Brief. Bioinformatics - Cancer gene prioritization by integrative analysis of mRNA expression and DNA copy number data: a comparative review.

Tópicos

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Resumo

A variety of genome-wide profiling techniques are available to investigate complementary aspects of genome structure and function. Integrative analysis of heterogeneous data sources can reveal higher level interactions that cannot be detected based on individual observations. A standard integration task in cancer studies is to identify altered genomic regions that induce changes in the expression of the associated genes based on joint analysis of genome-wide gene expression and copy number profiling measurements. In this review, we highlight common approaches to genomic data integration and provide a transparent benchmarking procedure to quantitatively compare method performances in cancer gene prioritization. Algorithms, data sets and benchmarking results are available at http://intcomp.r-forge.r-project.org.

Resumo Limpo

varieti genomewid profil techniqu avail investig complementari aspect genom structur function integr analysi heterogen data sourc can reveal higher level interact detect base individu observ standard integr task cancer studi identifi alter genom region induc chang express associ gene base joint analysi genomewid gene express copi number profil measur review highlight common approach genom data integr provid transpar benchmark procedur quantit compar method perform cancer gene priorit algorithm data set benchmark result avail httpintcomprforgerprojectorg

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