PyCogent: a toolkit for making sense from sequence1Department of Chemistry and Biochemistry, University of Colorado, Boulder, Colorado, USA 2Computational Genomics Laboratory, John Curtin School of Medical Research, The Australian National University, Canberra, Australian Capital Territory, Australia 3Thermo Fisher Scientific, Lafayette, Colorado, USA 4Seattle Biomedical Research Institute, Seattle, Washington, USA 5Department of Biochemistry and Molecular Genetics, University of Colorado Health Sciences Center, Aurora, Colorado, USA 6Science Applications International Corporation, Englewood, Colorado, USA 7Department of Computer Science, University of Colorado, Boulder, Colorado, USA 8Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA 9Walter and Eliza Hall Institute, Melbourne, Victoria, Australia
Genome Biology 2007, 8:R171doi:10.1186/gb-2007-8-8-r171
Subject areas: Bioinformatics, Evolution, Genome studies AbstractWe 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. |


on Google Scholar







author email
corresponding author email