Dataset Information

Peak identification and quantification by proteomic mass spectrogram decomposition

ABSTRACT: Recent advances in liquid chromatography/mass spectrometry (LC/MS) technology have improved the sensitivity, resolution, and speed of proteome analysis, resulting in demand for sophisticated algorithms to interpret complex mass spectrograms. Here, we propose a novel statistical method, proteomic mass spectrogram decomposition (ProtMSD), for joint identification and quantification of peptides and proteins. Given the proteomic mass spectrogram and the reference mass spectra of possible peptide ions associated with proteins from selected run or runs as a dictionary, ProtMSD estimates the chromatograms of those peptide ions, under a group sparsity constraint without using the conventional careful pre-processing (e.g., thresholding and peak picking). We show that the method was significantly improved by using protein-peptide hierarchical relationships, isotopic distribution profiles, reference retention times of peptide ions, and pre-learned mass spectra of noise. We examined the concept of database search, library search, and match-between-runs. This is the first attempt to use a matrix decomposition technique as a tool for LC/MS-based proteome identification and quantification.

ORGANISM(S): Homo Sapiens (human) Saccharomyces Cerevisiae (baker's Yeast)

SUBMITTER: Yasushi Ishihama

PROVIDER: PXD015189 | JPOST Repository | Sat Mar 06 00:00:00 GMT 2021

REPOSITORIES: jPOST

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Dataset's files

Source:

			Action	DRS
	171020pt_mNMF_standard48proteins_01.raw	Raw
	171020pt_mNMF_standard4proteins_01.raw	Raw
	190401pt_mNMF_standard48proteins.dat	Other
	190401pt_mNMF_standard4proteins.dat	Other
	190611pt_mNMF_standard4proteins.sky	Other

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Publications

Peak Identification and Quantification by Proteomic Mass Spectrogram Decomposition.

Taechawattananant Pasrawin P Yoshii Kazuyoshi K Ishihama Yasushi Y

Journal of proteome research 20210304 5

Recent advances in the liquid chromatography/mass spectrometry (LC/MS) technology have improved the sensitivity, resolution, and speed of proteome analysis, resulting in increasing demand for more sophisticated algorithms to interpret complex mass spectrograms. Here, we propose a novel statistical method, proteomic mass spectrogram decomposition (ProtMSD), for joint identification and quantification of peptides and proteins. Given the proteomic mass spectrogram and the reference mass spectra of ...[more]

PMID: 33661642

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