An "eFP‐Seq Browser" for Visualizing and Exploring RNA‐Seq Data

Alexander Sullivan, Priyank Purohit, Nowlan H. Freese, Asher Pasha, Eddi Esteban, Jamie Waese, Alison Wu, Michelle Chen, Chih Ying Chin, Richard Song, Sneha Ramesh Watharkar, Agnes P. Chan (+5 others)
<span title="2019-07-26">2019</span> <i title="Wiley"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/eyeekm765nepfjifdg2zcmtf2y" style="color: black;">The Plant Journal</a> </i> &nbsp;
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 sequencing (RNA-seq) for generating transcriptomic data is the most popular. By analysing 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
more &raquo; ... ta sets enable analyses and visualizations to help generate hypotheses about a gene's function. We 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 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 from 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 allow researchers to visualize and explore the details of RNA-seq alignments summarized in eFP-Seq Browser as coverage graphs. We present four cases of use of the eFP-Seq Browser for ABI3, SR34, SR45a and U2AF65B, where we examine expression levels and identify alternative splicing. The URL for the browser is https://bar.utoronto.ca/eFP-Seq_Browser/. OPEN RESEARCH BADGES: This article has earned an Open Data Badge for making publicly available the digitally-shareable data necessary to reproduce the reported results. Tool is at https://bar.utoronto.ca/eFP-Seq_Browser/; RNA-seq data at https://s3.amazonaws.com/iplant-cdn/iplant/home/araport/rnaseq_bam/ and https://s3.amazonaws.com/iplant-cdn/iplant/home/araport/rnaseq_bam/Klepikova/. Code is available at https://github.com/BioAnalyticResource/eFP-Seq-Browser.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1111/tpj.14468">doi:10.1111/tpj.14468</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/31350781">pmid:31350781</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC6899666/">pmcid:PMC6899666</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ihlbiyc27ffujmmuix2tfco3am">fatcat:ihlbiyc27ffujmmuix2tfco3am</a> </span>
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