PROTREC: a probability-based approach for recovering missing proteins based on biological networks

A novel network-based approach for predicting missing proteins (MPs) is proposed here. This approach, PROTREC (short for PROtein RECovery), dominates existing network-based methods - such as Functional Class Scoring (FCS), Hypergeometric Enrichment (HE), and Gene Set Enrichment Analysis (GSEA) - acr...

Full description

Saved in:
Bibliographic Details
Main Authors: Kong, Weijia, Wong, Bertrand Jern Han, Gao, Huanhuan, Guo, Tiannan, Liu, Xianming, Du, Xiaoxian, Wong, Limsoon, Goh, Wilson Wen Bin
Other Authors: School of Biological Sciences
Format: Article
Language:English
Published: 2022
Subjects:
Online Access:https://hdl.handle.net/10356/160171
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Nanyang Technological University
Language: English
id sg-ntu-dr.10356-160171
record_format dspace
spelling sg-ntu-dr.10356-1601712023-02-28T17:12:53Z PROTREC: a probability-based approach for recovering missing proteins based on biological networks Kong, Weijia Wong, Bertrand Jern Han Gao, Huanhuan Guo, Tiannan Liu, Xianming Du, Xiaoxian Wong, Limsoon Goh, Wilson Wen Bin School of Biological Sciences Lee Kong Chian School of Medicine (LKCMedicine) National University of Singapore Science::Biological sciences Bioinformatics Protein Complexes A novel network-based approach for predicting missing proteins (MPs) is proposed here. This approach, PROTREC (short for PROtein RECovery), dominates existing network-based methods - such as Functional Class Scoring (FCS), Hypergeometric Enrichment (HE), and Gene Set Enrichment Analysis (GSEA) - across a variety of proteomics datasets derived from different proteomics data acquisition paradigms: Higher PROTREC scores are much more closely correlated with higher recovery rates of MPs across sample replicates. The PROTREC score, unlike methods reporting p-values, can be directly interpreted as the probability that an unreported protein in a proteomic screen is actually present in the sample being screened. SIGNIFICANCE: Mass spectrometry (MS) has developed rapidly in recent years; however, an obvious proportion of proteins is still undetected, leading to missing protein problems. A few existing protein recovery methods are based on biological networks, but the performance is not satisfactory. We propose a new protein recovery method, PROTREC, a Bayesian-inspired approach based on biological networks, which shows exceptional performance across multiple validation strategies. It does not rely on peptide information, so it avoids the ambiguity issue that most protein assembly methods face. Ministry of Education (MOE) Published version This work is supported in part by a Singapore Ministry of Education tier-2 grant (MOE2019-T2-1-042). 2022-07-14T03:17:32Z 2022-07-14T03:17:32Z 2022 Journal Article Kong, W., Wong, B. J. H., Gao, H., Guo, T., Liu, X., Du, X., Wong, L. & Goh, W. W. B. (2022). PROTREC: a probability-based approach for recovering missing proteins based on biological networks. Journal of Proteomics, 250, 104392-. https://dx.doi.org/10.1016/j.jprot.2021.104392 1874-3919 https://hdl.handle.net/10356/160171 10.1016/j.jprot.2021.104392 34626823 2-s2.0-85117253996 250 104392 en MOE2019-T2-1-042 Journal of Proteomics © 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Science::Biological sciences
Bioinformatics
Protein Complexes
spellingShingle Science::Biological sciences
Bioinformatics
Protein Complexes
Kong, Weijia
Wong, Bertrand Jern Han
Gao, Huanhuan
Guo, Tiannan
Liu, Xianming
Du, Xiaoxian
Wong, Limsoon
Goh, Wilson Wen Bin
PROTREC: a probability-based approach for recovering missing proteins based on biological networks
description A novel network-based approach for predicting missing proteins (MPs) is proposed here. This approach, PROTREC (short for PROtein RECovery), dominates existing network-based methods - such as Functional Class Scoring (FCS), Hypergeometric Enrichment (HE), and Gene Set Enrichment Analysis (GSEA) - across a variety of proteomics datasets derived from different proteomics data acquisition paradigms: Higher PROTREC scores are much more closely correlated with higher recovery rates of MPs across sample replicates. The PROTREC score, unlike methods reporting p-values, can be directly interpreted as the probability that an unreported protein in a proteomic screen is actually present in the sample being screened. SIGNIFICANCE: Mass spectrometry (MS) has developed rapidly in recent years; however, an obvious proportion of proteins is still undetected, leading to missing protein problems. A few existing protein recovery methods are based on biological networks, but the performance is not satisfactory. We propose a new protein recovery method, PROTREC, a Bayesian-inspired approach based on biological networks, which shows exceptional performance across multiple validation strategies. It does not rely on peptide information, so it avoids the ambiguity issue that most protein assembly methods face.
author2 School of Biological Sciences
author_facet School of Biological Sciences
Kong, Weijia
Wong, Bertrand Jern Han
Gao, Huanhuan
Guo, Tiannan
Liu, Xianming
Du, Xiaoxian
Wong, Limsoon
Goh, Wilson Wen Bin
format Article
author Kong, Weijia
Wong, Bertrand Jern Han
Gao, Huanhuan
Guo, Tiannan
Liu, Xianming
Du, Xiaoxian
Wong, Limsoon
Goh, Wilson Wen Bin
author_sort Kong, Weijia
title PROTREC: a probability-based approach for recovering missing proteins based on biological networks
title_short PROTREC: a probability-based approach for recovering missing proteins based on biological networks
title_full PROTREC: a probability-based approach for recovering missing proteins based on biological networks
title_fullStr PROTREC: a probability-based approach for recovering missing proteins based on biological networks
title_full_unstemmed PROTREC: a probability-based approach for recovering missing proteins based on biological networks
title_sort protrec: a probability-based approach for recovering missing proteins based on biological networks
publishDate 2022
url https://hdl.handle.net/10356/160171
_version_ 1759857520093954048