{ "info": { "author": "Mark Ivanov", "author_email": "pyteomics@googlegroups.com", "bugtrack_url": null, "classifiers": [ "Intended Audience :: Science/Research", "Programming Language :: Python :: 2.7", "Topic :: Education", "Topic :: Scientific/Engineering :: Bio-Informatics", "Topic :: Scientific/Engineering :: Chemistry", "Topic :: Scientific/Engineering :: Physics" ], "description": "ms1searchpy - a proteomics search engine for LC-MS1 spectra\n---------------------------------------------------------------\n\nThe .tsv (or mzML) and .fasta files are required for basic operation of the script.\ntsv file is tab-separated text file with peptide features generated by Dinosaur software (J.Teleman et al., \"Dinosaur: A Refined Open-Source Peptide MS Feature Detector\", JPR 2016) from mzML file. This file can be generated by any other software for peak-picking and must contain columns 'massCalib', 'rtApex', 'charge' and 'nIsotopes' columns.\nFor a \u0441onvenient usage, mzML files can be used directly and the script will run an attached version of Dinosaur (installed Java is required). \nFor an efficient usage of retention time, user can install and use ELUDE prediction algorithm (-elude path_to_elude_binary should be used in parameters).\n\nAlgorithm can be run with following command:\n\n python search.py path_to_MZML -d path_to_fasta\n\n OR\n\n python search.py path_to_peptideFeatures -d path_to_fasta\n\nThe script output contains files: all identified proteins (filename_proteins_full.csv), filtered proteins (filename_proteins.csv), all matched peptide match fingerprints (filename_PFMs.csv).\n\nCiting ms1searchpy\n-------------------\nIvanov et al. 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