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Accelerating metagenomic read classification on CUDA-enabled GPUs

Identifieur interne : 000F02 ( Main/Exploration ); précédent : 000F01; suivant : 000F03

Accelerating metagenomic read classification on CUDA-enabled GPUs

Auteurs : Robin Kobus [Allemagne] ; Christian Hundt [Allemagne] ; André Müller [Allemagne] ; Bertil Schmidt [Allemagne]

Source :

RBID : PMC:5209836

Descripteurs français

English descriptors

Abstract

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.

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.

Conclusion

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.


Url:
DOI: 10.1186/s12859-016-1434-6
PubMed: 28049411
PubMed Central: 5209836


Affiliations:


Links toward previous steps (curation, corpus...)


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