sagepy 0.2.11

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SAGEpy

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

Installation

sagepy might eventually come to PyPI, but for now you need to follow these steps to install it:

1. Clone our fork of the SAGE repository:

git clone [e-mail]:theGreatHerrLebert/sage.git

2. 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.

3. Install the sagepy python package with poetry:

git clone [e-mail]: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, validate_mods, validate_var_mods, SAGE_KNOWN_MODS

# 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,
)

# generate static cysteine modification
static_mods = { k: v for k, v in [SAGE_KNOWN_MODS.cysteine_static()] }

# generate variable methionine modification
variable_mods = { k: v for k, v in [SAGE_KNOWN_MODS.methionine_variable()] }

# generate SAGE compatible mod representations
static = validate_mods(static_mods)
variab = validate_var_mods(variable_mods)

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,
    variable_mods=variab,
    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

scorer = Scorer(report_psms=2, min_matched_peaks=5)
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)]