Accelerating metagenomic read classification on CUDA-enabled GPUs

Kobus, Robin; Hundt, Christian; Müller, André C.; Schmidt, Bertil

doi:10.1186/s12859-016-1434-6

Cited by 14 publications

(10 citation statements)

References 18 publications

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“…On the other hand, small complex detection is a more challenging task [ 17 ], which is another focus of our future study. Thirdly, for better complex prediction performance, we will also consider building reliable and robust PPI networks by fusing multiple networks [ 47 ].…”

Section: Discussionmentioning

confidence: 99%

CPredictor3.0: detecting protein complexes from PPI networks with expression data and functional annotations

Zhou

et al. 2017

BMC Syst Biol

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BackgroundEffectively predicting protein complexes not only helps to understand the structures and functions of proteins and their complexes, but also is useful for diagnosing disease and developing new drugs. Up to now, many methods have been developed to detect complexes by mining dense subgraphs from static protein-protein interaction (PPI) networks, while ignoring the value of other biological information and the dynamic properties of cellular systems.ResultsIn this paper, based on our previous works CPredictor and CPredictor2.0, we present a new method for predicting complexes from PPI networks with both gene expression data and protein functional annotations, which is called CPredictor3.0. This new method follows the viewpoint that proteins in the same complex should roughly have similar functions and are active at the same time and place in cellular systems. We first detect active proteins by using gene express data of different time points and cluster proteins by using gene ontology (GO) functional annotations, respectively. Then, for each time point, we do set intersections with one set corresponding to active proteins generated from expression data and the other set corresponding to a protein cluster generated from functional annotations. Each resulting unique set indicates a cluster of proteins that have similar function(s) and are active at that time point. Following that, we map each cluster of active proteins of similar function onto a static PPI network, and get a series of induced connected subgraphs. We treat these subgraphs as candidate complexes. Finally, by expanding and merging these candidate complexes, the predicted complexes are obtained.We evaluate CPredictor3.0 and compare it with a number of existing methods on several PPI networks and benchmarking complex datasets. The experimental results show that CPredictor3.0 achieves the highest F1-measure, which indicates that CPredictor3.0 outperforms these existing method in overall.ConclusionCPredictor3.0 can serve as a promising tool of protein complex prediction.

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Section: Discussionmentioning

confidence: 99%

CPredictor3.0: detecting protein complexes from PPI networks with expression data and functional annotations

Zhou

et al. 2017

BMC Syst Biol

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“…Metagenomic Profilers. SOTA taxonomic profilers take one or a combination of three following directions to improve the accuracy and/or execution time of profiling: (1) Reference database's size reduction with pre-alignment filters [2,90] or heuristics for taxonomic classification [34,40,46,70,85]. (2) Post species-level classification presence and abundance estimation heuristics [40,41,51].…”

Section: Related Workmentioning

confidence: 99%

“…Many recent works have improved the speed and/or accuracy of taxonomic profiling by various means, e.g., directly as heuristics in pre-and post-processing steps of profiling [40,41], indirectly as pre-alignment filters [2] or innovative hardware designs for alignment [4,16,34]. However, the memory bandwidth and the (limited) cache capacities remain the two main bottlenecks even in these approaches [33,83].…”

Section: Introductionmentioning

confidence: 99%

KrakenOnMem

Shahroodi

Zahedi

Singh

et al. 2022

Proceedings of the 36th ACM International Conference on Supercomputing

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State-of-the-art taxonomic profilers that comprise the first step in larger-context metagenomic studies have proven to be computationally intensive, i.e., while accurate, they come at the cost of high latency and energy consumption. Table Lookup operation is a primary bottleneck of today's profilers. In this paper, we first propose TL-PIM, a hardware accelerator based on the processingin-memory (PIM) paradigm to accelerate Table Lookup. TL-PIM leverages the in-memory compute capability of emerging memory technologies along with intelligent data mapping. Then, we integrate TL-PIM into Kraken2, a state-of-the-art metagenomic profiler, and build an HW/SW co-designed profiler, called KrakenOnMem. Results from a silicon-based prototype of our emerging memory validate the design and required operations on a smaller scale. Our large-scale calibrated simulations show that KrakenOnMem can provide an average of 61.3% speedup compared to original Kraken2 for end-to-end profiling. Additionally, our design improves the energy consumption by orders of magnitude compared to the original Kraken2 while incurring a negligible area overhead. CCS CONCEPTS• Hardware → Memory and dense storage; Bio-embedded electronics.

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“…There has been a limited amount of prior work on using GPUs for metagenomic read classification. cuCLARK [18] accelerates CLARK using CUDA but only achieves speedups between 3.2 and 6.6 while keeping the high memory consumption of CLARK. MetaBinG2 [32] applies a hidden Markov model to estimate the distance of a read to organisms but is over an order-of-magnitude slower compared to CLARK.…”

Section: Related Workmentioning

confidence: 99%

MetaCache-GPU: Ultra-Fast Metagenomic Classification

Kobus

Müller

Jünger

et al. 2021

50th International Conference on Parallel Processing

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The cost of DNA sequencing has dropped exponentially over the past decade, making genomic data accessible to a growing number of scientists. In bioinformatics, localization of short DNA sequences (reads) within large genomic sequences is commonly facilitated by constructing index data structures which allow for efficient querying of substrings. Recent metagenomic classification pipelines annotate reads with taxonomic labels by analyzing their 𝑘-mer histograms with respect to a reference genome database. CPU-based index construction is often performed in a preprocessing phase due to the relatively high cost of building irregular data structures such as hash maps. However, the rapidly growing amount of available reference genomes establishes the need for index construction and querying at interactive speeds. In this paper, we introduce MetaCache-GPU -an ultra-fast metagenomic short read classifier specifically tailored to fit the characteristics of CUDA-enabled accelerators. Our approach employs a novel hash table variant featuring efficient minhash fingerprinting of reads for locality-sensitive hashing and their rapid insertion using warp-aggregated operations. Our performance evaluation shows that MetaCache-GPU is able to build large reference databases in a matter of seconds, enabling instantaneous operability, while popular CPU-based tools such as Kraken2 require over an hour for index construction on the same data. In the context of an ever-growing number of reference genomes, MetaCache-GPU is the first metagenomic classifier that makes analysis pipelines with on-demand composition of largescale reference genome sets practical. The source code is publicly available at https://github.com/muellan/metacache.

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Accelerating metagenomic read classification on CUDA-enabled GPUs

Cited by 14 publications

References 18 publications

CPredictor3.0: detecting protein complexes from PPI networks with expression data and functional annotations

CPredictor3.0: detecting protein complexes from PPI networks with expression data and functional annotations

KrakenOnMem

MetaCache-GPU: Ultra-Fast Metagenomic Classification

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