Putting microarrays in a context: integrated analysis of diverse biological data.
In recent years, multiple types of high-throughput functional genomic data that facilitate rapid functional annotation of sequenced genomes have become available. Gene expression microarrays are the most commonly available source of such data. However, genomic data often sacrifice specificity for scale, yielding very large quantities of relatively lower-quality data than traditional experimental methods. Thus sophisticated analysis methods are necessary to make accurate functional interpretation of these large-scale data sets. This review presents an overview of recently developed methods that integrate the analysis of microarray data with sequence, interaction, localisation and literature data, and further outlines current challenges in the field. The focus of this review is on the use of such methods for gene function prediction, understanding of protein regulation and modelling of biological networks.
Troyanskaya
Olga G
OG
Department of Computer Science and Lewis-Sigler Institute for Integrative Genomics, Princeton University, NJ 08544, USA. ogt@CS.Princeton.EDU
D000465:Algorithms; D000818:Animals; D001695:Biology / Q000379:methods; D002874:Chromosome Mapping / Q000379:methods* / Q000639:trends; D030541:Databases, Genetic; D020869:Gene Expression Profiling / Q000379:methods* / Q000639:trends; D005786:Gene Expression Regulation / Q000502:physiology*; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods / Q000639:trends; D008954:Models, Biological; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods* / Q000639:trends; D018511:Systems Integration; D014584:User-Computer Interface