Workflows

Transcribe DNA (1)

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The Transcribe DNA service takes the DNA sequence, here defaulted to 'gatcctccat' and outputs the corresponding transcribed RNA sequence, in this example, 'gauccuccau'.

Created: 2010-10-28

Reverse complement DNA (1)

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The Reverse Complement DNA service takes the DNA sequence, here defaulted to 'gatcctccat' and outputs the corresponding reverse complement sequence, in this example, 'atggaggatc'.

Created: 2010-10-28

Read SwissProt file (1)

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The Read_SwissProt_file service reads the file at the swiss_prot_file_url and converts the SwissProt data into Agave format. The result is then sent to the workflow's swiss_prot_data port.

Created: 2010-10-28 | Last updated: 2010-10-28

Read GenBank file (1)

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The Read_GenBank_file service reads the file at the genbank_file_url and converts the GenBank data into Agave format. The result is then sent to the workflow's gen_bank_data port.

Created: 2010-10-28 | Last updated: 2010-10-28

Encode byte[] to base64 (1)

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The encode byte[] to base 64 service encodes the bytes representing (in the local character set) 'hello'.

Created: 2010-10-28

Decode base64 to byte[] (1)

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The decode base64 to byte[] service decodes the base64 string. The byte array is then converted into the string 'Hello world'.

Created: 2010-10-28

Is Parameter Optimization Useful (1)

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This process compares two 10x10fold cross-validations with linear SVM for classification. One use the heuristic parameter choice C=1/avg(K(x,x)) and the other uses an internal 5fold cross-validation to tune the parameter C. Afterwards both performance values are compared via ANOVA and T-test to check whether there is a significant difference between both performance values. The behaviour of the optimized 10x10 XV is similar to that of the experiment 10x10fold_XV_with_optimizedSVM_classificat...

Created: 2010-10-28 | Last updated: 2010-10-28

10x10fold_XV_with_optimizedSVM_classification (1)

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This process performs 10 round of 10fold cross-validation for classification with a linear SVM. In each traning-phase the parameters C of the linear SVM is tuned by a 5fold cross-validation. The repetions are useful for estimating the real performance on a small sample high variance data set, e.g. microarray data.

Created: 2010-10-28

Blastn-ClustalW (1)

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Created: 2010-10-27 | Last updated: 2010-10-27

Credits: Kalpana

Polygon buffer - Using pyWPS (1)

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Basic example on how to integrate a Web Processing Service (WPS) using  PyWPS's WSDL/SOAP The workflow will accept a GML file with featureCollection where polyon is described using gml:coordinate element structure. This GMl is easely generated with GDAL. The coodinate structure will be converted to WKT and used to create a polygon using shapely, the output will be a buffer polygon with a defined width. Output polygon will replace the orginal polygon and the existing XML will be return...

Created: 2010-10-26 | Last updated: 2010-10-26

Credits: Jorgejesus