The EVcouplings Python framework for coevolutionary sequence analysis
| Autor: | Perry Palmedo, Christian Dallago, Thomas A. Hopf, Chan Kang, John Ingraham, Adam J. Riesselman, Benjamin Schubert, Agnes Toth-Petroczy, Eli J. Draizen, Kelly P Brock, Sophia Mersmann, Debora S. Marks, Anna G. Green, Charlotta P I Schärfe, Robert L. Sheridan, Chris Sander |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Statistics and Probability
Sequence analysis Computer science Sequence alignment computer.software_genre medicine.disease_cause Biochemistry 03 medical and health sciences Software medicine Molecular Biology 030304 developmental biology computer.programming_language 0303 health sciences Mutation business.industry Programming language 030302 biochemistry & molecular biology RNA Proteins Modular design Python (programming language) Applications Notes Computer Science Applications Structure and function ddc Computational Mathematics Computational Theory and Mathematics business computer Sequence Analysis Sequence Alignment |
| Zdroj: | Bioinformatics |
| Popis: | 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 |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|