Comparison of computational methods for the identification of cell cycle-regulated genes.
BACKGROUND
DNA microarrays have been used extensively to study the cell cycle transcription programme in a number of model organisms. The Saccharomyces cerevisiae data in particular have been subjected to a wide range of bioinformatics analysis methods, aimed at identifying the correct and complete set of periodically expressed genes.


RESULTS
Here, we provide the first thorough benchmark of such methods, surprisingly revealing that most new and more mathematically advanced methods actually perform worse than the analysis published with the original microarray data sets. We show that this loss of accuracy specifically affects methods that only model the shape of the expression profile without taking into account the magnitude of regulation. We present a simple permutation-based method that performs better than most existing methods.
de Lichtenberg
Ulrik
U
Center for Biological Sequence Analysis, Technical University of Denmark DK-2800 Lyngby, Denmark.
Jensen
Lars Juhl
LJ
Fausbøll
Anders
A
Jensen
Thomas S
TS
Bork
Peer
P
Brunak
Søren
S
D000465:Algorithms; D018797:Cell Cycle Proteins / Q000235:genetics / Q000378:metabolism*; D019295:Computational Biology / Q000379:methods; D020869:Gene Expression Profiling / Q000379:methods*; D015966:Gene Expression Regulation, Fungal / Q000502:physiology*; D018816:Genes, cdc / Q000502:physiology*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D012441:Saccharomyces cerevisiae / Q000502:physiology*; D029701:Saccharomyces cerevisiae Proteins / Q000032:analysis / Q000235:genetics / Q000378:metabolism; D015398:Signal Transduction / Q000502:physiology*
D018797:Cell Cycle Proteins; D029701:Saccharomyces cerevisiae Proteins