9 Transcript Analysis: A Microarray Approach

Publisher Summary Microarray transcript analysis sets itself a very ambitious goal in attempting to measure transcription changes in more than 5000 yeast genes simultaneously. This is particularly challenging because these 5000 genes have an expression range of 10–3–102 transcripts per cell, a dynamic range of five orders of magnitude. The potential for error is obviously large. The chapter describes the procedures that allow these errors to be minimized and discusses computational analysis and its role both in the extraction of knowledge from vast tables of data and in providing an assessment of the quality and reliability of that knowledge. Thus, a pipeline of analysis is described in the chapter that has been designed to minimize errors and assess data quality. The initial fundamental concept and limitation to be grasped is that with current microarray technology, quantification is relative not absolute. The goal is always to measure relative transcript abundances between two or more samples. Because of this, the technique is extremely sensitive to errors arising from differences in sample handling. This places high demands on sample preparation and technical performance of the microarray experiment.

[1]  R. Lempicki,et al.  Evaluation of gene expression measurements from commercial microarray platforms. , 2003, Nucleic acids research.

[2]  Terence P. Speed,et al.  A comparison of normalization methods for high density oligonucleotide array data based on variance and bias , 2003, Bioinform..

[3]  Jason E. Stewart,et al.  Design and implementation of microarray gene expression markup language (MAGE-ML) , 2002, Genome Biology.

[4]  Crispin J. Miller,et al.  Simpleaffy: a BioConductor package for Affymetrix Quality Control and data analysis , 2005, Bioinform..

[5]  Duccio Cavalieri,et al.  Standards for Microarray Data , 2002, Science.

[6]  Pierre Baldi,et al.  A Bayesian framework for the analysis of microarray expression data: regularized t -test and statistical inferences of gene changes , 2001, Bioinform..

[7]  John Quackenbush,et al.  A guide to microarray experiments-an open letter to the scientific journals , 2002, The Lancet.

[8]  J. Mattick.  RNA regulation: a new genetics? , 2004, Nature Reviews Genetics.

[9]  R. Tibshirani,et al.  Significance analysis of microarrays applied to the ionizing radiation response , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[10]  John Quackenbush.  Microarray data normalization and transformation , 2002, Nature Genetics.

[11]  Paul T. Spellman.  A Status Report on Mage , 2005, Bioinform..

[12]  Sergio Contrino,et al.  ArrayExpress—a public repository for microarray gene expression data at the EBI , 2004, Nucleic Acids Res..

[13]  Gavin Sherlock,et al.  The Longhorn Array Database (LAD): An Open-Source, MIAME compliant implementation of the Stanford Microarray Database (SMD) , 2003, BMC Bioinformatics.

[14]  Xuefeng Bruce Ling,et al.  Multiclass cancer classification and biomarker discovery using GA-based algorithms , 2005, Bioinform..

[15]  T. Speed,et al.  GOstat: find statistically overrepresented Gene Ontologies within a group of genes. , 2004, Bioinformatics.

[16]  Jason E. Stewart,et al.  Minimum information about a microarray experiment (MIAME)—toward standards for microarray data , 2001, Nature Genetics.

[17]  Wei Chen,et al.  Comparison of seven methods for producing Affymetrix expression scores based on False Discovery Rates in disease profiling data , 2005, BMC Bioinformatics.

[18]  Satoru Miyano,et al.  Open source clustering software , 2004 .

[19]  Gordon K. Smyth,et al.  Use of within-array replicate spots for assessing differential expression in microarray experiments , 2005, Bioinform..

[20]  Royston Goodacre,et al.  Identification of Novel Genes in Arabidopsis Involved in Secondary Cell Wall Formation Using Expression Profiling and Reverse Genetics , 2005, The Plant Cell Online.

[21]  Catherine Brooksbank,et al.  An open letter to the scientific journals , 2002, Bioinform..

[22]  Charles Auffray,et al.  Towards standardization of RNA quality assessment using user-independent classifiers of microcapillary electrophoresis traces , 2005, Nucleic acids research.

[23]  M Vingron,et al.  Transcriptional profiling on all open reading frames of Saccharomyces cerevisiae , 1998, Yeast.

[24]  John D. Storey,et al.  Statistical significance for genomewide studies , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[25]  Andrew Hayes,et al.  Hybridization array technology coupled with chemostat culture: Tools to interrogate gene expression in Saccharomyces cerevisiae. , 2002, Methods.

[26]  S. Gruvberger,et al.  BioArray Software Environment (BASE): a platform for comprehensive management and analysis of microarray data , 2002, Genome Biology.

[27]  Rafael A. Irizarry,et al.  A Model-Based Background Adjustment for Oligonucleotide Expression Arrays , 2004 .

[28]  Terence P. Speed,et al.  A benchmark for Affymetrix GeneChip expression measures , 2004, Bioinform..

[29]  Wei-Min Liu,et al.  Robust estimators for expression analysis , 2002, Bioinform..

[30]  R. Tibshirani,et al.  Diagnosis of multiple cancer types by shrunken centroids of gene expression , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[31]  Hubert Hackl,et al.  MARS: Microarray analysis, retrieval, and storage system , 2005, BMC Bioinformatics.

[32]  C. Li,et al.  Model-based analysis of oligonucleotide arrays: expression index computation and outlier detection. , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[33]  B. Barrell,et al.  Life with 6000 Genes , 1996, Science.

[34]  B. Barrell,et al.  The genome sequence of Schizosaccharomyces pombe , 2002, Nature.

[35]  John N. Weinstein,et al.  High-Throughput GoMiner, an 'industrial-strength' integrative gene ontology tool for interpretation of multiple-microarray experiments, with application to studies of Common Variable Immune Deficiency (CVID) , 2005, BMC Bioinformatics.

[36]  Michael J Gagen,et al.  Accelerating Networks , 2005, Science.

[37]  Steven C. Lawlor,et al.  MAPPFinder: using Gene Ontology and GenMAPP to create a global gene-expression profile from microarray data , 2003, Genome Biology.

[38]  Douglas B. Kell,et al.  maxdLoad2 and maxdBrowse: standards-compliant tools for microarray experimental annotation, data management and dissemination , 2005, BMC Bioinformatics.

[39]  Chung-Yen Lin,et al.  POWER: PhylOgenetic WEb Repeater—an integrated and user-optimized framework for biomolecular phylogenetic analysis , 2005, Nucleic Acids Res..

[40]  D. Botstein,et al.  Cluster analysis and display of genome-wide expression patterns. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[41]  Kathleen Marchal,et al.  M@cbeth: a Microarray Classification Benchmarking Tool , 2005 .

[42]  M. Rosbash,et al.  Number and distribution of polyadenylated RNA sequences in yeast , 1977, Cell.

[43]  Ben Shneiderman,et al.  Interactively optimizing signal-to-noise ratios in expression profiling: project-specific algorithm selection and detection p-value weighting in Affymetrix microarrays , 2004, Bioinform..

[44]  Tao Li,et al.  A comparative study of feature selection and multiclass classification methods for tissue classification based on gene expression , 2004, Bioinform..

[45]  Jae K. Lee,et al.  Local-pooled-error test for identifying differentially expressed genes with a small number of replicated microarrays , 2003, Bioinform..