Genomic Data Analyzing Workflow for Single Nucleotide Polymorphisms in Human Nervous System Cancers

Mutation data analysis related to the disease is one of the main objectives in genomic data analysis. Analyzing Single Nucleotide polymorphisms (SNPs) are time-consuming and intensive labour required process. Moreover, finding all relevant data for the mutation is also a complicated task. Cancer is a genetic disease which begins by accumulating mutations in an organ. Cancer genesis can be explored by analyzing genomic mutations and protein changes.Our objectives of this study are to build a user-friendly platform for data conversion, data enrichment and SNP data interpretation in human nervous system cancer mutations based on R programming. We have used 15 datasets to develop the workflow. R packages, UniProt conversion tool, Ensemble Genome conversion tool and VEP tool were used for the enrichment. Data enrichment was performed via using data in DBSNP, COSMIC, Clinvar and Uniprot databases. Mutation protein effect prediction was performed with SIFT and PolyPhen. Data filtration and SNP mutation analysis were completed with R packages and MS Excel.Initially, we performed data purification step which relies on genome build, database version and genome position. According to the results, the best method for genomic mapping is chromosome position mapping. Secondly, we enriched mutations with genomic data. We filtered protein mutated genomic level mutations. Protein position identification and data enrichment were the final steps of the workflow. We designed a reusable workflow for large mutation datasets analysis via using human nervous system cancer mutation data. As future work, we will try to merge other cancer datasets for this workflow.