Knowledge-Based Analysis for Detecting Key Signaling Events from Time-Series Phosphoproteomics Data.
Cell signaling underlies transcription/epigenetic control of a vast majority of cell-fate decisions. A key goal in cell signaling studies is to identify the set of kinases that underlie key signaling events. In a typical phosphoproteomics study, phosphorylation sites (substrates) of active kinases a...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2015-08-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1004403&type=printable |
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| _version_ | 1850125598287986688 |
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| author | Pengyi Yang Xiaofeng Zheng Vivek Jayaswal Guang Hu Jean Yee Hwa Yang Raja Jothi |
| author_facet | Pengyi Yang Xiaofeng Zheng Vivek Jayaswal Guang Hu Jean Yee Hwa Yang Raja Jothi |
| author_sort | Pengyi Yang |
| collection | DOAJ |
| description | Cell signaling underlies transcription/epigenetic control of a vast majority of cell-fate decisions. A key goal in cell signaling studies is to identify the set of kinases that underlie key signaling events. In a typical phosphoproteomics study, phosphorylation sites (substrates) of active kinases are quantified proteome-wide. By analyzing the activities of phosphorylation sites over a time-course, the temporal dynamics of signaling cascades can be elucidated. Since many substrates of a given kinase have similar temporal kinetics, clustering phosphorylation sites into distinctive clusters can facilitate identification of their respective kinases. Here we present a knowledge-based CLUster Evaluation (CLUE) approach for identifying the most informative partitioning of a given temporal phosphoproteomics data. Our approach utilizes prior knowledge, annotated kinase-substrate relationships mined from literature and curated databases, to first generate biologically meaningful partitioning of the phosphorylation sites and then determine key kinases associated with each cluster. We demonstrate the utility of the proposed approach on two time-series phosphoproteomics datasets and identify key kinases associated with human embryonic stem cell differentiation and insulin signaling pathway. The proposed approach will be a valuable resource in the identification and characterizing of signaling networks from phosphoproteomics data. |
| format | Article |
| id | doaj-art-2bac6a24fed24e19b888a09e2ca28889 |
| institution | OA Journals |
| issn | 1553-734X 1553-7358 |
| language | English |
| publishDate | 2015-08-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj-art-2bac6a24fed24e19b888a09e2ca288892025-08-20T02:34:06ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582015-08-01118e100440310.1371/journal.pcbi.1004403Knowledge-Based Analysis for Detecting Key Signaling Events from Time-Series Phosphoproteomics Data.Pengyi YangXiaofeng ZhengVivek JayaswalGuang HuJean Yee Hwa YangRaja JothiCell signaling underlies transcription/epigenetic control of a vast majority of cell-fate decisions. A key goal in cell signaling studies is to identify the set of kinases that underlie key signaling events. In a typical phosphoproteomics study, phosphorylation sites (substrates) of active kinases are quantified proteome-wide. By analyzing the activities of phosphorylation sites over a time-course, the temporal dynamics of signaling cascades can be elucidated. Since many substrates of a given kinase have similar temporal kinetics, clustering phosphorylation sites into distinctive clusters can facilitate identification of their respective kinases. Here we present a knowledge-based CLUster Evaluation (CLUE) approach for identifying the most informative partitioning of a given temporal phosphoproteomics data. Our approach utilizes prior knowledge, annotated kinase-substrate relationships mined from literature and curated databases, to first generate biologically meaningful partitioning of the phosphorylation sites and then determine key kinases associated with each cluster. We demonstrate the utility of the proposed approach on two time-series phosphoproteomics datasets and identify key kinases associated with human embryonic stem cell differentiation and insulin signaling pathway. The proposed approach will be a valuable resource in the identification and characterizing of signaling networks from phosphoproteomics data.https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1004403&type=printable |
| spellingShingle | Pengyi Yang Xiaofeng Zheng Vivek Jayaswal Guang Hu Jean Yee Hwa Yang Raja Jothi Knowledge-Based Analysis for Detecting Key Signaling Events from Time-Series Phosphoproteomics Data. PLoS Computational Biology |
| title | Knowledge-Based Analysis for Detecting Key Signaling Events from Time-Series Phosphoproteomics Data. |
| title_full | Knowledge-Based Analysis for Detecting Key Signaling Events from Time-Series Phosphoproteomics Data. |
| title_fullStr | Knowledge-Based Analysis for Detecting Key Signaling Events from Time-Series Phosphoproteomics Data. |
| title_full_unstemmed | Knowledge-Based Analysis for Detecting Key Signaling Events from Time-Series Phosphoproteomics Data. |
| title_short | Knowledge-Based Analysis for Detecting Key Signaling Events from Time-Series Phosphoproteomics Data. |
| title_sort | knowledge based analysis for detecting key signaling events from time series phosphoproteomics data |
| url | https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1004403&type=printable |
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