Advanced bioinformatics methods for practical applications in proteomics
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...
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| Format: | Journal Article |
| Language: | English |
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2020
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| Online Access: | https://hdl.handle.net/10356/144722 |
| _version_ | 1826129218236317696 |
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| author | Goh, Wilson Wen Bin Wong, Limsoon |
| author2 | School of Biological Sciences |
| author_facet | School of Biological Sciences Goh, Wilson Wen Bin Wong, Limsoon |
| author_sort | Goh, Wilson Wen Bin |
| collection | NTU |
| description | 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. |
| first_indexed | 2024-10-01T07:37:07Z |
| format | Journal Article |
| id | ntu-10356/144722 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T07:37:07Z |
| publishDate | 2020 |
| record_format | dspace |
| spelling | ntu-10356/1447222023-02-28T16:57:20Z Advanced bioinformatics methods for practical applications in proteomics Goh, Wilson Wen Bin Wong, Limsoon School of Biological Sciences Science::Biological sciences Proteomics Networks 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. Ministry of Education (MOE) Accepted version A Singapore Ministry of Education tier-2 grant (MOE2012-T2-1-061) to L. W. 2020-11-20T06:56:49Z 2020-11-20T06:56:49Z 2018 Journal Article 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 1467-5463 https://hdl.handle.net/10356/144722 10.1093/bib/bbx128 30657890 1 20 347 355 en Briefings in bioinformatics © 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. application/pdf |
| spellingShingle | Science::Biological sciences Proteomics Networks Goh, Wilson Wen Bin Wong, Limsoon Advanced bioinformatics methods for practical applications in proteomics |
| title | Advanced bioinformatics methods for practical applications in proteomics |
| title_full | Advanced bioinformatics methods for practical applications in proteomics |
| title_fullStr | Advanced bioinformatics methods for practical applications in proteomics |
| title_full_unstemmed | Advanced bioinformatics methods for practical applications in proteomics |
| title_short | Advanced bioinformatics methods for practical applications in proteomics |
| title_sort | advanced bioinformatics methods for practical applications in proteomics |
| topic | Science::Biological sciences Proteomics Networks |
| url | https://hdl.handle.net/10356/144722 |
| work_keys_str_mv | AT gohwilsonwenbin advancedbioinformaticsmethodsforpracticalapplicationsinproteomics AT wonglimsoon advancedbioinformaticsmethodsforpracticalapplicationsinproteomics |