The EVcouplings Python framework for coevolutionary sequence analysis
Journal Article
·
· Bioinformatics
- Harvard Medical School, Boston, MA (United States); DOE/OSTI
- Harvard Medical School, Boston, MA (United States)
- Harvard Medical School, Boston, MA (United States); Dana-Farber Cancer Institute, Boston, MA (United States)
- Harvard Medical School, Boston, MA (United States); Univ. of Tubingen (Germany)
- Harvard Medical School, Boston, MA (United States); Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)
- Memorial Sloan Kettering Cancer Center, New York, NY (United States)
- Univ. of Virginia, Charlottesville, VA (United States)
- Harvard Medical School, Boston, MA (United States); Technical Univ. of Munich (Germany)
Coevolutionary sequence analysis has become a commonly used technique for de novo prediction of the structure and function of proteins, RNA, and protein complexes. We present the EVcouplings framework, a fully integrated open-source application and Python package for coevolutionary analysis. The framework enables generation of sequence alignments, calculation and evaluation of evolutionary couplings (ECs), and de novo prediction of structure and mutation effects. The combination of an easy to use, flexible command line interface and an underlying modular Python package makes the full power of coevolutionary analyses available to entry-level and advanced users.
- Research Organization:
- Krell Institute, Ames, IA (United States)
- Sponsoring Organization:
- National Science Foundation (NSF); Pathway Commons; USDOE; USDOE Office of Science (SC)
- Grant/Contract Number:
- FG02-97ER25308
- OSTI ID:
- 1610349
- Journal Information:
- Bioinformatics, Journal Name: Bioinformatics Journal Issue: 9 Vol. 35; ISSN 1367-4803
- Publisher:
- International Society for Computational Biology - Oxford University PressCopyright Statement
- Country of Publication:
- United States
- Language:
- English
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