BGSA: a bit-parallel global sequence alignment toolkit for multi-core and many-core architectures

Zhang, Jikai; Lan, Haidong; Chan, Yuandong; Shang, Yuan; Schmidt, Bertil; Liu, Weiguo

doi:10.1093/bioinformatics/bty930

Cited by 19 publications

(10 citation statements)

References 11 publications

(10 reference statements)

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“…Our algorithm is defined with unit costs for mismatches and indels. Other approaches have extended bit-parallelism to generalized integer costs (Loving et al , 2014; Zhang et al , 2018). Using generalized integer costs with our graph-based approach would require extending the column merge and changed minimum value operations to the different score representation used by the generalized integer cost algorithms.…”

Section: Discussionmentioning

confidence: 99%

“…To measure the overhead added by this, we ran the bitvector algorithm on a graph consisting of a linear chain of nodes with 200 000 bp in total and a 100 000 bp query. This linear graph mimicks sequence-to-sequence alignment and we compared our performance with optimized implementations of Myers’ algorithm from BGSA (Zhang et al , 2018) and Seqan (Döring et al , 2008) on the same sequences. We also tested whole-column processing for the linear graph to see how much of the difference is due to code optimization and how much is due to the different processing methods.…”

Section: Methodsmentioning

confidence: 99%

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Bit-parallel sequence-to-graph alignment

2019

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Motivation Graphs are commonly used to represent sets of sequences. Either edges or nodes can be labeled by sequences, so that each path in the graph spells a concatenated sequence. Examples include graphs to represent genome assemblies, such as string graphs and de Bruijn graphs, and graphs to represent a pan-genome and hence the genetic variation present in a population. Being able to align sequencing reads to such graphs is a key step for many analyses and its applications include genome assembly, read error correction and variant calling with respect to a variation graph. Results We generalize two linear sequence-to-sequence algorithms to graphs: the Shift-And algorithm for exact matching and Myers’ bitvector algorithm for semi-global alignment. These linear algorithms are both based on processing w sequence characters with a constant number of operations, where w is the word size of the machine (commonly 64), and achieve a speedup of up to w over naive algorithms. For a graph with |V| nodes and |E| edges and a sequence of length m, our bitvector-based graph alignment algorithm reaches a worst case runtime of O(|V|+⌈mw⌉|E| log w) for acyclic graphs and O(|V|+m|E| log w) for arbitrary cyclic graphs. We apply it to five different types of graphs and observe a speedup between 3-fold and 20-fold compared with a previous (asymptotically optimal) alignment algorithm. Availability and implementation https://github.com/maickrau/GraphAligner Supplementary information Supplementary data are available at Bioinformatics online.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Bit-parallel sequence-to-graph alignment

2019

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“…Parasail's code is also customized during compilation for the instruction set of the host architecture. Another implementation, BGSA [40], implements the Needleman-Wunsh algorithm with the Myers' bit-parallel technique but supports multi-core, task-level, and instruction-level parallelism for batch execution There are also approaches to speed-up edit distance by using specialized hardware, such as GPUs, FPGAs, or even custom-designed processors (for references, see the introduction in [3]). These result in orders-of-magnitude constant time speedups over their CPU counterparts in practice.…”

Section: State-of-the-art Implementations and Algorithmsmentioning

confidence: 99%

“…The algorithm we refer to here solves the edit distance computation problem as stated, without knowing in advance. In some papers (e.g [40]…”

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confidence: 99%

Theoretical analysis of edit distance algorithms: an applied perspective

Medvedev¹

2022

Preprint

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Given its status as a classic problem and its importance to both theoreticians and practitioners, edit distance provides an excellent lens through which to understand how the theoretical analysis of algorithms impacts practical implementations. From an applied perspective, the goals of theoretical analysis are to predict the empirical performance of an algorithm and to serve as a yardstick to design novel algorithms that perform well in practice [Roughgarden 2019]. In this paper, we systematically survey the types of theoretical analysis techniques that have been applied to edit distance and evaluate the extent to which each one has achieved these two goals. These techniques include traditional worst-case analysis, worst-case analysis parametrized by edit distance or entropy or compressibility, average-case analysis, semi-random models, and advice-based models. We find that the track record is mixed. On one hand, two algorithms widely used in practice have been born out of theoretical analysis and their empirical performance is captured well by theoretical predictions. On the other hand, all the algorithms developed using theoretical analysis as a yardstick since then have not had any practical relevance. We conclude by discussing the remaining open problems and how they can be tackled.

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“…Due to its importance and performance impact in many applications, multiple libraries have emerged implementing those algorithms. Among the most widely used, it is worth mentioning Edlib [48] and BGSA [49], fast CPU implementations of the Myers bit-vector algorithm (BPM) [37]; DAligner [50], an efficient implementation of the O(ND) algorithm [45]; and NVBio [51], a GPU accelerated library for sequence alignment.…”

Section: Background a Edit-distance Sequence Alignmentmentioning

confidence: 99%

Accelerating Edit-Distance Sequence Alignment on GPU Using the Wavefront Algorithm

Aguado-Puig¹,

Marco-Sola²,

Moure³

et al. 2022

IEEE Access

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Sequence alignment remains a fundamental problem with practical applications ranging from pattern recognition to computational biology. Traditional algorithms based on dynamic programming are hard to parallelize, require significant amounts of memory, and fail to scale for large inputs. This work presents eWFA-GPU, a GPU (graphics processing unit)-accelerated tool to compute the exact editdistance sequence alignment based on the wavefront alignment algorithm (WFA). This approach exploits the similarities between the input sequences to accelerate the alignment process while requiring less memory than other algorithms. Our implementation takes full advantage of the massive parallel capabilities of modern GPUs to accelerate the alignment process. In addition, we propose a succinct representation of the alignment data that successfully reduces the overall amount of memory required, allowing the exploitation of the fast shared memory of a GPU. Our results show that our GPU implementation outperforms by 3-9× the baseline edit-distance WFA implementation running on a 20 core machine. As a result, eWFA-GPU is up to 265 times faster than state-of-the-art CPU implementation, and up to 56 times faster than state-of-the-art GPU implementations.

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BGSA: a bit-parallel global sequence alignment toolkit for multi-core and many-core architectures

Cited by 19 publications

References 11 publications

Bit-parallel sequence-to-graph alignment

Bit-parallel sequence-to-graph alignment

Theoretical analysis of edit distance algorithms: an applied perspective

Accelerating Edit-Distance Sequence Alignment on GPU Using the Wavefront Algorithm

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