PyCogent: a toolkit for making sense from sequence |
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Authors: | Rob Knight Peter Maxwell Amanda Birmingham Jason Carnes J Gregory Caporaso Brett C Easton Michael Eaton Micah Hamady Helen Lindsay Zongzhi Liu Catherine Lozupone Daniel McDonald Michael Robeson Raymond Sammut Sandra Smit Matthew J Wakefield Jeremy Widmann Shandy Wikman Stephanie Wilson Hua Ying Gavin A Huttley |
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Institution: | Department of Chemistry and Biochemistry, University of Colorado, Boulder, Colorado, USA. rob@spot.colorado.edu |
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Abstract: | We have implemented in Python the COmparative GENomic Toolkit, a fully integrated and thoroughly tested framework for novel probabilistic analyses of biological sequences, devising workflows, and generating publication quality graphics. PyCogent includes connectors to remote databases, built-in generalized probabilistic techniques for working with biological sequences, and controllers for third-party applications. The toolkit takes advantage of parallel architectures and runs on a range of hardware and operating systems, and is available under the general public license from http://sourceforge.net/projects/pycogent. |
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