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ADS-HCSpark: A scalable HaplotypeCaller leveraging adaptive data segmentation to accelerate variant calling on Spark
- Source :
- BMC Bioinformatics, BMC Bioinformatics, Vol 20, Iss 1, Pp 1-13 (2019)
- Publication Year :
- 2019
- Publisher :
- Springer Science and Business Media LLC, 2019.
-
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 variant calling based on Apache Spark framework. ADS-HCSpark accelerates the process of variant calling by implementing the parallelization of mainstream GATK HaplotypeCaller algorithm on multi-core and multi-node. Aiming at solving the problem of computation skew in HaplotypeCaller, a parallel strategy of adaptive data segmentation is proposed and a variant calling algorithm based on adaptive data segmentation is implemented, which achieves good scalability on both single-node and multi-node. For the requirement that adjacent data blocks should have overlapped boundaries, Hadoop-BAM library is customized to implement partitioning BAM file into overlapped blocks, further improving the accuracy of variant calling. Conclusions ADS-HCSpark is a scalable tool to achieve variant calling based on Apache Spark framework, implementing the parallelization of GATK HaplotypeCaller algorithm. ADS-HCSpark is evaluated on our cluster and in the case of best performance that could be achieved in this experimental platform, ADS-HCSpark is 74% faster than GATK3.8 HaplotypeCaller on single-node experiments, 57% faster than GATK4.0 HaplotypeCallerSpark and 27% faster than SparkGA on multi-node experiments, with better scalability and the accuracy of over 99%. The source code of ADS-HCSpark is publicly available at https://github.com/SCUT-CCNL/ADS-HCSpark.git. Electronic supplementary material The online version of this article (10.1186/s12859-019-2665-0) contains supplementary material, which is available to authorized users.
- Subjects :
- Time Factors
Source code
Computer science
media_common.quotation_subject
Parallel computing
lcsh:Computer applications to medicine. Medical informatics
Biochemistry
Genome
DNA sequencing
03 medical and health sciences
0302 clinical medicine
Structural Biology
Databases, Genetic
Variant calling
Spark (mathematics)
Humans
lcsh:QH301-705.5
Molecular Biology
Throughput (business)
030304 developmental biology
media_common
Spark
0303 health sciences
Applied Mathematics
Skew
Process (computing)
Genetic Variation
High-Throughput Nucleotide Sequencing
Sequence Analysis, DNA
Hadoop-BAM
Data segment
Computer Science Applications
lcsh:Biology (General)
Haplotypes
030220 oncology & carcinogenesis
Scalability
lcsh:R858-859.7
Adaptive data segmentation
DNA microarray
Algorithms
Software
Subjects
Details
- ISSN :
- 14712105
- Volume :
- 20
- Database :
- OpenAIRE
- Journal :
- BMC Bioinformatics
- Accession number :
- edsair.doi.dedup.....6a22fcc13d68089655bc5069c57f832f