Leveraging known genomic variants to improve detection of variants, especially close-by Indels

The impact of post-alignment processing procedures on whole-exome sequencing data

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.

Fast read alignment with incorporation of known genomic variants

Background

Many genetic variants have been reported from sequencing projects due to decreasing experimental costs. Compared to the current typical paradigm, read mapping incorporating existing variants can improve the performance of subsequent analysis. This method is supposed to map sequencing reads efficiently to a graphical index with a reference genome and known variation to increase alignment quality and variant calling accuracy. However, storing and indexing various types of variation require costly RAM space.

Methods

Aligning reads to a graph model-based index including the whole set of variants is ultimately an NP-hard problem in theory. Here, we propose a variation-aware read alignment algorithm (VARA), which generates the alignment between read and multiple genomic sequences simultaneously utilizing the schema of the Landau-Vishkin algorithm. VARA dynamically extracts regional variants to construct a pseudo tree-based structure on-the-fly for seed extension without loading the whole genome variation into memory space.

Results

We developed the novel high-throughput sequencing read aligner deBGA-VARA by integrating VARA into deBGA. The deBGA-VARA is benchmarked both on simulated reads and the NA12878 sequencing dataset. The experimental results demonstrate that read alignment incorporating genetic variation knowledge can achieve high sensitivity and accuracy.

Conclusions

Due to its efficiency, VARA provides a promising solution for further improvement of variant calling while maintaining small memory footprints. The deBGA-VARA is available at: https://github.com/hitbc/deBGA-VARA.