Skip to main content

No project description provided

Project description

SAGEpy

A python interface to the core SAGE search engine for mass spectrometry proteomics

logo

Installation

sagepy is now available via pip:

pip install sagepy

Build from source

  1. Clone our fork of the SAGE repository:
git clone git@github.com:theGreatHerrLebert/sage.git
  1. Install the sage-core bindings using maturin, optionally in a virtual environment:
cd sage/crates/sagepy-connector

# Install maturin
pip install maturin

# Build and install the bindings
maturin build --release

# Install the bindings
pip install target/wheels/sagepy_connector-0.1.0-cp38-cp38-manylinux2014_x86_64.whl [--force-reinstall]

This will provide you with a python exposed version of the core SAGE library.

  1. Install the sagepy python package with poetry:
git clone git@github.com:theGreatHerrLebert/sagepy.git

cd sagepy

# Install poetry
pip install poetry

# Install sagepy
poetry install

Usage

sagepy is a python interface to the core SAGE search engine. It exposes the core functionality of SAGE in a pythonic way, allowing you to use it for a direct integration into your python-based proteomics workflow. So far, it mainly mirrors structs that are available in the core SAGE library.

Example generation of a sage database

import numpy as np
from sagepy.core import EnzymeBuilder, SageSearchConfiguration

# configure a trypsin-like digestor of fasta files
enzyme_builder = EnzymeBuilder(
    missed_cleavages=2, 
    min_len=5, 
    max_len=50, 
    cleave_at='KR', 
    restrict='P', 
    c_terminal=True,
)

# UPDATE: Modification handling is simplified, using canonical UNIMOD notation
static_mods = {"C": "[UNIMOD:4]"}  # static cysteine modification
variable_mods = {"M": ["[UNIMOD:35]"]}

with open('path/to/reference.fasta', 'r') as infile:
    fasta = infile.read()

# set-up a config for a sage-database
sage_config = SageSearchConfiguration(
    fasta=fasta,
    static_mods=static_mods,
    variable_mods=variable_mods,
    enzyme_builder=enzyme_builder,
    generate_decoys=True,
    bucket_size=int(np.power(2, 14))
)

# generate the database for searching against
indexed_db = sage_config.generate_indexed_database()

Generate a query

import numpy as np
from sagepy.core import Precursor, RawSpectrum, ProcessedSpectrum, SpectrumProcessor, Tolerance, Scorer, Representation

### Example search of a sage database
precursor = Precursor(
    charge=2,
    mz=506.77,
)

intensity = np.array([ 202.,  170.,  205.,  152., 1069.,  595.,  198.,  805.,  187.,
        194.,  197.,  169.,  196.,  209.,  638.,  372.,  235.,  399.,
        194.,  185.,  181.,  170.,  407.,  150.,  157.,  175.,  273.,
       1135.,  881.,  337.,  311.,  243.,  310.,  153.,  162.,  210.,
        277.,  206.,  189.,  259.,  658.,  383.,  166.,  169.,  219.,
        186.,  221.,  193.,  367.,  283.,  237.,  157.,  372., 1276.,
       1618., 1102.,  404.,  232.,  456.,  765.,  507.,  223.,  258.,
        402.,  187.,  158.,  153.,  304.,  218.,  223.,  156., 1605.,
       1165., 1062.,  434.,  208.,  155.,  197.,  221.,  697.,  397.,
        180.,  195.,  512.,  252.,  367.,  305.,  335.,  175.,  174.,
        296.,  212.], dtype=np.float32)

mz = np.array([272.16873692, 356.16844797, 406.71079396, 406.71396814,
       406.71714233, 406.72031653, 407.21246768, 407.21564382,
       407.21881996, 407.22199612, 407.7144506 , 407.71762869,
       488.27537883, 488.28581266, 499.29228981, 499.29580676,
       499.29932372, 499.30284069, 506.75478369, 507.26157767,
       541.26272227, 553.29188809, 577.30432041, 577.30810217,
       595.32672633, 597.2907525 , 603.27568881, 614.32036769,
       614.32426881, 614.32816995, 615.3272682 , 615.33117252,
       616.33108578, 617.33572156, 636.30924838, 637.30619081,
       637.31016425, 665.36284673, 666.36197292, 674.35335834,
       674.35744565, 674.36153297, 675.35511968, 675.36330039,
       679.3531909 , 680.35044702, 680.35455247, 687.36822726,
       687.37648041, 688.37547678, 697.3616813 , 700.3617026 ,
       715.36157366, 715.36578342, 715.36999319, 715.37420297,
       715.37841277, 715.38262258, 716.36384605, 716.37227148,
       716.38069696, 717.37103577, 725.35228543, 749.39291293,
       749.39722166, 750.38424802, 786.44692356, 786.45575152,
       787.4492132 , 787.45804678, 795.39284711, 812.41777208,
       812.42225834, 812.42674462, 812.4312309 , 812.44020351,
       813.40504794, 813.41851494, 813.42300396, 813.427493  ,
       813.43198205, 813.44544927, 814.43784098, 828.42202737,
       828.4265576 , 851.43464868, 899.45327427, 899.46271517,
       912.45278821, 913.44673363, 915.45053417, 915.46482091], dtype=np.float32)

raw_spectrum = RawSpectrum(
    file_id=1,
    spec_id='DEMO-SPEC',
    total_ion_current=12667.0,
    precursors=[precursor],
    mz=mz,
    intensity=intensity
)

spec_processor = SpectrumProcessor(take_top_n=75)
query = spec_processor.process(raw_spectrum)

Search a database

from sagepy.core import Scorer

# UPDATE: pass modifications to the scorer, necessary for PTM handling
scorer = Scorer(report_psms=2, min_matched_peaks=5, variable_mods=variable_mods, static_mods=static_mods)
results = scorer.score(db=indexed_db, spectrum=query)

potential output:

[Feature(idx: PeptideIx(1009105), peptide_len: 9, spec_id: DEMO-SPEC, file_id: 1, rank: 1, label: 1, exp. mass: 1011.5254516601562, cal. mass: 1011.5347900390625, charge: 2, retention time: 0.0, aligned rt: 0.0, predicted rt: 0.0, delta rt model: 0.9990000128746033, delta mass: 2989.41943359375, isotope error: 3.010050058364868, average ppm: 5.889466285705566, hyperscore: 15.020833459653923, delta_next: 0.0, delta_best: 0.0, matched peaks: 5, longest b: 0,longest y: 4, longest y pct: 0.4444444477558136, missed cleavages: 0, matched intensity pct: 14.81151294708252, scored candidates: 9340, poisson: -2.177020383746938, discriminant score: 0.0, posterior error: 1.0, spectrum q: 1.0, peptide q: 1.0, protein q: 1.0, ms2 intensity: 4652.0, ms1 intensity: 0.0), Feature(idx: PeptideIx(1009105), peptide_len: 9, spec_id: DEMO-SPEC, file_id: 1, rank: 2, label: 1, exp. mass: 1011.5254516601562, cal. mass: 1011.5347900390625, charge: 2, retention time: 0.0, aligned rt: 0.0, predicted rt: 0.0, delta rt model: 0.9990000128746033, delta mass: 1001.641845703125, isotope error: 1.003350019454956, average ppm: 5.889466285705566, hyperscore: 15.020833459653923, delta_next: 0.0, delta_best: 0.0, matched peaks: 5, longest b: 0,longest y: 4, longest y pct: 0.4444444477558136, missed cleavages: 0, matched intensity pct: 14.81151294708252, scored candidates: 9340, poisson: -2.177020383746938, discriminant score: 0.0, posterior error: 1.0, spectrum q: 1.0, peptide q: 1.0, protein q: 1.0, ms2 intensity: 4652.0, ms1 intensity: 0.0)]

Hot TODOs

  • Documentation and in-detail examples

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

sagepy-0.2.27.tar.gz (38.8 kB view details)

Uploaded Source

Built Distribution

sagepy-0.2.27-py3-none-any.whl (45.9 kB view details)

Uploaded Python 3

File details

Details for the file sagepy-0.2.27.tar.gz.

File metadata

  • Download URL: sagepy-0.2.27.tar.gz
  • Upload date:
  • Size: 38.8 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: poetry/1.8.3 CPython/3.11.10 Linux/6.5.0-1025-azure

File hashes

Hashes for sagepy-0.2.27.tar.gz
Algorithm Hash digest
SHA256 680fd5083da9e656caadc734d1480ea5cfa64968ac4f3b9e9d5ef51817ffb0a9
MD5 9026671442b710d548c61a68eaab67f7
BLAKE2b-256 11a383335d7fe4a581c7d7d5fc71ef6746bfcd0b02f89d58c4dcb8b9b734fd97

See more details on using hashes here.

File details

Details for the file sagepy-0.2.27-py3-none-any.whl.

File metadata

  • Download URL: sagepy-0.2.27-py3-none-any.whl
  • Upload date:
  • Size: 45.9 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: poetry/1.8.3 CPython/3.11.10 Linux/6.5.0-1025-azure

File hashes

Hashes for sagepy-0.2.27-py3-none-any.whl
Algorithm Hash digest
SHA256 1569e5c403e7cafb2f457717db4b44720c810f05d0785a71276360f51d864f51
MD5 c2758e89db3b3308b67f2bb7496e0b95
BLAKE2b-256 65d64f817485b95a2c25a0af3e927381dc6c496f1e84cca0689b144cbd1f63e5

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page