Software
PyCogent: a toolkit for making sense from sequence
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* Corresponding authors: Rob Knight rob@spot.colorado.edu - Gavin A Huttley Gavin.Huttley@anu.edu.au
- † Equal contributors
1 Department of Chemistry and Biochemistry, University of Colorado, Boulder, Colorado, USA
2 Computational Genomics Laboratory, John Curtin School of Medical Research, The Australian National University, Canberra, Australian Capital Territory, Australia
3 Thermo Fisher Scientific, Lafayette, Colorado, USA
4 Seattle Biomedical Research Institute, Seattle, Washington, USA
5 Department of Biochemistry and Molecular Genetics, University of Colorado Health Sciences Center, Aurora, Colorado, USA
6 Science Applications International Corporation, Englewood, Colorado, USA
7 Department of Computer Science, University of Colorado, Boulder, Colorado, USA
8 Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA
9 Walter and Eliza Hall Institute, Melbourne, Victoria, Australia
Genome Biology 2007, 8:R171 doi:10.1186/gb-2007-8-8-r171
Published: 21 August 2007Abstract
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 webcite.