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Dinalankara, W.

Publications and source records attributed to Dinalankara, W..

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Divergence: An R Package for Divergence Analysis of Omics Data

Given the ever-increasing amount of high-dimensional and complex omics data becoming available, it is increasingly important to discover simple but effective methods of analysis. Divergence analysis transforms each entry of a high-dimensional omics profile into a digitized (binary or ternary) code based on the deviation of the entry from a given baseline population. This is a novel framework that is significantly different from existing omics data analysis methods: it allows digitization of continuous omics data at the univariate or multivariate level, facilitates sample level analysis, and is applicable on many different omics platforms. The divergence package, available on the R platform through the Bioconductor repository collection, provides easy-to-use functions for carrying out this transformation. Here we demonstrate how to use the package with data from the Cancer Genome Atlas.

bioinformatics

Recounting the FANTOM Cage Associated Transcriptome

Long non-coding RNAs (lncRNAs) have emerged as key coordinators of biological and cellular processes. Characterizing lncRNA expression across cells and tissues is key to understanding their role in determining phenotypes including human diseases. We present here FC-R2, a comprehensive expression atlas across a broadly-defined human transcriptome, inclusive of over 109,000 coding and non-coding genes, as described in the FANTOM CAGE-Associated Transcriptome (FANTOM-CAT) study. This atlas greatly extends the gene annotation used in the original recount2 resource. We demonstrate the utility of the FC-R2 atlas by reproducing key findings from published large studies and by generating new results across normal and diseased human samples. In particular, we (a) identify tissue specific transcription profiles for distinct classes of coding and non-coding genes, (b) perform differential expression analyses across thirteen cancer types, providing new insights linking promoter and enhancer lncRNAs expression to tumor pathogenesis, and (c) confirm the prognostic value of several enhancers in cancer. Comprised of over 70,000 samples, the FC-R2 atlas will empower other researchers to investigate functions and biological roles of both known coding genes and novel lncRNAs. Most importantly, access to the FC-R2 atlas is available from https://jhubiostatistics.shinyapps.io/recount/, the recount Bioconductor package, and http://marchionnilab.org/fcr2.html.

genomics