Literature DB >> 18310054

Are we overestimating the number of cell-cycling genes? The impact of background models on time-series analysis.

Matthias E Futschik1, Hanspeter Herzel.   

Abstract

MOTIVATION: Periodic processes play fundamental roles in organisms. Prominent examples are the cell cycle and the circadian clock. Microarray array technology has enabled us to screen complete sets of transcripts for possible association with such fundamental periodic processes on a system-wide level. Frequently, quite large numbers of genes have been detected as periodically expressed. However, the small overlap between genes identified in different studies has cast some doubts on the reliability of the periodic expression detected.
RESULTS: In this study, comparative analysis suggests that the lacking agreement between different cell-cycle studies might be due to inadequate background models for the determination of significance. We demonstrate that the choice of background model has considerable impact on the statistical significance of periodic expression. For illustration, we reanalyzed two microarray studies of the yeast cell cycle. Our evaluation strongly indicates that the results of previous analyses might have been overoptimistic and that the use of more suitable background model promises to give more realistic results. AVAILABILITY: R scripts are available on request from the corresponding author.

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Year:  2008        PMID: 18310054     DOI: 10.1093/bioinformatics/btn072

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  23 in total

1.  Design and analysis of large-scale biological rhythm studies: a comparison of algorithms for detecting periodic signals in biological data.

Authors:  Anastasia Deckard; Ron C Anafi; John B Hogenesch; Steven B Haase; John Harer
Journal:  Bioinformatics       Date:  2013-09-20       Impact factor: 6.937

2.  Shrinkage regression-based methods for microarray missing value imputation.

Authors:  Hsiuying Wang; Chia-Chun Chiu; Yi-Ching Wu; Wei-Sheng Wu
Journal:  BMC Syst Biol       Date:  2013-12-13

3.  Translational control of lipogenic enzymes in the cell cycle of synchronous, growing yeast cells.

Authors:  Heidi M Blank; Ricardo Perez; Chong He; Nairita Maitra; Richard Metz; Joshua Hill; Yuhong Lin; Charles D Johnson; Vytas A Bankaitis; Brian K Kennedy; Rodolfo Aramayo; Michael Polymenis
Journal:  EMBO J       Date:  2017-01-05       Impact factor: 11.598

4.  Analyzing circadian expression data by harmonic regression based on autoregressive spectral estimation.

Authors:  Rendong Yang; Zhen Su
Journal:  Bioinformatics       Date:  2010-06-15       Impact factor: 6.937

5.  Comparison and consolidation of microarray data sets of human tissue expression.

Authors:  Jenny Russ; Matthias E Futschik
Journal:  BMC Genomics       Date:  2010-05-14       Impact factor: 3.969

6.  Coordinated progression through two subtranscriptomes underlies the tachyzoite cycle of Toxoplasma gondii.

Authors:  Michael S Behnke; John C Wootton; Margaret M Lehmann; Josh B Radke; Olivier Lucas; Julie Nawas; L David Sibley; Michael W White
Journal:  PLoS One       Date:  2010-08-26       Impact factor: 3.240

7.  Building blocks are synthesized on demand during the yeast cell cycle.

Authors:  Kate Campbell; Jakub Westholm; Sergo Kasvandik; Francesca Di Bartolomeo; Maurizio Mormino; Jens Nielsen
Journal:  Proc Natl Acad Sci U S A       Date:  2020-03-25       Impact factor: 11.205

8.  Phase Coupled Meta-analysis: sensitive detection of oscillations in cell cycle gene expression, as applied to fission yeast.

Authors:  Saumyadipta Pyne; Roee Gutman; Chang Sik Kim; Bruce Futcher
Journal:  BMC Genomics       Date:  2009-09-17       Impact factor: 3.969

9.  How cyanobacteria pose new problems to old methods: challenges in microarray time series analysis.

Authors:  Robert Lehmann; Rainer Machné; Jens Georg; Manuela Benary; Ilka Axmann; Ralf Steuer
Journal:  BMC Bioinformatics       Date:  2013-04-21       Impact factor: 3.169

10.  Dynamic circadian protein-protein interaction networks predict temporal organization of cellular functions.

Authors:  Thomas Wallach; Katja Schellenberg; Bert Maier; Ravi Kiran Reddy Kalathur; Pablo Porras; Erich E Wanker; Matthias E Futschik; Achim Kramer
Journal:  PLoS Genet       Date:  2013-03-28       Impact factor: 5.917

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