Aligning sequences to general graphs in O(V + mE) time

Rautiainen, Mikko; Marschall, Tobias

doi:10.1101/216127

Cited by 32 publications

(39 citation statements)

References 27 publications

(35 reference statements)

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“…We formalize a reference graph as a tuple G r = (V r , E r ) of nodes V r and directed, labeled edges E r ⊆ V r × V r × Σ, where the alphabet Σ = {A, C, G, T} represents the four different nucleotides. Note that in contrast to sequence graphs [28], we label edges instead of nodes.…”

Section: Task Description: Alignment To Reference Graphsmentioning

confidence: 99%

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AStarix: Fast and Optimal Sequence-to-Graph Alignment

Ivanov

Bichsel

Mustafa

et al. 2020

Preprint

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We present an algorithm for the optimal alignment of sequences to genome graphs. It works by phrasing the edit distance minimization task as finding a shortest path on an implicit alignment graph. To find a shortest path, we instantiate the A paradigm with a novel domain-specific heuristic function that accounts for the upcoming subsequence in the query to be aligned, resulting in a provably optimal alignment algorithm called AStarix. Experimental evaluation of AStarix shows that it is 1-2 orders of magnitude faster than state-of-the-art optimal algorithms on the task of aligning Illumina reads to reference genome graphs. Implementations and evaluations are available at https://github.com/eth-sri/astarix.

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Section: Task Description: Alignment To Reference Graphsmentioning

confidence: 99%

“…. , |q|} (equivalent to [28]). Traversing a state v, i ∈ V q a represents the alignment of the first i query characters ending at node v. In particular, query position i = 0 indicates that we have not yet matched any letters from the query.…”

Section: Alignment On Editmentioning

confidence: 99%

AStarix: Fast and Optimal Sequence-to-Graph Alignment

Ivanov

Bichsel

Mustafa

et al. 2020

Preprint

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“…More efficient algorithms in terms of worst case time complexity have been earlier suggested in [18,19] for an important case of edit distance or Levenshtein distance (µ and σ equal to 1). Supplement Section "Shortest paths search in binary-weighted graphs" presents a simple modification of the basic approach described above achieving the same time complexity of O(|G| · |Sub|).…”

Section: Sequence To Graph Alignment Via Alignment Graphsmentioning

confidence: 99%

“…Again, while we only consider linear gap penalties with coefficient σ (in our experiments we used σ = 5) here, affine gap penalties can also be implemented without significant increase of running time or memory footprint [19].…”

Section: Alignment Of Amino-acid Sequencesmentioning

confidence: 99%

SPAligner: Alignment of Long Diverged Molecular Sequences to Assembly Graphs

Dvorkina

Antipov

Korobeynikov

et al. 2019

Preprint

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Background: Graph-based representation of genome assemblies has been recently used in different applications -from gene finding to haplotype separation. While most of these applications are based on the alignment of molecular sequences to assembly graphs, existing software tools for finding such alignments have important limitations. Results: We present a novel SPAligner tool for aligning long diverged molecular sequences to assembly graphs and demonstrate that SPAligner is an efficient solution for mapping third generation sequencing data and can also facilitate the identification of known genes in complex metagenomic datasets. Conclusions: Our work will facilitate accelerating the development of graph-based approaches in solving sequence to genome assembly alignment problem. SPAligner is implemented as a part of SPAdes tools library and is available on https://github.com/ablab/spades/archive/spaligner-paper.zip. BackgroundMany popular short read assemblers [1, 2, 3] provide the user not only with a set of contig sequences, but also with assembly graphs, encoding the information on the potential adjacencies of the assembled sequences. Naturally arising problem of sequence-to-graph alignment has been a topic of many recent studies [4,5,6,7,8]. Identifying alignments of long error-prone reads (such as Pacbio and ONT reads) to assembly graphs is particularly important and has recently been applied to hybrid genome assembly [9,10], read error correction [11], and haplotype separation [12]. At the same time, the choice of the practical aligners supporting long nucleotide sequences is currently limited to vg [4] and GraphAligner [13], both of which are under active development. Moreover, to the best of our knowledge, no existing graph-based aligner supports alignment of amino acid sequences.Here we present the SPAligner (Saint Petersburg Aligner) tool for aligning long diverged molecular (both nucleotide and amino acid) sequences against assembly graphs produced by the popular short-read assemblers. The project stemmed from our previous efforts on the long-read alignment within the hybridSPAdes assembler [9]. Our benchmarks on

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“…A few recent works have investigated extending Burrows-Wheeler-Transform-based indexing to sequence DAGs [41] and de-Bruijn graphs [2,29,40]. Similarly, there exist studies that have explored extension of the classic sequence-to-sequence alignment routines to graphs [19,20,30,36]. In our recent work [19], we presented new complexity results and algorithms for the alignment problem using general sequence-labeled graphs.…”

Section: Introductionmentioning

confidence: 99%

Validating Paired-end Read Alignments in Sequence Graphs

Jain

Zhang

Dilthey

et al. 2019

Preprint

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Graph based non-linear reference structures such as variation graphs and colored de Bruijn graphs enable incorporation of full genomic diversity within a population. However, transitioning from a simple string-based reference to graphs requires addressing many computational challenges, one of which concerns accurately mapping sequencing read sets to graphs. Paired-end Illumina sequencing is a commonly used sequencing platform in genomics, where the paired-end distance constraints allow disambiguation of repeats. Many recent works have explored provably good index-based and alignment-based strategies for mapping individual reads to graphs. However, validating distance constraints efficiently over graphs is not trivial, and existing sequence to graph mappers rely on heuristics. We introduce a mathematical formulation of the problem, and provide a new algorithm to solve it exactly. We take advantage of the high sparsity of reference graphs, and use sparse matrixmatrix multiplications (SpGEMM) to build an index which can be queried efficiently by a mapping algorithm for validating the distance constraints. Effectiveness of the algorithm is demonstrated using real reference graphs, including a human MHC variation graph, and a pan-genome de-Bruijn graph built using genomes of 20 B. anthracis strains. While the one-time indexing time can vary from a few minutes to a few hours using our algorithm, answering a million distance queries takes less than a second. AcknowledgementsThe authors thank Abdurrahman Yasar, Siva Rajamanickam and Srinivas Eswar for sharing their insights on sparse matrix manipulations. Problem FormulationDefinition 1. Sequence Graph: A sequence graph G(V, E) is a directed graph with vertices V and edges E, where each vertex v ∈ V is labeled with a character from alphabet Σ.

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Aligning sequences to general graphs in O(V + mE) time

Cited by 32 publications

References 27 publications

AStarix: Fast and Optimal Sequence-to-Graph Alignment

AStarix: Fast and Optimal Sequence-to-Graph Alignment

SPAligner: Alignment of Long Diverged Molecular Sequences to Assembly Graphs

Validating Paired-end Read Alignments in Sequence Graphs

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