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PyCogent: a toolkit for making sense from sequence
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
Institution:Department of Chemistry and Biochemistry, University of Colorado, Boulder, Colorado, USA. rob@spot.colorado.edu
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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