Accelerating metagenomic read classification on CUDA-enabled GPUs.
Identifieur interne : 000E27 ( PubMed/Curation ); précédent : 000E26; suivant : 000E28Accelerating metagenomic read classification on CUDA-enabled GPUs.
Auteurs : Robin Kobus [Allemagne] ; Christian Hundt [Allemagne] ; André Müller [Allemagne] ; Bertil Schmidt [Allemagne]Source :
- BMC bioinformatics [ 1471-2105 ] ; 2017.
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Abstract
Metagenomic sequencing studies are becoming increasingly popular with prominent examples including the sequencing of human microbiomes and diverse environments. A fundamental computational problem in this context is read classification; i.e. the assignment of each read to a taxonomic label. Due to the large number of reads produced by modern high-throughput sequencing technologies and the rapidly increasing number of available reference genomes software tools for fast and accurate metagenomic read classification are urgently needed.
DOI: 10.1186/s12859-016-1434-6
PubMed: 28049411
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<front><div type="abstract" xml:lang="en">Metagenomic sequencing studies are becoming increasingly popular with prominent examples including the sequencing of human microbiomes and diverse environments. A fundamental computational problem in this context is read classification; i.e. the assignment of each read to a taxonomic label. Due to the large number of reads produced by modern high-throughput sequencing technologies and the rapidly increasing number of available reference genomes software tools for fast and accurate metagenomic read classification are urgently needed.</div>
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<Abstract><AbstractText Label="BACKGROUND" NlmCategory="BACKGROUND">Metagenomic sequencing studies are becoming increasingly popular with prominent examples including the sequencing of human microbiomes and diverse environments. A fundamental computational problem in this context is read classification; i.e. the assignment of each read to a taxonomic label. Due to the large number of reads produced by modern high-throughput sequencing technologies and the rapidly increasing number of available reference genomes software tools for fast and accurate metagenomic read classification are urgently needed.</AbstractText>
<AbstractText Label="RESULTS" NlmCategory="RESULTS">We present cuCLARK, a read-level classifier for CUDA-enabled GPUs, based on the fast and accurate classification of metagenomic sequences using reduced k-mers (CLARK) method. Using the processing power of a single Titan X GPU, cuCLARK can reach classification speeds of up to 50 million reads per minute. Corresponding speedups for species- (genus-)level classification range between 3.2 and 6.6 (3.7 and 6.4) compared to multi-threaded CLARK executed on a 16-core Xeon CPU workstation.</AbstractText>
<AbstractText Label="CONCLUSION" NlmCategory="CONCLUSIONS">cuCLARK can perform metagenomic read classification at superior speeds on CUDA-enabled GPUs. It is free software licensed under GPL and can be downloaded at https://github.com/funatiq/cuclark free of charge.</AbstractText>
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