ArticlePLoS computational biology2015
Knowledge-Based Analysis for Detecting Key Signaling Events from Time-Series Phosphoproteomics Data.
Article in PLoS computational biology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Article
- Polyamines sustain epithelial regeneration in aged intestines by modulating protein homeostasis.Nature cell biology · 2025Article
- Integrating multi-omics to reveal the molecular mechanism of lipid metabolism regulated by a splice variant of Lysyl-oxidase like-2, LOXL2Δ13.Communications biology · 2025Article
- CLUEY enables knowledge-guided clustering and cell type detection from single-cell omics data.Bioinformatics (Oxford, England) · 2025Article
- Comprehensive evaluation of phosphoproteomic-based kinase activity inference.Nature communications · 2025Article
- Temporal phosphoproteomics reveals circuitry of phased propagation in insulin signaling.Nature communications · 2025Article
- SnapKin: a snapshot deep learning ensemble for kinase-substrate prediction from phosphoproteomics data.NAR genomics and bioinformatics · 2023Article
- An optogenetic-phosphoproteomic study reveals dynamic Akt1 signaling profiles in endothelial cells.Nature communications · 2023Article
- A basic phosphoproteomic-DIA workflow integrating precise quantification of phosphosites in systems biology.Biophysics reports · 2023Article
- Principles of phosphoproteomics and applications in cancer research.The Biochemical journal · 2023Article
- Pharmacological approaches to understanding protein kinase signaling networks.Frontiers in pharmacology · 2023Review
- Time-resolved phosphoproteome and proteome analysis reveals kinase signaling on master transcription factors during myogenesis.iScience · 2022Article
- Inference of kinase-signaling networks in human myeloid cell line models by Phosphoproteomics using kinase activity enrichment analysis (KAEA).BMC cancer · 2021Article
- Bioinformatic Analysis of Temporal and Spatial Proteome Alternations During Infections.Frontiers in genetics · 2021Review
- Article
- Proteomics and phosphoproteomics in precision medicine: applications and challenges.Briefings in bioinformatics · 2019Review
- The transcriptional response to oxidative stress is part of, but not sufficient for, insulin resistance in adipocytes.Scientific reports · 2018Article
- Phosphoproteomics-Based Profiling of Kinase Activities in Cancer Cells.Methods in molecular biology (Clifton, N.J.) · 2018Article
- The KSEA App: a web-based tool for kinase activity inference from quantitative phosphoproteomics.Bioinformatics (Oxford, England) · 2017Article
- Positive-unlabeled ensemble learning for kinase substrate prediction from dynamic phosphoproteomics data.Bioinformatics (Oxford, England) · 2016Article
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6 authors.
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Abstract
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.
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