Dataset Information

ABySS 2.0: resource-efficient assembly of large genomes using a Bloom filter.

ABSTRACT: The assembly of DNA sequences de novo is fundamental to genomics research. It is the first of many steps toward elucidating and characterizing whole genomes. Downstream applications, including analysis of genomic variation between species, between or within individuals critically depend on robustly assembled sequences. In the span of a single decade, the sequence throughput of leading DNA sequencing instruments has increased drastically, and coupled with established and planned large-scale, personalized medicine initiatives to sequence genomes in the thousands and even millions, the development of efficient, scalable and accurate bioinformatics tools for producing high-quality reference draft genomes is timely. With ABySS 1.0, we originally showed that assembling the human genome using short 50-bp sequencing reads was possible by aggregating the half terabyte of compute memory needed over several computers using a standardized message-passing system (MPI). We present here its redesign, which departs from MPI and instead implements algorithms that employ a Bloom filter, a probabilistic data structure, to represent a de Bruijn graph and reduce memory requirements. We benchmarked ABySS 2.0 human genome assembly using a Genome in a Bottle data set of 250-bp Illumina paired-end and 6-kbp mate-pair libraries from a single individual. Our assembly yielded a NG50 (NGA50) scaffold contiguity of 3.5 (3.0) Mbp using <35 GB of RAM. This is a modest memory requirement by today's standards and is often available on a single computer. We also investigate the use of BioNano Genomics and 10x Genomics' Chromium data to further improve the scaffold NG50 (NGA50) of this assembly to 42 (15) Mbp.

SUBMITTER: Jackman SD

PROVIDER: S-EPMC5411771 | biostudies-literature | 2017 May

REPOSITORIES: biostudies-literature

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ABySS 2.0: resource-efficient assembly of large genomes using a Bloom filter.

Jackman Shaun D SD Vandervalk Benjamin P BP Mohamadi Hamid H Chu Justin J Yeo Sarah S Hammond S Austin SA Jahesh Golnaz G Khan Hamza H Coombe Lauren L Warren Rene L RL Birol Inanc I

Genome research 20170223 5

The assembly of DNA sequences de novo is fundamental to genomics research. It is the first of many steps toward elucidating and characterizing whole genomes. Downstream applications, including analysis of genomic variation between species, between or within individuals critically depend on robustly assembled sequences. In the span of a single decade, the sequence throughput of leading DNA sequencing instruments has increased drastically, and coupled with established and planned large-scale, pers ...[more]

PMID: 28232478

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Project description:Whole genome sequencing has become fast, accurate, and cheap, paving the way towards the large-scale collection and processing of human genome data. Unfortunately, this dawning genome era does not only promise tremendous advances in biomedical research but also causes unprecedented privacy risks for the many. Handling storage and processing of large genome datasets through cloud services greatly aggravates these concerns. Current research efforts thus investigate the use of strong cryptographic methods and protocols to implement privacy-preserving genomic computations.We propose FHE-BLOOM and PHE-BLOOM, two efficient approaches for genetic disease testing using homomorphically encrypted Bloom filters. Both approaches allow the data owner to securely outsource storage and computation to an untrusted cloud. FHE-BLOOM is fully secure in the semi-honest model while PHE-BLOOM slightly relaxes security guarantees in a trade-off for highly improved performance.We implement and evaluate both approaches on a large dataset of up to 50 patient genomes each with up to 1000000 variations (single nucleotide polymorphisms). For both implementations, overheads scale linearly in the number of patients and variations, while PHE-BLOOM is faster by at least three orders of magnitude. For example, testing disease susceptibility of 50 patients with 100000 variations requires only a total of 308.31 s (σ=8.73 s) with our first approach and a mere 0.07 s (σ=0.00 s) with the second. We additionally discuss security guarantees of both approaches and their limitations as well as possible extensions towards more complex query types, e.g., fuzzy or range queries.Both approaches handle practical problem sizes efficiently and are easily parallelized to scale with the elastic resources available in the cloud. The fully homomorphic scheme, FHE-BLOOM, realizes a comprehensive outsourcing to the cloud, while the partially homomorphic scheme, PHE-BLOOM, trades a slight relaxation of security guarantees against performance improvements by at least three orders of magnitude.

Dataset Information

ABySS 2.0: resource-efficient assembly of large genomes using a Bloom filter.

Publications

ABySS 2.0: resource-efficient assembly of large genomes using a Bloom filter.

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