Microarray analysis techniques: Difference between revisions

Revision as of 13:11, 7 April 2013

Revision as of 14:09, 8 April 2013

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==Pattern recognition==

Commercial systems for gene network analysis such as Ingenuity<ref>{{cite web |url=http://www.ingenuity.com/ |title=Ingenuity Systems |accessdate=2007-12-31 |format= |work=}}</ref> and Pathway studio<ref>{{cite web |url=http://www.ariadnegenomicselsevier.com/products/pathway-studio/ |title=Ariadne GenomicsElsevier: Pathway Studio |accessdate=2007-12-31 |format= |work=}}</ref> create visual representations of differentially expressed genes based on current scientific literature. Non-commercial tools such as [[GenMAPP]] and [[Anduril_(workflow_engine)#Moksiskaan|Moksiskaan]] also aid in organizing and visualizing gene network data procured from one or several microarray experiments. A wide variety of microarray analysis tools are available through [[Bioconductor]] written in the [[R programming language]]. The frequently cited SAM Excel module and other microarray tools<ref>[{{cite web |url=http://www-stat.stanford.edu/~tibs/SAM/ |title=Significance Analysis of Microarrays |accessdate=2007-12-31 |format= |work=}}]</ref> are available through Stanford University. Another set is available from Harvard and MIT.<ref>{{cite web |url=http://www.broad.mit.edu/tools/software.html |title=Software - Broad |accessdate=2007-12-31 |work=}}</ref>

Specialized software tools for statistical analysis to determine the extent of over- or under-expression of a gene in a microarray experiment relative to a reference state have also been developed to aid in identifying genes or gene sets associated with particular [[phenotype]]s. One such method of analysis, known as Gene Set Enrichment Analysis (GSEA), uses a [[Kolmogorov-Smirnov]]-style statistic to identify groups of genes that are regulated together.<ref>{{cite journal |author=Subramanian A, Tamayo P, Mootha VK, ''et al.'' |title=Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles |journal=Proc. Natl. Acad. Sci. U.S.A. |volume=102 |issue=43 |pages=15545–50 |year=2005 |pmid=16199517 |doi=10.1073/pnas.0506580102 |pmc=1239896}}</ref> This third-party statistics package offers the user information on the genes or gene sets of interest, including links to entries in databases such as NCBI's [[GenBank]] and curated databases such as Biocarta<ref>{{cite web |url=http://www.biocarta.com/ |title=BioCarta - Charting Pathways of Life |accessdate=2007-12-31 |format= |work=}}</ref> and [[Gene Ontology]]. Protein complex enrichment analysis tool (COMPLEAT) provides similar enrichment analysis at the level of protein complexes <ref>{{cite journal |author=Vinayagam A, Hu Y, Kulkarni M, Roesel C, ''et al.'' |title= Protein Complex-Based Analysis Framework for High-Throughput Data Sets. 6, rs5 (2013). |journal= Sci. Signal. |volume=6 |issue=r5 |year=2013 |pmid= 23443684 |doi= 10.1126/scisignal.2003629 |url= http://www.flyrnai.org/compleat/}}</ref>. The tool can identify the dynamic protein complex regulation under different condition or time points. Related system, PAINT<ref>{{cite web |url=http://www.dbi.tju.edu/dbi/staticpages.php?page=tools&menu=37 |title=DBI Web |accessdate=2007-12-31 |format= |work=}}</ref> and SCOPE<ref>{{cite web |url=http://genie.dartmouth.edu/scope/ |title=SCOPE |accessdate=2007-12-31 |format= |work=}}</ref> performs a statistical analysis on gene promoter regions, identifying over and under representation of previously identified [[transcription factor]] response elements. Another statistical analysis tool is Rank Sum Statistics for Gene Set Collections (RssGsc), which uses rank sum probability distribution functions to find gene sets that explain experimental data.<ref>{{cite web |url=http://rssgsc.sourceforge.net/ |title=RssGsc |accessdate=2008-10-15 |format= |work=}}</ref> A further approach is contextual meta-analysis, i.e. finding out how a gene cluster responds to a variety of experimental contexts. [[Genevestigator]] is a public tool to perform contextual meta-analysis across contexts such as anatomical parts, stages of development, and response to diseases, chemicals, stresses, and [[neoplasms]].