An “eFP-Seq Browser” for visualizing and exploring RNA-seq data

Improvements in next-generation sequencing technologies have resulted in dramatically reduced sequencing costs. This has led to an explosion of “-seq”-based methods, of which RNA-seq for generating transcriptomic data is the most popular. By analyzing global patterns of gene expression in organs/tissues/cells of interest or in response to chemical or environmental perturbations, researchers can better understand an organism’s biology. Tools designed to work with large RNA-seq data sets enable analyses and visualizations to help generate hypotheses about a gene’s function. University of Toronto researchers present here a user-friendly RNA-seq data exploration tool, called the eFP-Seq Browser, that shows the read map coverage of a gene of interest in each of the samples along with “electronic fluorescent pictographic” (eFP) images that serve as visual representations of expression levels. The tool also summarizes the details of each RNA-seq experiment, providing links to archival databases and publications. It automatically computes the Reads per Kilobase per Million reads mapped (RPKM) expression level summaries and point biserial correlation scores to sort the samples based on a gene’s expression level or by how dissimilar the read map profile is to a gene splice variant, to quickly identify samples with the strongest expression level or where alternative splicing might be occurring. Links to the Integrated Genome Browser desktop visualization tool let researchers visualize and explore the details of RNA-seq alignments summarized in eFP-Seq Browser as coverage graphs. The researchers present 4 use cases of the eFP-Seq Browser for ABI3, SR34, SR45a, and U2AF65B, where they examine expression levels and identify alternative splicing.



Sullivan A, Purohit P, Freese NH, Pasha A, Esteban E, Waese J, Wu A, Chen M, Chin CY, Song R, Watharkar SR, Chan AP, Krishnakumar V, Vaughn MW, Town C, Loraine AE, Provart NJ. (2019) An “eFP-Seq Browser” for Visualizing and Exploring RNA-Seq Data. Plant J [Epub ahead of print]. [abstract]

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