Measuring pathway database coverage of the phosphoproteome
Protein phosphorylation is one of the best known post-translational mechanisms playing a key role in the regulation of cellular processes. Over 100,000 distinct phosphorylation sites have been discovered through constant improvement of mass spectrometry based phosphoproteomics in the last decade. Ho...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
PeerJ Inc.
2021-05-01
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| Series: | PeerJ |
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| Online Access: | https://peerj.com/articles/11298.pdf |
| _version_ | 1827607508527611904 |
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| author | Hannah Huckstep Liam G. Fearnley Melissa J. Davis |
| author_facet | Hannah Huckstep Liam G. Fearnley Melissa J. Davis |
| author_sort | Hannah Huckstep |
| collection | DOAJ |
| description | Protein phosphorylation is one of the best known post-translational mechanisms playing a key role in the regulation of cellular processes. Over 100,000 distinct phosphorylation sites have been discovered through constant improvement of mass spectrometry based phosphoproteomics in the last decade. However, data saturation is occurring and the bottleneck of assigning biologically relevant functionality to phosphosites needs to be addressed. There has been finite success in using data-driven approaches to reveal phosphosite functionality due to a range of limitations. The alternate, more suitable approach is making use of prior knowledge from literature-derived databases. Here, we analysed seven widely used databases to shed light on their suitability to provide functional insights into phosphoproteomics data. We first determined the global coverage of each database at both the protein and phosphosite level. We also determined how consistent each database was in its phosphorylation annotations compared to a global standard. Finally, we looked in detail at the coverage of each database over six experimental datasets. Our analysis highlights the relative strengths and weaknesses of each database, providing a guide in how each can be best used to identify biological mechanisms in phosphoproteomic data. |
| first_indexed | 2024-03-09T06:55:13Z |
| format | Article |
| id | doaj.art-7bfb676172584c3c93475e5fcd94713e |
| institution | Directory Open Access Journal |
| issn | 2167-8359 |
| language | English |
| last_indexed | 2024-03-09T06:55:13Z |
| publishDate | 2021-05-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ |
| spelling | doaj.art-7bfb676172584c3c93475e5fcd94713e2023-12-03T10:05:33ZengPeerJ Inc.PeerJ2167-83592021-05-019e1129810.7717/peerj.11298Measuring pathway database coverage of the phosphoproteomeHannah Huckstep0Liam G. Fearnley1Melissa J. Davis2Division of Bioinformatics, The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, AustraliaDepartment of Medical Biology, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Parkville, Victoria, AustraliaDivision of Bioinformatics, The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, AustraliaProtein phosphorylation is one of the best known post-translational mechanisms playing a key role in the regulation of cellular processes. Over 100,000 distinct phosphorylation sites have been discovered through constant improvement of mass spectrometry based phosphoproteomics in the last decade. However, data saturation is occurring and the bottleneck of assigning biologically relevant functionality to phosphosites needs to be addressed. There has been finite success in using data-driven approaches to reveal phosphosite functionality due to a range of limitations. The alternate, more suitable approach is making use of prior knowledge from literature-derived databases. Here, we analysed seven widely used databases to shed light on their suitability to provide functional insights into phosphoproteomics data. We first determined the global coverage of each database at both the protein and phosphosite level. We also determined how consistent each database was in its phosphorylation annotations compared to a global standard. Finally, we looked in detail at the coverage of each database over six experimental datasets. Our analysis highlights the relative strengths and weaknesses of each database, providing a guide in how each can be best used to identify biological mechanisms in phosphoproteomic data.https://peerj.com/articles/11298.pdfPhosphoproteomicsDatabasesProteomicsBioinformatics |
| spellingShingle | Hannah Huckstep Liam G. Fearnley Melissa J. Davis Measuring pathway database coverage of the phosphoproteome PeerJ Phosphoproteomics Databases Proteomics Bioinformatics |
| title | Measuring pathway database coverage of the phosphoproteome |
| title_full | Measuring pathway database coverage of the phosphoproteome |
| title_fullStr | Measuring pathway database coverage of the phosphoproteome |
| title_full_unstemmed | Measuring pathway database coverage of the phosphoproteome |
| title_short | Measuring pathway database coverage of the phosphoproteome |
| title_sort | measuring pathway database coverage of the phosphoproteome |
| topic | Phosphoproteomics Databases Proteomics Bioinformatics |
| url | https://peerj.com/articles/11298.pdf |
| work_keys_str_mv | AT hannahhuckstep measuringpathwaydatabasecoverageofthephosphoproteome AT liamgfearnley measuringpathwaydatabasecoverageofthephosphoproteome AT melissajdavis measuringpathwaydatabasecoverageofthephosphoproteome |