Abstract The use of post-alignment procedures has been suggested to prevent the identification of false-positives in massive DNA sequencing data. Insertions and deletions are most likely to be misinterpreted by variant calling algorithms. Using known genetic variants as references for post-processing pipelines can minimize mismatches. They allow reads to be correctly realigned and recalibrated, resulting in more parsimonious variant calling. In this work, we aim to investigate the impact of using different sets of common variants as references to facilitate variant calling from whole-exome sequencing data. We selected reference variants from common insertions and deletions available within the 1K Genomes project data and from databases from the Latin American Database of Genetic Variation (LatinGen). We used the Genome Analysis Toolkit to perform post-processing procedures like local realignment, quality recalibration procedures, and variant calling in whole exome samples. We identified an increased number of variants from the call set for all groups when no post-processing procedure was performed. We found that there was a higher concordance rate between variants called using 1K Genomes and LatinGen. Therefore, we believe that the increased number of rare variants identified in the analysis without realignment or quality recalibration indicated that they were likely false-positives.

The RNA interference (RNAi) technique is a recent technology that uses double-stranded RNA molecules to promote potent and specific gene silencing. The application of this technique to molecular biology has increased considerably, from gene function identification to disease treatment. However, not all small interfering RNAs (siRNAs) are equally efficient, making target selection an essential procedure. Here we present Strand Analysis (SA), a free online software tool able to identify and classify the best RNAi targets based on Gibbs free energy (deltaG). Furthermore, particular features of the software, such as the free energy landscape and deltaG gradient, may be used to shed light on RNA-induced silencing complex (RISC) activity and RNAi mechanisms, which makes the SA software a distinct and innovative tool.