Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/144722
Title: Advanced bioinformatics methods for practical applications in proteomics
Authors: Goh, Wilson Wen Bin
Wong, Limsoon
Keywords: Science::Biological sciences
Issue Date: 2018
Source: Goh, W. W. B., & Wong, L. (2017). Advanced bioinformatics methods for practical applications in proteomics. Briefings in Bioinformatics, 20(1), 347–355. doi:10.1093/bib/bbx128
Journal: Briefings in bioinformatics
Abstract: Mass spectrometry (MS)-based proteomics has undergone rapid advancements in recent years, creating challenging problems for bioinformatics. We focus on four aspects where bioinformatics plays a crucial role (and proteomics is needed for clinical application): peptide-spectra matching (PSM) based on the new data-independent acquisition (DIA) paradigm, resolving missing proteins (MPs), dealing with biological and technical heterogeneity in data and statistical feature selection (SFS). DIA is a brute-force strategy that provides greater width and depth but, because it indiscriminately captures spectra such that signal from multiple peptides is mixed, getting good PSMs is difficult. We consider two strategies: simplification of DIA spectra to pseudo-data-dependent acquisition spectra or, alternatively, brute-force search of each DIA spectra against known reference libraries. The MP problem arises when proteins are never (or inconsistently) detected by MS. When observed in at least one sample, imputation methods can be used to guess the approximate protein expression level. If never observed at all, network/protein complex-based contextualization provides an independent prediction platform. Data heterogeneity is a difficult problem with two dimensions: technical (batch effects), which should be removed, and biological (including demography and disease subpopulations), which should be retained. Simple normalization is seldom sufficient, while batch effect-correction algorithms may create errors. Batch effect-resistant normalization methods are a viable alternative. Finally, SFS is vital for practical applications. While many methods exist, there is no best method, and both upstream (e.g. normalization) and downstream processing (e.g. multiple-testing correction) are performance confounders. We also discuss signal detection when class effects are weak.
URI: https://hdl.handle.net/10356/144722
ISSN: 1467-5463
DOI: 10.1093/bib/bbx128
Rights: © 2017 Oxford University Press. All rights reserved. This is a pre-copyedited, author-produced PDF of an article accepted for publication in Briefings in bioinformatics following peer review. The definitive publisher-authenticated version Goh, W. W. B., & Wong, L. (2017). Advanced bioinformatics methods for practical applications in proteomics. Briefings in Bioinformatics, 20(1), 347–355. is available online at:https://doi.org/10.1093/bib/bbx128.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SBS Journal Articles

Files in This Item:
File Description SizeFormat 
Advanced bioinformatics methods for practical applications in proteomics.pdf349.74 kBAdobe PDFView/Open

SCOPUSTM   
Citations

7
Updated on Jan 15, 2021

PublonsTM
Citations

7
Updated on Jan 13, 2021

Page view(s)

8
Updated on Jan 20, 2021

Download(s)

6
Updated on Jan 20, 2021

Google ScholarTM

Check

Altmetric


Plumx

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.