopic_YN></meshQualifier></meshQualifierList></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Cluster Analysis</descriptorName></meshHeading><meshHeading><majorTopic_YN>Y</majorTopic_YN><descriptorName>Phylogeny</descriptorName></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Gene Library</descriptorName></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Species Specificity</descriptorName></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Proteomics</descriptorName><meshQualifierList><meshQualifier><abbreviation>MT</abbreviation><qualifierName>methods</qualifierName><majorTopic_YN>Y</majorTopic_YN></meshQualifier></meshQualifierList></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Tandem Mass Spectrometry</descriptorName><meshQualifierList><meshQualifier><abbreviation>VE</abbreviation><qualifierName>veterinary</qualifierName><majorTopic_YN>N</majorTopic_YN></meshQualifier></meshQualifierList></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Chromatography, Liquid</descriptorName></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>DNA Barcoding, Taxonomic</descriptorName><meshQualifierList><meshQualifier><abbreviation>VE</abbreviation><qualifierName>veterinary</qualifierName><majorTopic_YN>N</majorTopic_YN></meshQualifier></meshQualifierList></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Seafood</descriptorName><meshQualifierList><meshQualifier><abbreviation>AN</abbreviation><qualifierName>analysis</qualifierName><majorTopic_YN>Y</majorTopic_YN></meshQualifier><meshQualifier><abbreviation>ST</abbreviation><qualifierName>standards</qualifierName><majorTopic_YN>N</majorTopic_YN></meshQualifier></meshQualifierList></meshHeading></meshHeadingList><subsetList><subset><code>IM</code><name>Index Medicus</name></subset></subsetList><fullTextUrlList><fullTextUrl><availability>Subscription required</availability><availabilityCode>S</availabilityCode><documentStyle>doi</documentStyle><site>DOI</site><url>http://dx.doi.org/10.1021/pr4006525</url></fullTextUrl></fullTextUrlList><inEPMC>N</inEPMC><inPMC>N</inPMC><citedByCount>0</citedByCount><hasReferences>N</hasReferences><hasTextMinedTerms>N</hasTextMinedTerms><hasDbCrossReferences>N</hasDbCrossReferences><hasLabsLinks>N</hasLabsLinks><hasTMAccessionNumbers>N</hasTMAccessionNumbers><dateOfCompletion>2014-06-09</dateOfCompletion><dateOfCreation>2013-11-01</dateOfCreation><electronicPublicationDate>2013-10-15</electronicPublicationDate><firstPublicationDate>2013-10-15</firstPublicationDate><luceneScore>755.33105</luceneScore></result><result><id>23742691</id><source>MED</source><pmid>23742691</pmid><pmcid>PMC3686625</pmcid><DOI>10.1186/1756-0500-6-224</DOI><title>Use of expressed sequence tags as an alternative approach for the identification of Taenia solium metacestode excretion/secretion proteins.</title><authorString>Victor B, Dorny P, Kanobana K, Polman K, Lindh J, Deelder AM, Palmblad M, Gabriël S.</authorString><authorList><author><fullName>Victor B</fullName><firstName>Bjorn</firstName><lastName>Victor</lastName><initials>B</initials></author><author><fullName>Dorny P</fullName><firstName>Pierre</firstName><lastName>Dorny</lastName><initials>P</initials></author><author><fullName>Kanobana K</fullName><firstName>Kirezi</firstName><lastName>Kanobana</lastName><initials>K</initials></author><author><fullName>Polman K</fullName><firstName>Katja</firstName><lastName>Polman</lastName><initials>K</initials></author><author><fullName>Lindh J</fullName><firstName>Johan</firstName><lastName>Lindh</lastName><initials>J</initials></author><author><fullName>Deelder AM</fullName><firstName>André M</firstName><lastName>Deelder</lastName><initials>AM</initials></author><author><fullName>Palmblad M</fullName><firstName>Magnus</firstName><lastName>Palmblad</lastName><initials>M</initials></author><author><fullName>Gabriël S</fullName><firstName>Sarah</firstName><lastName>Gabriël</lastName><initials>S</initials></author></authorList><journalInfo><volume>6</volume><journalIssueId>2000656</journalIssueId><dateOfPublication>2013 </dateOfPublication><monthOfPublication>0</monthOfPublication><yearOfPublication>2013</yearOfPublication><printPublicationDate>2013-01-01</printPublicationDate><journal><title>BMC research notes</title><ISOAbbreviation>BMC Res Notes</ISOAbbreviation><medlineAbbreviation>BMC Res Notes</medlineAbbreviation><NLMid>101462768</NLMid><ESSN>1756-0500</ESSN></journal></journalInfo><pageInfo>224</pageInfo><abstractText> BACKGROUND: Taenia solium taeniasis/cysticercosis is a zoonotic helminth infection mainly found in rural regions of Africa, Asia and Latin America. In endemic areas, diagnosis of cysticercosis largely depends on serology, but these methods have their drawbacks and require improvement. This implies better knowledge of the proteins secreted and excreted by the parasite. In a previous study, we used a custom protein database containing protein sequences from related helminths to identify T. solium metacestode excretion/secretion proteins. An alternative or complementary approach would be to use expressed sequence tags combined with BLAST and protein mapping to supercontigs of Echinococcus granulosus, a closely related cestode. In this study, we evaluate this approach and compare the results to those obtained in the previous study. FINDINGS: We report 297 proteins organized in 106 protein groups based on homology. Additional classification was done using Gene Ontology information on biological process and molecular function. Of the 106 protein groups, 58 groups were newly identified, while 48 groups confirmed previous findings. Blast2GO analysis revealed that the majority of the proteins were involved in catalytic activities and binding. CONCLUSIONS: In this study, we used translated expressed sequence tags combined with BLAST and mapping strategies to both confirm and complement previous research. Our findings are comparable to recent studies on other helminth genera like Echinococcus, Schistosoma and Clonorchis, indicating similarities between helminth excretion/secretion proteomes.</abstractText><affiliation>Veterinary Helminthology Unit, Department of Biomedical Sciences, Institute of Tropical Medicine, Antwerp, Belgium. bvictor@itg.be</affiliation><language>eng</language><pubModel>Electronic</pubModel><pubTypeList><pubType>Journal Article</pubType><pubType>Research Support, Non-U.S. Gov't</pubType></pubTypeList><meshHeadingList><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Animals</descriptorName></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Cestoda</descriptorName><meshQualifierList><meshQualifier><abbreviation>ME</abbreviation><qualifierName>metabolism</qualifierName><majorTopic_YN>Y</majorTopic_YN></meshQualifier></meshQualifierList></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Helminth Proteins</descriptorName><meshQualifierList><meshQualifier><abbreviation>ME</abbreviation><qualifierName>metabolism</qualifierName><majorTopic_YN>Y</majorTopic_YN></meshQualifier></meshQualifierList></meshHeading><meshHeading><majorTopic_YN>Y</majorTopic_YN><descriptorName>Expressed Sequence Tags</descriptorName></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Taenia solium</descriptorName><meshQualifierList><meshQualifier><abbreviation>GE</abbreviation><qualifierName>genetics</qualifierName><majorTopic_YN>N</majorTopic_YN></meshQualifier><meshQualifier><abbreviation>PH</abbreviation><qualifierName>physiology</qualifierName><majorTopic_YN>Y</majorTopic_YN></meshQualifier></meshQualifierList></meshHeading></meshHeadingList><chemicalList><chemical><name>Helminth Proteins</name><registryNumber>0</registryNumber></chemical></chemicalList><subsetList><subset><code>IM</code><name>Index Medicus</name></subset></subsetList><fullTextUrlList><fullTextUrl><availability>Free</availability><availabilityCode>F</availabilityCode><documentStyle>html</documentStyle><site>PubMedCentral</site><url>http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=EBI&amp;pubmedid=23742691</url></fullTextUrl><fullTextUrl><availability>Free</availability><availabilityCode>F</availabilityCode><documentStyle>pdf</documentStyle><site>PubMedCentral</site><url>http://www.pubmedcentral.nih.gov/picrender.fcgi?tool=EBI&amp;pubmedid=23742691&amp;action=stream&amp;blobtype=pdf</url></fullTextUrl><fullTextUrl><availability>Subscription required</availability><availabilityCode>S</availabilityCode><documentStyle>doi</documentStyle><site>DOI</site><url>http://dx.doi.org/10.1186/1756-0500-6-224</url></fullTextUrl><fullTextUrl><availability>Open access</availability><availabilityCode>OA</availabilityCode><documentStyle>html</documentStyle><site>Europe_PMC</site><url>http://europepmc.org/articles/PMC3686625</url></fullTextUrl><fullTextUrl><availability>Open access</availability><availabilityCode>OA</availabilityCode><documentStyle>pdf</documentStyle><site>Europe_PMC</site><url>http://europepmc.org/articles/PMC3686625?pdf=render</url></fullTextUrl></fullTextUrlList><isOpenAccess>Y</isOpenAccess><inEPMC>Y</inEPMC><inPMC>Y</inPMC><citedByCount>1</citedByCount><hasReferences>Y</hasReferences><hasTextMinedTerms>Y</hasTextMinedTerms><hasDbCrossReferences>N</hasDbCrossReferences><hasLabsLinks>N</hasLabsLinks><hasTMAccessionNumbers>N</hasTMAccessionNumbers><dateOfCompletion>2013-11-11</dateOfCompletion><dateOfCreation>2013-06-19</dateOfCreation><electronicPublicationDate>2013-06-06</electronicPublicationDate><firstPublicationDate>2013-06-06</firstPublicationDate><luceneScore>674.4574</luceneScore></result><result><id>23678963</id><source>MED</source><pmid>23678963</pmid><DOI>10.1021/ac400826z</DOI><title>Detection and structural elucidation of esterified oxylipids in human synovial fluid by electrospray ionization-fourier transform ion-cyclotron mass spectrometry and liquid chromatography-ion trap-MS(3): detection of esterified hydroxylated docosapentaenoic acid containing phospholipids.</title><authorString>Jónasdóttir HS, Nicolardi S, Jonker W, Derks R, Palmblad M, Ioan-Facsinay A, Toes R, van der Burgt YE, Deelder AM, Mayboroda OA, Giera M.</authorString><authorList><author><fullName>Jónasdóttir HS</fullName><firstName>Hulda S</firstName><lastName>Jónasdóttir</lastName><initials>HS</initials><affiliation>Leiden University Medical Center (LUMC), Center for Proteomics and Metabolomics, Albinusdreef 2, 2300RC Leiden, The Netherlands.</affiliation></author><author><fullName>Nicolardi S</fullName><firstName>Simone</firstName><lastName>Nicolardi</lastName><initials>S</initials></author><author><fullName>Jonker W</fullName><firstName>Willem</firstName><lastName>Jonker</lastName><initials>W</initials></author><author><fullName>Derks R</fullName><firstName>Rico</firstName><lastName>Derks</lastName><initials>R</initials></author><author><fullName>Palmblad M</fullName><firstName>Magnus</firstName><lastName>Palmblad</lastName><initials>M</initials></author><author><fullName>Ioan-Facsinay A</fullName><firstName>Andreea</firstName><lastName>Ioan-Facsinay</lastName><initials>A</initials></author><author><fullName>Toes R</fullName><firstName>René</firstName><lastName>Toes</lastName><initials>R</initials></author><author><fullName>van der Burgt YE</fullName><firstName>Yuri E M</firstName><lastName>van der Burgt</lastName><initials>YE</initials></author><author><fullName>Deelder AM</fullName><firstName>André M</firstName><lastName>Deelder</lastName><initials>AM</initials></author><author><fullName>Mayboroda OA</fullName><firstName>Oleg A</firstName><lastName>Mayboroda</lastName><initials>OA</initials></author><author><fullName>Giera M</fullName><firstName>Martin</firstName><lastName>Giera</lastName><initials>M</initials></author></authorList><journalInfo><issue>12</issue><volume>85</volume><journalIssueId>2049259</journalIssueId><dateOfPublication>2013 Jun</dateOfPublication><monthOfPublication>6</monthOfPublication><yearOfPublication>2013</yearOfPublication><printPublicationDate>2013-06-01</printPublicationDate><journal><title>Analytical chemistry</title><ISOAbbreviation>Anal. Chem.</ISOAbbreviation><medlineAbbreviation>Anal Chem</medlineAbbreviation><NLMid>0370536</NLMid><ISSN>0003-2700</ISSN><ESSN>1520-6882</ESSN></journal></journalInfo><pageInfo>6003-6010</pageInfo><abstractText>Here, we present the application of a cross-platform approach, combining rapid direct infusion high-resolution/accurate mass electrospray ionization Fourier transform ion-cyclotron mass spectrometry (ESI-FTICRMS) with in-depth data-dependent LC-MS(2) and LC-MS(3) analysis for lipid profiling. The analytical approach as well as the subsequent data handling is described. The method was applied to human synovial fluid samples from osteo- and rheumatoid arthritis patients. Multivariate statistical analysis revealed esterified oxylipids as molecular features in a subset of the patient samples. Employing LC-MS(2) and LC-MS(3) analysis of these species, we were able to clarify the hypothesized lipid structures initially based on the accurate mass measurements performed on the ESI-FTICRMS platform. LC-MS(3) analysis of intact esterified oxy-lipids and LC-MS(2) analysis of the hydrolysis products allowed for the detection of positional isomers. The approach led to the structural elucidation of hydroxylated docosapentaenoic acid-containing diacyl-phosphatidylcholine type phospholipids in human synovial fluid.</abstractText><affiliation>Leiden University Medical Center (LUMC), Center for Proteomics and Metabolomics, Albinusdreef 2, 2300RC Leiden, The Netherlands.</affiliation><language>eng</language><pubModel>Print-Electronic</pubModel><pubTypeList><pubType>Journal Article</pubType><pubType>Research Support, Non-U.S. Gov't</pubType></pubTypeList><meshHeadingList><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Humans</descriptorName></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Synovial Fluid</descriptorName><meshQualifierList><meshQualifier><abbreviation>CH</abbreviation><qualifierName>chemistry</qualifierName><majorTopic_YN>Y</majorTopic_YN></meshQualifier></meshQualifierList></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Fatty Acids, Unsaturated</descriptorName><meshQualifierList><meshQualifier><abbreviation>AN</abbreviation><qualifierName>analysis</qualifierName><majorTopic_YN>Y</majorTopic_YN></meshQualifier><meshQualifier><abbreviation>CH</abbreviation><qualifierName>chemistry</qualifierName><majorTopic_YN>N</majorTopic_YN></meshQualifier></meshQualifierList></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Phospholipids</descriptorName><meshQualifierList><meshQualifier><abbreviation>AN</abbreviation><qualifierName>analysis</qualifierName><majorTopic_YN>Y</majorTopic_YN></meshQualifier><meshQualifier><abbreviation>CH</abbreviation><qualifierName>chemistry</qualifierName><majorTopic_YN>N</majorTopic_YN></meshQualifier></meshQualifierList></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Spectroscopy, Fourier Transform Infrared</descriptorName><meshQualifierList><meshQualifier><abbreviation>MT</abbreviation><qualifierName>methods</qualifierName><majorTopic_YN>N</majorTopic_YN></meshQualifier></meshQualifierList></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Spectrometry, Mass, Electrospray Ionization</descriptorName><meshQualifierList><meshQualifier><abbreviation>MT</abbreviation><qualifierName>methods</qualifierName><majorTopic_YN>Y</majorTopic_YN></meshQualifier></meshQualifierList></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Esterification</descriptorName></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Hydroxylation</descriptorName></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Chromatography, Liquid</descriptorName><meshQualifierList><meshQualifier><abbreviation>MT</abbreviation><qualifierName>methods</qualifierName><majorTopic_YN>N</majorTopic_YN></meshQualifier></meshQualifierList></meshHeading><meshHeading><majorTopic_YN>Y</majorTopic_YN><descriptorName>Cyclotrons</descriptorName></meshHeading></meshHeadingList><chemicalList><chemical><name>docosapentaenoic acid</name><registryNumber>25448-00-4</registryNumber></chemical><chemical><name>Phospholipids</name><registryNumber>0</registryNumber></chemical><chemical><name>Fatty Acids, Unsaturated</name><registryNumber>0</registryNumber></chemical></chemicalList><subsetList><subset><code>IM</code><name>Index Medicus</name></subset></subsetList><fullTextUrlList><fullTextUrl><availability>Subscription required</availability><availabilityCode>S</availabilityCode><documentStyle>doi</documentStyle><site>DOI</site><url>http://dx.doi.org/10.1021/ac400826z</url></fullTextUrl></fullTextUrlList><inEPMC>N</inEPMC><inPMC>N</inPMC><citedByCount>1</citedByCount><hasReferences>N</hasReferences><hasTextMinedTerms>N</hasTextMinedTerms><hasDbCrossReferences>N</hasDbCrossReferences><hasLabsLinks>N</hasLabsLinks><hasTMAccessionNumbers>N</hasTMAccessionNumbers><dateOfCompletion>2014-04-22</dateOfCompletion><dateOfCreation>2013-06-18</dateOfCreation><electronicPublicationDate>2013-05-30</electronicPublicationDate><firstPublicationDate>2013-05-30</firstPublicationDate><luceneScore>597.2597</luceneScore></result><result><id>23487758</id><source>MED</source><pmid>23487758</pmid><pmcid>PMC3606982</pmcid><DOI>10.1073/pnas.1217238110</DOI><title>Identification of genetic variants influencing the human plasma proteome.</title><authorString>Johansson Å, Enroth S, Palmblad M, Deelder AM, Bergquist J, Gyllensten U.</authorString><authorList><author><fullName>Johansson Å</fullName><firstName>Åsa</firstName><lastName>Johansson</lastName><initials>Å</initials><authorId type="ORCID">0000-0002-2915-4498</authorId></author><author><fullName>Enroth S</fullName><firstName>Stefan</firstName><lastName>Enroth</lastName><initials>S</initials><authorId type="ORCID">0000-0002-5056-9137</authorId></author><author><fullName>Palmblad M</fullName><firstName>Magnus</firstName><lastName>Palmblad</lastName><initials>M</initials></author><author><fullName>Deelder AM</fullName><firstName>André M</firstName><lastName>Deelder</lastName><initials>AM</initials></author><author><fullName>Bergquist J</fullName><firstName>Jonas</firstName><lastName>Bergquist</lastName><initials>J</initials></author><author><fullName>Gyllensten U</fullName><firstName>Ulf</firstName><lastName>Gyllensten</lastName><initials>U</initials></author></authorList><authorIdList><authorId type="ORCID">0000-0002-2915-4498</authorId><authorId type="ORCID">0000-0002-5056-9137</authorId></authorIdList><journalInfo><issue>12</issue><volume>110</volume><journalIssueId>2021511</journalIssueId><dateOfPublication>2013 Mar</dateOfPublication><monthOfPublication>3</monthOfPublication><yearOfPublication>2013</yearOfPublication><printPublicationDate>2013-03-01</printPublicationDate><journal><title>Proceedings of the National Academy of Sciences of the United States of America</title><ISOAbbreviation>Proc. Natl. Acad. Sci. U.S.A.</ISOAbbreviation><medlineAbbreviation>Proc Natl Acad Sci U S A</medlineAbbreviation><NLMid>7505876</NLMid><ISSN>0027-8424</ISSN><ESSN>1091-6490</ESSN></journal></journalInfo><pageInfo>4673-4678</pageInfo><abstractText>Genetic variants influencing the transcriptome have been extensively studied. However, the impact of the genetic factors on the human proteome is largely unexplored, mainly due to lack of suitable high-throughput methods. Here we present unique and comprehensive identification of genetic variants affecting the human plasma protein profile by combining high-throughput and high-resolution mass spectrometry (MS) with genome-wide SNP data. We identified and quantified the abundance of 1,056 tryptic-digested peptides, representing 163 proteins in the plasma of 1,060 individuals from two population-based cohorts. The abundance level of almost one-fifth (19%) of the peptides was found to be heritable, with heritability ranging from 0.08 to 0.43. The levels of 60 peptides from 25 proteins, 15% of the proteins studied, were influenced by cis-acting SNPs. We identified and replicated individual cis-acting SNPs (combined P value ranging from 3.1 × 10(-52) to 2.9 × 10(-12)) influencing 11 peptides from 5 individual proteins. These SNPs represent both regulatory SNPs and nonsynonymous changes defining well-studied disease alleles such as the ε4 allele of apolipoprotein E (APOE), which has been shown to increase risk of Alzheimer's disease. Our results show that high-throughput mass spectrometry represents a promising method for large-scale characterization of the human proteome, allowing for both quantification and sequencing of individual proteins. Abundance and peptide composition of a protein plays an important role in the etiology, diagnosis, and treatment of a number of diseases. A better understanding of the genetic impact on the plasma proteome is therefore important for evaluating potential biomarkers and therapeutic agents for common diseases.</abstractText><affiliation>Department of Immunology, Genetics, and Pathology, Rudbeck Laboratory, SciLifeLab, Uppsala University, 75185 Uppsala, Sweden.</affiliation><language>eng</language><pubModel>Print-Electronic</pubModel><pubTypeList><pubType>Clinical Trial</pubType><pubType>Journal Article</pubType><pubType>Multicenter Study</pubType><pubType>Research Support, Non-U.S. Gov't</pubType></pubTypeList><meshHeadingList><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Humans</descriptorName></meshHeading><meshHeading><majorTopic_YN>Y</majorTopic_YN><descriptorName>Blood Proteins</descriptorName><meshQualifierList><meshQualifier><abbreviation>GE</abbreviation><qualifierName>genetics</qualifierName><majorTopic_YN>N</majorTopic_YN></meshQualifier><meshQualifier><abbreviation>ME</abbreviation><qualifierName>metabolism</qualifierName><majorTopic_YN>N</majorTopic_YN></meshQualifier></meshQualifierList></meshHeading><meshHeading><majorTopic_YN>Y</majorTopic_YN><descriptorName>Proteome</descriptorName><meshQualifierList><meshQualifier><abbreviation>GE</abbreviation><qualifierName>genetics</qualifierName><majorTopic_YN>N</majorTopic_YN></meshQualifier><meshQualifier><abbreviation>ME</abbreviation><qualifierName>metabolism</qualifierName><majorTopic_YN>N</majorTopic_YN></meshQualifier></meshQualifierList></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Biological Markers</descriptorName><meshQualifierList><meshQualifier><abbreviation>BL</abbreviation><qualifierName>blood</qualifierName><majorTopic_YN>N</majorTopic_YN></meshQualifier></meshQualifierList></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Genetic Markers</descriptorName></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Cohort Studies</descriptorName></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Adolescent</descriptorName></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Adult</descriptorName></meshHeading><meshHeading><majorTopic_YN>Y</majorTopic_YN><descriptorName>Alleles</descriptorName></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Middle Aged</descriptorName></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Genome-Wide Association Study</descriptorName></meshHeading><meshHeading><majorTopic_YN>Y</majorTopic_YN><descriptorName>Polymorphism, Single Nucleotide</descriptorName></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Female</descriptorName></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Male</descriptorName></meshHeading></meshHeadingList><chemicalList><chemical><name>Blood Proteins</name><registryNumber>0</registryNumber></chemical><chemical><name>Biological Markers</name><registryNumber>0</registryNumber></chemical><chemical><name>Genetic Markers</name><registryNumber>0</registryNumber></chemical><chemical><name>Proteome</name><registryNumber>0</registryNumber></chemical></chemicalList><subsetList><subset><code>IM</code><name>Index Medicus</name></subset></subsetList><fullTextUrlList><fullTextUrl><availability>Free</availability><availabilityCode>F</availabilityCode><documentStyle>pdf</documentStyle><site>PubMedCentral</site><url>http://www.pubmedcentral.nih.gov/picrender.fcgi?tool=EBI&amp;pubmedid=23487758&amp;action=stream&amp;blobtype=pdf</url></fullTextUrl><fullTextUrl><availability>Free</availability><availabilityCode>F</availabilityCode><documentStyle>html</documentStyle><site>PubMedCentral</site><url>http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=EBI&amp;pubmedid=23487758</url></fullTextUrl><fullTextUrl><availability>Subscription required</availability><availabilityCode>S</availabilityCode><documentStyle>doi</documentStyle><site>DOI</site><url>http://dx.doi.org/10.1073/pnas.1217238110</url></fullTextUrl><fullTextUrl><availability>Free</availability><availabilityCode>F</availabilityCode><documentStyle>html</documentStyle><site>Europe_PMC</site><url>http://europepmc.org/articles/PMC3606982</url></fullTextUrl><fullTextUrl><availability>Free</availability><availabilityCode>F</availabilityCode><documentStyle>pdf</documentStyle><site>Europe_PMC</site><url>http://europepmc.org/articles/PMC3606982?pdf=render</url></fullTextUrl></fullTextUrlList><isOpenAccess>N</isOpenAccess><inEPMC>Y</inEPMC><inPMC>Y</inPMC><citedByCount>10</citedByCount><hasReferences>Y</hasReferences><hasTextMinedTerms>Y</hasTextMinedTerms><hasDbCrossReferences>N</hasDbCrossReferences><hasLabsLinks>N</hasLabsLinks><hasTMAccessionNumbers>Y</hasTMAccessionNumbers><tmAccessionTypeList><accessionType>refsnp</accessionType></tmAccessionTypeList><dateOfCompletion>2013-05-23</dateOfCompletion><dateOfCreation>2013-03-20</dateOfCreation><dateOfRevision>2013-09-19</dateOfRevision><electronicPublicationDate>2013-03-04</electronicPublicationDate><firstPublicationDate>2013-03-04</firstPublicationDate><luceneScore>747.9789</luceneScore></result><result><id>23666722</id><source>MED</source><pmid>23666722</pmid><DOI>10.1007/978-1-62703-392-3_3</DOI><title>Isotopic distributions.</title><authorString>Rockwood AL, Palmblad M.</authorString><authorList><author><fullName>Rockwood AL</fullName><firstName>Alan L</firstName><lastName>Rockwood</lastName><initials>AL</initials><affiliation>Department of Pathology, University of Utah, Salt Lake City, UT, USA.</affiliation></author><author><fullName>Palmblad M</fullName><firstName>Magnus</firstName><lastName>Palmblad</lastName><initials>M</initials></author></authorList><journalInfo><volume>1007</volume><journalIssueId>2038613</journalIssueId><dateOfPublication>2013 </dateOfPublication><monthOfPublication>0</monthOfPublication><yearOfPublication>2013</yearOfPublication><printPublicationDate>2013-01-01</printPublicationDate><journal><title>Methods in molecular biology (Clifton, N.J.)</title><ISOAbbreviation>Methods Mol. Biol.</ISOAbbreviation><medlineAbbreviation>Methods Mol Biol</medlineAbbreviation><NLMid>9214969</NLMid><ISSN>1064-3745</ISSN><ESSN>1940-6029</ESSN></journal></journalInfo><pageInfo>65-99</pageInfo><abstractText>Isotopic information determined by mass spectrometry can be used in a wide variety of applications. Broadly speaking these could be classified as "passive" applications, meaning that they use naturally occurring isotopic information, and "active" applications, meaning that the isotopic distributions are manipulated in some way. The classic passive application is the determination of chemical composition by comparing observed isotopic patterns of molecules to theoretically calculated isotopic patterns. Active applications include isotope exchange experiments of a variety of types, as well as isotope labeling in tracing studies and to provide references for quantitation. Regardless of the type of application considered, the problem of theoretical calculation of isotopic patterns almost invariably arises. This paper reviews a number of application examples and computational approaches for isotopic studies in mass spectrometry.</abstractText><affiliation>Department of Pathology, University of Utah, Salt Lake City, UT, USA.</affiliation><language>eng</language><pubModel>Print</pubModel><pubTypeList><pubType>Journal Article</pubType></pubTypeList><meshHeadingList><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Isotopes</descriptorName><meshQualifierList><meshQualifier><abbreviation>CH</abbreviation><qualifierName>chemistry</qualifierName><majorTopic_YN>Y</majorTopic_YN></meshQualifier></meshQualifierList></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Monte Carlo Method</descriptorName></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Mass Spectrometry</descriptorName><meshQualifierList><meshQualifier><abbreviation>MT</abbreviation><qualifierName>methods</qualifierName><majorTopic_YN>Y</majorTopic_YN></meshQualifier></meshQualifierList></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Isotope Labeling</descriptorName><meshQualifierList><meshQualifier><abbreviation>MT</abbreviation><qualifierName>methods</qualifierName><majorTopic_YN>N</majorTopic_YN></meshQualifier></meshQualifierList></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Algorithms</descriptorName></meshHeading></meshHeadingList><chemicalList><chemical><name>Isotopes</name><registryNumber>0</registryNumber></chemical></chemicalList><subsetList><subset><code>IM</code><name>Index Medicus</name></subset></subsetList><fullTextUrlList><fullTextUrl><availability>Subscription required</availability><availabilityCode>S</availabilityCode><documentStyle>doi</documentStyle><site>DOI</site><url>http://dx.doi.org/10.1007/978-1-62703-392-3_3</url></fullTextUrl></fullTextUrlList><inEPMC>N</inEPMC><inPMC>N</inPMC><citedByCount>1</citedByCount><hasReferences>N</hasReferences><hasTextMinedTerms>N</hasTextMinedTerms><hasDbCrossReferences>N</hasDbCrossReferences><hasLabsLinks>N</hasLabsLinks><hasTMAccessionNumbers>N</hasTMAccessionNumbers><dateOfCompletion>2013-12-02</dateOfCompletion><dateOfCreation>2013-05-13</dateOfCreation><firstPublicationDate>2013-01-01</firstPublicationDate><luceneScore>1218.5171</luceneScore></result><result><id>23666723</id><source>MED</source><pmid>23666723</pmid><DOI>10.1007/978-1-62703-392-3_4</DOI><title>Retention time prediction and protein identification.</title><authorString>Henneman AA, Palmblad M.</authorString><authorList><author><fullName>Henneman AA</fullName><firstName>Alex A</firstName><lastName>Henneman</lastName><initials>AA</initials><affiliation>Center for Proteomics and Metabolomics, Leiden University Medical Center, Leiden, The Netherlands.</affiliation></author><author><fullName>Palmblad M</fullName><firstName>Magnus</firstName><lastName>Palmblad</lastName><initials>M</initials></author></authorList><journalInfo><volume>1007</volume><journalIssueId>2038613</journalIssueId><dateOfPublication>2013 </dateOfPublication><monthOfPublication>0</monthOfPublication><yearOfPublication>2013</yearOfPublication><printPublicationDate>2013-01-01</printPublicationDate><journal><title>Methods in molecular biology (Clifton, N.J.)</title><ISOAbbreviation>Methods Mol. Biol.</ISOAbbreviation><medlineAbbreviation>Methods Mol Biol</medlineAbbreviation><NLMid>9214969</NLMid><ISSN>1064-3745</ISSN><ESSN>1940-6029</ESSN></journal></journalInfo><pageInfo>101-118</pageInfo><abstractText>In bottom-up proteomics, proteins are typically identified by enzymatic digestion into peptides, tandem mass spectrometry and comparison of the tandem mass spectra with those predicted from a sequence database for peptides within measurement uncertainty from the experimentally obtained mass. Although now decreasingly common, isolated proteins or simple protein mixtures can also be identified by measuring only the masses of the peptides resulting from the enzymatic digest, without any further fragmentation. Separation methods such as liquid chromatography and electrophoresis are often used to fractionate complex protein or peptide mixtures prior to analysis by mass spectrometry. Although the primary reason for this is to avoid ion suppression and improve data quality, these separations are based on physical and chemical properties of the peptides or proteins and therefore also provide information about them. Depending on the separation method, this could be protein molecular weight (SDS-PAGE), isoelectric point (IEF), charge at a known pH (ion exchange chromatography), or hydrophobicity (reversed phase chromatography). These separations produce approximate measurements on properties that to some extent can be predicted from amino acid sequences. In the case of molecular weight of proteins without posttranslational modifications this is straightforward: simply add the molecular weights of the amino acid residues in the protein. For IEF, charge and hydrophobicity, the order of the amino acids, and folding state of the peptide or protein also matter, but it is nevertheless possible to predict the behavior of peptides and proteins in these separation methods to a degree which renders such predictions useful. This chapter reviews the topic of using data from separation methods for identification and validation in proteomics, with special emphasis on predicting retention times of tryptic peptides in reversed-phase chromatography under acidic conditions, as this is one of the most commonly used separation methods in proteomics.</abstractText><affiliation>Center for Proteomics and Metabolomics, Leiden University Medical Center, Leiden, The Netherlands.</affiliation><language>eng</language><pubModel>Print</pubModel><pubTypeList><pubType>Journal Article</pubType></pubTypeList><meshHeadingList><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Trypsin</descriptorName><meshQualifierList><meshQualifier><abbreviation>ME</abbreviation><qualifierName>metabolism</qualifierName><majorTopic_YN>N</majorTopic_YN></meshQualifier></meshQualifierList></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Proteins</descriptorName><meshQualifierList><meshQualifier><abbreviation>AN</abbreviation><qualifierName>analysis</qualifierName><majorTopic_YN>Y</majorTopic_YN></meshQualifier></meshQualifierList></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Proteomics</descriptorName><meshQualifierList><meshQualifier><abbreviation>MT</abbreviation><qualifierName>methods</qualifierName><majorTopic_YN>N</majorTopic_YN></meshQualifier></meshQualifierList></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Chromatography, Reverse-Phase</descriptorName><meshQualifierList><meshQualifier><abbreviation>MT</abbreviation><qualifierName>methods</qualifierName><majorTopic_YN>Y</majorTopic_YN></meshQualifier></meshQualifierList></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Peptide Fragments</descriptorName><meshQualifierList><meshQualifier><abbreviation>AN</abbreviation><qualifierName>analysis</qualifierName><majorTopic_YN>Y</majorTopic_YN></meshQualifier><meshQualifier><abbreviation>IP</abbreviation><qualifierName>isolation &amp; purification</qualifierName><majorTopic_YN>N</majorTopic_YN></meshQualifier><meshQualifier><abbreviation>ME</abbreviation><qualifierName>metabolism</qualifierName><majorTopic_YN>N</majorTopic_YN></meshQualifier><meshQualifier><abbreviation>CH</abbreviation><qualifierName>chemistry</qualifierName><majorTopic_YN>N</majorTopic_YN></meshQualifier></meshQualifierList></meshHeading></meshHeadingList><chemicalList><chemical><name>Trypsin</name><registryNumber>EC 3.4.21.4</registryNumber></chemical><chemical><name>Proteins</name><registryNumber>0</registryNumber></chemical><chemical><name>Peptide Fragments</name><registryNumber>0</registryNumber></chemical></chemicalList><subsetList><subset><code>IM</code><name>Index Medicus</name></subset></subsetList><fullTextUrlList><fullTextUrl><availability>Subscription required</availability><availabilityCode>S</availabilityCode><documentStyle>doi</documentStyle><site>DOI</site><url>http://dx.doi.org/10.1007/978-1-62703-392-3_4</url></fullTextUrl></fullTextUrlList><inEPMC>N</inEPMC><inPMC>N</inPMC><citedByCount>0</citedByCount><hasReferences>N</hasReferences><hasTextMinedTerms>N</hasTextMinedTerms><hasDbCrossReferences>N</hasDbCrossReferences><hasLabsLinks>N</hasLabsLinks><hasTMAccessionNumbers>N</hasTMAccessionNumbers><dateOfCompletion>2013-12-02</dateOfCompletion><dateOfCreation>2013-05-13</dateOfCreation><firstPublicationDate>2013-01-01</firstPublicationDate><luceneScore>1218.5171</luceneScore></result><result><id>23666736</id><source>MED</source><pmid>23666736</pmid><DOI>10.1007/978-1-62703-392-3_17</DOI><title>Simple proteomics data analysis in the object-oriented PowerShell.</title><authorString>Mohammed Y, Palmblad M.</authorString><authorList><author><fullName>Mohammed Y</fullName><firstName>Yassene</firstName><lastName>Mohammed</lastName><initials>Y</initials><affiliation>Center for Proteomics and Metabolomics, Leiden University Medical Center, Leiden, The Netherlands.</affiliation></author><author><fullName>Palmblad M</fullName><firstName>Magnus</firstName><lastName>Palmblad</lastName><initials>M</initials></author></authorList><journalInfo><volume>1007</volume><journalIssueId>2038613</journalIssueId><dateOfPublication>2013 </dateOfPublication><monthOfPublication>0</monthOfPublication><yearOfPublication>2013</yearOfPublication><printPublicationDate>2013-01-01</printPublicationDate><journal><title>Methods in molecular biology (Clifton, N.J.)</title><ISOAbbreviation>Methods Mol. Biol.</ISOAbbreviation><medlineAbbreviation>Methods Mol Biol</medlineAbbreviation><NLMid>9214969</NLMid><ISSN>1064-3745</ISSN><ESSN>1940-6029</ESSN></journal></journalInfo><pageInfo>379-391</pageInfo><abstractText>Scripting languages such as Perl and Python are appreciated for solving simple, everyday tasks in bioinformatics. A more recent, object-oriented command shell and scripting language, Windows PowerShell, has many attractive features: an object-oriented interactive command line, fluent navigation and manipulation of XML files, ability to consume Web services from the command line, consistent syntax and grammar, rich regular expressions, and advanced output formatting. The key difference between classical command shells and scripting languages, such as bash, and object-oriented ones, such as PowerShell, is that in the latter the result of a command is a structured object with inherited properties and methods rather than a simple stream of characters. Conveniently, PowerShell is included in all new releases of Microsoft Windows and therefore already installed on most computers in classrooms and teaching labs. In this chapter we demonstrate how PowerShell in particular allows easy interaction with mass spectrometry data in XML formats, connection to Web services for tools such as BLAST, and presentation of results as formatted text or graphics. These features make PowerShell much more than "yet another scripting language."</abstractText><affiliation>Center for Proteomics and Metabolomics, Leiden University Medical Center, Leiden, The Netherlands.</affiliation><language>eng</language><pubModel>Print</pubModel><pubTypeList><pubType>Journal Article</pubType></pubTypeList><meshHeadingList><meshHeading><majorTopic_YN>Y</majorTopic_YN><descriptorName>Proteomics</descriptorName></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Mass Spectrometry</descriptorName></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Internet</descriptorName></meshHeading><meshHeading><majorTopic_YN>Y</majorTopic_YN><descriptorName>Software</descriptorName></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Computational Biology</descriptorName><meshQualifierList><meshQualifier><abbreviation>MT</abbreviation><qualifierName>methods</qualifierName><majorTopic_YN>Y</majorTopic_YN></meshQualifier></meshQualifierList></meshHeading><meshHeading><majorTopic_YN>Y</majorTopic_YN><descriptorName>Programming Languages</descriptorName></meshHeading></meshHeadingList><subsetList><subset><code>IM</code><name>Index Medicus</name></subset></subsetList><fullTextUrlList><fullTextUrl><availability>Subscription required</availability><availabilityCode>S</availabilityCode><documentStyle>doi</documentStyle><site>DOI</site><url>http://dx.doi.org/10.1007/978-1-62703-392-3_17</url></fullTextUrl></fullTextUrlList><inEPMC>N</inEPMC><inPMC>N</inPMC><citedByCount>0</citedByCount><hasReferences>N</hasReferences><hasTextMinedTerms>N</hasTextMinedTerms><hasDbCrossReferences>N</hasDbCrossReferences><hasLabsLinks>N</hasLabsLinks><hasTMAccessionNumbers>N</hasTMAccessionNumbers><dateOfCompletion>2013-12-02</dateOfCompletion><dateOfCreation>2013-05-13</dateOfCreation><firstPublicationDate>2013-01-01</firstPublicationDate><luceneScore>1380.2645</luceneScore></result><result><id>22916831</id><source>MED</source><pmid>22916831</pmid><DOI>10.1021/pr300561q</DOI><title>Cloud parallel processing of tandem mass spectrometry based proteomics data.</title><authorString>Mohammed Y, Mostovenko E, Henneman AA, Marissen RJ, Deelder AM, Palmblad M.</authorString><authorList><author><fullName>Mohammed Y</fullName><firstName>Yassene</firstName><lastName>Mohammed</lastName><initials>Y</initials></author><author><fullName>Mostovenko E</fullName><firstName>Ekaterina</firstName><lastName>Mostovenko</lastName><initials>E</initials></author><author><fullName>Henneman AA</fullName><firstName>Alex A</firstName><lastName>Henneman</lastName><initials>AA</initials></author><author><fullName>Marissen RJ</fullName><firstName>Rob J</firstName><lastName>Marissen</lastName><initials>RJ</initials></author><author><fullName>Deelder AM</fullName><firstName>André M</firstName><lastName>Deelder</lastName><initials>AM</initials></author><author><fullName>Palmblad M</fullName><firstName>Magnus</firstName><lastName>Palmblad</lastName><initials>M</initials></author></authorList><journalInfo><issue>10</issue><volume>11</volume><journalIssueId>1971890</journalIssueId><dateOfPublication>2012 Oct</dateOfPublication><monthOfPublication>10</monthOfPublication><yearOfPublication>2012</yearOfPublication><printPublicationDate>2012-10-01</printPublicationDate><journal><title>Journal of proteome research</title><ISOAbbreviation>J. Proteome Res.</ISOAbbreviation><medlineAbbreviation>J Proteome Res</medlineAbbreviation><NLMid>101128775</NLMid><ISSN>1535-3893</ISSN><ESSN>1535-3907</ESSN></journal></journalInfo><pageInfo>5101-5108</pageInfo><abstractText>Data analysis in mass spectrometry based proteomics struggles to keep pace with the advances in instrumentation and the increasing rate of data acquisition. Analyzing this data involves multiple steps requiring diverse software, using different algorithms and data formats. Speed and performance of the mass spectral search engines are continuously improving, although not necessarily as needed to face the challenges of acquired big data. Improving and parallelizing the search algorithms is one possibility; data decomposition presents another, simpler strategy for introducing parallelism. We describe a general method for parallelizing identification of tandem mass spectra using data decomposition that keeps the search engine intact and wraps the parallelization around it. We introduce two algorithms for decomposing mzXML files and recomposing resulting pepXML files. This makes the approach applicable to different search engines, including those relying on sequence databases and those searching spectral libraries. We use cloud computing to deliver the computational power and scientific workflow engines to interface and automate the different processing steps. We show how to leverage these technologies to achieve faster data analysis in proteomics and present three scientific workflows for parallel database as well as spectral library search using our data decomposition programs, X!Tandem and SpectraST.</abstractText><affiliation>Biomolecular Mass Spectrometry Unit, Department of Parasitology, Leiden University Medical Center, The Netherlands. y.mohammed@lumc.nl</affiliation><language>eng</language><pubModel>Print-Electronic</pubModel><pubTypeList><pubType>Journal Article</pubType><pubType>Research Support, Non-U.S. Gov't</pubType></pubTypeList><meshHeadingList><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Humans</descriptorName></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Escherichia coli Proteins</descriptorName><meshQualifierList><meshQualifier><abbreviation>IP</abbreviation><qualifierName>isolation &amp; purification</qualifierName><majorTopic_YN>N</majorTopic_YN></meshQualifier><meshQualifier><abbreviation>CH</abbreviation><qualifierName>chemistry</qualifierName><majorTopic_YN>N</majorTopic_YN></meshQualifier></meshQualifierList></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Blood Proteins</descriptorName><meshQualifierList><meshQualifier><abbreviation>IP</abbreviation><qualifierName>isolation &amp; purification</qualifierName><majorTopic_YN>N</majorTopic_YN></meshQualifier><meshQualifier><abbreviation>CH</abbreviation><qualifierName>chemistry</qualifierName><majorTopic_YN>N</majorTopic_YN></meshQualifier></meshQualifierList></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Peptide Mapping</descriptorName><meshQualifierList><meshQualifier><abbreviation>MT</abbreviation><qualifierName>methods</qualifierName><majorTopic_YN>Y</majorTopic_YN></meshQualifier></meshQualifierList></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Proteomics</descriptorName></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Automatic Data Processing</descriptorName></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Computer Communication Networks</descriptorName></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Data Compression</descriptorName></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Data Mining</descriptorName></meshHeading><meshHeading><majorTopic_YN>Y</majorTopic_YN><descriptorName>Search Engine</descriptorName></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Tandem Mass Spectrometry</descriptorName><meshQualifierList><meshQualifier><abbreviation>MT</abbreviation><qualifierName>methods</qualifierName><majorTopic_YN>Y</majorTopic_YN></meshQualifier></meshQualifierList></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Chromatography, Liquid</descriptorName></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Algorithms</descriptorName></meshHeading></meshHeadingList><chemicalList><chemical><name>Blood Proteins</name><registryNumber>0</registryNumber></chemical><chemical><name>Escherichia coli Proteins</name><registryNumber>0</registryNumber></chemical></chemicalList><subsetList><subset><code>IM</code><name>Index Medicus</name></subset></subsetList><fullTextUrlList><fullTextUrl><availability>Subscription required</availability><availabilityCode>S</availabilityCode><documentStyle>doi</documentStyle><site>DOI</site><url>http://dx.doi.org/10.1021/pr300561q</url></fullTextUrl></fullTextUrlList><inEPMC>N</inEPMC><inPMC>N</inPMC><citedByCount>4</citedByCount><hasReferences>N</hasReferences><hasTextMinedTerms>N</hasTextMinedTerms><hasDbCrossReferences>N</hasDbCrossReferences><hasLabsLinks>N</hasLabsLinks><hasTMAccessionNumbers>N</hasTMAccessionNumbers><dateOfCompletion>2013-02-26</dateOfCompletion><dateOfCreation>2012-10-05</dateOfCreation><electronicPublicationDate>2012-09-05</electronicPublicationDate><firstPublicationDate>2012-09-05</firstPublicationDate><luceneScore>719.4867</luceneScore></result><result><id>22623400</id><source>MED</source><pmid>22623400</pmid><DOI>10.1002/pmic.201100496</DOI><title>Proteomic analysis of Taenia solium metacestode excretion-secretion proteins.</title><authorString>Victor B, Kanobana K, Gabriël S, Polman K, Deckers N, Dorny P, Deelder AM, Palmblad M.</authorString><authorList><author><fullName>Victor B</fullName><firstName>Bjorn</firstName><lastName>Victor</lastName><initials>B</initials></author><author><fullName>Kanobana K</fullName><firstName>Kirezi</firstName><lastName>Kanobana</lastName><initials>K</initials></author><author><fullName>Gabriël S</fullName><firstName>Sarah</firstName><lastName>Gabriël</lastName><initials>S</initials></author><author><fullName>Polman K</fullName><firstName>Katja</firstName><lastName>Polman</lastName><initials>K</initials></author><author><fullName>Deckers N</fullName><firstName>Nynke</firstName><lastName>Deckers</lastName><initials>N</initials></author><author><fullName>Dorny P</fullName><firstName>Pierre</firstName><lastName>Dorny</lastName><initials>P</initials></author><author><fullName>Deelder AM</fullName><firstName>André M</firstName><lastName>Deelder</lastName><initials>AM</initials></author><author><fullName>Palmblad M</fullName><firstName>Magnus</firstName><lastName>Palmblad</lastName><initials>M</initials></author></authorList><journalInfo><issue>11</issue><volume>12</volume><journalIssueId>1944125</journalIssueId><dateOfPublication>2012 Jun</dateOfPublication><monthOfPublication>6</monthOfPublication><yearOfPublication>2012</yearOfPublication><printPublicationDate>2012-06-01</printPublicationDate><journal><title>Proteomics</title><ISOAbbreviation>Proteomics</ISOAbbreviation><medlineAbbreviation>Proteomics</medlineAbbreviation><NLMid>101092707</NLMid><ISSN>1615-9853</ISSN><ESSN>1615-9861</ESSN></journal></journalInfo><pageInfo>1860-1869</pageInfo><abstractText>The metacestode larval stage of Taenia solium is the causal agent of a zoonotic disease called cysticercosis. The disease has an important impact on pork trade (due to porcine cysticercosis) and public health (due to human neurocysticercosis). In order to improve the current diagnostic tools and to get a better understanding of the interaction between T. solium metacestodes and their host, there is a need for more information about the proteins that are released by the parasite. In this study, we used protein sequences from different helminths, 1DE, reversed-phase LC, and MS/MS to analyze the excretion-secretion proteins produced by T. solium metacestodes from infected pigs. This is the first report of the T. solium metacestode excretion-secretion proteome. We report 76 proteins including 27 already described T. solium proteins, 17 host proteins and 32 proteins likely to be of T. solium origin, but identified using sequences from other helminths.</abstractText><affiliation>Veterinary Helminthology Unit, Department of Biomedical Sciences, Institute of Tropical Medicine, Antwerp, Belgium. bvictor@itg.be</affiliation><language>eng</language><pubModel>Print</pubModel><pubTypeList><pubType>Journal Article</pubType><pubType>Research Support, Non-U.S. Gov't</pubType></pubTypeList><grantsList><grant><grantId>221948</grantId><agency>European Commission FP7</agency><acronym>ICONZ</acronym><orderIn>0</orderIn></grant></grantsList><meshHeadingList><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Animals</descriptorName></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Cysticercosis</descriptorName><meshQualifierList><meshQualifier><abbreviation>PS</abbreviation><qualifierName>parasitology</qualifierName><majorTopic_YN>N</majorTopic_YN></meshQualifier><meshQualifier><abbreviation>VE</abbreviation><qualifierName>veterinary</qualifierName><majorTopic_YN>Y</majorTopic_YN></meshQualifier></meshQualifierList></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Swine Diseases</descriptorName><meshQualifierList><meshQualifier><abbreviation>DI</abbreviation><qualifierName>diagnosis</qualifierName><majorTopic_YN>N</majorTopic_YN></meshQualifier><meshQualifier><abbreviation>PS</abbreviation><qualifierName>parasitology</qualifierName><majorTopic_YN>Y</majorTopic_YN></meshQualifier></meshQualifierList></meshHeading><meshHeading><majorTopic_YN>Y</majorTopic_YN><descriptorName>Proteome</descriptorName></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Helminth Proteins</descriptorName><meshQualifierList><meshQualifier><abbreviation>AN</abbreviation><qualifierName>analysis</qualifierName><majorTopic_YN>Y</majorTopic_YN></meshQualifier></meshQualifierList></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Sequence Analysis, Protein</descriptorName></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Amino Acid Sequence</descriptorName></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Proteomics</descriptorName></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Mass Spectrometry</descriptorName></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Taenia solium</descriptorName><meshQualifierList><meshQualifier><abbreviation>ME</abbreviation><qualifierName>metabolism</qualifierName><majorTopic_YN>Y</majorTopic_YN></meshQualifier></meshQualifierList></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Swine</descriptorName></meshHeading></meshHeadingList><chemicalList><chemical><name>Helminth Proteins</name><registryNumber>0</registryNumber></chemical><chemical><name>Proteome</name><registryNumber>0</registryNumber></chemical></chemicalList><subsetList><subset><code>IM</code><name>Index Medicus</name></subset></subsetList><fullTextUrlList><fullTextUrl><availability>Subscription required</availability><availabilityCode>S</availabilityCode><documentStyle>doi</documentStyle><site>DOI</site><url>http://dx.doi.org/10.1002/pmic.201100496</url></fullTextUrl></fullTextUrlList><inEPMC>N</inEPMC><inPMC>N</inPMC><citedByCount>5</citedByCount><hasReferences>N</hasReferences><hasTextMinedTerms>N</hasTextMinedTerms><hasDbCrossReferences>N</hasDbCrossReferences><hasLabsLinks>N</hasLabsLinks><hasTMAccessionNumbers>N</hasTMAccessionNumbers><dateOfCompletion>2012-12-21</dateOfCompletion><dateOfCreation>2012-06-28</dateOfCreation><firstPublicationDate>2012-06-01</firstPublicationDate><luceneScore>638.61304</luceneScore></result><result><id>22368051</id><source>MED</source><pmid>22368051</pmid><DOI>10.1002/rcm.6162</DOI><title>Molecular phylogenetics by direct comparison of tandem mass spectra.</title><authorString>Palmblad M, Deelder AM.</authorString><authorList><author><fullName>Palmblad M</fullName><firstName>Magnus</firstName><lastName>Palmblad</lastName><initials>M</initials></author><author><fullName>Deelder AM</fullName><firstName>André M</firstName><lastName>Deelder</lastName><initials>AM</initials></author></authorList><journalInfo><issue>7</issue><volume>26</volume><journalIssueId>1908667</journalIssueId><dateOfPublication>2012 Apr</dateOfPublication><monthOfPublication>4</monthOfPublication><yearOfPublication>2012</yearOfPublication><printPublicationDate>2012-04-01</printPublicationDate><journal><title>Rapid communications in mass spectrometry : RCM</title><ISOAbbreviation>Rapid Commun. Mass Spectrom.</ISOAbbreviation><medlineAbbreviation>Rapid Commun Mass Spectrom</medlineAbbreviation><NLMid>8802365</NLMid><ISSN>0951-4198</ISSN><ESSN>1097-0231</ESSN></journal></journalInfo><pageInfo>728-732</pageInfo><abstractText> RATIONALE: Molecular phylogenetics is the study of evolution and relatedness of organisms or genes. Mass spectrometry is used routinely for bacterial identification and has also been used for phylogenetic analysis, for instance from bone material. Unfortunately, only a small fraction of the acquired tandem mass spectra allow direct interpretation. METHODS: We describe a new algorithm and software for molecular phylogenetics using pairwise comparisons of tandem mass spectra from enzymatically digested proteins. The spectra need not be annotated and all acquired data is used in the analysis. To demonstrate the method, we analyzed tryptic digests of sera from four great apes and two other primates. RESULTS: The distribution of spectra dot products for thousands of tandem mass spectra collected from two samples provides a measure on the fraction of shared peptides between the two samples. When inverted, this becomes a distance metric. By pairwise comparison between species and averaging over four individuals per species, it was possible to reconstruct the unique correct phylogenetic tree for the great apes and other primates. CONCLUSIONS: The new method described here has several attractive features compared with existing methods, among them simplicity, the unbiased use of all acquired data rather than a small subset of spectra, and the potential use of heavily degraded proteins or proteins with a priori unknown modifications.</abstractText><affiliation>Biomolecular Mass Spectrometry Unit, Department of Parasitology, Leiden University Medical Center, P.O. Box 9600, 2300, RC, Leiden, The Netherlands. n.m.palmblad@lumc.nl</affiliation><language>eng</language><pubModel>Print</pubModel><pubTypeList><pubType>Journal Article</pubType></pubTypeList><meshHeadingList><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Animals</descriptorName></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Humans</descriptorName></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Trypsin</descriptorName><meshQualifierList><meshQualifier><abbreviation>GE</abbreviation><qualifierName>genetics</qualifierName><majorTopic_YN>N</majorTopic_YN></meshQualifier><meshQualifier><abbreviation>CH</abbreviation><qualifierName>chemistry</qualifierName><majorTopic_YN>N</majorTopic_YN></meshQualifier></meshQualifierList></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Blood Proteins</descriptorName><meshQualifierList><meshQualifier><abbreviation>GE</abbreviation><qualifierName>genetics</qualifierName><majorTopic_YN>N</majorTopic_YN></meshQualifier><meshQualifier><abbreviation>CH</abbreviation><qualifierName>chemistry</qualifierName><majorTopic_YN>N</majorTopic_YN></meshQualifier></meshQualifierList></meshHeading><meshHeading><majorTopic_YN>Y</majorTopic_YN><descriptorName>Evolution, Molecular</descriptorName></meshHeading><meshHeading><majorTopic_YN>Y</majorTopic_YN><descriptorName>Phylogeny</descriptorName></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Software</descriptorName></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Catarrhini</descriptorName><meshQualifierList><meshQualifier><abbreviation>GE</abbreviation><qualifierName>genetics</qualifierName><majorTopic_YN>N</majorTopic_YN></meshQualifier></meshQualifierList></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Tandem Mass Spectrometry</descriptorName><meshQualifierList><meshQualifier><abbreviation>MT</abbreviation><qualifierName>methods</qualifierName><majorTopic_YN>Y</majorTopic_YN></meshQualifier></meshQualifierList></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Peptide Fragments</descriptorName><meshQualifierList><meshQualifier><abbreviation>GE</abbreviation><qualifierName>genetics</qualifierName><majorTopic_YN>Y</majorTopic_YN></meshQualifier><meshQualifier><abbreviation>CH</abbreviation><qualifierName>chemistry</qualifierName><majorTopic_YN>N</majorTopic_YN></meshQualifier></meshQualifierList></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Chromatography, Liquid</descriptorName></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Computational Biology</descriptorName><meshQualifierList><meshQualifier><abbreviation>MT</abbreviation><qualifierName>methods</qualifierName><majorTopic_YN>Y</majorTopic_YN></meshQualifier></meshQualifierList></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Algorithms</descriptorName></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Female</descriptorName></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Male</descriptorName></meshHeading></meshHeadingList><chemicalList><chemical><name>Trypsin</name><registryNumber>EC 3.4.21.4</registryNumber></chemical><chemical><name>Blood Proteins</name><registryNumber>0</registryNumber></chemical><chemical><name>Peptide Fragments</name><registryNumber>0</registryNumber></chemical></chemicalList><subsetList><subset><code>IM</code><name>Index Medicus</name></subset></subsetList><fullTextUrlList><fullTextUrl><availability>Subscription required</availability><availabilityCode>S</availabilityCode><documentStyle>doi</documentStyle><site>DOI</site><url>http://dx.doi.org/10.1002/rcm.6162</url></fullTextUrl></fullTextUrlList><inEPMC>N</inEPMC><inPMC>N</inPMC><citedByCount>0</citedByCount><hasReferences>Y</hasReferences><hasTextMinedTerms>N</hasTextMinedTerms><hasDbCrossReferences>N</hasDbCrossReferences><hasLabsLinks>N</hasLabsLinks><hasTMAccessionNumbers>N</hasTMAccessionNumbers><dateOfCompletion>2012-06-25</dateOfCompletion><dateOfCreation>2012-02-27</dateOfCreation><firstPublicationDate>2012-04-01</firstPublicationDate><luceneScore>1175.3207</luceneScore></result><result><id>22411703</id><source>MED</source><pmid>22411703</pmid><pmcid>PMC3394934</pmcid><DOI>10.1074/mcp.m111.010595</DOI><title>Scientific workflow management in proteomics.</title><authorString>de Bruin JS, Deelder AM, Palmblad M.</authorString><authorList><author><fullName>de Bruin JS</fullName><firstName>Jeroen S</firstName><lastName>de Bruin</lastName><initials>JS</initials></author><author><fullName>Deelder AM</fullName><firstName>André M</firstName><lastName>Deelder</lastName><initials>AM</initials></author><author><fullName>Palmblad M</fullName><firstName>Magnus</firstName><lastName>Palmblad</lastName><initials>M</initials></author></authorList><journalInfo><issue>7</issue><volume>11</volume><journalIssueId>1947312</journalIssueId><dateOfPublication>2012 Jul</dateOfPublication><monthOfPublication>7</monthOfPublication><yearOfPublication>2012</yearOfPublication><printPublicationDate>2012-07-01</printPublicationDate><journal><title>Molecular &amp; cellular proteomics : MCP</title><ISOAbbreviation>Mol. Cell Proteomics</ISOAbbreviation><medlineAbbreviation>Mol Cell Proteomics</medlineAbbreviation><NLMid>101125647</NLMid><ISSN>1535-9476</ISSN><ESSN>1535-9484</ESSN></journal></journalInfo><pageInfo>M111.010595</pageInfo><abstractText>Data processing in proteomics can be a challenging endeavor, requiring extensive knowledge of many different software packages, all with different algorithms, data format requirements, and user interfaces. In this article we describe the integration of a number of existing programs and tools in Taverna Workbench, a scientific workflow manager currently being developed in the bioinformatics community. We demonstrate how a workflow manager provides a single, visually clear and intuitive interface to complex data analysis tasks in proteomics, from raw mass spectrometry data to protein identifications and beyond.</abstractText><affiliation>Section for Medical Expert and Knowledge-Based Systems, Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Spitalgasse 23, A-1090 Vienna, Austria.</affiliation><language>eng</language><pubModel>Print-Electronic</pubModel><pubTypeList><pubType>Journal Article</pubType></pubTypeList><meshHeadingList><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Proteins</descriptorName><meshQualifierList><meshQualifier><abbreviation>AN</abbreviation><qualifierName>analysis</qualifierName><majorTopic_YN>Y</majorTopic_YN></meshQualifier></meshQualifierList></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Proteomics</descriptorName></meshHeading><meshHeading><majorTopic_YN>Y</majorTopic_YN><descriptorName>User-Computer Interface</descriptorName></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Workflow</descriptorName></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Tandem Mass Spectrometry</descriptorName></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Chromatography, Liquid</descriptorName></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Algorithms</descriptorName></meshHeading></meshHeadingList><chemicalList><chemical><name>Proteins</name><registryNumber>0</registryNumber></chemical></chemicalList><subsetList><subset><code>IM</code><name>Index Medicus</name></subset></subsetList><fullTextUrlList><fullTextUrl><availability>Free</availability><availabilityCode>F</availabilityCode><documentStyle>html</documentStyle><site>PubMedCentral</site><url>http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=EBI&amp;pubmedid=22411703</url></fullTextUrl><fullTextUrl><availability>Free</availability><availabilityCode>F</availabilityCode><documentStyle>pdf</documentStyle><site>PubMedCentral</site><url>http://www.pubmedcentral.nih.gov/picrender.fcgi?tool=EBI&amp;pubmedid=22411703&amp;action=stream&amp;blobtype=pdf</url></fullTextUrl><fullTextUrl><availability>Subscription required</availability><availabilityCode>S</availabilityCode><documentStyle>doi</documentStyle><site>DOI</site><url>http://dx.doi.org/10.1074/mcp.M111.010595</url></fullTextUrl><fullTextUrl><availability>Free</availability><availabilityCode>F</availabilityCode><documentStyle>html</documentStyle><site>Europe_PMC</site><url>http://europepmc.org/articles/PMC3394934</url></fullTextUrl></fullTextUrlList><isOpenAccess>N</isOpenAccess><inEPMC>Y</inEPMC><inPMC>Y</inPMC><citedByCount>3</citedByCount><hasReferences>Y</hasReferences><hasTextMinedTerms>Y</hasTextMinedTerms><hasDbCrossReferences>N</hasDbCrossReferences><hasLabsLinks>N</hasLabsLinks><hasTMAccessionNumbers>N</hasTMAccessionNumbers><dateOfCompletion>2012-10-30</dateOfCompletion><dateOfCreation>2012-07-09</dateOfCreation><dateOfRevision>2013-07-02</dateOfRevision><electronicPublicationDate>2012-03-12</electronicPublicationDate><firstPublicationDate>2012-03-12</firstPublicationDate><luceneScore>719.4867</luceneScore></result><result><id>22339108</id><source>MED</source><pmid>22339108</pmid><DOI>10.1021/pr201035r</DOI><title>Partially sequenced organisms, decoy searches and false discovery rates.</title><authorString>Victor B, Gabriël S, Kanobana K, Mostovenko E, Polman K, Dorny P, Deelder AM, Palmblad M.</authorString><authorList><author><fullName>Victor B</fullName><firstName>Bjorn</firstName><lastName>Victor</lastName><initials>B</initials></author><author><fullName>Gabriël S</fullName><firstName>Sarah</firstName><lastName>Gabriël</lastName><initials>S</initials></author><author><fullName>Kanobana K</fullName><firstName>Kirezi</firstName><lastName>Kanobana</lastName><initials>K</initials></author><author><fullName>Mostovenko E</fullName><firstName>Ekaterina</firstName><lastName>Mostovenko</lastName><initials>E</initials></author><author><fullName>Polman K</fullName><firstName>Katja</firstName><lastName>Polman</lastName><initials>K</initials></author><author><fullName>Dorny P</fullName><firstName>Pierre</firstName><lastName>Dorny</lastName><initials>P</initials></author><author><fullName>Deelder AM</fullName><firstName>André M</firstName><lastName>Deelder</lastName><initials>AM</initials></author><author><fullName>Palmblad M</fullName><firstName>Magnus</firstName><lastName>Palmblad</lastName><initials>M</initials></author></authorList><journalInfo><issue>3</issue><volume>11</volume><journalIssueId>1910857</journalIssueId><dateOfPublication>2012 Mar</dateOfPublication><monthOfPublication>3</monthOfPublication><yearOfPublication>2012</yearOfPublication><printPublicationDate>2012-03-01</printPublicationDate><journal><title>Journal of proteome research</title><ISOAbbreviation>J. Proteome Res.</ISOAbbreviation><medlineAbbreviation>J Proteome Res</medlineAbbreviation><NLMid>101128775</NLMid><ISSN>1535-3893</ISSN><ESSN>1535-3907</ESSN></journal></journalInfo><pageInfo>1991-1995</pageInfo><abstractText>Tandem mass spectrometry is commonly used to identify peptides, typically by comparing their product ion spectra with those predicted from a protein sequence database and scoring these matches. The most reported quality metric for a set of peptide identifications is the false discovery rate (FDR), the fraction of expected false identifications in the set. This metric has so far only been used for completely sequenced organisms or known protein mixtures. We have investigated whether FDR estimations are also applicable in the case of partially sequenced organisms, where many high-quality spectra fail to identify the correct peptides because the latter are not present in the searched sequence database. Using real data from human plasma and simulated partial sequence databases derived from two complete human sequence databases with different levels of redundancy, we could demonstrate that the mixture model approach in PeptideProphet is robust for partial databases, particularly if used in combination with decoy sequences. We therefore recommend using this method when estimating the FDR and reporting peptide identifications from incompletely sequenced organisms.</abstractText><affiliation>Veterinary Helminthology Unit, Department of Biomedical Sciences, Institute of Tropical Medicine (ITM) , Antwerp, Belgium. bvictor@itg.be</affiliation><language>eng</language><pubModel>Print-Electronic</pubModel><pubTypeList><pubType>Journal Article</pubType><pubType>Research Support, Non-U.S. Gov't</pubType></pubTypeList><grantsList><grant><grantId>221948</grantId><agency>European Commission FP7</agency><acronym>ICONZ</acronym><orderIn>0</orderIn></grant></grantsList><meshHeadingList><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Humans</descriptorName></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Blood Proteins</descriptorName><meshQualifierList><meshQualifier><abbreviation>ME</abbreviation><qualifierName>metabolism</qualifierName><majorTopic_YN>Y</majorTopic_YN></meshQualifier><meshQualifier><abbreviation>CH</abbreviation><qualifierName>chemistry</qualifierName><majorTopic_YN>N</majorTopic_YN></meshQualifier></meshQualifierList></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Peptide Mapping</descriptorName><meshQualifierList><meshQualifier><abbreviation>MT</abbreviation><qualifierName>methods</qualifierName><majorTopic_YN>Y</majorTopic_YN></meshQualifier><meshQualifier><abbreviation>ST</abbreviation><qualifierName>standards</qualifierName><majorTopic_YN>N</majorTopic_YN></meshQualifier></meshQualifierList></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Computer Simulation</descriptorName></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Peptide Fragments</descriptorName><meshQualifierList><meshQualifier><abbreviation>CH</abbreviation><qualifierName>chemistry</qualifierName><majorTopic_YN>N</majorTopic_YN></meshQualifier></meshQualifierList></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Algorithms</descriptorName></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Models, Biological</descriptorName></meshHeading><meshHeading><majorTopic_YN>Y</majorTopic_YN><descriptorName>Databases, Protein</descriptorName></meshHeading></meshHeadingList><chemicalList><chemical><name>Blood Proteins</name><registryNumber>0</registryNumber></chemical><chemical><name>Peptide Fragments</name><registryNumber>0</registryNumber></chemical></chemicalList><subsetList><subset><code>IM</code><name>Index Medicus</name></subset></subsetList><fullTextUrlList><fullTextUrl><availability>Subscription required</availability><availabilityCode>S</availabilityCode><documentStyle>doi</documentStyle><site>DOI</site><url>http://dx.doi.org/10.1021/pr201035r</url></fullTextUrl></fullTextUrlList><inEPMC>N</inEPMC><inPMC>N</inPMC><citedByCount>0</citedByCount><hasReferences>N</hasReferences><hasTextMinedTerms>N</hasTextMinedTerms><hasDbCrossReferences>N</hasDbCrossReferences><hasLabsLinks>N</hasLabsLinks><hasTMAccessionNumbers>N</hasTMAccessionNumbers><dateOfCompletion>2012-07-09</dateOfCompletion><dateOfCreation>2012-03-02</dateOfCreation><electronicPublicationDate>2012-02-16</electronicPublicationDate><firstPublicationDate>2012-02-16</firstPublicationDate><luceneScore>638.61304</luceneScore></result><result><id>22095492</id><source>MED</source><pmid>22095492</pmid><DOI>10.1002/rcm.5246</DOI><title>Precision profiling and identification of human serum peptides using Fourier transform ion cyclotron resonance mass spectrometry.</title><authorString>Nicolardi S, Palmblad M, Hensbergen PJ, Tollenaar RA, Deelder AM, van der Burgt YE.</authorString><authorList><author><fullName>Nicolardi S</fullName><firstName>Simone</firstName><lastName>Nicolardi</lastName><initials>S</initials></author><author><fullName>Palmblad M</fullName><firstName>Magnus</firstName><lastName>Palmblad</lastName><initials>M</initials></author><author><fullName>Hensbergen PJ</fullName><firstName>Paul J</firstName><lastName>Hensbergen</lastName><initials>PJ</initials></author><author><fullName>Tollenaar RA</fullName><firstName>Rob A E M</firstName><lastName>Tollenaar</lastName><initials>RA</initials></author><author><fullName>Deelder AM</fullName><firstName>André M</firstName><lastName>Deelder</lastName><initials>AM</initials></author><author><fullName>van der Burgt YE</fullName><firstName>Yuri E M</firstName><lastName>van der Burgt</lastName><initials>YE</initials></author></authorList><journalInfo><issue>23</issue><volume>25</volume><journalIssueId>1881910</journalIssueId><dateOfPublication>2011 Dec</dateOfPublication><monthOfPublication>12</monthOfPublication><yearOfPublication>2011</yearOfPublication><printPublicationDate>2011-12-01</printPublicationDate><journal><title>Rapid communications in mass spectrometry : RCM</title><ISOAbbreviation>Rapid Commun. Mass Spectrom.</ISOAbbreviation><medlineAbbreviation>Rapid Commun Mass Spectrom</medlineAbbreviation><NLMid>8802365</NLMid><ISSN>0951-4198</ISSN><ESSN>1097-0231</ESSN></journal></journalInfo><pageInfo>3457-3463</pageInfo><abstractText>Many biomarker discovery studies are based on matrix-assisted laser desorption/ionisation (MALDI) peptide profiles. In this study, 96 human serum samples were analysed on a Bruker solariX(TM) MALDI Fourier transform ion cyclotron resonance (FTICR) system equipped with a 15 tesla magnet. Isotopically resolved peptides were observed in ultrahigh resolution FTICR profiles up to m/z 6500 with mass measurement errors (MMEs) of previously identified peptides at a sub-ppm level. For comparison with our previous platform for peptide profile mass analysis (i.e. Ultraflex II) the corresponding time-of-flight (TOF) spectra were obtained with isotopically resolved peptides up to m/z 3500. The FTICR and TOF systems performed rather similar with respect to the repeatability of the signal intensities. However, the mass measurement precision improved at least 10-fold in ultrahigh resolution data and thus simplified spectral alignment necessary for robust and quantitatively precise comparisons of profiles in large-scale clinical studies. From each single MALDI-FTICR spectrum an m/z-list was obtained with sub-ppm precision for all different species, which is beneficial for identification purposes and interlaboratory comparisons. Furthermore, the FTICR system allowed new peptide identifications from collision-induced dissociation (CID) spectra using direct infusion of reversed-phase (RP) C(18)-fractionated serum samples on an electrospray ionisation (ESI) source.</abstractText><affiliation>Department of Parasitology, Leiden University Medical Center (LUMC), Biomolecular Mass Spectrometry Unit, Albinusdreef 2, 2300 RC Leiden, The Netherlands.</affiliation><language>eng</language><pubModel>Print</pubModel><pubTypeList><pubType>Journal Article</pubType><pubType>Research Support, Non-U.S. Gov't</pubType></pubTypeList><meshHeadingList><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Humans</descriptorName></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Peptides</descriptorName><meshQualifierList><meshQualifier><abbreviation>BL</abbreviation><qualifierName>blood</qualifierName><majorTopic_YN>Y</majorTopic_YN></meshQualifier></meshQualifierList></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Biological Markers</descriptorName><meshQualifierList><meshQualifier><abbreviation>BL</abbreviation><qualifierName>blood</qualifierName><majorTopic_YN>N</majorTopic_YN></meshQualifier></meshQualifierList></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Reproducibility of Results</descriptorName></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Amino Acid Sequence</descriptorName></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Molecular Sequence Data</descriptorName></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Chromatography, Reverse-Phase</descriptorName></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization</descriptorName><meshQualifierList><meshQualifier><abbreviation>IS</abbreviation><qualifierName>instrumentation</qualifierName><majorTopic_YN>N</majorTopic_YN></meshQualifier><meshQualifier><abbreviation>MT</abbreviation><qualifierName>methods</qualifierName><majorTopic_YN>Y</majorTopic_YN></meshQualifier></meshQualifierList></meshHeading><meshHeading><majorTopic_YN>Y</majorTopic_YN><descriptorName>Fourier Analysis</descriptorName></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Equipment Design</descriptorName></meshHeading></meshHeadingList><chemicalList><chemical><name>Peptides</name><registryNumber>0</registryNumber></chemical><chemical><name>Biological Markers</name><registryNumber>0</registryNumber></chemical></chemicalList><subsetList><subset><code>IM</code><name>Index Medicus</name></subset></subsetList><fullTextUrlList><fullTextUrl><availability>Subscription required</availability><availabilityCode>S</availabilityCode><documentStyle>doi</documentStyle><site>DOI</site><url>http://dx.doi.org/10.1002/rcm.5246</url></fullTextUrl></fullTextUrlList><inEPMC>N</inEPMC><inPMC>N</inPMC><citedByCount>3</citedByCount><hasReferences>Y</hasReferences><hasTextMinedTerms>N</hasTextMinedTerms><hasDbCrossReferences>N</hasDbCrossReferences><hasLabsLinks>N</hasLabsLinks><hasTMAccessionNumbers>N</hasTMAccessionNumbers><dateOfCompletion>2012-03-05</dateOfCompletion><dateOfCreation>2011-11-18</dateOfCreation><firstPublicationDate>2011-12-01</firstPublicationDate><luceneScore>677.0246</luceneScore></result><result><id>22120122</id><source>MED</source><pmid>22120122</pmid><DOI>10.1016/j.jprot.2011.11.003</DOI><title>Fc specific IgG glycosylation profiling by robust nano-reverse phase HPLC-MS using a sheath-flow ESI sprayer interface.</title><authorString>Selman MH, Derks RJ, Bondt A, Palmblad M, Schoenmaker B, Koeleman CA, van de Geijn FE, Dolhain RJ, Deelder AM, Wuhrer M.</authorString><authorList><author><fullName>Selman MH</fullName><firstName>Maurice H J</firstName><lastName>Selman</lastName><initials>MH</initials></author><author><fullName>Derks RJ</fullName><firstName>Rico J E</firstName><lastName>Derks</lastName><initials>RJ</initials></author><author><fullName>Bondt A</fullName><firstName>Albert</firstName><lastName>Bondt</lastName><initials>A</initials><authorId type="ORCID">0000-0002-0985-7903</authorId></author><author><fullName>Palmblad M</fullName><firstName>Magnus</firstName><lastName>Palmblad</lastName><initials>M</initials></author><author><fullName>Schoenmaker B</fullName><firstName>Bart</firstName><lastName>Schoenmaker</lastName><initials>B</initials></author><author><fullName>Koeleman CA</fullName><firstName>Carolien A M</firstName><lastName>Koeleman</lastName><initials>CA</initials></author><author><fullName>van de Geijn FE</fullName><firstName>Fleur E</firstName><lastName>van de Geijn</lastName><initials>FE</initials></author><author><fullName>Dolhain RJ</fullName><firstName>Radboud J E M</firstName><lastName>Dolhain</lastName><initials>RJ</initials></author><author><fullName>Deelder AM</fullName><firstName>André M</firstName><lastName>Deelder</lastName><initials>AM</initials></author><author><fullName>Wuhrer M</fullName><firstName>Manfred</firstName><lastName>Wuhrer</lastName><initials>M</initials></author></authorList><authorIdList><authorId type="ORCID">0000-0002-0985-7903</authorId></authorIdList><journalInfo><issue>4</issue><volume>75</volume><journalIssueId>1897240</journalIssueId><dateOfPublication>2012 Feb</dateOfPublication><monthOfPublication>2</monthOfPublication><yearOfPublication>2012</yearOfPublication><printPublicationDate>2012-02-01</printPublicationDate><journal><title>Journal of proteomics</title><ISOAbbreviation>J Proteomics</ISOAbbreviation><medlineAbbreviation>J Proteomics</medlineAbbreviation><NLMid>101475056</NLMid><ISSN>1874-3919</ISSN><ESSN>1876-7737</ESSN></journal></journalInfo><pageInfo>1318-1329</pageInfo><abstractText>Biological activities of immunoglobulin G such as effector functions via Fc receptor interactions are influenced by Fc-linked N-glycans. Here we describe a fast, robust and sensitive nano-LC-ESI-MS method for detailed subclass specific analysis of IgG Fc N-glycosylation. A sheath-flow ESI sprayer was used as a sensitive zero dead volume plug-and-play interface for online MS coupling, generating a very constant spray and ionization over the entire LC gradient. The propionic acid containing sheath-liquid effectively suppressed TFA gas-phase ion-pairing, enabling the use of TFA containing mobile phases. The fixed position of the sheath-flow ESI sprayer, far away from the glass capillary inlet, reduced MS contamination as compared to conventional nano-ESI. The method was found to be suitable for fast and detailed subclass specific IgG Fc N-glycosylation profiling in human plasma. The obtained subclass specific IgG Fc N-glycosylation profiles were processed automatically using in house developed software tools. For each of the IgG subclasses the 8 major glycoforms showed an interday analytical variation below 5%. The method was used to profile the IgG Fc N-glycosylation of 26 women at several time points during pregnancy and after delivery, revealing pregnancy-associated changes in IgG galactosylation, sialylation and incidence of bisecting N-acetylglucosamine.</abstractText><affiliation>Biomolecular Mass Spectrometry Unit, Department of Parasitology, Leiden University Medical Center, Leiden, The Netherlands.</affiliation><language>eng</language><pubModel>Print-Electronic</pubModel><pubTypeList><pubType>Journal Article</pubType><pubType>Research Support, Non-U.S. Gov't</pubType></pubTypeList><meshHeadingList><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Humans</descriptorName></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Immunoglobulin G</descriptorName><meshQualifierList><meshQualifier><abbreviation>CH</abbreviation><qualifierName>chemistry</qualifierName><majorTopic_YN>Y</majorTopic_YN></meshQualifier></meshQualifierList></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Galactose</descriptorName><meshQualifierList><meshQualifier><abbreviation>CH</abbreviation><qualifierName>chemistry</qualifierName><majorTopic_YN>N</majorTopic_YN></meshQualifier></meshQualifierList></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Polysaccharides</descriptorName><meshQualifierList><meshQualifier><abbreviation>CH</abbreviation><qualifierName>chemistry</qualifierName><majorTopic_YN>N</majorTopic_YN></meshQualifier></meshQualifierList></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Spectrometry, Mass, Electrospray Ionization</descriptorName><meshQualifierList><meshQualifier><abbreviation>MT</abbreviation><qualifierName>methods</qualifierName><majorTopic_YN>Y</majorTopic_YN></meshQualifier></meshQualifierList></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Postpartum Period</descriptorName></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Glycosylation</descriptorName></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Mass Spectrometry</descriptorName><meshQualifierList><meshQualifier><abbreviation>MT</abbreviation><qualifierName>methods</qualifierName><majorTopic_YN>N</majorTopic_YN></meshQualifier></meshQualifierList></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Software</descriptorName></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Acetylglucosamine</descriptorName><meshQualifierList><meshQualifier><abbreviation>CH</abbreviation><qualifierName>chemistry</qualifierName><majorTopic_YN>N</majorTopic_YN></meshQualifier></meshQualifierList></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>N-Acetylneuraminic Acid</descriptorName><meshQualifierList><meshQualifier><abbreviation>CH</abbreviation><qualifierName>chemistry</qualifierName><majorTopic_YN>N</majorTopic_YN></meshQualifier></meshQualifierList></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Chromatography, High Pressure Liquid</descriptorName><meshQualifierList><meshQualifier><abbreviation>MT</abbreviation><qualifierName>methods</qualifierName><majorTopic_YN>N</majorTopic_YN></meshQualifier></meshQualifierList></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Pregnancy</descriptorName></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Female</descriptorName></meshHeading><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Immunoglobulin Fc Fragments</descriptorName><meshQualifierList><meshQualifier><abbreviation>ME</abbreviation><qualifierName>metabolism</qualifierName><majorTopic_YN>Y</majorTopic_YN></meshQualifier></meshQualifierList></meshHeading></meshHeadingList><chemicalList><chemical><name>glycosylated IgG</name><registryNumber>0</registryNumber></chemical><chemical><name>Polysaccharides</name><registryNumber>0</registryNumber></chemical><chemical><name>Immunoglobulin G</name><registryNumber>0</registryNumber></chemical><chemical><name>Galactose</name><registryNumber>26566-61-0</registryNumber></chemical><chemical><name>Acetylglucosamine</name><registryNumber>7512-17-6</registryNumber></chemical><chemical><name>N-Acetylneuraminic Acid</name><registryNumber>131-48-6</registryNumber></chemical><chemical><name>Immunoglobulin Fc Fragments</name><registryNumber>0</registryNumber></chemical></chemicalList><subsetList><subset><code>IM</code><name>Index Medicus</name></subset></subsetList><fullTextUrlList><fullTextUrl><availability>Subscription 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M.</authorString><authorList><author><fullName>Mostovenko E</fullName><firstName>Ekaterina</firstName><lastName>Mostovenko</lastName><initials>E</initials></author><author><fullName>Deelder AM</fullName><firstName>André M</firstName><lastName>Deelder</lastName><initials>AM</initials></author><author><fullName>Palmblad M</fullName><firstName>Magnus</firstName><lastName>Palmblad</lastName><initials>M</initials></author></authorList><journalInfo><volume>11</volume><journalIssueId>1800645</journalIssueId><dateOfPublication>2011 </dateOfPublication><monthOfPublication>0</monthOfPublication><yearOfPublication>2011</yearOfPublication><printPublicationDate>2011-01-01</printPublicationDate><journal><title>BMC microbiology</title><ISOAbbreviation>BMC Microbiol.</ISOAbbreviation><medlineAbbreviation>BMC Microbiol</medlineAbbreviation><NLMid>100966981</NLMid><ESSN>1471-2180</ESSN></journal></journalInfo><pageInfo>126</pageInfo><abstractText> BACKGROUND: Escherichia coli is a well-studied anaerobic bacteria which is able to regulate metabolic pathways depending on the type of sugar presented in the medium. We have studied the glucose-lactose shift in E. coli at the protein level using a recently developed mass spectrometry platform. METHOD: Cells were grown in minimal medium containing two sugars (glucose and lactose) and analyzed using novel mass spectrometry cluster. The cluster combines the high resolving power and dynamic range of Fourier transform ion cyclotron resonance (FTICR) for accurate mass measurement and quantitation with multiple ion traps for fast and sensitive tandem mass spectrometry. The protein expression profile was followed in time across the glucose-lactose diauxic shift using label-free quantitation from the FTICR data. RESULTS AND CONCLUSION: The entire dataset was interrogated by KEGG pathway analysis, mapping measured changes in protein abundance onto known metabolic pathways. The obtained results were consistent with previously published gene expression data, with β-galactosidase being the most strongly induced protein during the diauxic shift.</abstractText><affiliation>Department of Parasitology, Biomolecular Mass Spectrometry Unit, Leiden University Medical Center, 2300 RC Leiden, Netherlands. e.mostovenko@lumc.nl</affiliation><language>eng</language><pubModel>Electronic</pubModel><pubTypeList><pubType>Journal Article</pubType></pubTypeList><meshHeadingList><meshHeading><majorTopic_YN>N</majorTopic_YN><descriptorName>Escherichia coli</descriptorName><meshQualifierList><meshQualifier><abbreviation>GE</abbreviation><qualifierName>genetics</qualifierName><majorTopic_YN>N</majorTopic_YN></meshQualifier><meshQualifier><abbreviation>GD</abbreviation><qualifierName>growth &amp; 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access</availability><availabilityCode>OA</availabilityCode><documentStyle>html</documentStyle><site>Europe_PMC</site><url>http://europepmc.org/articles/PMC3121593</url></fullTextUrl><fullTextUrl><availability>Open access</availability><availabilityCode>OA</availabilityCode><documentStyle>pdf</documentStyle><site>Europe_PMC</site><url>http://europepmc.org/articles/PMC3121593?pdf=render</url></fullTextUrl></fullTextUrlList><isOpenAccess>Y</isOpenAccess><inEPMC>Y</inEPMC><inPMC>Y</inPMC><citedByCount>1</citedByCount><hasReferences>Y</hasReferences><hasTextMinedTerms>Y</hasTextMinedTerms><hasDbCrossReferences>N</hasDbCrossReferences><hasLabsLinks>N</hasLabsLinks><hasTMAccessionNumbers>N</hasTMAccessionNumbers><dateOfCompletion>2011-09-08</dateOfCompletion><dateOfCreation>2011-06-24</dateOfCreation><dateOfRevision>2013-06-28</dateOfRevision><electronicPublicationDate>2011-06-01</electronicPublicationDate><firstPublicationDate>2011-06-01</firstPublicationDate><luceneScore>985.8153</luceneScore></result><result><id>20541438</id><source>MED</source><pmid>20541438</pmid><DOI>10.1016/j.jasms.2010.05.004</DOI><title>Quality control based on isotopic distributions for high-throughput MALDI-TOF and MALDI-FTICR serum peptide profiling.</title><authorString>Nicolardi S, Palmblad M, Dalebout H, Bladergroen M, Tollenaar RA, Deelder AM, van der Burgt YE.</authorString><authorList><author><fullName>Nicolardi S</fullName><firstName>Simone</firstName><lastName>Nicolardi</lastName><initials>S</initials></author><author><fullName>Palmblad M</fullName><firstName>Magnus</firstName><lastName>Palmblad</lastName><initials>M</initials></author><author><fullName>Dalebout H</fullName><firstName>Hans</firstName><lastName>Dalebout</lastName><initials>H</initials></author><author><fullName>Bladergroen M</fullName><firstName>Marco</firstName><lastName>Bladergroen</lastName><initials>M</initials><authorId type="ORCID">0000-0003-4434-4799</authorId></author><author><fullName>Tollenaar RA</fullName><firstName>Rob A E M</firstName><lastName>Tollenaar</lastName><initials>RA</initials></author><author><fullName>Deelder AM</fullName><firstName>André M</firstName><lastName>Deelder</lastName><initials>AM</initials></author><author><fullName>van der Burgt YE</fullName><firstName>Yuri E M</firstName><lastName>van der Burgt</lastName><initials>YE</initials></author></authorList><authorIdList><authorId type="ORCID">0000-0003-4434-4799</authorId></authorIdList><journalInfo><issue>9</issue><volume>21</volume><journalIssueId>1757452</journalIssueId><dateOfPublication>2010 Sep</dateOfPublication><monthOfPublication>9</monthOfPublication><yearOfPublication>2010</yearOfPublication><printPublicationDate>2010-09-01</printPublicationDate><journal><title>Journal of the American Society for Mass Spectrometry</title><ISOAbbreviation>J. Am. Soc. Mass Spectrom.</ISOAbbreviation><medlineAbbreviation>J Am Soc Mass Spectrom</medlineAbbreviation><NLMid>9010412</NLMid><ISSN>1044-0305</ISSN><ESSN>1879-1123</ESSN></journal></journalInfo><pageInfo>1515-1525</pageInfo><abstractText>In this study, we have implemented a new quality control (QC) parameter for peptide profiling based on isotopic distributions. This QC parameter is an objective measure and facilitates automatic sorting of large numbers of peptide spectra. Peptides in human serum samples were enriched using reversed-phase C(18)-functionalized magnetic beads using a high-throughput robotic platform. High-resolution MALDI-TOF and ultrahigh resolution MALDI-FTICR mass spectra were obtained and a workflow was developed for automated analysis and evaluation of these profiles. To this end, the isotopic distributions of multiple peptides were quantified from both MALDI-TOF and MALDI-FTICR spectra. Odd peptide isotope distributions in TOF spectra could be rationalized from ultrahigh resolution FTICR spectra that showed overlap of different peptides. The comparison of isotope patterns with estimated polyaveragine distributions was used to calculate a QC value for each single mass spectrum. Sorting these QC values enabled the best MALDI spectrum to be selected from replicate spots. Moreover, using this approach spectra containing high intensities of polymers or other contaminants and lacking peptides of interest can be efficiently removed from a clinical dataset. In general, this method simplifies the exclusion of low quality spectra from further statistical analysis.</abstractText><affiliation>Department of Parasitology, Biomolecular Mass Spectrometry Unit, Leiden University Medical Center, Leiden, The Netherlands.</affiliation><language>eng</language><pubModel>Print-Electronic</pubModel><pubTypeList><pubType>Journal Article</pubType><pubType>Research Support, Non-U.S. 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required</availability><availabilityCode>S</availabilityCode><documentStyle>doi</documentStyle><site>DOI</site><url>http://dx.doi.org/10.1016/j.jasms.2010.05.004</url></fullTextUrl></fullTextUrlList><inEPMC>N</inEPMC><inPMC>N</inPMC><citedByCount>6</citedByCount><hasReferences>Y</hasReferences><hasTextMinedTerms>N</hasTextMinedTerms><hasDbCrossReferences>N</hasDbCrossReferences><hasLabsLinks>N</hasLabsLinks><hasTMAccessionNumbers>N</hasTMAccessionNumbers><dateOfCompletion>2010-11-30</dateOfCompletion><dateOfCreation>2010-08-24</dateOfCreation><electronicPublicationDate>2010-05-12</electronicPublicationDate><firstPublicationDate>2010-05-12</firstPublicationDate><luceneScore>429.7506</luceneScore></result></resultList></return> /PubmedArticleSet/PubmedArticle/MedlineCitation/Article/AuthorList/Author/AffiliationInfo/Affiliation | /combined/return/resultList/result/affiliation net.sf.taverna.t2.coreworkflowmodel-impl1.5net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Parallelize 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qname="bookOrReportDetails&gt;monthOfPublication" /><s:basetype optional="true" unbounded="false" typename="short" name="yearOfPublication" qname="bookOrReportDetails&gt;yearOfPublication" /><s:basetype optional="true" unbounded="false" typename="string" name="numberOfPages" qname="bookOrReportDetails&gt;numberOfPages" /><s:basetype optional="true" unbounded="false" typename="string" name="edition" qname="bookOrReportDetails&gt;edition" /><s:basetype optional="true" unbounded="false" typename="string" name="isbn10" qname="bookOrReportDetails&gt;isbn10" /><s:basetype optional="true" unbounded="false" typename="string" name="isbn13" qname="bookOrReportDetails&gt;isbn13" /><s:basetype optional="true" unbounded="false" typename="string" name="seriesName" qname="bookOrReportDetails&gt;seriesName" /><s:basetype optional="true" unbounded="false" typename="string" name="seriesIssn" qname="bookOrReportDetails&gt;seriesIssn" /><s:basetype optional="true" unbounded="false" typename="string" name="comprisingTitle" qname="bookOrReportDetails&gt;comprisingTitle" /><s:basetype optional="true" unbounded="false" typename="string" name="comprisingTitleNonAscii" qname="bookOrReportDetails&gt;comprisingTitleNonAscii" /><s:basetype optional="true" unbounded="false" typename="string" name="extraInformation" qname="bookOrReportDetails&gt;extraInformation" /></s:elements></s:complextype><s:complextype optional="true" unbounded="false" typename="patentDetailsInfo" name="patentDetails" qname="result&gt;patentDetails"><s:elements><s:basetype optional="true" unbounded="false" typename="string" name="countryCode" qname="patentDetailsInfo&gt;countryCode" /><s:basetype optional="true" unbounded="false" typename="string" name="country" qname="patentDetailsInfo&gt;country" /><s:basetype optional="true" unbounded="false" typename="string" name="typeCode" qname="patentDetailsInfo&gt;typeCode" /><s:basetype optional="true" unbounded="false" typename="string" name="typeDescription" 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qname="patentDetailsInfo&gt;application"><s:elements><s:basetype optional="true" unbounded="false" typename="string" name="applicationNumber" qname="patentApplication&gt;applicationNumber" /><s:basetype optional="true" unbounded="false" typename="string" name="applicationDate" qname="patentApplication&gt;applicationDate" /><s:basetype optional="true" unbounded="false" typename="short" name="orderIn" qname="patentApplication&gt;orderIn" /></s:elements></s:complextype><s:arraytype optional="true" unbounded="false" wrapped="true" typename="patentPriorityList" name="priorityList" qname="patentDetailsInfo&gt;priorityList"><s:elementtype><s:complextype optional="false" unbounded="false" typename="patentPriorityData" name="" qname="{http://webservice.cdb.ebi.ac.uk/}patentPriorityData"><s:elements><s:basetype optional="true" unbounded="false" typename="string" name="priorityNumber" qname="patentPriorityData&gt;priorityNumber" /><s:basetype optional="true" unbounded="false" typename="string" 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name="dbCrossReferenceInfo" qname="dbCrossReference&gt;dbCrossReferenceInfo"><s:elementtype><s:complextype optional="false" unbounded="false" typename="dbCrossReferenceInfo" name="" qname="{http://webservice.cdb.ebi.ac.uk/}dbCrossReferenceInfo"><s:elements><s:basetype optional="true" unbounded="false" typename="string" name="info1" qname="dbCrossReferenceInfo&gt;info1" /><s:basetype optional="true" unbounded="false" typename="string" name="info2" qname="dbCrossReferenceInfo&gt;info2" /><s:basetype optional="true" unbounded="false" typename="string" name="info3" qname="dbCrossReferenceInfo&gt;info3" /><s:basetype optional="true" unbounded="false" typename="string" name="info4" qname="dbCrossReferenceInfo&gt;info4" /></s:elements></s:complextype></s:elementtype></s:arraytype></s:elements></s:complextype></s:elementtype></s:arraytype><s:arraytype optional="true" unbounded="false" wrapped="true" typename="citationList" name="citationList" qname="responseWrapper&gt;citationList"><s:elementtype><s:complextype optional="false" unbounded="false" typename="CitationData" name="" qname="{http://webservice.cdb.ebi.ac.uk/}CitationData"><s:elements><s:basetype optional="true" unbounded="false" typename="string" name="id" qname="CitationData&gt;id" /><s:basetype optional="true" unbounded="false" typename="string" name="source" qname="CitationData&gt;source" /><s:basetype optional="true" unbounded="false" typename="string" name="citationType" qname="CitationData&gt;citationType" /><s:basetype optional="true" unbounded="false" typename="string" name="title" qname="CitationData&gt;title" /><s:basetype optional="true" unbounded="false" typename="string" name="authorString" qname="CitationData&gt;authorString" /><s:basetype optional="true" unbounded="false" typename="string" name="journalAbbreviation" qname="CitationData&gt;journalAbbreviation" /><s:basetype optional="false" unbounded="false" typename="int" name="pubYear" qname="CitationData&gt;pubYear" /><s:basetype optional="true" unbounded="false" typename="string" name="volume" qname="CitationData&gt;volume" /><s:basetype optional="true" unbounded="false" typename="string" name="ISSN" qname="CitationData&gt;ISSN" /><s:basetype optional="true" unbounded="false" typename="string" name="issue" qname="CitationData&gt;issue" /><s:basetype optional="true" unbounded="false" typename="string" name="pageInfo" qname="CitationData&gt;pageInfo" /><s:basetype optional="false" unbounded="false" typename="int" name="citedByCount" qname="CitationData&gt;citedByCount" /><s:basetype optional="true" unbounded="false" typename="string" name="text" qname="CitationData&gt;text" /></s:elements></s:complextype></s:elementtype></s:arraytype><s:arraytype optional="true" unbounded="false" wrapped="true" typename="referencesList" name="referenceList" qname="responseWrapper&gt;referenceList"><s:elementtype><s:complextype optional="false" unbounded="false" typename="referenceInfo" name="" qname="{http://webservice.cdb.ebi.ac.uk/}referenceInfo"><s:elements><s:basetype optional="true" unbounded="false" typename="string" name="id" qname="referenceInfo&gt;id" /><s:basetype optional="true" unbounded="false" typename="string" name="source" qname="referenceInfo&gt;source" /><s:basetype optional="true" unbounded="false" typename="string" name="citationType" qname="referenceInfo&gt;citationType" /><s:basetype optional="true" unbounded="false" typename="string" name="title" qname="referenceInfo&gt;title" /><s:basetype optional="true" unbounded="false" typename="string" name="authorString" qname="referenceInfo&gt;authorString" /><s:basetype optional="true" unbounded="false" typename="string" name="journalAbbreviation" qname="referenceInfo&gt;journalAbbreviation" /><s:basetype optional="true" unbounded="false" typename="string" name="issue" qname="referenceInfo&gt;issue" /><s:basetype optional="false" unbounded="false" typename="int" name="pubYear" qname="referenceInfo&gt;pubYear" /><s:basetype optional="true" unbounded="false" typename="string" name="volume" qname="referenceInfo&gt;volume" /><s:basetype optional="true" unbounded="false" typename="string" name="ISSN" qname="referenceInfo&gt;ISSN" /><s:basetype optional="true" unbounded="false" typename="string" name="ESSN" qname="referenceInfo&gt;ESSN" /><s:basetype optional="true" unbounded="false" typename="string" name="ISBN" qname="referenceInfo&gt;ISBN" /><s:basetype optional="true" unbounded="false" typename="string" name="pageInfo" qname="referenceInfo&gt;pageInfo" /><s:basetype optional="true" unbounded="false" typename="string" name="publicationTitle" qname="referenceInfo&gt;publicationTitle" /><s:basetype optional="true" unbounded="false" typename="string" name="publisherLoc" qname="referenceInfo&gt;publisherLoc" /><s:basetype optional="true" unbounded="false" typename="string" name="publisherName" qname="referenceInfo&gt;publisherName" /><s:basetype optional="true" unbounded="false" typename="string" name="seriesName" qname="referenceInfo&gt;seriesName" /><s:basetype optional="true" unbounded="false" typename="string" name="edition" qname="referenceInfo&gt;edition" /><s:basetype optional="true" unbounded="false" typename="string" name="editors" qname="referenceInfo&gt;editors" /><s:basetype optional="true" unbounded="false" typename="string" name="doi" qname="referenceInfo&gt;doi" /><s:basetype optional="true" unbounded="false" typename="string" name="unstructuredInformation" qname="referenceInfo&gt;unstructuredInformation" /><s:basetype optional="true" unbounded="false" typename="string" name="externalLink" qname="referenceInfo&gt;externalLink" /><s:basetype optional="true" unbounded="false" typename="string" name="comments" qname="referenceInfo&gt;comments" /><s:basetype optional="false" unbounded="false" typename="int" name="citedOrder" qname="referenceInfo&gt;citedOrder" /><s:basetype optional="true" unbounded="false" typename="string" name="match" qname="referenceInfo&gt;match" /></s:elements></s:complextype></s:elementtype></s:arraytype><s:arraytype optional="true" unbounded="false" wrapped="true" typename="semanticTypeCounts" name="semanticTypeCountList" qname="responseWrapper&gt;semanticTypeCountList"><s:elementtype><s:complextype optional="false" unbounded="false" typename="semanticCounts" name="" qname="{http://webservice.cdb.ebi.ac.uk/}semanticCounts"><s:elements><s:basetype optional="true" unbounded="false" typename="string" name="name" qname="semanticCounts&gt;name" /><s:basetype optional="true" unbounded="false" typename="int" name="count" qname="semanticCounts&gt;count" /></s:elements></s:complextype></s:elementtype></s:arraytype><s:arraytype optional="true" unbounded="false" wrapped="true" typename="minedTermsList" name="semanticTypeList" qname="responseWrapper&gt;semanticTypeList"><s:elementtype><s:complextype optional="false" unbounded="false" typename="minedTerms" name="" qname="{http://webservice.cdb.ebi.ac.uk/}minedTerms"><s:elements><s:basetype optional="true" unbounded="false" typename="string" name="name" qname="minedTerms&gt;name" /><s:basetype optional="false" unbounded="false" typename="int" name="total" qname="minedTerms&gt;total" /><s:arraytype optional="true" unbounded="true" wrapped="false" typename="minedSummary" name="tmSummary" qname="minedTerms&gt;tmSummary"><s:elementtype><s:complextype optional="false" unbounded="false" typename="minedSummary" name="" qname="{http://webservice.cdb.ebi.ac.uk/}minedSummary"><s:elements><s:basetype optional="true" unbounded="false" typename="string" name="term" qname="minedSummary&gt;term" /><s:basetype optional="false" unbounded="false" typename="int" name="count" qname="minedSummary&gt;count" /><s:arraytype optional="true" unbounded="false" wrapped="true" typename="minedAltName" name="altNameList" qname="minedSummary&gt;altNameList"><s:elementtype><s:basetype optional="false" unbounded="false" typename="string" name="" qname="{http://www.w3.org/2001/XMLSchema}string" /></s:elementtype></s:arraytype><s:basetype optional="true" unbounded="false" typename="string" name="dbName" qname="minedSummary&gt;dbName" /><s:arraytype optional="true" unbounded="false" wrapped="true" typename="minedDbIDs" name="dbIdList" qname="minedSummary&gt;dbIdList"><s:elementtype><s:basetype optional="false" unbounded="false" typename="string" name="" qname="{http://www.w3.org/2001/XMLSchema}string" /></s:elementtype></s:arraytype></s:elements></s:complextype></s:elementtype></s:arraytype></s:elements></s:complextype></s:elementtype></s:arraytype><s:arraytype optional="true" unbounded="false" wrapped="true" typename="searchTerms" name="searchTermList" qname="responseWrapper&gt;searchTermList"><s:elementtype><s:complextype optional="false" unbounded="false" typename="SearchTerm" name="" qname="{http://webservice.cdb.ebi.ac.uk/}SearchTerm"><s:elements><s:basetype optional="true" unbounded="false" typename="string" name="term" qname="SearchTerm&gt;term" /><s:arraytype optional="true" unbounded="true" wrapped="false" typename="string" name="dataSets" qname="SearchTerm&gt;dataSets"><s:elementtype><s:basetype optional="false" unbounded="false" typename="string" name="" qname="{http://www.w3.org/2001/XMLSchema}string" /></s:elementtype></s:arraytype></s:elements></s:complextype></s:elementtype></s:arraytype><s:arraytype optional="true" unbounded="false" wrapped="true" typename="labsProviderCountList" name="linksCountList" qname="responseWrapper&gt;linksCountList"><s:elementtype><s:complextype optional="false" unbounded="false" typename="labsProviderCounts" name="" qname="{http://webservice.cdb.ebi.ac.uk/}labsProviderCounts"><s:elements><s:basetype optional="true" unbounded="false" typename="string" name="providerName" qname="labsProviderCounts&gt;providerName" /><s:basetype optional="true" unbounded="false" typename="int" name="linksCount" qname="labsProviderCounts&gt;linksCount" /></s:elements></s:complextype></s:elementtype></s:arraytype><s:arraytype optional="true" unbounded="false" wrapped="true" typename="labsProviders" name="providers" qname="responseWrapper&gt;providers"><s:elementtype><s:complextype optional="false" unbounded="false" typename="labsProvider" name="" qname="{http://webservice.cdb.ebi.ac.uk/}labsProvider"><s:elements><s:basetype optional="true" unbounded="false" typename="int" name="id" qname="labsProvider&gt;id" /><s:basetype optional="true" unbounded="false" typename="string" name="name" qname="labsProvider&gt;name" /><s:basetype optional="true" unbounded="false" typename="string" name="description" qname="labsProvider&gt;description" /><s:basetype optional="true" unbounded="false" typename="string" name="frontTab" qname="labsProvider&gt;frontTab" /><s:arraytype optional="true" unbounded="true" wrapped="false" typename="labsLinks" name="link" qname="labsProvider&gt;link"><s:elementtype><s:complextype optional="false" unbounded="false" typename="labsLinks" name="" qname="{http://webservice.cdb.ebi.ac.uk/}labsLinks"><s:elements><s:basetype optional="true" unbounded="false" typename="string" name="title" qname="labsLinks&gt;title" /><s:basetype optional="true" unbounded="false" typename="string" name="url" qname="labsLinks&gt;url" /></s:elements></s:complextype></s:elementtype></s:arraytype></s:elements></s:complextype></s:elementtype></s:arraytype></s:elements></s:complextype></s:elements></s:complextype></s:extensions> net.sf.taverna.t2.coreworkflowmodel-impl1.5net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Parallelize 1 net.sf.taverna.t2.coreworkflowmodel-impl1.5net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.ErrorBouncenet.sf.taverna.t2.coreworkflowmodel-impl1.5net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Failovernet.sf.taverna.t2.coreworkflowmodel-impl1.5net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Retry 1.0 1000 5000 0 net.sf.taverna.t2.coreworkflowmodel-impl1.5net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Invokebuild_queryauthor_name0queryString00offSets11pageSize00resultType00net.sf.taverna.t2.activitiesbeanshell-activity1.5net.sf.taverna.t2.activities.beanshell.BeanshellActivity author_name 0 text/plain java.lang.String true offSets 1 1 pageSize 0 0 queryString 0 0 resultType 0 0 workflow net.sf.taverna.t2.coreworkflowmodel-impl1.5net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Parallelize 1 net.sf.taverna.t2.coreworkflowmodel-impl1.5net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.ErrorBouncenet.sf.taverna.t2.coreworkflowmodel-impl1.5net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Failovernet.sf.taverna.t2.coreworkflowmodel-impl1.5net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Retry 1.0 1000 5000 0 net.sf.taverna.t2.coreworkflowmodel-impl1.5net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.InvokesearchPublications_inputqueryString0offSet0pageSize0resultType0output00net.sf.taverna.t2.activitieswsdl-activity1.5net.sf.taverna.t2.activities.wsdl.xmlsplitter.XMLInputSplitterActivity queryString 0 'text/plain' false resultType 0 'text/plain' false offSet 0 'text/plain' false pageSize 0 'text/plain' false synonym 0 'text/plain' false email 0 'text/plain' false output 0 'text/xml' 0 <s:extensions xmlns:s="http://org.embl.ebi.escience/xscufl/0.1alpha"><s:complextype optional="false" unbounded="false" typename="searchPublications" name="parameters" qname="{http://webservice.cdb.ebi.ac.uk/}searchPublications"><s:elements><s:basetype optional="true" unbounded="false" typename="string" name="queryString" qname="searchPublications&gt;queryString" /><s:basetype optional="true" unbounded="false" typename="string" name="resultType" qname="searchPublications&gt;resultType" /><s:basetype optional="true" unbounded="false" typename="int" name="offSet" qname="searchPublications&gt;offSet" /><s:basetype optional="true" unbounded="false" typename="string" name="pageSize" qname="searchPublications&gt;pageSize" /><s:basetype optional="true" unbounded="false" typename="boolean" name="synonym" qname="searchPublications&gt;synonym" /><s:basetype optional="true" unbounded="false" typename="string" name="email" qname="searchPublications&gt;email" /></s:elements></s:complextype></s:extensions> net.sf.taverna.t2.coreworkflowmodel-impl1.5net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Parallelize 1 net.sf.taverna.t2.coreworkflowmodel-impl1.5net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.ErrorBouncenet.sf.taverna.t2.coreworkflowmodel-impl1.5net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Failovernet.sf.taverna.t2.coreworkflowmodel-impl1.5net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Retry 1.0 1000 5000 0 net.sf.taverna.t2.coreworkflowmodel-impl1.5net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Invokemerge_resultsstringlist1concatenated00net.sf.taverna.t2.activitieslocalworker-activity1.5net.sf.taverna.t2.activities.localworker.LocalworkerActivity stringlist 1 l('text/plain') java.lang.String true seperator 0 'text/plain' java.lang.String true concatenated 0 0 workflow org.embl.ebi.escience.scuflworkers.java.StringListMerge UserNameHere 2015-06-19 14:40:24.760 UTC net.sf.taverna.t2.coreworkflowmodel-impl1.5net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Parallelize 1 net.sf.taverna.t2.coreworkflowmodel-impl1.5net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.ErrorBouncenet.sf.taverna.t2.coreworkflowmodel-impl1.5net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Failovernet.sf.taverna.t2.coreworkflowmodel-impl1.5net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Retry 1.0 1000 5000 0 net.sf.taverna.t2.coreworkflowmodel-impl1.5net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.InvokesearchPublicationsparameterssearchPublications_inputoutputsearchPublications_outputinputsearchPublicationsparametersbuild_queryauthor_nameauthor_namesearchPublications_inputqueryStringbuild_queryqueryStringsearchPublications_inputoffSetbuild_queryoffSetssearchPublications_inputpageSizebuild_querypageSizesearchPublications_inputresultTypebuild_queryresultTypemerge_resultsstringlistsearchPublications_outputreturnpublicationsmerge_resultsconcatenated 33c81e10-61cd-42ac-8e83-058a935d1094 2015-06-17 15:17:42.126 UTC f0d3e185-9f45-4a06-9eb5-00a648d2d7ff 2015-09-07 11:26:01.20 UTC 0d039122-0f12-4c1e-aa6f-1b556fa907b5 2015-06-17 15:42:02.258 UTC 2fa75b66-7aff-4719-afbe-0e171859b51e 2015-06-17 14:48:29.871 UTC 37c1d08e-22a8-438d-8c7b-a4a1a0a6e6d3 2015-06-17 15:30:33.882 UTC 8c229ff8-0ea4-4589-a79a-d74ae268e461 2015-09-07 11:09:30.966 UTC 767b33cf-2a10-4717-8bc0-80ef91da61bd 2015-06-17 14:45:24.545 UTC be914dc7-eed6-4f6a-b110-88703fc321db 2015-09-07 11:13:15.906 UTC 384475f1-0572-41ac-a485-a3eedb18463d 2015-06-17 15:49:48.583 UTC 9b2bf78f-a79f-41f2-be2f-f550435ed96e 2015-09-07 11:51:55.851 UTC 5c2fe851-3ca8-41ac-b814-c28552a0a48c 2015-09-07 11:33:05.932 UTC 15b76c3e-980b-4498-bfad-4b563b0f9257 2015-06-17 15:06:27.191 UTC This small workflow demonstrates how to connect to the new (August 2015) version of Europe PMC (http://europepmc.org/RestfulWebService). Changes from previous versions include the addition of a pageSize parameter, allowing the client to request up to 1,000 records at once. Since this may still not be sufficient, the offSet paremeter is used to retrieve data in chunks (here 5) of 1,000 records. The Flatten List service is then used to concatenate the results, a single list of all article titles for the author specified in the query. 2015-09-07 11:38:28.150 UTC 4729d116-3482-4ae9-9029-ba932485d36f 2015-06-17 15:48:39.671 UTC Magnus Palmblad 2015-09-07 11:17:40.475 UTC Get article titles from Europe PMC for single author - using single BeanShell to prepare all inputs 2015-09-07 11:25:29.106 UTC 72630699-e507-4a86-9dff-8199093ee0e6 2015-09-07 11:38:29.463 UTC d72d7b75-14e1-4aa5-a179-1b090ee2f22c 2015-06-17 14:47:41.206 UTC 8c32cc05-cb12-4112-9992-e81bb4026d5a 2015-09-07 11:17:46.315 UTC b75da20d-9e6c-475f-9e51-df8f16205fc8 2015-09-07 11:34:02.237 UTC 65ae77f0-5d4b-4147-a539-8489e03aea19 2015-06-17 14:33:05.146 UTC 49b50497-1aa9-45ae-b22c-914dc312f707 2015-06-17 15:05:10.52 UTC db00d1bf-ba0d-4a56-9c66-18b2cb5e3005 2015-09-07 11:14:43.596 UTC 10d7966c-b99d-4c1e-a8a5-8ff138fa4471 2015-06-17 15:45:31.358 UTC 8852a376-0f8c-4e3d-88d4-8cf6d801cd22 2015-09-07 11:25:31.627 UTC 38d67a9b-0698-4db8-b1f7-6dc11a5d63fb 2015-09-07 11:22:10.705 UTC