pyteomics
A framework for proteomics data analysis.
Decision gist · record as of 2026-08-14
Yes. Pyteomics is a mature, actively maintained library with no security vulnerabilities, zero runtime dependencies, and straightforward installation. It directly addresses core proteomics data analysis tasks. Install it if you work with mass spectrometry data, peptide sequences, or proteomics search results in Python.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python >=3.10
- Low friction installation with no runtime dependencies.
- Active maintenance: last commit 2026-07-22, released 23 days ago.
License · maintenance · safety
Apache-2.0 (permissive) — Apache-2.0 is permissive; you can use, modify, and distribute this package freely in commercial or private projects with minimal restrictions.
last release 2026-07-22 (23 days) · last repo commit 2026-07-22 · 161 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 286,369 downloads/mo, #8,044 on PyPI
Alternatives
Verify before relying
pip install pyteomics
from pyteomics import mass
mass_value = mass.calculate_mass(sequence='PEPTIDE')- Scope and completeness of supported MS data formats and search engine output types
- Performance characteristics for large-scale proteomics datasets
- Availability and quality of example workflows or tutorials beyond the documentation
What it is and what it does
Pyteomics is a collection of Python modules designed to handle proteomics data analysis tasks. It provides utilities for calculating physico-chemical properties of peptides and proteins (mass, isotopic distribution, charge, pI, retention time), accessing common proteomics data formats (MS/LC-MS data, FASTA databases, search engine outputs), and manipulating sequences of modified peptides and proteins. The library targets reproducible statistical analysis and rapid prototyping in proteomics research.
The package has no external runtime dependencies, making it lightweight and straightforward to integrate into existing Python workflows. It is actively maintained, with current Python version support (>=3.10), and has been in development since 2012, indicating a mature codebase for the proteomics community.
Use it for
- Calculate molecular mass and isotopic distributions for peptide sequences in mass spectrometry workflows
- Parse and process FASTA database files for proteomics searches and sequence analysis
- Extract and analyze results from proteomics search engines (e.g., Mascot, SEQUEST output)
- Compute physicochemical properties (pI, charge, retention time) for peptide characterization
- Manipulate and annotate sequences containing post-translational modifications
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
Pyteomics is a mature, actively maintained library with no security vulnerabilities, zero runtime dependencies, and straightforward installation. It directly addresses core proteomics data analysis tasks. Install it if you work with mass spectrometry data, peptide sequences, or proteomics search results in Python.
Install
pyteomics on PyPI
Before you install
Low friction installation with no runtime dependencies. Active maintenance: last commit 2026-07-22, released 23 days ago. Supported on current Python versions (requires Python >=3.10).
Requires Python >=3.10
License in practice
Apache-2.0 is permissive; you can use, modify, and distribute this package freely in commercial or private projects with minimal restrictions.
Quickstart
pip install pyteomics
from pyteomics import mass
mass_value = mass.calculate_mass(sequence='PEPTIDE')Verify before relying
- Scope and completeness of supported MS data formats and search engine output types
- Performance characteristics for large-scale proteomics datasets
- Availability and quality of example workflows or tutorials beyond the documentation
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | None |
| Maintenance | Actively maintained 23 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 286,369 / month, #8,044 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Intended Audience :: Science/ResearchProgramming Language :: Python :: 3Topic :: EducationTopic :: Scientific/Engineering :: Bio-InformaticsTopic :: Scientific/Engineering :: ChemistryTopic :: Scientific/Engineering :: PhysicsTopic :: Software Development :: Libraries |
Evidence: pyteomics-5.0.1-py3-none-any.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “proteomics data analysis”
- pyteomicsPyteomics provides Python tools for proteomics data analysis,…
- deepbiopDeepBioP is a deep learning preprocessing library for biological…
- pymzmlpymzml parses mzML mass spectrometry data files in Python, providing…
Give your agent the search over MCP, or paste the wish link into any chat.
More Libraries packages
urllib3 is an HTTP client library that provides thread-safe connection pooling, SSL/TLS verification, multipart file uploads, request retries, compression support, and proxy handling for Python applications.
Requests is a Python HTTP library that simplifies sending HTTP/1.1 requests with automatic handling of headers, authentication, cookies, and response parsing.
Pluggy provides a plugin system that lets you define hook specifications and register implementations to be called in sequence, enabling extensible Python applications without tight coupling.
Install it if you're building an extensible application or framework.
Provides parsing, arithmetic, and recurrence rule computation for dates and times, with timezone support and iCalendar RFC compliance.
Install it if you need to parse flexible date strings, compute relative dates, handle timezones, or work with recurrence rules—it's the de facto choice for these tasks.
Six provides utility functions to write Python code that runs on both Python 2.7 and Python 3.3+, smoothing over language differences between the two versions.
pytest is a testing framework that lets you write test functions using plain assert statements and automatically discovers and runs them, with detailed failure reporting.
See also mhctools · pipebio · pymzml · biotite · primer3-py · pysam · dnaio · pyhmmer · bio · pyfaidx