The EVcouplings Python framework for coevolutionary sequence analysis
| Autor: | Benjamin Schubert, Sophia Mersmann, Chris Sander, Adam J. Riesselman, Debora S. Marks, Thomas A. Hopf, Chan Kang, Charlotta P I Schärfe, Agnes Toth-Petroczy, Kelly P Brock, Christian Dallago, John Ingraham, Anna G. Green |
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| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
0303 health sciences
Mutation Sequence analysis business.industry Programming language Computer science RNA Modular design Python (programming language) medicine.disease_cause computer.software_genre Structure and function 03 medical and health sciences 0302 clinical medicine medicine Extensive data business computer 030217 neurology & neurosurgery 030304 developmental biology computer.programming_language |
| DOI: | 10.1101/326918 |
| Popis: | SummaryCoevolutionary sequence analysis has become a commonly used technique for de novo prediction of the structure and function of proteins, RNA, and protein complexes. This approach requires extensive computational pipelines that integrate multiple tools, databases, and data processing steps. 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 application has an easy to use command line interface to run workflows with user control over all analysis parameters, while the underlying modular Python package allows interactive data analysis and rapid development of new workflows. Through this multi-layered approach, the EVcouplings framework makes the full power of coevolutionary analyses available to entry-level and advanced users.Availabilityhttps://github.com/debbiemarkslab/evcouplingsContactsander.research@gmail.com, debbie@hms.harvard.edu |
| Databáze: | OpenAIRE |
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