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Characterization of peptide-protein relationships in protein ambiguity groups via bipartite graphs - data set D2

ABSTRACT: Motivation: In bottom-up mass spectrometry proteins are enzymatically digested before measurement. The relationship between proteins and peptides can be represented by bipartite graphs that can be split into connected components. This representation is useful to aid protein inference and quantification, which is complex due to the occurrence of shared peptides. We conducted a comprehensive analysis of these bipartite graphs using peptides from an in silico digestion of protein databases as well as quantified peptides. Results: The graphs based on quantified peptides are smaller and have less complex structures compared to the database level. However, the proportion of protein nodes without unique peptides and the proportion of graphs that contain these proteins increase. Large differences between the two underlying organisms (mouse and yeast) on database as well as quantitative level could be observed. Insights of this analysis may be useful for the development of protein inference and quantification algorithms. Link to preprint: https://www.biorxiv.org/content/10.1101/2021.07.28.454128v1?ct=

INSTRUMENT(S): LTQ Orbitrap Velos

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

TISSUE(S): Cell Culture

DISEASE(S): Disease Free

SUBMITTER: Karin Schork

LAB HEAD: Martin Eisenacher

PROVIDER: PXD024305 | Pride | 2021-07-30

REPOSITORIES: Pride

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Dataset's files

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			Action	DRS
	2020_01_31_proteome_S_cerevisae_with_isoforms.fasta	Fasta
	MaxQuant_output_txt.zip	Other
	UPS1_12500amol_R1.raw	Raw
	UPS1_12500amol_R2.raw	Raw
	UPS1_12500amol_R3.raw	Raw

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Characterization of peptide-protein relationships in protein ambiguity groups via bipartite graphs - data set D2 (without isoforms)

Project description:Motivation: In bottom-up mass spectrometry proteins are enzymatically digested before measurement. The relationship between proteins and peptides can be represented by bipartite graphs that can be split into connected components. This representation is useful to aid protein inference and quantification, which is complex due to the occurrence of shared peptides. We conducted a comprehensive analysis of these bipartite graphs using peptides from an in silico digestion of protein databases as well as quantified peptides. Results: The graphs based on quantified peptides are smaller and have less complex structures compared to the database level. However, the proportion of protein nodes without unique peptides and the proportion of graphs that contain these proteins increase. Large differences between the different underlying organisms on database as well as quantitative level could be observed. Insights of this analysis may be useful for the development of protein inference and quantification algorithms. Link to preprint: https://www.biorxiv.org/content/10.1101/2021.07.28.454128v1?ct=

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