Pack: Open Data: Bilateral Perisylvian Polymicrogyria

Created: 2010-11-16 16:20:43 Last updated: 2011-09-23 10:55:08

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Title: Open Data: Bilateral Perisylvian Polymicrogyria

Description

This pack contains freely accessible data for scientific researchers to try and identify key genes linked to Bilateral Perisylvian Polymicrogyria. For those who want more information about this condition before they download the data, please see this link, which describes the condition and traits: http://omim.org/entry/300388?search=bilateral%20perisylvian%20polymicrogyria

The results conatined within this pack are the output from running a scientific workflow that identifies candidate genes within a chromosome region linked to a disease, known as a Quantitative Trait Loci (QTL). I've run this workflow for bilateral perisylvian polymicrogyria. The results in this pack are freely available to all; not just people on myExperiment, but the entire world, to make use of and help understand this condition.

I'm making these data sets open to try and increase our understanding of the causes of this condition, and increase the possibility of developing therapeuitic treatments.

Please download this data and help to identify candidate genes, pathways, and genetic mutations that may influence this condition. I want all people investigating epilepsy to have a look at this data and see if it is of any use to their research. If it is not, then nothing has been lost.

If you know anyone working on this condition, or anyone working in a similar field - such as Epilepsy, please tell them and pass on the link to this data.

It should be noted that I have no involvement with the generation of the QTL regions from http://www.ncbi.nlm.nih.gov/sites/pubmed/11822025, nor can be held accountable for the validity of the underlying data collected from numerous databases. I merely offer you the data as I have collected, and list the workflows used to gather the data.

Use the data as you wish, but please reference this URL: http://www.myexperiment.org/packs/157/ and this original paper: http://www.ncbi.nlm.nih.gov/pubmed/17709344 if you plan to publish the data. Details of the text mining methods are in preparation for publication.

Items (7)

File: Bilateral Perisylvian Polymicrogyria (Paul Fisher)

This file contains QTL genes and pathways linked to this condition

Added by Paul Fisher ... more than 1 year ago (2011-03-17 11:14:07)

File: Bilateral Perisylvian Polymicrogyria - Single Nucleotide Polymorphisms (Paul Fisher)

This file contains SNP found within the BPP QTL regions, as published by Villard et al., (2002)

Added by Paul Fisher ... more than 1 year ago (2011-03-17 11:21:41)

Workflow: Pathway and Gene to Pubmed (Paul Fisher)

Added by Paul Fisher ... more than 1 year ago (2011-03-22 11:40:50)

Description:

This workflow takes in a list of gene names and KEGG pathway descriptions, and searches the PubMed database for corresponding articles. Any matches to the genes are then retrieved (abstracts only). These abstracts are then used to calculate a cosine vector space between two sets of corpora (gene and phenotype, or pathway and phenotype). The workflow counts the number of articles in the pubmed database in which each term occurs, and identifies the total number of articles in the entire PubMed database. It also identified the total number of articles within pubmed so that a term enrichment score may be calculated. The workflow also takes in a document containing abstracts that are related to a particular phenotype. Scientiifc terms are then extracted from this text and given a weighting according to the number of terms that appear in the document. The higher the value the better the score. This is given as: X = log((a / b) / (c / d)) where: a = number of occurnaces of individual terms in phenotype corpus b = number of abstracts in entire phenotype corpus c = number of occurnaces of individual terms in entire pubmed d = number of articles in entire pubmed Once this has been created, the pathways obtained from the QTL and microarray pathway analysis workflows are analysed. The documents from a search of each pathway in pubmed are merged into a single document of pathway abstracts. The (unweighted) phenotype terms are then searched in the pathways corpus. This will determine if the phenotype term is listed with the given pathway. The higher the value the better the score. Each term is then assigned a weight as: Y = log((e / f) / (c /d)) where: a = number of occurnaces of individual terms in pathway corpus b = number of abstracts in pathway corpus (per pathway) c = number of occurnaces of individual terms in entire pubmed d = number of articles in entire pubmed The weighted terms are then given a link score. This is the total of: X + Y. This gives the link between the pathway and the phenotype a score / significance value. The higher the score the more "appropriate/interesting" the link between the pathway and the phenotype. The terms are also ranked according to the number of pathways which have been given a weight. This is calculated as: W = ( X + Y). The higher the value the better the score.

Workflow: Pathways and Gene annotations forQTL region (Paul Fisher)

Added by Paul Fisher ... more than 1 year ago (2011-03-22 11:40:18)