Dataset Information

A dataset for training/testing machine learning models of peptide feature detection: Human HELA LC-MSMS

ABSTRACT: We introduce MSTracer, a tool for peptide feature detection from MS1, which incorporates a machine-learning-combined scoring function based on peptide isotopic distribution and peptide intensity shape on the LC-MS map. By using Support Vector Regression (SVR), the quality of detected peptide features is remarkably improved. By utilising Neural Networks (NN), scores that indicate the quality of features are assigned for detected features as well. We use the Human HELA LC-MSMS dataset to train and test the results and compare with MaxQuant, OpenMS, and Dinosaur.

INSTRUMENT(S): Orbitrap Fusion

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Hela Cell

SUBMITTER: Xiangyuan Zeng

LAB HEAD: Bin Ma

PROVIDER: PXD022287 | Pride | 2021-06-18

REPOSITORIES: Pride

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Dataset's files

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			Action	DRS
	HELA-DDA-60C-rep1.mzML	Mzml
	HELA-DDA-60C-rep1.mzid.gz	Mzid
	HELA-DDA-60C-rep1.raw	Raw
	HELA-DDA-60C-rep1_method.txt	Txt
	HELA-DDA-60C-rep1_msgf_.tsv	Tabular

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Project description:Chromatin structure and function is maintained by dynamic protein-protein and protein-nucleic acid interactions. Histones are a family of proteins that are abundant chromatin constituents and that carry numerous post-translational modifications (PTMs). Histone PTMs mediate a variety of biological activities, including recruitment of enzymatic readers, writers and erasers that modulate protein activities, DNA replication, transcription and repair. Individual histone molecules contain multiple co-existing PTMs some of which exhibit crosstalk, i.e. coordinated or mutually exclusive activities. We here present an integrated experimental and computational approach for systems level molecular characterization of PTMs and PTM crosstalk. Using wildtype and engineered mouse embryonic stem cells with perturbations in the Polycomb Repressive Complex 1 (PRC1, suz12-/-), PRC2 (Ring1A/b-/-) and DNA methyltransferases (Dnmt1/3a/3b-/-) we performed comprehensive PTM analysis of histone H3 tails. We identified unique histone H3 PTM features of each of the four cell lines and we detected common combinatorial PTM features across cell lines. Using quantitative middle-down proteomics combined with probabilistic and statistical data analysis we extracted histone H3 PTM profiles for all four mESC systems. PTM crosstalk emerged as mutually exclusive histone PTMs or coordinately regulated PTMs independent of histone peptide abundance in the four model systems. We detected positive crosstalk between adjacent mono-methylated marks but strong negative crosstalk among most of the seven characterized di- and tri-methylations on lysines. We report novel features of PTM interplay involving hitherto poorly characterized arginine methylation and lysine methylation sites in histone H3, including H3R2me, H3R8me and H3K37me, which exhibited specific PTM codes suggesting a particular role in chromatin. All histone H3 PTM data is available in our publicly available CrossTalkDB repository at http://crosstalkdb.bmb.sdu.dk

Dataset Information

A dataset for training/testing machine learning models of peptide feature detection: Human HELA LC-MSMS

Dataset's files

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