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2Association Analysis for Large-Scale Gene Set DataStefan A. Kirov, Bing Zhang, and Jay R. SnoddySummaryHigh-throughput experiments in biology often produce sets of genes of potential interests.Some of those gene sets might be of considerable size. Therefore, computer-assisted analysis isnecessary for the biological interpretation of the gene sets, and for creating working hypotheses,which can be tested experimentally. One obvious way to analyze gene set data is to associate thegenes with a particular biological feature, for example, a given pathway. Statistical analysis couldbe used to evaluate if a gene set is truly associated with a feature. Over the past few years manytools that perform such analysis have been created. In this chapter, using WebGestalt as an example,it will be explained in detail how to associate gene sets with functional annotations, pathways,publication records, and protein domains.Key Words: Association analysis; data interpretation; gene expression; gene set; WebGestalt;genome-scale; high-throughput analysis.1. IntroductionBecause of the first large-scale expression analysis in 1995 (1), numerousstudies have tried to correlate the observed expression patterns with other significantbiological data, such as phenotypes, regulatory sequences, pathways, andso on. Such types of correlation analysis could potentially reveal mechanismsthat are associated with the observed expression patterns. The results fromlarge-scale biological experiments, such as expression analysis is often complex.In many cases, it will not be possible to infer the aforementioned associationsby manual analysis because of the data size and complexity. An overview of themicroarray technology and some of the computer-assisted inference analyses isreviewed by Stoughton (2).A large number of studies use gene ontology (GO) annotation (3) to assist inthe analysis of gene expression data. For example, Bono et al. used GO toreconstruct metabolic pathways (4). The GO consortium (3) provides a powerfulFrom: Methods in Molecular Biology, vol. 408: Gene Function AnalysisEdited by: M. Ochs © Humana Press Inc., Totowa, NJ19
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Gene Function Analysis
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METHODS IN MOLECULAR BIOLOGYGene Fu
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46 Wang and Ochsresults posttreatme
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Prediction Using PAINT 51even in si
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Prediction Using PAINT 53Fig. 1. A
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Prediction Using PAINT 55first exon
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Table 1 (Continued)Server name URL
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Sybil: Multiple Genome Comparison a
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Estimating Protein Function Using P
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Table 3 (Continued)Class MYCMAX MYC
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IIIEXPERIMENTAL METHODS
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194 Caldwell et al.for sequences th
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196 Caldwell et al.query because it
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198 Caldwell et al.Fig. 1. (A) Prot
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200 Caldwell et al.outside primer o
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Screening in Yeast With a Bacterial
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318 Thibodeau-Beganny and Joungbeen
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336 IndexKknockin (gene knockin) 19
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