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Functional studies of the PI(3)-kinase signalling pathway employing synthetic and expressed siRNA

Authors
Journal
Nucleic Acids Research
0305-1048
Publisher
Oxford University Press
Publication Date
Keywords
  • Articles
Disciplines
  • Biology
  • Computer Science
  • Design
  • Mathematics

Abstract

gkn923 1..13 Published online 25 November 2008 Nucleic Acids Research, 2009, Vol. 37, No. 1 1–13 doi:10.1093/nar/gkn923 SURVEY AND SUMMARY Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists Da Wei Huang, Brad T. Sherman and Richard A. Lempicki* Laboratory of Immunopathogenesis and Bioinformatics, Clinical Services Program, SAIC-Frederick, Inc., National Cancer Institute at Frederick, Frederick, MD 21702, USA Received September 10, 2008; Revised October 24, 2008; Accepted November 3, 2008 ABSTRACT Functional analysis of large gene lists, derived in most cases from emerging high-throughput genomic, proteomic and bioinformatics scanning approaches, is still a challenging and daunting task. The gene-annotation enrichment analysis is a promising high-throughput strategy that increases the likelihood for investigators to identify biol- ogical processes most pertinent to their study. Approximately 68 bioinformatics enrichment tools that are currently available in the community are collected in this survey. Tools are uniquely categor- ized into three major classes, according to their underlying enrichment algorithms. The comprehen- sive collections, unique tool classifications and associated questions/issues will provide a more comprehensive and up-to-date view regarding the advantages, pitfalls and recent trends in a simpler tool-class level rather than by a tool-by- tool approach. Thus, the survey will help tool designers/developers and experienced end users understand the underlying algorithms and pertinent details of particular tool categories/tools, enabling them to make the best choices for their particular research interests. INTRODUCTION The traditional biological research approaches typically study one gene or a few genes at a time. In contrast, high-throughput genomic, proteomic and bioinformatics scanning approaches (such as expression microarray, pro- moter microarray, proteomics, ChIP-on-CHIPs, etc.) are emerging as alt

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