Cargando…
PyHMMER: a Python library binding to HMMER for efficient sequence analysis
SUMMARY: PyHMMER provides Python integration of the popular profile Hidden Markov Model software HMMER via Cython bindings. This allows the annotation of protein sequences with profile HMMs and building new ones directly with Python. PyHMMER increases flexibility of use, allowing creating queries di...
Autores principales: | , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10159651/ https://www.ncbi.nlm.nih.gov/pubmed/37074928 http://dx.doi.org/10.1093/bioinformatics/btad214 |
_version_ | 1785037146214629376 |
---|---|
author | Larralde, Martin Zeller, Georg |
author_facet | Larralde, Martin Zeller, Georg |
author_sort | Larralde, Martin |
collection | PubMed |
description | SUMMARY: PyHMMER provides Python integration of the popular profile Hidden Markov Model software HMMER via Cython bindings. This allows the annotation of protein sequences with profile HMMs and building new ones directly with Python. PyHMMER increases flexibility of use, allowing creating queries directly from Python code, launching searches, and obtaining results without I/O, or accessing previously unavailable statistics like uncorrected P-values. A new parallelization model greatly improves performance when running multithreaded searches, while producing the exact same results as HMMER. AVAILABILITY AND IMPLEMENTATION: PyHMMER supports all modern Python versions (Python 3.6+) and similar platforms as HMMER (x86 or PowerPC UNIX systems). Pre-compiled packages are released via PyPI (https://pypi.org/project/pyhmmer/) and Bioconda (https://anaconda.org/bioconda/pyhmmer). The PyHMMER source code is available under the terms of the open-source MIT licence and hosted on GitHub (https://github.com/althonos/pyhmmer); its documentation is available on ReadTheDocs (https://pyhmmer.readthedocs.io). |
format | Online Article Text |
id | pubmed-10159651 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-101596512023-05-05 PyHMMER: a Python library binding to HMMER for efficient sequence analysis Larralde, Martin Zeller, Georg Bioinformatics Applications Note SUMMARY: PyHMMER provides Python integration of the popular profile Hidden Markov Model software HMMER via Cython bindings. This allows the annotation of protein sequences with profile HMMs and building new ones directly with Python. PyHMMER increases flexibility of use, allowing creating queries directly from Python code, launching searches, and obtaining results without I/O, or accessing previously unavailable statistics like uncorrected P-values. A new parallelization model greatly improves performance when running multithreaded searches, while producing the exact same results as HMMER. AVAILABILITY AND IMPLEMENTATION: PyHMMER supports all modern Python versions (Python 3.6+) and similar platforms as HMMER (x86 or PowerPC UNIX systems). Pre-compiled packages are released via PyPI (https://pypi.org/project/pyhmmer/) and Bioconda (https://anaconda.org/bioconda/pyhmmer). The PyHMMER source code is available under the terms of the open-source MIT licence and hosted on GitHub (https://github.com/althonos/pyhmmer); its documentation is available on ReadTheDocs (https://pyhmmer.readthedocs.io). Oxford University Press 2023-04-19 /pmc/articles/PMC10159651/ /pubmed/37074928 http://dx.doi.org/10.1093/bioinformatics/btad214 Text en © The Author(s) 2023. Published by Oxford University Press. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Applications Note Larralde, Martin Zeller, Georg PyHMMER: a Python library binding to HMMER for efficient sequence analysis |
title | PyHMMER: a Python library binding to HMMER for efficient sequence analysis |
title_full | PyHMMER: a Python library binding to HMMER for efficient sequence analysis |
title_fullStr | PyHMMER: a Python library binding to HMMER for efficient sequence analysis |
title_full_unstemmed | PyHMMER: a Python library binding to HMMER for efficient sequence analysis |
title_short | PyHMMER: a Python library binding to HMMER for efficient sequence analysis |
title_sort | pyhmmer: a python library binding to hmmer for efficient sequence analysis |
topic | Applications Note |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10159651/ https://www.ncbi.nlm.nih.gov/pubmed/37074928 http://dx.doi.org/10.1093/bioinformatics/btad214 |
work_keys_str_mv | AT larraldemartin pyhmmerapythonlibrarybindingtohmmerforefficientsequenceanalysis AT zellergeorg pyhmmerapythonlibrarybindingtohmmerforefficientsequenceanalysis |