Optimizing performance of GATK workflows using Apache Arrow In-Memory data framework

Background: Immense improvements in sequencing technologies enable producing large amounts of high throughput and cost effective next-generation sequencing (NGS) data. This data needs to be processed efficiently for further downstream analyses. Computing systems need this large amounts of data closer to the processor (with low latency) for...

ArrowSAM: In-Memory Genomics Data Processing Using Apache Arrow

The rapidly growing size of genomics data bases, driven by advances in sequencing technologies, demands fast and cost-effective processing. However, processing this data creates many challenges, particularly in selecting appropriate algorithms and computing platforms. Computing systems need data closer to the processor for fast processing....

GPU acceleration of Darwin read overlapper for de novo assembly of long DNA reads

BACKGROUND: In Overlap-Layout-Consensus (OLC) based de novo assembly, all reads must be compared with every other read to find overlaps. This makes the process rather slow and limits the practicality of using de novo assembly methods at a large scale in the field. Darwin is a fast and accurate read overlapper that can be used for de novo...

A Comparison of Seed-and-Extend Techniques in Modern DNA Read Alignment Algorithms

DNA read alignment is a major step in genome analysis. However, as DNA reads continue to become longer, new approaches need to be developed to effectively use these longer reads in the alignment process. Modern aligners commonly use a two-step approach for read alignment: 1. seeding, 2. extension. In this paper, we have investigated various...