
doi: 10.5281/zenodo.10783806 , 10.5281/zenodo.18010220 , 10.5281/zenodo.7883686 , 10.5281/zenodo.7950885 , 10.5281/zenodo.18331838 , 10.5281/zenodo.8214448 , 10.5281/zenodo.10031166 , 10.5281/zenodo.18342603 , 10.5281/zenodo.18237097 , 10.5281/zenodo.12167428 , 10.5281/zenodo.10888565 , 10.5281/zenodo.10777932 , 10.5281/zenodo.20136421 , 10.5281/zenodo.8015581 , 10.5281/zenodo.15366954 , 10.5281/zenodo.13904632 , 10.5281/zenodo.10810269 , 10.5281/zenodo.17455353 , 10.5281/zenodo.8252801 , 10.5281/zenodo.11068566 , 10.5281/zenodo.12750566 , 10.5281/zenodo.10829699 , 10.5281/zenodo.8267526 , 10.5281/zenodo.10671468 , 10.5281/zenodo.15366398 , 10.5281/zenodo.18302263 , 10.5281/zenodo.10051328 , 10.5281/zenodo.18331230 , 10.5281/zenodo.18235948 , 10.5281/zenodo.8255803 , 10.5281/zenodo.18262092 , 10.5281/zenodo.10687723 , 10.5281/zenodo.17841754 , 10.5281/zenodo.14625478 , 10.5281/zenodo.4270012
doi: 10.5281/zenodo.10783806 , 10.5281/zenodo.18010220 , 10.5281/zenodo.7883686 , 10.5281/zenodo.7950885 , 10.5281/zenodo.18331838 , 10.5281/zenodo.8214448 , 10.5281/zenodo.10031166 , 10.5281/zenodo.18342603 , 10.5281/zenodo.18237097 , 10.5281/zenodo.12167428 , 10.5281/zenodo.10888565 , 10.5281/zenodo.10777932 , 10.5281/zenodo.20136421 , 10.5281/zenodo.8015581 , 10.5281/zenodo.15366954 , 10.5281/zenodo.13904632 , 10.5281/zenodo.10810269 , 10.5281/zenodo.17455353 , 10.5281/zenodo.8252801 , 10.5281/zenodo.11068566 , 10.5281/zenodo.12750566 , 10.5281/zenodo.10829699 , 10.5281/zenodo.8267526 , 10.5281/zenodo.10671468 , 10.5281/zenodo.15366398 , 10.5281/zenodo.18302263 , 10.5281/zenodo.10051328 , 10.5281/zenodo.18331230 , 10.5281/zenodo.18235948 , 10.5281/zenodo.8255803 , 10.5281/zenodo.18262092 , 10.5281/zenodo.10687723 , 10.5281/zenodo.17841754 , 10.5281/zenodo.14625478 , 10.5281/zenodo.4270012
PyHMMER provides Python integration of the popular profile Hidden Markov Model software HMMER via Cython bindings. This allows 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. 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). Supplementary data are available at Bioinformatics online.
If you use this software, please cite it using the metadata from this file.
bioinformatics, hmm
bioinformatics, hmm
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
