To help you get started, we've collected the most common ways that pyteomics is being used within popular public projects.

statisticalbiotechnology / diffacto / diffacto / diffacto.py View on Github

def _load_fasta(db, id_regex):
    prot_dict = dict()
    for header, seq in fasta.read(db):
        seq = seq.replace("I", "L").upper()  # convert DB sequence I -> L
        prot_id = header.split()[0]
        if id_regex is not None:
            find_id = re.findall(id_regex, header)
            if len(find_id) > 0:
                prot_id = find_id[0]
        prot_dict[prot_id] = seq

    return prot_dict

View more ways to use pyteomics

What is Pyteomics?

Pyteomics is a collection of lightweight and handy tools for Python that help to handle various sorts of proteomics data. Pyteomics provides a growing set of modules to facilitate the most common tasks in proteomics data analysis, such as:

• calculation of basic physico-chemical properties of polypeptides:
  • mass and isotopic distribution
  • charge and pI
  • chromatographic retention time
• access to common proteomics data:
  • MS or LC-MS data
  • FASTA databases
  • search engines output
• easy manipulation of sequences of modified peptides and proteins

The goal of the Pyteomics project is to provide a versatile, reliable and well-documented set of open tools for the wide proteomics community. One of the project's key features is Python itself, an open source language increasingly popular in scientific programming. The main applications of the library are reproducible statistical data analysis and rapid software prototyping.

Supported Python versions

Pyteomics supports Python 2.7 and Python 3.3+.

Install with pip

The main way to obtain Pyteomics is via pip Python package manager:

pip install pyteomics

Install with conda

You can also install Pyteomics from Bioconda using conda:

conda install -c bioconda pyteomics

Arch-based distros

On Arch Linux and related distros, you can install Pyteomics from AUR: python-pyteomics

Project dependencies

Some functionality in Pyteomics relies on other packages:

> - numpy; > - matplotlib (used by > pyteomics.pylab_aux); > - lxml (used by XML parsing modules and > pyteomics.mass.mass.Unimod); > - pandas (can be used with > pyteomics.pepxml, pyteomics.tandem, pyteomics.mzid, > pyteomics.auxiliary); > - sqlalchemy (used by > pyteomics.mass.unimod); > - pynumpress (adds support > for Numpress compression in mzML); > - h5py and optionally > hdf5plugin (used > by pyteomics.mzmlb); > - psims > (used py pyteomics.proforma); > - spectrum_utils > (optionally used for spectrum annotation in > pyteomics.pylab_aux).

All dependencies are optional.

Installing a subset of dependencies with pip

You can quickly install just the dependencies you need by specifying an "extra". For example:

pip install pyteomics[XML]

This will install Pyteomics, NumPy and lxml, which are needed to read XML format. Currently provided identifiers are: XML, TDA, graphics, DF, Unimod, numpress, mzMLb, proforma.

You can also use these specs as dependencies in your own packages which require specific Pyteomics functionality.