Visualizing data in a Genome Browser
The ability to visualize your raw sequencing aligned to the
genome is enabled through the use of genome browsers.
Visualizing your data is incredibly important - it enables
you to investigate your data and get a feel for how it
"looks". This is incredibly important for quality
control - programs such as FASTQC, HOMER, etc. can only do
so much. Years of experience and knowledge of
biological systems make the human brain a good tool to
investigate data quality. Also, visualization of
sequencing data may help you come up with new ideas about
how to analyze the data.
Popular Genome Browsers:
This is an awesome genome browser that puts
lots of different information at your finger tips,
including lots of published studies and ENCODE
data. Big pluses: data integration.
Negatives: slower (web based), a little more difficult
to upload large custom data sets.
(Integrative Genome Viewer)
This browser runs locally on your own computer
(the more memory you have the better). It is Java
based, and is easy to use on almost any computer.
It doesn't have the same degree of shared information
available as UCSC, but it is much faster for browsing
across the genome. Also, it is better for looking
at individual reads/looking for variants.
There are tons of genome browsers out there
that serve many different needs. For a list, check
out this link.
Types of custom data files
A general list of common file formats can be
Popular formats are shown below:
- bed - (*.bed) - BED files are very basic as
they simply describe a simple region in the
genome. They are usually used to describe
ChIP-Seq peaks and things of that nature. Nearly
every genome browser supports visualization of BED
- wiggle - (*.wig) - Wiggle files are used to
display quantitative information across genomic
regions. Usually used to display read depth from
ChIP- or RNA-seq experiments. Wiggle format is
compact and displays data at regular intervals.
- bedGraph - (*.bedGraph) - Similar to Wiggle
files, these are used to display quantitative data
across genomic regions. They use variable length
intervals instead of constant intervals found in
wiggle files, and are usually a little bigger in size.
- bam - (*.bam) - Display individual
reads. Bam files need to be sorted, and they need to have an index file
along with the bam file to help the genome browser
efficiently find reads in the bam file. It's
best to use a local browser like IGV when visualizing
- GFF/GTF - (*.gff *.gtf) - Extensible file
formats for specifying spliced transcripts and
genes. Transcript assembly programs like
cufflinks will generate GTF files that you can then
upload to a genome browser.
Server Resident Files:
In the case of web-based genome browsers such
as UCSC, it can be difficult to upload large data
files. To get around this issue, UCSC set up
protocols to allow you to post your files on a webserver
and then create a track that "points" to the location of
your files. This requires a working webserver, but
can be a powerful way to visualize bigwig files. (more
Creating genome browser files
There are a bunch of specialized programs for
creating genome browser files. For example, HOMER
has specialized routines
for creating browser files. Often, the output of a
program is already suited to be loaded into a genome
browser. For example, cufflinks generates a
"transcripts.gtf" file. Macs creates a peak bed
file. Other common examples:
Loading custom data into the UCSC Genome Browser
To look at your own data using the UCSC Genome
Browser, click on the "Genomes" at the top and look for
the "Add Custom Tracks" or "Manage Custom Tracks" button:
After uploading your track, the data should appear in the
genome browser. At the bottom of the browser image
you'll find a variety of track settings. The section
at the top controls the settings for custom tracks:
You can change how the track is visualized by clicking on
the drop down menu, shown above. You can also click
on the 'blue' link name and change other custom settings.
Visual Quality Control
There are several things to look out for when
viewing your data in the browser. Below is a
checklist to help guide you.
- Look for spikes in the data. These may be
caused by contaminants, and may cause problems with
- Are there nice, defined peaks in the data? Or are
there regions of continuous coverage (histone
- Are there reads on all expected chromsomes?
- Does the pattern match the experiment?
- TFs: Spikes of enrichment near the TSS and
distal regulatory elements
- H3K4me3 - enriched near TSS
- H3K4me1/2, H3/H4ac, DNase - enriched near TSS
and near distal regulatory elements
- H3K36me3 - enriched across gene bodies
- H3K27me3 - enriched near CpG Islands of inactive
- H3K9me3 - enriched across broad domains and
- Is the background low, or almost as high as the
- Are most reads found on exons? Or is there a
lot of reads in introns/other regions?
- Do you have even read coverage across exons, or is
it full of strong spikes?
- Is there a 3' or 5' bias in the data?
- If strand specific, is it the correct strand?