Design and analysis of large-scale biological rhythm studies: a comparison of algorithms for detecting periodic signals in biological data.
BACKGROUND
To discover and study periodic processes in biological systems, we sought to identify periodic patterns in their gene expression data. We surveyed a large number of available methods for identifying periodicity in time series data and chose representatives of different mathematical perspectives that performed well on both synthetic data and biological data. Synthetic data were used to evaluate how each algorithm responds to different curve shapes, periods, phase shifts, noise levels and sampling rates. The biological datasets we tested represent a variety of periodic processes from different organisms, including the cell cycle and metabolic cycle in Saccharomyces cerevisiae, circadian rhythms in Mus musculus and the root clock in Arabidopsis thaliana.


RESULTS
From these results, we discovered that each algorithm had different strengths. Based on our findings, we make recommendations for selecting and applying these methods depending on the nature of the data and the periodic patterns of interest. Additionally, these results can also be used to inform the design of large-scale biological rhythm experiments so that the resulting data can be used with these algorithms to detect periodic signals more effectively.
Program in Computational Biology and Bioinformatics, Department of Mathematics, Duke University, Durham, NC 27708, USA, Department of Medicine, Department of Pharmacology, Institute for Translational Medicine and Therapeutics, University of Pennsylvania School of Medicine, Philadelphia, PA 19104, USA and Department of Biology, Duke University, Durham, NC 27708, USA.
Anastasia
A
Deckard
Ron C
RC
Anafi
John B
JB
Hogenesch
Steven B
SB
Haase
John
J
Harer
D000465:Algorithms; D000818:Animals; D017360:Arabidopsis / Q000235:genetics; D002453:Cell Cycle / Q000235:genetics / Q000502:physiology*; D057906:Circadian Clocks / Q000502:physiology*; D002940:Circadian Rhythm / Q000502:physiology*; D019295:Computational Biology; D020869:Gene Expression Profiling / Q000379:methods; D005786:Gene Expression Regulation; D053858:Metabolic Networks and Pathways; D051379:Mice; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods; D010363:Pattern Recognition, Automated; D018517:Plant Roots / Q000254:growth & development / Q000378:metabolism; D012441:Saccharomyces cerevisiae / Q000235:genetics