ArticleBioinformatics (Oxford, England)2019
The EVcouplings Python framework for coevolutionary sequence analysis.
Article in Bioinformatics (Oxford, England), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 160 papers.
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160 citing papers in PubMed.
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- Structural Mechanism of Prestin-Membrane Mechanotransduction.bioRxiv : the preprint server for biology · 2025Article
- Proteome-wide model for human disease genetics.Nature genetics · 2025Article
- Influence of contact map topology on RNA structure prediction.Nucleic acids research · 2025Article
- Sequence-based calculation of local energetic frustration in proteins.Structural dynamics (Melville, N.Y.) · 2025Article
100 more citing papers are in PubMed but not listed here.
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16 authors.
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Abstract
summaryCoevolutionary 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.
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