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Abstract
RNAseq has become a preferred method of data generation, particularly when dealing with non-model organisms or organisms for which no reference genome has yet been completed. Similarly, single nucleotide polymorphisms (SNPs) and their phenotypic effects have become a favorite subject of study throughout the genetic community. While several tools exist and are being created to call and analyze SNPs from RNAseq data, there are still some gaps in the situations that the current technologies can address. The Snv Heuristic Identification, Exploration, and Location Detector (SHIELD) is a fully automated pipeline tailored to take mapped RNA-seq reads from studies with small sample sizes, find unique SNVs between user-defined groups, and generate Circos plots for visualization of the data and results. SHIELD's use is demonstrated in an analysis of M. mulatta subjects infected with malaria. SHIELD identified several high density SNV regions that confirm the importance of genetic variations in innate immune functions.