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

Authors:
Ulrik De Lichtenberg;Lars Juhl Jensen;Anders Fausbøll;Thomas S. Jensen;Peer Bork;Søren Brunak
Affiliations:
Center for Biological Sequence Analysis, Technical University of Denmark DK-2800 Lyngby, Denmark;European Molecular Biology Laboratory D-69117 Heidelberg, Germany;Center for Biological Sequence Analysis, Technical University of Denmark DK-2800 Lyngby, Denmark;Center for Biological Sequence Analysis, Technical University of Denmark DK-2800 Lyngby, Denmark;European Molecular Biology Laboratory D-69117 Heidelberg, Germany;Center for Biological Sequence Analysis, Technical University of Denmark DK-2800 Lyngby, Denmark
Venue:
Bioinformatics
Year:
2005

Citing 0
Cited 12

Clustering and visualization approaches for human cell cycle gene expression data analysis

International Journal of Approximate Reasoning
On Finding and Interpreting Patterns in Gene Expression Data from Time Course Experiments

PRIB '08 Proceedings of the Third IAPR International Conference on Pattern Recognition in Bioinformatics
Using dynamic bayesian networks to infer gene regulatory networks from expression profiles

Proceedings of the 2009 ACM symposium on Applied Computing
Study of microarray time series data based on Forward-Backward Linear Prediction and Singular Value Decomposition

International Journal of Data Mining and Bioinformatics
Identifying genes involved in cyclic processes by combining gene expression analysis and prior knowledge

EURASIP Journal on Bioinformatics and Systems Biology - Special issue on applications of signal procesing techniques to bioinformatics, genomics, and proteomics
Inference from Low Precision Transcriptome Data Representation

Journal of Signal Processing Systems
Using a state-space model and location analysis to infer time-delayed regulatory networks

EURASIP Journal on Bioinformatics and Systems Biology
Ranking through integration of protein-similarity for identification of cell-cyclic genes

International Journal of Bioinformatics Research and Applications
A novel statistical model for finding patterns in cell-cycle transcription data

Pattern Recognition Letters
A scalable approach for inferring transcriptional regulation in the yeast cell cycle

Proceedings of the 2nd ACM Conference on Bioinformatics, Computational Biology and Biomedicine
Pooling evidence to identify cell cycle–regulated genes

ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part II
Comparison of methods for identifying periodically varying genes

International Journal of Bioinformatics Research and Applications

Quantified Score

Hi-index	3.84

Visualization

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. Supplementary information: Results and benchmark sets are available at http://www.cbs.dtu.dk/cellcycle Contact: brunak@cbs.dtu.dk