Integrated Genome Browser now includes RNA-Seq data sets from Arabidopsis, =
maize, and sorghum.
To download the latest version of Integrated Genome Browser, visit: www.bio=
IGB is a desktop genome visualization tool mainly used to analyze and explo=
re big data sets, such as read alignments from RNASeq experiments.
Two tutorials are available to help you get started visualizing RNASeq data=
(as alignments and coverage graphs) in IGB.
To view the tutorials, visit the IGB User's Guide: http://wiki.transvar.or=
The tutorial titled "RNA-Seq coverage graphs" uses maize RNASeq data to sho=
w how coverage graphs can reveal new genes and differentially expressed gen=
es. (If you are new to IGB, start with this one.)
The tutorial titled "Assessing splicing" explains how to use IGB to look fo=
r alternative splicing evidence in read alignments data.
There is also a simple, interactive tutorial describing basic navigation fu=
nctions. To run the interactive tutorial, go to Help > Tutorials after laun=
The currently available Arabidopsis RNASeq data set includes alignments and=
coverage graphs from a cold stress experiment performed here at UNCC. Info=
rmation about the experiment is available from the main IGBQuickLoad site: =
For potential data providers:
IGB QuickLoad is a very simple data integration and sharing system IGB uses=
to combine data from multiple sites. If you have big data sets to share, y=
ou can add them to a QuickLoad section of your lab Web site and then publis=
h the URL. IGB users can then enter the URL into IGB and see your data alon=
gside other data sets (gene models, ESTs, TDNA insertions, RNASeq, etc) pro=
vided by other groups. You can also password protect the data if you want t=
o keep it private prior to publication. So far as I know, none of the other=
desktop genome browsers support this; this is unique to IGB. However, they=
may adopt it as well =96 it is very simple and requires no specialized sof=
If you are interested in trying this in your lab, the IGB team would be gla=
d to provide advice and help, as this will help us improve the documentatio=
n and give us ideas for new features. More information about QuickLoad is h=
Ann Loraine, Ph.D.
Department of Bioinformatics and Genomics
University of North Carolina at Charlotte
North Carolina Research Campus
600 Laureate Way
Kannapolis, NC 28081
aloraine from uncc.edu<mailto:aloraine from uncc.edu>