Literature DB >> 10890920

Fundamental patterns underlying gene expression profiles: simplicity from complexity.

N S Holter1, M Mitra, A Maritan, M Cieplak, J R Banavar, N V Fedoroff.   

Abstract

Analysis of previously published sets of DNA microarray gene expression data by singular value decomposition has uncovered underlying patterns or "characteristic modes" in their temporal profiles. These patterns contribute unequally to the structure of the expression profiles. Moreover, the essential features of a given set of expression profiles are captured using just a small number of characteristic modes. This leads to the striking conclusion that the transcriptional response of a genome is orchestrated in a few fundamental patterns of gene expression change. These patterns are both simple and robust, dominating the alterations in expression of genes throughout the genome. Moreover, the characteristic modes of gene expression change in response to environmental perturbations are similar in such distant organisms as yeast and human cells. This analysis reveals simple regularities in the seemingly complex transcriptional transitions of diverse cells to new states, and these provide insights into the operation of the underlying genetic networks.

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Year:  2000        PMID: 10890920      PMCID: PMC26961          DOI: 10.1073/pnas.150242097

Source DB:  PubMed          Journal:  Proc Natl Acad Sci U S A        ISSN: 0027-8424            Impact factor:   11.205


  8 in total

1.  The transcriptional program of sporulation in budding yeast.

Authors:  S Chu; J DeRisi; M Eisen; J Mulholland; D Botstein; P O Brown; I Herskowitz
Journal:  Science       Date:  1998-10-23       Impact factor: 47.728

2.  The transcriptional program in the response of human fibroblasts to serum.

Authors:  V R Iyer; M B Eisen; D T Ross; G Schuler; T Moore; J C Lee; J M Trent; L M Staudt; J Hudson; M S Boguski; D Lashkari; D Shalon; D Botstein; P O Brown
Journal:  Science       Date:  1999-01-01       Impact factor: 47.728

3.  Quantitative monitoring of gene expression patterns with a complementary DNA microarray.

Authors:  M Schena; D Shalon; R W Davis; P O Brown
Journal:  Science       Date:  1995-10-20       Impact factor: 47.728

4.  Light-generated oligonucleotide arrays for rapid DNA sequence analysis.

Authors:  A C Pease; D Solas; E J Sullivan; M T Cronin; C P Holmes; S P Fodor
Journal:  Proc Natl Acad Sci U S A       Date:  1994-05-24       Impact factor: 11.205

5.  Sum1 and Hst1 repress middle sporulation-specific gene expression during mitosis in Saccharomyces cerevisiae.

Authors:  J Xie; M Pierce; V Gailus-Durner; M Wagner; E Winter; A K Vershon
Journal:  EMBO J       Date:  1999-11-15       Impact factor: 11.598

6.  Cluster analysis and display of genome-wide expression patterns.

Authors:  M B Eisen; P T Spellman; P O Brown; D Botstein
Journal:  Proc Natl Acad Sci U S A       Date:  1998-12-08       Impact factor: 11.205

7.  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

8.  Induction of meiosis in Saccharomyces cerevisiae depends on conversion of the transcriptional represssor Ume6 to a positive regulator by its regulated association with the transcriptional activator Ime1.

Authors:  I Rubin-Bejerano; S Mandel; K Robzyk; Y Kassir
Journal:  Mol Cell Biol       Date:  1996-05       Impact factor: 4.272

  8 in total
  114 in total

1.  Dynamic modeling of gene expression data.

Authors:  N S Holter; A Maritan; M Cieplak; N V Fedoroff; J R Banavar
Journal:  Proc Natl Acad Sci U S A       Date:  2001-02-13       Impact factor: 11.205

2.  Genome wide oscillations in expression. Wavelet analysis of time series data from yeast expression arrays uncovers the dynamic architecture of phenotype.

Authors:  R R Klevecz; D B Murray
Journal:  Mol Biol Rep       Date:  2001       Impact factor: 2.316

Review 3.  Microarray data quality analysis: lessons from the AFGC project. Arabidopsis Functional Genomics Consortium.

Authors:  David Finkelstein; Rob Ewing; Jeremy Gollub; Fredrik Sterky; J Michael Cherry; Shauna Somerville
Journal:  Plant Mol Biol       Date:  2002-01       Impact factor: 4.076

4.  Argus--a new database system for Web-based analysis of multiple microarray data sets.

Authors:  J Comander; G M Weber; M A Gimbrone; G García-Cardeña
Journal:  Genome Res       Date:  2001-09       Impact factor: 9.043

5.  Statistical modeling of large microarray data sets to identify stimulus-response profiles.

Authors:  L P Zhao; R Prentice; L Breeden
Journal:  Proc Natl Acad Sci U S A       Date:  2001-05-08       Impact factor: 11.205

Review 6.  Network genomics--a novel approach for the analysis of biological systems in the post-genomic era.

Authors:  Christian V Forst
Journal:  Mol Biol Rep       Date:  2002-09       Impact factor: 2.316

7.  Interactive exploration of microarray gene expression patterns in a reduced dimensional space.

Authors:  Jatin Misra; William Schmitt; Daehee Hwang; Li-Li Hsiao; Steve Gullans; George Stephanopoulos; Gregory Stephanopoulos
Journal:  Genome Res       Date:  2002-07       Impact factor: 9.043

8.  Generalized singular value decomposition for comparative analysis of genome-scale expression data sets of two different organisms.

Authors:  Orly Alter; Patrick O Brown; David Botstein
Journal:  Proc Natl Acad Sci U S A       Date:  2003-03-11       Impact factor: 11.205

9.  Network component analysis: reconstruction of regulatory signals in biological systems.

Authors:  James C Liao; Riccardo Boscolo; Young-Lyeol Yang; Linh My Tran; Chiara Sabatti; Vwani P Roychowdhury
Journal:  Proc Natl Acad Sci U S A       Date:  2003-12-12       Impact factor: 11.205

Review 10.  Gene expression profiling in ecotoxicology.

Authors:  Terry W Snell; Sara E Brogdon; Michael B Morgan
Journal:  Ecotoxicology       Date:  2003-12       Impact factor: 2.823

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