An ‘eFP‐Seq Browser’ for visualizing and exploring RNA sequencing data

Abstract

SummaryImprovements 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 data 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 http://sps:urlprefix::https; RNA‐seq data at http://sps:urlprefix::https and http://sps:urlprefix::https. Code is available at http://sps:urlprefix::https

Arabidopsis RNA Polymerase IV generates 21-22 nucleotide small RNAs that can participate in RNA-directed DNA methylation and may regulate genes

“…Publicly available small RNA-seq (GSE41755, GSE74398, GSE57191, GSE118705, GSE84122, GSE79780), RNA-seq (GSE99691), and PARE-seq (GSM1263708) were used. [41] and analyzed in the eFP-RNA-Seq Browser [42]. sRNA accumulation from different loci is shown in total, AGO1-IP or negative control mock-IP samples in wt Col inflorescence tissue.…”

BioVizConnect: Web application linking CyVerse cloud resources to genomic visualization in the Integrated Genome Browser

“…As users interact with BioViz Connect Web pages, JavaScript functions running in the Web browser make REST-style calls to this IGB localhost endpoint, directing IGB to load and display data pulled from CyVerse. This is an enhancement of the REST-based mechanism IGB uses to interact with and consume data from other sites, including the BioAnalytics Resource (BAR) eFP-Seq browser [22] and the Galaxy workflow server [15]. The chief difference or innovation of BioViz Connect compared to these older schemes is that BioViz Connect transmits meta-data about a data set to IGB, thus enabling users to configure the appearance of their data using the BioViz Connect user interface as a dashboard-style application.…”

Transcriptome-guided parsimonious flux analysis improves predictions with metabolic networks in complex environments

“…Subsequently, transcript mapping is reduced to only genes that appear in the genome-scale network reconstruction of interest, therefore not skewing the distribution with data that is uninformative to the current analysis. To generate reaction coefficients (weights) that closely reflect the normalized transcript abundance [37] for their associated genes, we calculated the ratio of transcription for each gene with the maximum abundance in the dataset (Fig 1A). In line with PLOS COMPUTATIONAL BIOLOGY During the pruning step, reactions for genes that recruit greater abundances of transcript are assigned smaller linear coefficients which in turn result in higher likelihood of usage in an overall flux minimization objective.…”