Structural and Computational Biology Unit, EMBL, Meyerhofstraße 1, Heidelberg 69117, Germany.
Bioinformatics. 2023 May 4;39(5). doi: 10.1093/bioinformatics/btad214.
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.
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).
PyHMMER 通过 Cython 绑定为流行的蛋白质结构域隐马尔可夫模型软件 HMMER 提供了 Python 集成。这允许使用蛋白质序列进行结构域 HMM 注释，并直接使用 Python 构建新的 HMM。PyHMMER 提高了使用的灵活性，允许直接从 Python 代码创建查询、启动搜索，并在没有 I/O 的情况下获得结果，或者访问以前无法获得的统计信息，如未校正的 P 值。新的并行化模型在运行多线程搜索时极大地提高了性能，同时产生与 HMMER 完全相同的结果。
PyHMMER 支持所有现代 Python 版本（Python 3.6+）和类似的 HMMER 平台（x86 或 PowerPC UNIX 系统）。预编译的软件包通过 PyPI（https://pypi.org/project/pyhmmer/）和 Bioconda（https://anaconda.org/bioconda/pyhmmer）发布。PyHMMER 的源代码根据开源的 MIT 许可证条款提供，并托管在 GitHub（https://github.com/althonos/pyhmmer）上；其文档可在 ReadTheDocs（https://pyhmmer.readthedocs.io）上查阅。
