PyCogent: A Toolkit for Analyzing Biological Sequences
Author Information
Author(s): 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
Primary Institution: Department of Chemistry and Biochemistry, University of Colorado, Boulder, Colorado, USA
Hypothesis
Can a toolkit be developed to facilitate probabilistic analyses of biological sequences and improve workflow control?
Conclusion
PyCogent provides a unique framework that enhances the application of comparative genomics data to various biological problems.
Supporting Evidence
- PyCogent includes connectors to remote databases and built-in probabilistic techniques for biological sequences.
- The toolkit allows for flexible parameterization of probabilistic models.
- PyCogent supports many key evolutionary analyses directly, including sequence alignment and phylogenetic reconstruction.
Takeaway
PyCogent is a software tool that helps scientists analyze DNA and other biological data more easily and effectively.
Methodology
The toolkit was implemented in Python and includes features for querying databases, conducting analyses, and visualizing results.
Limitations
The toolkit's capabilities may be limited by the existing software packages it integrates with.
Digital Object Identifier (DOI)
Want to read the original?
Access the complete publication on the publisher's website