pepmatch 0.8.2

Peptide and epitope search against a reference proteome with specified mismatches.

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Author: Daniel Marrama

Peptide search against a reference proteome, or sets of proteins, with residue subtitutions.

Two step process: preprocessing and matching.

Preprocessed data is stored in a SQLite or pickle format and only has to be performed once.

As a competition to improve tool performance, we created a benchmarking framework with instructions here.

Requirements

• Python 3.7+
• Pandas
• NumPy
• Biopython
• Levenshtein

Installation

pip install pepmatch

Inputs

Preprocessor

proteome - Path to proteome file to search against.
split - k-mer size to break up proteome into.
preprocessed_format - SQLite ("sqlite") or "pickle".
preprocessed_files_path - Directory where you want preprocessed files to go. Default is current directory.
gene_priority_proteome - Subset of proteome with prioritized protein IDs.
versioned_ids - UniProt or NCBI ID versioning included.

Matcher

query - Query of peptides to search either in .fasta file or as a Python list.
proteome - Name of preprocessed proteome to search against.
max_mismatches - Maximum number of mismatches (substitutions) for query.
split - k-mer size of the preprocessed proteome.
preprocessed_files_path - Directory where preprocessed files are. Default is current directory.
one_match - (optional) Returns only one match per query peptide. It will output the best match.
output_format - (optional) Outputs results into a file (CSV, XLSX, JSON, HTML) or just as a dataframe.
output_name - (optional) Specify name of file for output. Leaving blank will generate a name.

Note: For now, due to performance, SQLite is used for exact matching and pickle is used for mismatching.

Command Line Example

pepmatch-preprocess -p human.fasta -k 5 -f sql
pepmatch-match -q peptides.fasta -p 9606 -m 0 -k 5

Exact Matching Example

from pepmatch import Preprocessor, Matcher

# proteome, k, preprocessed_format, target directory, gene_priority_proteome
Preprocessor('proteomes/human.fasta', 'sql', '.', 'proteomes/human_gp.fasta').preprocess(k=5)
# PREPROCESSING ONLY NEEDS TO BE DONE ONCE!

# query, proteome, max_mismatches, k, preprocessed files directory
Matcher('queries/mhc_ligands_test.fasta', 'proteomes/human.fasta', 0, 5, '.').match()

Mismatching Example

from pepmatch import Preprocessor, Matcher

# proteome, k, preprocessed_format, target directory
Preprocessor('proteomes/human.fasta', 'pickle', '.').preprocess(k=3)
# PREPROCESSING ONLY NEEDS TO BE DONE ONCE!

# query, proteome, max_mismatches, k, preprocessed files directory
Matcher('queries/neoepitopes_test.fasta', 'proteomes/human.fasta', 3, 3, '.').match()

Outputs

As mentioned above, outputs can be specified with the output_format parameter in the Matcher class. The following formats are allowed: 'dataframe', 'csv', 'xlsx', 'json', and 'html'.

If specifying 'dataframe', the match() method will return a pandas dataframe which can be stored as a variable as so:

df = Matcher('queries/neoepitopes_test.fasta', 'human.fasta', 3, 3, output_format='dataframe').match()

TODO

• Add test workflows for GitHub Actions
• Add multithreading option
• Remove dependency on Levenshtein (this is not maintained very well)