Detection and decomposition: treatment-induced cyclic gene expression disruption in high-throughput time-series datasets.
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.
MSU-DOE Plant Research Laboratory, Michigan State University, East Lansing, MI 48824, USA. yuhjiao@msu.edu
Yuhua
Y
Jiao
Bruce A
BA
Rosa
Sookyung
S
Oh
Beronda L
BL
Montgomery
Wensheng
W
Qin
Jin
J
Chen
D000465:Algorithms; D016208:Databases, Factual; D015870:Gene Expression; D020869:Gene Expression Profiling / Q000379:methods; D010363:Pattern Recognition, Automated / Q000379:methods*