ProInfer: An interpretable protein inference tool leveraging on biological networks.

Peng, H; Wong, L; Goh, WWB

ProInfer: An interpretable protein inference tool leveraging on biological networks.

Peng, H Wong, L Goh, WWB

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Publisher:: Public Library of Science (PLoS)
Publication Type:: Journal Article
Citation:: PLoS Comput Biol, 2023, 19, (3), pp. e1010961
Issue Date:: 2023-03

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Field	Value	Language
dc.contributor.author	Peng, H
dc.contributor.author	Wong, L
dc.contributor.author	Goh, WWB
dc.contributor.editor	Elofsson, A
dc.date.accessioned	2024-01-16T03:01:41Z
dc.date.available	2023-02-20
dc.date.available	2024-01-16T03:01:41Z
dc.date.issued	2023-03
dc.identifier.citation	PLoS Comput Biol, 2023, 19, (3), pp. e1010961
dc.identifier.issn	1553-734X
dc.identifier.issn	1553-7358
dc.identifier.uri	http://hdl.handle.net/10453/174555
dc.description.abstract	In mass spectrometry (MS)-based proteomics, protein inference from identified peptides (protein fragments) is a critical step. We present ProInfer (Protein Inference), a novel protein assembly method that takes advantage of information in biological networks. ProInfer assists recovery of proteins supported only by ambiguous peptides (a peptide which maps to more than one candidate protein) and enhances the statistical confidence for proteins supported by both unique and ambiguous peptides. Consequently, ProInfer rescues weakly supported proteins thereby improving proteome coverage. Evaluated across THP1 cell line, lung cancer and RAW267.4 datasets, ProInfer always infers the most numbers of true positives, in comparison to mainstream protein inference tools Fido, EPIFANY and PIA. ProInfer is also adept at retrieving differentially expressed proteins, signifying its usefulness for functional analysis and phenotype profiling. Source codes of ProInfer are available at https://github.com/PennHui2016/ProInfer.
dc.format	Electronic-eCollection
dc.language	eng
dc.publisher	Public Library of Science (PLoS)
dc.relation.ispartof	PLoS Comput Biol
dc.relation.isbasedon	10.1371/journal.pcbi.1010961
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	01 Mathematical Sciences, 06 Biological Sciences, 08 Information and Computing Sciences
dc.subject.classification	Bioinformatics
dc.subject.mesh	Algorithms
dc.subject.mesh	Peptides
dc.subject.mesh	Proteome
dc.subject.mesh	Mass Spectrometry
dc.subject.mesh	Proteomics
dc.subject.mesh	Databases, Protein
dc.subject.mesh	Software
dc.subject.mesh	Peptides
dc.subject.mesh	Proteome
dc.subject.mesh	Proteomics
dc.subject.mesh	Algorithms
dc.subject.mesh	Software
dc.subject.mesh	Databases, Protein
dc.subject.mesh	Mass Spectrometry
dc.subject.mesh	Algorithms
dc.subject.mesh	Peptides
dc.subject.mesh	Proteome
dc.subject.mesh	Mass Spectrometry
dc.subject.mesh	Proteomics
dc.subject.mesh	Databases, Protein
dc.subject.mesh	Software
dc.title	ProInfer: An interpretable protein inference tool leveraging on biological networks.
dc.type	Journal Article
utslib.citation.volume	19
utslib.location.activity	United States
utslib.for	01 Mathematical Sciences
utslib.for	06 Biological Sciences
utslib.for	08 Information and Computing Sciences
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	open_access	*
dc.date.updated	2024-01-16T03:01:39Z
pubs.issue	3
pubs.publication-status	Published online
pubs.volume	19
utslib.citation.issue	3

Abstract:

In mass spectrometry (MS)-based proteomics, protein inference from identified peptides (protein fragments) is a critical step. We present ProInfer (Protein Inference), a novel protein assembly method that takes advantage of information in biological networks. ProInfer assists recovery of proteins supported only by ambiguous peptides (a peptide which maps to more than one candidate protein) and enhances the statistical confidence for proteins supported by both unique and ambiguous peptides. Consequently, ProInfer rescues weakly supported proteins thereby improving proteome coverage. Evaluated across THP1 cell line, lung cancer and RAW267.4 datasets, ProInfer always infers the most numbers of true positives, in comparison to mainstream protein inference tools Fido, EPIFANY and PIA. ProInfer is also adept at retrieving differentially expressed proteins, signifying its usefulness for functional analysis and phenotype profiling. Source codes of ProInfer are available at https://github.com/PennHui2016/ProInfer.

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http://hdl.handle.net/10453/174555