Download ashgPoster2011ver3.pdf

Transparent, accessible, and robust analysis of SNPs
http://usegalaxy.org
Belinda M. Giardine, Burhans R., Riemer C., Ratan A., Harris R., Von Kuster G.,
Galaxy Development Team, Hardison R.C., Zhang Y., Miller W.
The Pennsylvania State University, Center for Comparative Genomics and Bioinformatics, University Park, PA
The first tool demonstrated is the aaChanges tool. Given a set of SNPs and a set of
genes the tool computes which SNPs cause a change in an amino acid. For this
example we are using filtered GWAS catalog SNPs brought in from the Galaxy library
“Putative SNP Phenotypes” and RefSeq genes from the BX Browser (a Penn State
mirror of the UCSC Genome Browser).
This example uses the Galaxy server to find
potentially functional SNPs within the GWAS
catalog. This catalog contains SNPs that are
associated genetically with phenotypes; they
are tag SNPS, but not necessarily the functional
SNP. However, a subset of them could actually
be functional, and we will search for these to
illustrate the power of Galaxy tools for finding
candidate functional SNPs. A similar analysis
can be done on all SNPs within linkage
disequilibrium of the tag SNPs for a more
thorough examination of a particular locus.
Only a few of the steps are shown in detail
here. There are also many tools for working
with SNPs that are not used in this example.
Shown on the right are the categories for the
current Galaxy tools, and below is a stock
library of SNP data provided by Galaxy.
The results of the aaChanges tool are saved in a file that can be used in further
analysis. The tool adds the RefSeq ID for the gene used, the amino acid change
and position, and the reference allele that was substituted. A SNP will have one
line in the file for every gene that intersects that position and has an amino acid
change.
With the coding SNPs we now have genes. We narrowed the SNPs to just
those with “cholesterol” in the phenotype. We then asked the Comparative
Toxicogenomics Database (CTD) for the pathways associated with those
genes. Here we show both the options used for running CTD and a peek at
the results in the history panel.
Links to gene names
RefSeq Genes
GWAS SNPs
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PolyPhen predictions
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PRPs
Conserved TFBS
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ENCODE segments
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* outputs
Genome browser view of the tracks used in the example.
SMARCA4
LDLR
KANK2
DOCK6
ENCODE segmentations
green = predicted transcribed region
red = predicted promoter region including TSS and promoter flanking region
yellow = predicted enhancer or open chromatin cis regulatory element
turquoise = CTCF enriched element
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This is the workflow made from the
history of this example showing all
steps and the connections between
them. A workflow allows easy
rerunning of a pipeline with different
inputs. The workflow can be used
for yourself or shared with others.
Various other tools were used for filtering and genomic operations (e.g.
intersection of chromosome coordinates with annotations from browsers).
A summary of what was learned from the analysis done in this example:
110 SNPs cause an amino acid change
61 SNPs predicted to be [possibly] damaging by PolyPhen2
63 SNPs predicted to be [possibly] damaging by SIFT
68 SNPs in PRPs (computationally predicted regulatory regions)
154 SNPs in conserved transcription factor binding sites
1,082 SNPs in regions enriched for function according to ENCODE
Through the Galaxy history all the steps done and results are stored in one
location, and can be easily shared with others. The history from this example
is publicly available on Galaxy under Shared Data.
Background image is the Tadpole Galaxy, from hubblesite.org