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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 * * * * PolyPhen predictions * PRPs Conserved TFBS * ENCODE segments * * 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 * 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