PyCogent: A toolkit for making sense from sequence

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 YingGavin A. Huttley

Research output: Contribution to journalArticlepeer-review

149 Scopus citations


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

Original languageEnglish (US)
Article numberR171
JournalGenome biology
Issue number8
StatePublished - Aug 21 2007

ASJC Scopus subject areas

  • Ecology, Evolution, Behavior and Systematics
  • Genetics
  • Cell Biology


Dive into the research topics of 'PyCogent: A toolkit for making sense from sequence'. Together they form a unique fingerprint.

Cite this