GPMeta: a GPU-accelerated method for ultrarapid pathogen identification from metagenomic sequences

Abstract

Metagenomic sequencing (mNGS) is a powerful diagnostic tool to detect causative pathogens in clinical microbiological testing owing to its unbiasedness and substantially reduced costs. Rapid and accurate classification of metagenomic sequences is a critical procedure for pathogen identification in dry-lab step of mNGS test. However, clinical practices of the testing technology are hampered by the challenge of classifying sequences within a clinically relevant timeframe. Here, we present GPMeta, a novel GPU-accelerated approach to ultrarapid pathogen identification from complex mNGS data, allowing users to bypass this limitation. Using mock microbial community datasets and public real metagenomic sequencing datasets from clinical samples, we show that GPMeta has not only higher accuracy but also significantly higher speed than existing state-of-the-art tools such as Bowtie2, Bwa, Kraken2 and Centrifuge. Furthermore, GPMeta offers GPMetaC clustering algorithm, a statistical model for clustering and rescoring ambiguous alignments to improve the discrimination of highly homologous sequences from microbial genomes with average nucleotide identity >95%. GPMetaC exhibits higher precision and recall rate than others. GPMeta underlines its key role in the development of the mNGS test in infectious diseases that require rapid turnaround times. Further study will discern how to best and easily integrate GPMeta into routine clinical practices. GPMeta is freely accessible to non-commercial users at https://github.com/Bgi-LUSH/GPMeta.

GenStore: a high-performance in-storage processing system for genome sequence analysis

“…Hardware Acceleration of Metagenomics. Several works use GPUs (e.g., [62,63,66,70,[235][236][237]), FPGAs (e.g., [238][239][240]), and PIM (e.g., [61,64,65,67,68,197]) to accelerate metagenomics by alleviating its computation or main memory overheads. These works do not reduce I/O overheads, whose impact on end-to-end performance becomes even larger when other bottlenecks are alleviated.…”

“…Some works (e.g., [48][49][50][51][52][53]) aim to alleviate this overhead by applying sampling techniques to reduce the database size, but they incur accuracy loss, which is problematic for many use cases (e.g., [18,26,42,[54][55][56][57][58][59][60]). Various other works (e.g., [61][62][63][64][65][66][67][68][69][70]) accelerate other bottlenecks in metagenomic analysis, such as computation and main memory bottlenecks. These works do not alleviate I/O overheads, whose impact on end-to-end performance becomes even larger (as shown in §3) when other bottlenecks are alleviated.…”