Literature DB >> 18584052

Detecting periodic genes from irregularly sampled gene expressions: a comparison study.

Wentao Zhao1, Kwadwo Agyepong, Erchin Serpedin, Edward R Dougherty.   

Abstract

Time series microarray measurements of gene expressions have been exploited to discover genes involved in cell cycles. Due to experimental constraints, most microarray observations are obtained through irregular sampling. In this paper three popular spectral analysis schemes, namely, Lomb-Scargle, Capon and missing-data amplitude and phase estimation (MAPES), are compared in terms of their ability and efficiency to recover periodically expressed genes. Based on in silico experiments for microarray measurements of Saccharomyces cerevisiae, Lomb-Scargle is found to be the most efficacious scheme. 149 genes are then identified to be periodically expressed in the Drosophila melanogaster data set.

Entities:  

Year:  2008        PMID： 18584052      PMCID： PMC3171399          DOI： 10.1155/2008/769293

Source DB:  PubMed          Journal:  EURASIP J Bioinform Syst Biol        ISSN： 1687-4145


  13 in total

1.  Identifying periodically expressed transcripts in microarray time series data.

Authors:  Sofia Wichert; Konstantinos Fokianos; Korbinian Strimmer
Journal:  Bioinformatics       Date:  2004-01-01       Impact factor: 6.937

2.  Statistical resynchronization and Bayesian detection of periodically expressed genes.

Authors:  Xin Lu; Wen Zhang; Zhaohui S Qin; Kurt E Kwast; Jun S Liu
Journal:  Nucleic Acids Res       Date:  2004-01-22       Impact factor: 16.971

Review 3.  Rethinking synchronization of mammalian cells for cell cycle analysis.

Authors:  S Cooper
Journal:  Cell Mol Life Sci       Date:  2003-06       Impact factor: 9.261

4.  Model-based methods for identifying periodically expressed genes based on time course microarray gene expression data.

Authors:  Y Luan; H Li
Journal:  Bioinformatics       Date:  2004-02-12       Impact factor: 6.937

5.  Detecting periodic patterns in unevenly spaced gene expression time series using Lomb-Scargle periodograms.

Authors:  Earl F Glynn; Jie Chen; Arcady R Mushegian
Journal:  Bioinformatics       Date:  2005-11-22       Impact factor: 6.937

6.  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
Journal:  Bioinformatics       Date:  2004-10-28       Impact factor: 6.937

7.  New weakly expressed cell cycle-regulated genes in yeast.

Authors:  Ulrik de Lichtenberg; Rasmus Wernersson; Thomas Skøt Jensen; Henrik Bjørn Nielsen; Anders Fausbøll; Peer Schmidt; Flemming Bryde Hansen; Steen Knudsen; Søren Brunak
Journal:  Yeast       Date:  2005-11       Impact factor: 3.239

8.  Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization.

Authors:  P T Spellman; G Sherlock; M Q Zhang; V R Iyer; K Anders; M B Eisen; P O Brown; D Botstein; B Futcher
Journal:  Mol Biol Cell       Date:  1998-12       Impact factor: 4.138

9.  Robust regression for periodicity detection in non-uniformly sampled time-course gene expression data.

Authors:  Miika Ahdesmäki; Harri Lähdesmäki; Andrew Gracey; Llya Shmulevich; Olli Yli-Harja
Journal:  BMC Bioinformatics       Date:  2007-07-02       Impact factor: 3.169

10.  Gene expression during the life cycle of Drosophila melanogaster.

Authors:  Michelle N Arbeitman; Eileen E M Furlong; Farhad Imam; Eric Johnson; Brian H Null; Bruce S Baker; Mark A Krasnow; Matthew P Scott; Ronald W Davis; Kevin P White
Journal:  Science       Date:  2002-09-27       Impact factor: 47.728
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  10 in total

1.  Spectral preprocessing for clustering time-series gene expressions.

Authors:  Wentao Zhao; Erchin Serpedin; Edward R Dougherty
Journal:  EURASIP J Bioinform Syst Biol       Date:  2009-04-08

2.  Identifying genes involved in cyclic processes by combining gene expression analysis and prior knowledge.

Authors:  Wentao Zhao; Erchin Serpedin; Edward R Dougherty
Journal:  EURASIP J Bioinform Syst Biol       Date:  2009-04-15

3.  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

4.  Personal omics profiling reveals dynamic molecular and medical phenotypes.

Authors:  Rui Chen; George I Mias; Jennifer Li-Pook-Than; Lihua Jiang; Hugo Y K Lam; Rong Chen; Elana Miriami; Konrad J Karczewski; Manoj Hariharan; Frederick E Dewey; Yong Cheng; Michael J Clark; Hogune Im; Lukas Habegger; Suganthi Balasubramanian; Maeve O'Huallachain; Joel T Dudley; Sara Hillenmeyer; Rajini Haraksingh; Donald Sharon; Ghia Euskirchen; Phil Lacroute; Keith Bettinger; Alan P Boyle; Maya Kasowski; Fabian Grubert; Scott Seki; Marco Garcia; Michelle Whirl-Carrillo; Mercedes Gallardo; Maria A Blasco; Peter L Greenberg; Phyllis Snyder; Teri E Klein; Russ B Altman; Atul J Butte; Euan A Ashley; Mark Gerstein; Kari C Nadeau; Hua Tang; Michael Snyder
Journal:  Cell       Date:  2012-03-16       Impact factor: 41.582

5.  MathIOmica: An Integrative Platform for Dynamic Omics.

Authors:  George I Mias; Tahir Yusufaly; Raeuf Roushangar; Lavida R K Brooks; Vikas V Singh; Christina Christou
Journal:  Sci Rep       Date:  2016-11-24       Impact factor: 4.379

6.  Identifying stochastic oscillations in single-cell live imaging time series using Gaussian processes.

Authors:  Nick E Phillips; Cerys Manning; Nancy Papalopulu; Magnus Rattray
Journal:  PLoS Comput Biol       Date:  2017-05-11       Impact factor: 4.475

7.  TimeTrial: An Interactive Application for Optimizing the Design and Analysis of Transcriptomic Time-Series Data in Circadian Biology Research.

Authors:  Elan Ness-Cohn; Marta Iwanaszko; William L Kath; Ravi Allada; Rosemary Braun
Journal:  J Biol Rhythms       Date:  2020-07-02       Impact factor: 3.182

8.  Spectral analysis on time-course expression data: detecting periodic genes using a real-valued iterative adaptive approach.

Authors:  Kwadwo S Agyepong; Fang-Han Hsu; Edward R Dougherty; Erchin Serpedin
Journal:  Adv Bioinformatics       Date:  2013-02-28

9.  Metabolome progression during early gut microbial colonization of gnotobiotic mice.

Authors:  Angela Marcobal; Tahir Yusufaly; Steven Higginbottom; Michael Snyder; Justin L Sonnenburg; George I Mias
Journal:  Sci Rep       Date:  2015-06-29       Impact factor: 4.996

10.  Methods detecting rhythmic gene expression are biologically relevant only for strong signal.

Authors:  David Laloum; Marc Robinson-Rechavi
Journal:  PLoS Comput Biol       Date:  2020-03-17       Impact factor: 4.475
  10 in total

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