VC@Scale: Scalable and high-performance variant calling on cluster environments.

ABSTRACT:

Background

Recently many new deep learning-based variant-calling methods like DeepVariant have emerged as more accurate compared with conventional variant-calling algorithms such as GATK HaplotypeCaller, Sterlka2, and Freebayes albeit at higher computational costs. Therefore, there is a need for more scalable and higher performance workflows of these deep learning methods. Almost all existing cluster-scaled variant-calling workflows that use Apache Spark/Hadoop as big data frameworks loosely integrate existing single-node pre-processing and variant-calling applications. Using Apache Spark just for distributing/scheduling data among loosely coupled applications or using I/O-based storage for storing the output of intermediate applications does not exploit the full benefit of Apache

SUBMITTER: Ahmad T 

PROVIDER: S-EPMC8424057 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature