Systematic Protein Prioritization for Targeted Proteomics Studies through Literature Mining
There are more than 3.7 million published articles on the biological functions or disease implications of proteins, constituting an important resource of proteomics knowledge. However, it is difficult to summarize the millions of proteomics findings in the literature manually and quantify their relevance to the biology and diseases of interest. We developed a fully automated bioinformatics framework to identify and prioritize proteins associated with any biological entity. We used the 22 targeted areas of the Biology/Disease-driven (B/D)-Human Proteome Project (HPP) as examples, prioritized the relevant proteins through their Protein Universal Reference Publication-Originated Search Engine (PURPOSE) scores, validated the relevance of the score by comparing the protein prioritization results with a curated database, computed the scores of proteins across the topics of B/D-HPP, and characterized the top proteins in the common model organisms. We further extended the bioinformatics workflow to identify the relevant proteins in all organ systems and human diseases and deployed a cloud-based tool to prioritize proteins related to any custom search terms in real time. Our tool can facilitate the prioritization of proteins for any organ system or disease of interest and can contribute to the development of targeted proteomic studies for precision medicine.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2018 |
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| Erschienen: |
2018 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:17 |
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| Enthalten in: |
Journal of proteome research - 17(2018), 4 vom: 06. Apr., Seite 1383-1396 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Yu, Kun-Hsing [VerfasserIn] |
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| Links: |
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| Anmerkungen: |
Date Completed 03.06.2019 Date Revised 15.04.2023 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1021/acs.jproteome.7b00772 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM281623600 |
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| 245 | 1 | 0 | |a Systematic Protein Prioritization for Targeted Proteomics Studies through Literature Mining |
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| 500 | |a Date Revised 15.04.2023 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a There are more than 3.7 million published articles on the biological functions or disease implications of proteins, constituting an important resource of proteomics knowledge. However, it is difficult to summarize the millions of proteomics findings in the literature manually and quantify their relevance to the biology and diseases of interest. We developed a fully automated bioinformatics framework to identify and prioritize proteins associated with any biological entity. We used the 22 targeted areas of the Biology/Disease-driven (B/D)-Human Proteome Project (HPP) as examples, prioritized the relevant proteins through their Protein Universal Reference Publication-Originated Search Engine (PURPOSE) scores, validated the relevance of the score by comparing the protein prioritization results with a curated database, computed the scores of proteins across the topics of B/D-HPP, and characterized the top proteins in the common model organisms. We further extended the bioinformatics workflow to identify the relevant proteins in all organ systems and human diseases and deployed a cloud-based tool to prioritize proteins related to any custom search terms in real time. Our tool can facilitate the prioritization of proteins for any organ system or disease of interest and can contribute to the development of targeted proteomic studies for precision medicine | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, N.I.H., Extramural | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 650 | 4 | |a Research Support, U.S. Gov't, Non-P.H.S. | |
| 650 | 4 | |a Human Proteome Project | |
| 650 | 4 | |a bioinformatics | |
| 650 | 4 | |a information retrieval | |
| 650 | 4 | |a literature mining | |
| 650 | 4 | |a proteomics | |
| 700 | 1 | |a Lee, Tsung-Lu Michael |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Chi-Shiang |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Yu-Ju |e verfasserin |4 aut | |
| 700 | 1 | |a Ré, Christopher |e verfasserin |4 aut | |
| 700 | 1 | |a Kou, Samuel C |e verfasserin |4 aut | |
| 700 | 1 | |a Chiang, Jung-Hsien |e verfasserin |4 aut | |
| 700 | 1 | |a Kohane, Isaac S |e verfasserin |4 aut | |
| 700 | 1 | |a Snyder, Michael |e verfasserin |4 aut | |
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