Semi-supervised learning for sensitive open modification spectral library searching - dataset PXD009476

This is the analysis results of ANN-SoLo + Rescoring as an integrated module. ANN-SoLo spectral library search engine is a tool for efficient open modification searching. ANN-SoLo uses a cascade search strategy to optimally identify both unmodified and modified peptides: in the first stage a standard search is performed to identify unmodified peptides, after which the remaining unidentified spectra are submitted to second stage during which an open search is performed to additionally identify modified peptides. We have augmented this approach by natively integrating PSM rescoring into ANN-SoLo using the mokapot Python framework for semi-supervised learning for peptide detection.

The repository includes the results and search database used to analyze a human glycoproteomics dataset that was acquired from human kidney tissue, serum, and T cells to study O-linked glycosylation. Samples were trypsin-digested and separated into 24 fractions after enrichment of intact glycopeptides and release of O-linked glycopeptides. Next, the data was acquired on a Fusion Lumos mass spectrometer with an Easy-nLC 1200 system or a Q-Exactive HF mass spectrometer with a Waters NanoAcquity UPLC. From this dataset, four raw files derived from kidney tissue samples were retrieved from PRIDE (project PXD009476) and converted to MGF files using ThermoRawFileParser (version 1.7.2).

The four files are available both in RAW and MGF format below. 

Arab, Issar, William E. Fondrie, Kris Laukens, and Wout Bittremieux. "Semisupervised Machine Learning for Sensitive Open Modification Spectral Library Searching." Journal of Proteome Research 22, no. 2 (2023): 585-593. doi.org/10.1021/acs.jproteome.2c00616