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 Ying
  • Gavin A. Huttley

Research output: Contribution to journalArticlepeer-review

157 Scopus citations

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.

Original languageEnglish (US)
Article numberR171
JournalGenome biology
Volume8
Issue number8
DOIs
StatePublished - Aug 21 2007

ASJC Scopus subject areas

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

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