cff-version: 1.2.0
title: eFP-Seq Browser
message: >-
    An 'eFP-Seq Browser' for visualizing and exploring RNA
    sequencing data
type: software
authors:
    - given-names: Alexander
      family-names: Sullivan
      email: alexjsully.connect@outlook.com
      orcid: "https://orcid.org/0000-0002-4463-4473"
    - given-names: Priyank K.
      family-names: Purohit
    - given-names: Nowlan H.
      family-names: Freese
    - given-names: Asher
      family-names: Pasha
      orcid: "https://orcid.org/0000-0002-9315-0520"
    - given-names: Eddi
      family-names: Esteban
      orcid: "https://orcid.org/0000-0001-9016-9202"
    - given-names: Jamie
      family-names: Waese
    - given-names: Alison
      family-names: Wu
    - given-names: Michelle
      family-names: Chen
    - given-names: Chih Y.
      family-names: Chin
    - given-names: Richard
      family-names: Song
    - given-names: Sneha R.
      family-names: Watharkar
    - given-names: Agnes P.
      family-names: Chan
    - given-names: Vivek
      family-names: Krishnakumar
    - given-names: Matthew W.
      family-names: Vaughn
    - given-names: Chris
      family-names: Town
    - given-names: Ann E.
      family-names: Loraine
      affiliation: University of North Carolina
      email: ann.loraine@uncc.edu
    - given-names: Nicholas J.
      family-names: Provart
      orcid: "https://orcid.org/0000-0001-5551-7232"
      email: nicholas.provart@utoronto.ca
      affiliation: University of Toronto
identifiers:
    - type: doi
      value: 10.1111/tpj.14468
repository-code: "https://github.com/BioAnalyticResource/eFP-Seq_Browser"
url: "https://bar.utoronto.ca/eFP-Seq_Browser/"
abstract: >-
    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 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/.
keywords:
    - bioinformatics
    - computational biology
    - rna-seq
    - data visualization
    - arabidopsis thaliana
    - rna-seq mapping
    - alternative splicing
    - web application
license: GPL-2.0
commit: a5b952bf2df11e9c8538b372f2ee0d3c23d1fa90
version: 1.3.15
date-released: "2024-01-15"