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 Citationer (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.

Originalsprog	Engelsk
Tidsskrift	Bioinformatics
Vol/bind	21
Udgave nummer	7
Sider (fra-til)	1164-71
Antal sider	8
ISSN	1367-4803
DOI	https://doi.org/10.1093/bioinformatics/bti093
Status	Udgivet - 2005
Udgivet eksternt	Ja

Adgang til dokumentet

10.1093/bioinformatics/bti093

Citationsformater

@article{3ff1a1a0a86345dcabcee5ce093a3555,

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.",

author = "{de Lichtenberg}, Ulrik and Jensen, {Lars Juhl} and Anders Fausb{\o}ll and Jensen, {Thomas Sk{\o}t} and Peer Bork and S{\o}ren Brunak",

year = "2005",

doi = "10.1093/bioinformatics/bti093",

language = "English",

volume = "21",

pages = "1164--71",

journal = "Bioinformatics",

issn = "1367-4803",

publisher = "Oxford University Press",

number = "7",

}

TY - JOUR

T1 - Comparison of computational methods for the identification of cell cycle-regulated genes

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.

U2 - 10.1093/bioinformatics/bti093

DO - 10.1093/bioinformatics/bti093

M3 - Journal article

C2 - 15513999

SN - 1367-4803

VL - 21

SP - 1164

EP - 1171

JO - Bioinformatics

JF - Bioinformatics

IS - 7

ER -

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

Abstract

Adgang til dokumentet

Fingeraftryk

Citationsformater