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...
| Main Authors: | , , , , , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2022
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/160171 |
| _version_ | 1811697164461539328 |
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| author | Kong, Weijia Wong, Bertrand Jern Han Gao, Huanhuan Guo, Tiannan Liu, Xianming Du, Xiaoxian Wong, Limsoon Goh, Wilson Wen Bin |
| 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 |
| author_sort | Kong, Weijia |
| collection | NTU |
| 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. |
| first_indexed | 2024-10-01T07:50:54Z |
| format | Journal Article |
| id | ntu-10356/160171 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T07:50:54Z |
| publishDate | 2022 |
| record_format | dspace |
| spelling | ntu-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 |
| 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 |
| title | 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_short | 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 |
| topic | Science::Biological sciences Bioinformatics Protein Complexes |
| url | https://hdl.handle.net/10356/160171 |
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