AStarix: Fast and Optimal Sequence-to-Graph Alignment

Ivanov, Pesho; Bichsel, Benjamin; Mustafa, Harun; Kahles, André; Rätsch, Gunnar; Vechev, Martin

doi:10.1007/978-3-030-45257-5_7

Cited by 15 publications

(11 citation statements)

References 37 publications

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“…From a state ⟨ i, j ⟩ where a i = b j , it is sufficient to only consider the matching edge to ⟨ i +1, j +1⟩ (Allison, 1992; Ivanov et al ., 2020), and ignore the insertion and deletion edges to ⟨ i, j +1⟩ and ⟨ i +1, j ⟩. During alignment, we greedily match as many letters as possible within the current seed before inserting only the last open state in the priority queue, but we do not cross seed boundaries in order to not interfere with match pruning.…”

Section: Pseudocodementioning

confidence: 99%

Exact global alignment using A* with chaining seed heuristic and match pruning

Koerkamp

Ivanov

2022

Preprint

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Motivation. Sequence alignment has been a core problem in computational biology for the last half-century. It is an open problem whether exact pairwise alignment is possible in linear time for related sequences (Medvedev, 2022b). Methods. We solve exact global pairwise alignment with respect to edit distance by using the A* shortest path algorithm on the edit graph. In order to efficiently align long sequences with high error rate, we extend the seed heuristic for A* (Ivanov et al., 2022) with match chaining, inexact matches, and the novel match pruning optimization. We prove the correctness of our algorithm and provide an efficient implementation in A*PA. Motivation. Sequence alignment has been a core problem in computational biology for the last half-century. It is an open problem whether exact pairwise alignment is possible in linear time for related sequences (Medvedev, 2022b). Methods. We solve exact global pairwise alignment with respect to edit distance by using the A* shortest path algorithm on the edit graph. In order to efficiently align long sequences with high error rate, we extend the seed heuristic for A* (Ivanov et al., 2022) with match chaining, inexact matches, and the novel match pruning optimization. We prove the correctness of our algorithm and provide an efficient implementation in A*PA. Results. We evaluate A*PA on synthetic data (random sequences of length n with uniform mutations with error rate e) and on real long ONT reads of human data. On the synthetic data with e=5% and n≤10^7 bp, A*PA exhibits a near-linear empirical runtime scaling of n^1.08 and achieves >250x speedup compared to the leading exact aligners EDLIB and BIWFA. Even for a high error rate of e=15%, the empirical scaling is n^1.28 for n≤107 bp. On two real datasets, A*PA is the fastest aligner for 58% of the alignments when the reads contain only sequencing errors, and for 17% of the alignments when the reads also include biological variation. Availability. github.com/RagnarGrootKoerkamp/astar-pairwise-aligner Contact. ragnar.grootkoerkamp@inf.ethz.ch, pesho@inf.ethz.ch

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Section: Pseudocodementioning

confidence: 99%

Exact global alignment using A* with chaining seed heuristic and match pruning

Koerkamp

Ivanov

2022

Preprint

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“…If allowing for approximate string matching under the minimum number of edits in the sequence, then the problem can be solved in quadratic time ( Amir et al 2000 ), and extensions to consider affine gap costs ( Jain et al 2020 ) and various practical optimizations were developed later. These practical optimizations were implemented into fast exact aligners such as ( Jain et al 2019 ), ( Rautiainen et al 2019 , Rautiainen and Marschall 2020 ), and ( Ivanov et al 2020 , 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Chaining for accurate alignment of erroneous long reads to acyclic variation graphs

Cáceres

Salmela

et al. 2023

Bioinformatics

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Motivation Aligning reads to a variation graph is a standard task in pangenomics, with downstream applications such as improving variant calling. While the vg toolkit (Garrison et al., 2018) is a popular aligner of short reads, GraphAligner (Rautiainen and Marschall, 2020) is the state-of-the-art aligner of erroneous long reads. GraphAligner works by finding candidate read occurrences based on individually extending the best seeds of the read in the variation graph. However, a more principled approach recognized in the community is to co-linearly chain multiple seeds. Results We present a new algorithm to co-linearly chain a set of seeds in a string labeled acyclic graph, together with the first efficient implementation of such a co-linear chaining algorithm into a new aligner of erroneous long reads to acyclic variation graphs, GraphChainer. We run experiments aligning real and simulated PacBio CLR reads with average error rates 15% and 5%. Compared to GraphAligner, GraphChainer aligns 12% to 17% more reads, and 21% to 28% more total read length, on real PacBio CLR reads from human chromosomes 1, 22 and the whole human pangenome. On both simulated and real data, GraphChainer aligns between 95% and 99% of all reads, and of total read length. We also show that minigraph (Li et al., 2020) and minichain (Chandra and Jain, 2023) obtain an accuracy of less than 60% on this setting. Availability GraphChainer is freely available at https://github.com/algbio/GraphChainer. The datasets and evaluation pipeline can be reached from the previous address. Supplementary information Supplementary data are available at Bioinformatics online.

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“…In essence, it is a modified version of the common sequence alignment with dynamic programming (DP) algorithms, where all the incoming edges connecting a certain node in the graph to other nodes are considered while calculating the cell’s score to find the best path of the sequence through the graph. In recent years, several tools have been introduced to perform sequence-to-graph alignments with better speeds and accuracy ( Ivanov et al 2020 , Rautiainen and Marschall 2020 , Sirén et al 2021 ).…”

Section: Introductionmentioning

confidence: 99%

`PanPA`: generation and alignment of panproteome graphs

Dabbaghie,

Srikakulam,

Marschall

et al. 2023

Bioinformatics Advances

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Motivation Compared to eukaryotes, prokaryote genomes are more diverse through different mechanisms, including a higher mutation rate and horizontal gene transfer. Therefore, using a linear representative reference can cause a reference bias. Graph-based pangenome methods have been developed to tackle this problem. However, comparisons in DNA space are still challenging due to this high diversity. In contrast, amino acid sequences have higher similarity due to evolutionary constraints, whereby a single amino acid may be encoded by several synonymous codons. Coding regions cover the majority of the genome in prokaryotes. Thus, panproteomes present an attractive alternative leveraging the higher sequence similarity while not losing much of the genome in non-coding regions. Results We present PanPA, a method that takes a set of multiple sequence alignments of protein sequences, indexes them, and builds a graph for each multiple sequence alignment. In the querying step, it can align DNA or amino acid sequences back to these graphs. We first showcase that PanPA generates correct alignments on a panproteome from 1350 Escherichia coli. To demonstrate that panproteomes allow comparisons at longer phylogenetic distances, we compare DNA and protein alignments from 1073 Salmonella enterica assemblies against E.coli reference genome, pangenome, and panproteome using BWA, GraphAligner, and PanPA, respectively; with PanPA aligning around 22% more sequences. We also aligned a DNA short-reads whole genome sequencing (WGS) sample from S.enterica against the E.coli reference with BWA and the panproteome with PanPA, where PanPA was able to find alignment for 68% of the reads compared to 5% with BWA. Availalability and implementation PanPA is available at https://github.com/fawaz-dabbaghieh/PanPA.

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AStarix: Fast and Optimal Sequence-to-Graph Alignment

Cited by 15 publications

References 37 publications

Exact global alignment using A* with chaining seed heuristic and match pruning

Exact global alignment using A* with chaining seed heuristic and match pruning

Chaining for accurate alignment of erroneous long reads to acyclic variation graphs

`PanPA`: generation and alignment of panproteome graphs

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AStarix: Fast and Optimal Sequence-to-Graph Alignment

Cited by 15 publications

References 37 publications

Exact global alignment using A* with chaining seed heuristic and match pruning

Exact global alignment using A* with chaining seed heuristic and match pruning

Chaining for accurate alignment of erroneous long reads to acyclic variation graphs

PanPA: generation and alignment of panproteome graphs

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`PanPA`: generation and alignment of panproteome graphs