Differential Expression Table

DESeq2 results for all genes (n = 32250)





Note: If your miR of interest does not appear, try searching in the Differential Expression Table below. Select the desired row(s), and then click 'Add selected rows to gene list'


Differential Expression Table

DESeq2 results for all miRNAs (n = 338)


About this app

Authors: Chelsea Herdman and Bradley Demarest

The Gene Expression Explorer app allows users to visualize differential expression results from RNA-seq datasets.

This Shiny app is supported by the B2B Consortium Grant (http://www.benchtobassinet.com) and hosted on http://b2b.hci.utah.edu/shiny/zebrafish_heart_timecourse/


Background and Methods

Experimental Methods

Zebrafish fish hearts were mechanically separated from embyronic zebrafish at five ages: 24, 36, 48, 60 and 72 hours post-fertilization (hpf). The procedure was repeated 4 times for 24, 36 and 48hpf timepoints and 5 times for 60 and 72hpf timepoints for a total of 22 biological samples.
RNA was purified from approximately n = 100 hearts per replicate per timepoint and the sample was split equally to produce a total RNA library (Illumina RiboZero Gold Library Kit) and a small RNA library (QIAseq miRNA Library Kit). All libraries for both RNA classes were multiplexed into two single RNA-Seq libraries and sequenced on an Illumina HiSeq 2500. The Total RNA sequencing library was sequenced paired-end over eight lanes and the Small RNA sequencing library was sequenced single-end over two lanes.

Data is publicly available at https://b2b.hci.utah.edu/gnomex/gnomexFlex.jsp?requestNumber=468R

Computational Methods

RiboZero Data

Transcript abundances were quantified using kallisto (Bray et al. 2016) and genome build GRCz11 release 99 (jan2020.archive.ensembl.org). The R package Tximport was used to give a bias-corrected gene-level abundance estimation (Soneson et al. 2015).
Summed estimated counts were rounded to the nearest integer in order to run DESeq2 (Love et al. 2014) using a negative binomial LRT model correcting for replicate (counts~ replicate + timepoint). This model tests in an anova-like way whether one timepoint differs from any other timepoint across the series.

Visualization Features

Gene panel plots can be displayed using normalized counts or rlog values from DESeq2 or transcripts per million from kallisto. The light grey dot represents the mean expression value for that gene at each timepoint and the dark grey dots represent the value for each replicate. The vertical line at each timepoint depicts the range of the data and a line has been drawn to connect the mean at each timepoint to show the expression profile.

The heatmap displays z-scores (computed using DESeq2 normalized counts) for the selected gene list using a red-blue color scale.


References

  • Bray N, Pimentel H, Melsted P, Pachter L (2016), Near-optimal probabilistic RNA-seq quantification, Nature Biotechnology, 34, 525–527. doi:10.1038/nbt.3519
  • Soneson C, Love MI, Robinson MD (2015), Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences, F1000Research, 4, 1521. doi:10.12688/f1000research.7563.2
  • Love MI, Huber W, Anders S (2014), Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2, Genome Biology, 15, 550. doi:10.1186/s13059-014-0550-8

Please email Chelsea Herdman cherdman@genetics.utah.edu with any questions or issues