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University of Louisville Bioinformatics

Bioinformatics Research at the University of Louisville


Project:

categoryCompare: High-throughput data meta-analysis using gene annotations


Authors:

Robert M. Flight1, Benjamin J. Harrison2,3, Fahim Mohammad4, Mary B. Bunge5, Lawrence D.F. Moon6, Jeffrey C. Petruska2,3, Eric C. Rouchka7

  1. Department of Molecular and Cellular Biochemistry, University of Kentucky.
  2. Deparmtent of Anatomical Sciences and Neurobiology, University of Louisville.
  3. Deparmtent of Neurological Surgery, Kentucky Spinal Cord Injury Research Center, University of Louisville.
  4. Deparmtent of Pathology, Beth Israel Deaconess Medical Center, Harvard Medical School.
  5. Deparmtent of Pathology, Beth Israel Deaconess Medical Center, Harvard Medical School.
Motivation:
Assessment of high-throughput --omics data initially focuses on relative or raw levels of a particular feature, such as an expression value for a transcript, protein, or metabolite. At a second level, analyses of annotations including known or predicted functions and associations of each individual feature, attempt to distill biological context. Most currently available comparative- and meta-analyses methods are dependent on the availability of identical features across data sets, and concentrate on determining features that are differentially expressed across experiments, some of which may be considered .biomarkers.. The heterogeneity of measurement platforms and inherent variability of biological systems confounds the search for robust biomarkers indicative of a particular condition. In many instances, however, multiple data sets show involvement of common biological processes or signaling pathways, even though individual features are not commonly measured or differentially expressed between them. We developed a methodology, CATEGORYCOMPARE, for cross-platform and cross-sample comparison of high-throughput data at the annotation level. We assessed the utility of the approach using hypothetical data, as well as determining similarities and differences in the set of processes in two instances: (1) denervated skin vs. denervated muscle, and (2) colon from Crohn's disease vs. colon from ulcerative colitis (UC). The hypothetical data showed that in many cases comparing annotations gave superior results to comparing only at the gene level. Improved analytical results depended as well on the number of genes included in the annotation term, the amount of noise in relation to the number of genes expressing in unenriched annotation categories, and the specific method in which samples are combined. In the skin vs. muscle denervation comparison, the tissues demonstrated markedly different responses. The Crohn's vs. UC comparison showed gross similarities in inflammatory response in the two diseases, with particular processes specific to each disease.
Availability:

CategoryCompareWEB coming soon

VERSION 1.8.0

VERSION 1.6.15

ccPaper

Citation:
Please Cite:

Flight RM, Harrison BJ, Mohammad F, Bunge MB, Moon LDF, Petruska JC, Rouchka EC: categoryCompare, an analytical tool based on feature annotations. Frontiers in Genetics 2014, 5:98. doi: 10.3389/fgene.2014.00098

Funding provided by the Kentucky Spinal Cord and Head Injury Research Trust (Grant 09-12A to Jeffrey C. Petruska); Paralyzed Veterans of America (Fellowship to Benjamin J. Harrison); National Institutes of Health (NIH) grants P20RR016481 (ECR), 3P20RR016481- 09S1 (Eric C. Rouchka, Robert M. Flight, Benjamin J. Harrison), P20GM103436 (Eric C. Rouchka), R21NS080091 (Jeffrey C. Petruska), and R21NS 071299 (Jeffrey C. Petruska); and Department of Energy contract DE-EM0000197 (Eric C. Rouchka). The contents of this research are solely the responsibility of the authors and do not represent the official views of the funding organizations.


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