CGDM: collaborative genomic data model for molecular profiling data using NoSQL

Wang, Shicai; Mares, Mihaela A.; Guo, Yike

doi:10.1093/bioinformatics/btw531

Cited by 4 publications

(1 citation statement)

References 26 publications

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“…The CloudBurst [ 11 ], Seal [ 12 ], Hadoop-BAM [ 13 ], and Crossbow software [ 14 ] take advantage of the Hadoop framework to accelerate sequencing read mapping, aligning, and manipulations as well as single-nucleotide polymorphism (SNP) calling. The Collaborative Genomic Data Model [ 15 ] adopts HBase to boost the querying speed for the main classes of queries on genomic databases. MetaSpark [ 16 ] utilizes Spark's distributed dataset to recruit large scales of metagenomics reads to reference genomes and achieves better scalability and sensitivity than single-machine based programs [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

Optimized distributed systems achieve significant performance improvement on sorted merging of massive VCF files

et al. 2018

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BackgroundSorted merging of genomic data is a common data operation necessary in many sequencing-based studies. It involves sorting and merging genomic data from different subjects by their genomic locations. In particular, merging a large number of variant call format (VCF) files is frequently required in large-scale whole-genome sequencing or whole-exome sequencing projects. Traditional single-machine based methods become increasingly inefficient when processing large numbers of files due to the excessive computation time and Input/Output bottleneck. Distributed systems and more recent cloud-based systems offer an attractive solution. However, carefully designed and optimized workflow patterns and execution plans (schemas) are required to take full advantage of the increased computing power while overcoming bottlenecks to achieve high performance.FindingsIn this study, we custom-design optimized schemas for three Apache big data platforms, Hadoop (MapReduce), HBase, and Spark, to perform sorted merging of a large number of VCF files. These schemas all adopt the divide-and-conquer strategy to split the merging job into sequential phases/stages consisting of subtasks that are conquered in an ordered, parallel, and bottleneck-free way. In two illustrating examples, we test the performance of our schemas on merging multiple VCF files into either a single TPED or a single VCF file, which are benchmarked with the traditional single/parallel multiway-merge methods, message passing interface (MPI)–based high-performance computing (HPC) implementation, and the popular VCFTools.ConclusionsOur experiments suggest all three schemas either deliver a significant improvement in efficiency or render much better strong and weak scalabilities over traditional methods. Our findings provide generalized scalable schemas for performing sorted merging on genetics and genomics data using these Apache distributed systems.

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