NDLI: Distributed Memory Partitioning of High-Throughput Sequencing Datasets for Enabling Parallel Genomics Analyses

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Distributed Memory Partitioning of High-Throughput Sequencing Datasets for Enabling Parallel Genomics Analyses

Content Provider	ACM Digital Library
Author	Chockalingam, Sriram P. Aluru, Srinivas Jammula, Nagakishore
Abstract	State-of-the-art high-throughput sequencing instruments decipher in excess of a billion short genomic fragments per run. The output sequences are referred to as 'reads'. These read datasets facilitate a wide variety of analyses with applications in areas such as genomics, metagenomics, and transcriptomics. Owing to the large size of the read datasets, such analyses are often compute and memory intensive. In this paper, we present a parallel algorithm for partitioning large-scale read datasets in order to facilitate distributed-memory parallel analyses. During the process of partitioning the read datasets, we construct and partition the associated de Bruijn graph in parallel. This allows applications that make use of a variant of the de Bruijn graph, such as de novo assembly, to directly leverage the generated de Bruijn graph partitions. In addition, we propose a mechanism for evaluating the quality of the generated partitions of reads and demonstrate that our algorithm produces high quality partitions. Our implementation is available at github.com/ParBLiSS/read_partitioning.
Starting Page	417
Ending Page	424
Page Count	8
File Format	PDF
ISBN	9781450347228
DOI	10.1145/3107411.3107491
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2017-08-20
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Memory partitioning
Content Type	Text
Resource Type	Article

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