Comparison of computational methods for the identification of cell cycle-regulated genes

Ulrik de Lichtenberg; Lars Juhl Jensen; Anders Fausbøll; Thomas Skøt Jensen; Peer Bork; Søren Brunak

doi:10.1093/bioinformatics/bti093

Comparison of computational methods for the identification of cell cycle-regulated genes

Ulrik de Lichtenberg, Lars Juhl Jensen, Anders Fausbøll, Thomas Skøt Jensen, Peer Bork, Søren Brunak

155 Citations (Scopus)

Abstract

MOTIVATION: 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.

Original language	English
Journal	Bioinformatics
Volume	21
Issue number	7
Pages (from-to)	1164-71
Number of pages	8
ISSN	1367-4803
DOIs	https://doi.org/10.1093/bioinformatics/bti093
Publication status	Published - 2005
Externally published	Yes

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10.1093/bioinformatics/bti093

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title = "Comparison of computational methods for the identification of cell cycle-regulated genes",

abstract = "MOTIVATION: 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.",

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AU - de Lichtenberg, Ulrik

AU - Jensen, Lars Juhl

AU - Fausbøll, Anders

AU - Jensen, Thomas Skøt

AU - Bork, Peer

AU - Brunak, Søren

PY - 2005

Y1 - 2005

N2 - MOTIVATION: 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.

AB - MOTIVATION: 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.

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JO - Bioinformatics

JF - Bioinformatics

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Comparison of computational methods for the identification of cell cycle-regulated genes

Abstract

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