Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/165441
Title: Resolving missing protein problems using functional class scoring
Authors: Wong, Bertrand Jern Han
Kong, Weijia
Wong, Limsoon
Goh, Wilson Wen Bin
Keywords: Science::Biological sciences
Science::Medicine
Issue Date: 2022
Source: Wong, B. J. H., Kong, W., Wong, L. & Goh, W. W. B. (2022). Resolving missing protein problems using functional class scoring. Scientific Reports, 12(1), 11358-. https://dx.doi.org/10.1038/s41598-022-15314-3
Project: RG35/20 
MOE2019-T2-1-042 
Journal: Scientific Reports 
Abstract: Despite technological advances in proteomics, incomplete coverage and inconsistency issues persist, resulting in "data holes". These data holes cause the missing protein problem (MPP), where relevant proteins are persistently unobserved, or sporadically observed across samples, hindering biomarker discovery and proper functional characterization. Network-based approaches can provide powerful solutions for resolving these issues. Functional Class Scoring (FCS) is one such method that uses protein complex information to recover missing proteins with weak support. However, FCS has not been evaluated on more recent proteomic technologies with higher coverage, and there is no clear way to evaluate its performance. To address these issues, we devised a more rigorous evaluation schema based on cross-verification between technical replicates and evaluated its performance on data acquired under recent Data-Independent Acquisition (DIA) technologies (viz. SWATH). Although cross-replicate examination reveals some inconsistencies amongst same-class samples, tissue-differentiating signal is nonetheless strongly conserved, confirming that FCS selects for biologically meaningful networks. We also report that predicted missing proteins are statistically significant based on FCS p values. Despite limited cross-replicate verification rates, the predicted missing proteins as a whole have higher peptide support than non-predicted proteins. FCS also predicts missing proteins that are often lost due to weak specific peptide support.
URI: https://hdl.handle.net/10356/165441
ISSN: 2045-2322
DOI: 10.1038/s41598-022-15314-3
Schools: Lee Kong Chian School of Medicine (LKCMedicine) 
School of Biological Sciences 
Organisations: School of Computing, NUS
Research Centres: Center for Biomedical Informatics, NTU
Rights: © The Author(s) 2022. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:LKCMedicine Journal Articles
SBS Journal Articles

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