Accelerating metagenomic read classification on CUDA-enabled GPUs. Issue 1 (December 2017)

• Record Type: Journal Article
• Authors: Kobus, Robin; Hundt, Christian; Müller, André; Schmidt, Bertil
• Is Part Of: BMC bioinformatics. Volume 18:Issue 1(2017)
• Page Start: 1
• Page End: 10
• Publication Date: 2017-12
• Subjects: Metagenomics -- Taxonomic assignment -- Exact k-mer matching -- CUDA -- GPUs; Bioinformatics -- Periodicals; Computational biology -- Periodicals; 570.285
• DOI: 10.1186/s12859-016-1434-6
• Languages: English
• ISSNs: 1471-2105

Abstract

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 reducedk -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 athttps://github.com/funatiq/cuclark free of charge.