The EVcouplings Python framework for coevolutionary sequence analysis Journal Article


Authors: Hopf, T. A.; Green, A. G.; Schubert, B.; Mersmann, S.; Schärfe, C. P. I.; Ingraham, J. B.; Toth-Petroczy, A.; Brock, K.; Riesselman, A. J.; Palmedo, P.; Kang, C.; Sheridan, R.; Draizen, E. J.; Dallago, C.; Sander, C.; Marks, D. S.
Article Title: The EVcouplings Python framework for coevolutionary sequence analysis
Abstract: SUMMARY: 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. AVAILABILITY AND IMPLEMENTATION: https://github.com/debbiemarkslab/evcouplings. © The Author(s) 2018. Published by Oxford University Press.
Journal Title: Bioinformatics
Volume: 35
Issue: 9
ISSN: 1367-4803
Publisher: Oxford University Press  
Date Published: 2019-05-01
Start Page: 1582
End Page: 1584
Language: English
DOI: 10.1093/bioinformatics/bty862
PUBMED: 30304492
PROVIDER: scopus
PMCID: PMC6499242
DOI/URL:
Notes: Article -- Export Date: 3 June 2019 -- Source: Scopus
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