Detection and decomposition: Treatment-induced cyclic gene expression disruption in high-throughput time-series datasets

Yuhua Jiao, Bruce A. Rosa, Sookyung Oh, Beronda L. Montgomery, Wensheng Qin, Jin Chen

Research output: Contribution to journalArticle

Abstract

Higher organisms possess many genes which cycle under normal conditions, to allow the organism to adapt to expected environmental conditions throughout the course of a day. However, treatment-induced disruption of regular cyclic gene expression patterns presents a significant challenge in novel gene discovery experiments because these disruptions can induce strong differential regulation events for genes that are not involved in an adaptive response to the treatment. To address this cycle disruption problem, we reviewed the state-of-art periodic pattern detection algorithms and a pattern decomposition algorithm (PRIISM), which is a knowledge-based Fourier analysis algorithm designed to distinguish the cyclic patterns from the rest gene expression patterns, and discussed potential future improvements.

Original languageEnglish
Article number1271002
JournalJournal of Bioinformatics and Computational Biology
Volume10
Issue number6
DOIs
StatePublished - Dec 1 2012
Externally publishedYes

Keywords

  • Cyclic gene expression
  • pattern decomposition
  • pattern detection
  • time-series

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