{"categories":[{"label":"Bio-Informatics","url":"https://skillfed.io/packages/category/scientific-engineering-bio-informatics"},{"label":"Medical Science Apps.","url":"https://skillfed.io/packages/category/scientific-engineering-medical-science-apps"}],"enrichment":{"capability":"PyHMMER provides Python bindings to HMMER3, a biological sequence analysis tool that uses profile hidden Markov models to search for sequence homologs in protein or DNA databases.","skillfed_tags":["bioinformatics","sequence-analysis","cython-bindings"],"use_cases":["Annotate protein domains in genomic data by searching sequences against Pfam or custom HMM libraries within a Python pipeline.","Build HMMs from multiple sequence alignments and immediately search them against a sequence database without leaving Python.","Integrate sequence homology detection into automated genome analysis or metagenomics workflows that already use Biopython or similar libraries.","Retrieve and filter high-scoring hits from HMMER searches programmatically, sorting or post-processing results in memory.","Parallelize hmmsearch or hmmscan across multiple CPUs for faster annotation of large sequence sets."],"what_it_does":"PyHMMER wraps HMMER3, a mature bioinformatics tool for finding sequence homologs using profile hidden Markov models. Instead of spawning external binaries and parsing fixed-width text output, it exposes HMMER's C internals directly through Cython, letting you work entirely in memory with Python objects. You load sequences and HMM profiles, run searches like hmmsearch or hmmscan, and get back structured hit objects you can query and filter in Python\u2014no temporary files, no output parsing, no intermediate format conversions.\n\nThe package is designed for researchers and pipeline developers who want to embed HMMER searches into Python workflows without managing separate HMMER installations. It supports building HMMs from alignments, scanning sequences against profile databases, and aligning sequences to profiles. Parallelization uses Python threads and can adapt to multi-CPU systems. The single runtime dependency is psutil, and pre-built wheels cover most common platforms, though Unix-only HMMER internals mean Windows is not supported.","worth_installing":"Yes. PyHMMER is actively maintained, has no known vulnerabilities, carries permissive licenses, and solves a real problem for bioinformatics workflows\u2014embedding HMMER searches in Python without subprocess overhead or output parsing. Medium install friction is acceptable given the availability of pre-built wheels and the value of in-memory, structured access to HMMER results. Suitable for research and production use."},"id":"pyhmmer","links":{"html":"https://skillfed.io/packages/pyhmmer","md":"https://skillfed.io/packages/pyhmmer.md","pypi":"https://pypi.org/project/pyhmmer/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-13","license_spdx":"MIT AND BSD-3-Clause AND BSD-2-Clause","license_treatment":"permissive","name":"pyhmmer","python_support":"supports_current","summary":"Cython bindings and Python interface to HMMER3."},"popularity":{"monthly_downloads":1556065,"position":3769,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"0.12.2"}