PhosphoChain: A novel algorithm to predict kinase and phosphatase networks from high-throughput expression data

Wei Ming Chen, Samuel A. Danziger, Jung Hsien Chiang, John D. Aitchison

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)

Abstract

Motivation: Protein phosphorylation is critical for regulating cellular activities by controlling protein activities, localization and turnover, and by transmitting information within cells through signaling networks. However, predictions of protein phosphorylation and signaling networks remain a significant challenge, lagging behind predictions of transcriptional regulatory networks into which they often feed. Results: We developed PhosphoChain to predict kinases, phosphatases and chains of phosphorylation events in signaling networks by combining mRNA expression levels of regulators and targets with a motif detection algorithm and optional prior information. PhosphoChain correctly reconstructed ∼78% of the yeast mitogenactivated protein kinase pathway from publicly available data. When tested on yeast phosphoproteomic data from large-scale mass spectrometry experiments, PhosphoChain correctly identified -27% more phosphorylation sites than existing motif detection tools (NetPhosYeast and GPS2.0), and predictions of kinase-phosphatase interactions overlapped with ∼59% of known interactions present in yeast databases. PhosphoChain provides a valuable framework for predicting condition-specific phosphorylation events from highthroughput data.

Original languageEnglish
Pages (from-to)2435-2444
Number of pages10
JournalBioinformatics
Volume29
Issue number19
DOIs
Publication statusPublished - 2013

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

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