BLVector: Fast BLAST-Like Algorithm for Manycore CPU With Vectorization

Gálvez, Sergio; Agostini, Federico; Caselli, Javier; Hernández, Pilar; Dorado, Gabriel

doi:10.3389/fgene.2021.618659

Cited by 8 publications

(4 citation statements)

References 27 publications

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“…They have included clock-frequency increases for the Central Processing Units (CPU) of computers, node reduction, multicore (a few), many core (higher number) and integration through System on a Chip (SoC) with unified memory between the CPU and Graphics Processing Units (GPU). Machine learning, artificial intelligence, dedicated artificial neural network (ANN) analyses, and massive parallelism are enhanced using multi-core architectures (Gálvez et al, 2016(Gálvez et al, , 2021. All this has contributed to our ability to sequence de novo, assemble, and annotate extremely large and complex genomes.…”

Section: Wheat Reference-genome and In Silico Bioinformaticsmentioning

confidence: 99%

Capturing Wheat Phenotypes at the Genome Level

et al. 2022

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Recent technological advances in next-generation sequencing (NGS) technologies have dramatically reduced the cost of DNA sequencing, allowing species with large and complex genomes to be sequenced. Although bread wheat (Triticum aestivum L.) is one of the world’s most important food crops, efficient exploitation of molecular marker-assisted breeding approaches has lagged behind that achieved in other crop species, due to its large polyploid genome. However, an international public–private effort spanning 9 years reported over 65% draft genome of bread wheat in 2014, and finally, after more than a decade culminated in the release of a gold-standard, fully annotated reference wheat-genome assembly in 2018. Shortly thereafter, in 2020, the genome of assemblies of additional 15 global wheat accessions was released. As a result, wheat has now entered into the pan-genomic era, where basic resources can be efficiently exploited. Wheat genotyping with a few hundred markers has been replaced by genotyping arrays, capable of characterizing hundreds of wheat lines, using thousands of markers, providing fast, relatively inexpensive, and reliable data for exploitation in wheat breeding. These advances have opened up new opportunities for marker-assisted selection (MAS) and genomic selection (GS) in wheat. Herein, we review the advances and perspectives in wheat genetics and genomics, with a focus on key traits, including grain yield, yield-related traits, end-use quality, and resistance to biotic and abiotic stresses. We also focus on reported candidate genes cloned and linked to traits of interest. Furthermore, we report on the improvement in the aforementioned quantitative traits, through the use of (i) clustered regularly interspaced short-palindromic repeats/CRISPR-associated protein 9 (CRISPR/Cas9)-mediated gene-editing and (ii) positional cloning methods, and of genomic selection. Finally, we examine the utilization of genomics for the next-generation wheat breeding, providing a practical example of using in silico bioinformatics tools that are based on the wheat reference-genome sequence.

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Section: Wheat Reference-genome and In Silico Bioinformaticsmentioning

confidence: 99%

Capturing Wheat Phenotypes at the Genome Level

et al. 2022

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“…Advancements made possible by a dualstrategy approach focused on hardware and software have included clock-frequency increases, node reduction, multicores and integration through System on a Chip (SoC) with unified memory between the Central Processing Unit (CPU), Graphics Processing Units (GPU). machine learning, artificial intelligence, Artificial Neural Network (ANN) analysis, and massive parallelism allowed by multi-core and many-core architectures [274,275]. All this has contributed to our ability to sequence de novo, assemble, and annotate extremely large and complex genomes.…”

Section: Wheat Reference Genome and In Silico Bioinformaticsmentioning

confidence: 99%

Wheat genomics and breeding: bridging the gap.

et al. 2021

Self Cite

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Recent technological advances in next-generation sequencing (NGS) technologies have dramatically reduced the cost of DNA sequencing, allowing species with large and complex genomes to be sequenced. Although bread wheat (Triticum aestivum L.) is one of the world's most important food crops, until very recently efficient exploitation of molecular marker-assisted breeding approaches has lagged behind that achieved in other crop species due to its large polyploid genome. However, an international public-private effort spanning nine years reported over 65% draft genome of bread wheat in 2014, and finally, after more than a decade culminated in the release of a gold-standard, fully annotated reference wheat genome assembly in 2017. Shortly thereafter, in 2020, the genome of assemblies of additional fifteen global wheat accessions were released. Wheat has now entered into the pan-genomic era where basic resources can be efficiently exploited. Wheat genotyping with a few hundred markers has been replaced by genotyping arrays capable of genotyping hundreds of wheat lines using thousands of markers, providing fast, relatively inexpensive, and reliable data for exploitation in wheat breeding. These advances have opened up a new horizon for marker-assisted selection (MAS) and genomic selection (GS) in wheat. Herein, we review the advances and perspectives in wheat genetics and genomics, with a focus on key traits including grain yield, yield-related traits, end-use quality and resistance to biotic and abiotic stresses. We also enlisted several reported candidate and cloned candidate genes responsible for the aforesaid traits of interest. Furthermore, we report on the improvement in the aforementioned quantitative traits through the use of (i) clustered regularly interspaced short palindromic repeats/CRISPR-associated protein 9 (CRISPR/Cas9) mediated gene-editing, (ii) positional cloning methods, and of genomic selection. Finally, we make recommendations on the utilization of genomics for the next-generation wheat breeding and provide a practical example of using the latest, in silico bioinformatics tools that were based on the wheat reference genome sequence.

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“…One such strategy leverages the SIMD (Single instruction, multiple data) vector instructions available on all modern CPUs [22]. SIMD sequence alignment implementations [23][24][25][26] have achieved impressive speed gains. This technique serves as a core part of the acceleration strategy used in popular pHMM alignment tools [21,27,28].…”

Section: Introductionmentioning

confidence: 99%

An FPGA-based hardware accelerator supporting sensitive sequence homology filtering with profile hidden Markov models

Anderson,

Wheeler

2023

Preprint

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Sequence alignment lies at the heart of genome sequence annotation. While the BLAST suite of alignment tools has long held an important role in alignment-based sequence database search, greater sensitivity is achieved through the use of profile hidden Markov models (pHMMs). The Forward algorithm that provides much of pHMMs' sensitivity is relatively slow, motivating extensive efforts to increase speed. Numerous researchers have devised methods to improve pHMM alignment speed using hardware accelerators such as graphics processing units (GPUs) and field programmable gate arrays (FPGAs). Here, we describe an FPGA hardware accelerator for a key bottleneck step in the analysis pipeline employed by the popular pHMM aligment tool, HMMER. HMMER accelerates pHMM Forward alignment by screening most sequence with a series of filters that rapidly approximate the result of computing full Forward alignment. The first of these filters, the Single Segment ungapped Viterbi (SSV) algorithm, is designed to filter out 98% of non-related inputs and accounts for ~70% of the overall runtime of the DNA search tool nhmmer in common use cases. SSV is an ideal target for hardware acceleration due to its limited data dependency structure. We present Hardware Accelerated single segment Viterbi Additional Coprocessor (HAVAC), an FPGA-based hardware accelerator for the SSV algorithm. The core HAVAC kernel calculates the SSV matrix at 1739 GCUPS on a Xilinx Alveo U50 FPGA accelerator card, ~227x faster than the optimized SSV implementation in nhmmer. Accounting for PCI-e data transfer data processing, HAVAC is 65x faster than nhmmer's SSV with one thread and 35x faster than nhmmer with four threads, and uses ~31% the energy of a traditional high end Intel CPU. Because these computations are performed on a co-processor, the host CPU remain free to simultaneously compute downstream pHMM alignment and later post-processing.

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BLVector: Fast BLAST-Like Algorithm for Manycore CPU With Vectorization

Cited by 8 publications

References 27 publications

Capturing Wheat Phenotypes at the Genome Level

Capturing Wheat Phenotypes at the Genome Level

Wheat genomics and breeding: bridging the gap.

An FPGA-based hardware accelerator supporting sensitive sequence homology filtering with profile hidden Markov models

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