Accelerating Variant Calling on Human Genomes Using a Commodity Cluster
Accelerating Variant Calling on Human Genomes Using a Commodity Cluster
复制标题
使用商品簇加速人类基因组的变异调用
DOI：
10.1145/3459637.3482047
复制
发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Simoes, Eduardo
中科院分区：
文献类型：
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作者：
Rao, Praveen;Zachariah, Arun;Rao, Deepthi;Tonellato, Peter;Warren, Wesley;Simoes, Eduardo
变异识别是一项基本任务，用于识别个体基因组中与参考人类基因组相比的变异。这项任务可以更好地了解个人的疾病风险，并最终导致精准医学和药物发现的新创新。然而，对大量人类基因组序列的变异调用需要大量的计算和存储资源。虽然对这些资源的访问在今天是可能的（例如，通过云计算），降低分析基因组的成本已成为一个主要挑战。出于这些原因，我们解决了使用商品集群在大量人类基因组序列上加速变体调用管道的问题。我们提出了一种新的方法，协同结合数据和任务并行的不同阶段的变异调用管道在不同的序列与最小的同步。我们的方法采用期货启用异步计算，以提高整体集群利用率，从而加速变异调用管道。在一个16节点的集群上，我们观察到我们的方法比最先进的大数据基因组学软件快3X-4.7X。
Variant calling is a fundamental task that is performed to identify variants in an individual's genome compared to a reference human genome. This task can enable better understanding of an individual's risk to diseases and eventually lead to new innovations in precision medicine and drug discovery. However, variant calling on a large number of human genome sequences requires significant computing and storage resources. While access to such resources is possible today (e.g., through cloud computing), reducing the cost of analyzing genomes has become a major challenge. Motivated by these reasons, we address the problem of accelerating the variant calling pipeline on a large number of human genome sequences using a commodity cluster. We propose a novel approach that synergistically combines data and task parallelism for different stages of the variant calling pipeline across different sequences with minimal synchronization. Our approach employs futures to enable asynchronous computations in order to improve the overall cluster utilization and thereby, accelerate the variant calling pipeline. On a 16-node cluster, we observed that our approach was 3X-4.7X faster than the state-of-the-art Big Data Genomics software.
影响因子：
9.8
作者：
Stephens ZD;Lee SY;Faghri F;Campbell RH;Zhai C;Efron MJ;Iyer R;Schatz MC;Sinha S;Robinson GE
通讯作者：
Robinson GE
DOI：
10.1093/bioinformatics/btv179
发表时间：
2015-08-01
期刊：
Bioinformatics (Oxford, England)
影响因子：
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作者：
Decap D;Reumers J;Herzeel C;Costanza P;Fostier J
通讯作者：
Fostier J
DOI：
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发表时间：
2020
期刊：
Definitions
影响因子：
--
作者：
L. Marshall;Alison C. Bested;R. Bray
通讯作者：
R. Bray