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The EVcouplings Python framework for coevolutionary sequence analysis
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...
Autores principales: | , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6499242/ https://www.ncbi.nlm.nih.gov/pubmed/30304492 http://dx.doi.org/10.1093/bioinformatics/bty862 |
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author | Hopf, Thomas A Green, Anna G Schubert, Benjamin Mersmann, Sophia Schärfe, Charlotta P I Ingraham, John B Toth-Petroczy, Agnes Brock, Kelly Riesselman, Adam J Palmedo, Perry Kang, Chan Sheridan, Robert Draizen, Eli J Dallago, Christian Sander, Chris Marks, Debora S |
author_facet | Hopf, Thomas A Green, Anna G Schubert, Benjamin Mersmann, Sophia Schärfe, Charlotta P I Ingraham, John B Toth-Petroczy, Agnes Brock, Kelly Riesselman, Adam J Palmedo, Perry Kang, Chan Sheridan, Robert Draizen, Eli J Dallago, Christian Sander, Chris Marks, Debora S |
author_sort | Hopf, Thomas A |
collection | PubMed |
description | 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 |
format | Online Article Text |
id | pubmed-6499242 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-64992422019-05-07 The EVcouplings Python framework for coevolutionary sequence analysis Hopf, Thomas A Green, Anna G Schubert, Benjamin Mersmann, Sophia Schärfe, Charlotta P I Ingraham, John B Toth-Petroczy, Agnes Brock, Kelly Riesselman, Adam J Palmedo, Perry Kang, Chan Sheridan, Robert Draizen, Eli J Dallago, Christian Sander, Chris Marks, Debora S Bioinformatics Applications Notes 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 Oxford University Press 2019-05-01 2018-10-09 /pmc/articles/PMC6499242/ /pubmed/30304492 http://dx.doi.org/10.1093/bioinformatics/bty862 Text en © The Author(s) 2018. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Applications Notes Hopf, Thomas A Green, Anna G Schubert, Benjamin Mersmann, Sophia Schärfe, Charlotta P I Ingraham, John B Toth-Petroczy, Agnes Brock, Kelly Riesselman, Adam J Palmedo, Perry Kang, Chan Sheridan, Robert Draizen, Eli J Dallago, Christian Sander, Chris Marks, Debora S The EVcouplings Python framework for coevolutionary sequence analysis |
title | The EVcouplings Python framework for coevolutionary sequence analysis |
title_full | The EVcouplings Python framework for coevolutionary sequence analysis |
title_fullStr | The EVcouplings Python framework for coevolutionary sequence analysis |
title_full_unstemmed | The EVcouplings Python framework for coevolutionary sequence analysis |
title_short | The EVcouplings Python framework for coevolutionary sequence analysis |
title_sort | evcouplings python framework for coevolutionary sequence analysis |
topic | Applications Notes |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6499242/ https://www.ncbi.nlm.nih.gov/pubmed/30304492 http://dx.doi.org/10.1093/bioinformatics/bty862 |
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