SegAlign: A Scalable GPU-Based Whole Genome Aligner

Goenka, Sneha D.; Turakhia, Yatish; Paten, Benedict; Horowitz, Mark

doi:10.1109/sc41405.2020.00043

Cited by 15 publications

(8 citation statements)

References 46 publications

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“…We already provide the SIMD implementation to calculate the hash values BLEND. We encourage implementing our mechanism for the applications that use seeds to find sequence similarity using processing-inmemory and near-data processing [102][103][104][105][106][107][108][109][110][111][112][113][114], GPUs [115][116][117], and FPGAs and ASICs [118][119][120][121][122][123] to exploit the massive amount of embarrassingly parallel bitwise operations in BLEND to find fuzzy seed matches. Third, we believe it is possible to apply the hashing technique we use in BLEND for many seeding techniques with a proper design.…”

Section: Discussionmentioning

confidence: 99%

BLEND: A Fast, Memory-Efficient, and Accurate Mechanism to Find Fuzzy Seed Matches in Genome Analysis

Fırtına

Park

Alser

et al. 2022

Preprint

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Generating the hash values of short subsequences, called seeds, enables quickly identifying similarities between genomic sequences by matching seeds with a single lookup of their hash values. However, these hash values can be used only for finding exact-matching seeds as the conventional hashing methods assign distinct hash values for different seeds, including highly similar seeds. Finding only exact-matching seeds causes either 1) increasing the use of the costly sequence alignment or 2) limited sensitivity. We introduce BLEND, the first efficient and accurate mechanism that can identify both exact-matching and highly similar seeds with a single lookup of their hash values, called fuzzy seeds matches. BLEND 1) utilizes a technique called SimHash, that can generate the same hash value for similar sets, and 2) provides the proper mechanisms for using seeds as sets with the SimHash technique to find fuzzy seed matches efficiently. We show the benefits of BLEND when used in read overlapping and read mapping. For read overlapping, BLEND is faster by 2.4x-83.9x (on average 19.3x), has a lower memory footprint by 0.9x-14.1x (on average 3.8x), and finds higher quality overlaps leading to accurate de novo assemblies than the state-of-the-art tool, minimap2. For read mapping, BLEND is faster by 0.8x-4.1x (on average 1.7x) than minimap2. Source code is available at https://github.com/CMU-SAFARI/BLEND.

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Section: Discussionmentioning

confidence: 99%

BLEND: A Fast, Memory-Efficient, and Accurate Mechanism to Find Fuzzy Seed Matches in Genome Analysis

Fırtına

Park

Alser

et al. 2022

Preprint

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“…Haley J. Abel 1 ,Lucinda L Antonacci-Fulton 2 , Mobin Asri 3 , Gunjan Baid 4 , Carl A. Baker 5 , Anastasiya Belyaeva 4 , Konstantinos Billis 6 , Guillaume Bourque 7,8,9 , Silvia Buonaiuto 10 , Andrew Carroll 4 , Mark JP Chaisson 11 , Pi-Chuan Chang 4 , Xian H. Chang 3 , Haoyu Cheng 12,13 , Justin Chu 12 , Sarah Cody 2 , Vincenza Colonna 10,14 , Daniel E. Cook 4 , Robert M. Cook-Deegan 15 , Omar E. Cornejo 16 , Mark Diekhans 3 , Daniel Doerr 17 , Peter Ebert 17 , Evan E. Eichler 5,18 , Jordan M. Eizenga 3 , Susan Fairley 6 , Olivier Fedrigo 19 , Adam L. Felsenfeld 20 , Xiaowen Feng 12,13 , Christian Fischer 14 , Paul Flicek 6 , Giulio Formenti 19 , Adam Frankish 6 , Robert S. Fulton 2 , Shilpa Garg 22 , Erik Garrison 14 , Carlos Garcia Giron 6 , Richard E. Green 23,24 , Cristian Groza 25 , Andrea Guarracino 26 , Leanne Haggerty 6 , Ira Hall 27,28 , William T Harvey 5 , Marina Haukness 3 , David Haussler 3,18 , Simon Heumos 29,30 , Glenn Hickey 3 , Kendra Hoekzema 5 , Thibaut Hourlier 6 , Kerstin Howe 31 , Miten Jain 32 , Erich D. Jarvis 33,18 , Hanlee P. Ji 34 , Alexey Kolesnikov 4 , Jan O. Korbel 35 , Jennifer Kordosky 5 , Sergey Koren 36 , HoJoon Lee 34 , Alexandra P. Lewis 5 , Heng Li 12,13 , Wen-Wei Liao…”

Section: Acknowledgementsmentioning

confidence: 99%

“…Progressive Cactus is a more recent tool for large vertebrate scale multiple alignments (Armstrong et al, 2020). It also uses LASTZ, or the GPU-accelerated successor SegAlign (Goenka et al, 2020), to perform pairwise alignments. However, it does so by progressively reconstructing ancestral sequences using a phylogenetic guide tree.…”

Section: Introductionmentioning

confidence: 99%

Pangenome Graph Construction from Genome Alignment with Minigraph-Cactus

Hickey

Monlong

Ebler

et al. 2022

Preprint

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Reference genomes provide mapping targets and coordinate systems but introduce biases when samples under study diverge sufficiently from them. Pangenome references seek to address this by storing a representative set of diverse haplotypes and their alignment, usually as a graph. Alternate alleles determined by variant callers can be used to construct pangenome graphs, but thanks to advances in long-read sequencing, high-quality phased assemblies are becoming widely available. Constructing a pangenome graph directly from assemblies, as opposed to variant calls, leverages the graph's ability to consistently represent variation at different scales and reduces biases introduced by reference-based variant calls. Pangenome construction in this way is equivalent to multiple genome alignment, which is extremely computationally demanding. Here we present the Minigraph-Cactus pangenome pipeline and demonstrate its ability to build a pangenome graph by aligning 90 human haplotypes. This tool was designed to build graphs with as much information as possible while still being practical for use with current mapping and genotyping tools. We show that this graph is useful both for studying variation within the input haplotypes, but also as a basis for mapping and variant calling. We also demonstrate that this method can apply to nonhuman data by showing improved mapping and variant detection sensitivity with a Drosophila melanogaster pangenome.

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“…With the advent of extremely powerful GPUs in recent decades has also come GPU adaptations to many alignment algorithms [128][129][130]. Notably, both MUMmer and LastZ have been fitted for GPU use [131,132], with the latter improvement yielding a consistent 13x speedup on a vertebrate dataset. While GPUs and large compute clusters, whose use has been made easier by workflow engines such as Toil [133] and Nextflow [134], may provide significant wall-clock time speedups in proportion to the resources available, they are only beneficial to those with the necessary resources.…”

Section: Area 2: Reducing Computational Requirementsmentioning

confidence: 99%

Multiple genome alignment in the telomere-to-telomere assembly era

et al. 2022

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With the arrival of telomere-to-telomere (T2T) assemblies of the human genome comes the computational challenge of efficiently and accurately constructing multiple genome alignments at an unprecedented scale. By identifying nucleotides across genomes which share a common ancestor, multiple genome alignments commonly serve as the bedrock for comparative genomics studies. In this review, we provide an overview of the algorithmic template that most multiple genome alignment methods follow. We also discuss prospective areas of improvement of multiple genome alignment for keeping up with continuously arriving high-quality T2T assembled genomes and for unlocking clinically-relevant insights.

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SegAlign: A Scalable GPU-Based Whole Genome Aligner

Cited by 15 publications

References 46 publications

BLEND: A Fast, Memory-Efficient, and Accurate Mechanism to Find Fuzzy Seed Matches in Genome Analysis

BLEND: A Fast, Memory-Efficient, and Accurate Mechanism to Find Fuzzy Seed Matches in Genome Analysis

Pangenome Graph Construction from Genome Alignment with Minigraph-Cactus

Multiple genome alignment in the telomere-to-telomere assembly era

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