ADS-HCSpark: A scalable HaplotypeCaller leveraging adaptive data segmentation to accelerate variant calling on Spark.

ABSTRACT:

Background

The advance of next generation sequencing enables higher throughput with lower price, and as the basic of high-throughput sequencing data analysis, variant calling is widely used in disease research, clinical treatment and medicine research. However, current mainstream variant caller tools have a serious problem of computation bottlenecks, resulting in some long tail tasks when performing on large datasets. This prevents high scalability on clusters of multi-node and multi-core, and leads to long runtime and inefficient usage of computing resources. Thus, a high scalable tool which could run in distributed environment will be highly useful to accelerate variant calling on large scale genome data.

Results

In this paper, we present ADS-HCSpark, a scalable tool for v

SUBMITTER: Xiao A 

PROVIDER: S-EPMC6376756 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature