Sequence to graph alignment using gap-sensitive co-linear chaining

Chandra, Ghanshyam; Jain, Chirag

doi:10.1101/2022.08.29.505691

Cited by 8 publications

(44 citation statements)

References 59 publications

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“…The chaining algorithm we present does not take into account any genomic coordinate or graph traversal information, as is typically done with co-linear chaining algorithms [39,33,2,42,9]. Since coordinates are only defined for the input sequences, applying this technique for coordinates assigned to contigs from low-coverage assembly graphs would produce short alignments, as demonstrated by minigraph during our evaluation.…”

Section: Discussionmentioning

confidence: 99%

“…Finally, anchor extension is the alignment of the query to the graph via forward and backward search from the ends of each anchor. A common anchor filtration method is co-linear chaining [38,39,2,33,42,9], a dynamic programming algorithm that finds high-scoring anchor chains. A chain is a series of anchors that appear in the correct order with respect to the query such that each anchor can reach the subsequent anchor in the chain via graph traversal.…”

Section: Introductionmentioning

confidence: 99%

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Label-guided seed-chain-extend alignment on annotated De Bruijn graphs

Mustafa

Karasikov

Rätsch

et al. 2022

Preprint

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The amount of data stored in genomic sequence databases is growing exponentially, far exceeding traditional indexing strategies' processing capabilities. Many recent indexing methods organize sequence data into a sequence graph to succinctly represent large genomic data sets from reference genome and sequencing read set databases. These methods typically use De Bruijn graphs as the graph model or the underlying index model, with auxiliary graph annotation data structures to associate graph nodes with various metadata. Examples of metadata can include a node's source samples (called labels), genomic coordinates, expression levels, etc. An important property of these graphs is that the set of sequences spelled by graph walks is a superset of the set of input sequences. Thus, when aligning to graphs indexing samples derived from low-coverage sequencing sets, sequence information present in many target samples can compensate for missing information resulting from a lack of sequence coverage. Aligning a query against an entire sequence graph (as in traditional sequence-to-graph alignment) using state-of-the-art algorithms can be computationally intractable for graphs constructed from thousands of samples, potentially searching through many non-biological combinations of samples before converging on the best alignment. To address this problem, we propose a novel alignment strategy called multi-label alignment (MLA) and an algorithm implementing this strategy using annotated De Bruijn graphs within the MetaGraph framework, called MetaGraph-MLA. MLA extends current sequence alignment scoring models with additional label change operations for incorporating mixtures of samples into an alignment, penalizing mixtures that are dissimilar in their sequence content. To overcome disconnects in the graph that result from a lack of sequencing coverage, we further extend our graph index to utilize a variable-order De Bruijn graph and introduce node length change as an operation. In this model, traversal between nodes that share a suffix of length < k-1 acts as a proxy for inserting nodes into the graph. MetaGraph-MLA constructs an MLA of a query by chaining single-label alignments using sparse dynamic programming. We evaluate MetaGraph-MLA on simulated data against state-of-the-art sequence-to-graph aligners. We demonstrate increases in alignment lengths for simulated viral Illumina-type (by 6.5%), PacBio CLR-type (by 6.2%), and PacBio CCS-type (by 6.7%) sequencing reads, respectively, and show that the graph walks incorporated into our MLAs originate predominantly from samples of the same strain as the reads' ground-truth samples. We envision MetaGraph-MLA as a step towards establishing sequence graph tools for sequence search against a wide variety of target sequence types.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Label-guided seed-chain-extend alignment on annotated De Bruijn graphs

Mustafa

Karasikov

Rätsch

et al. 2022

Preprint

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“…Unfortunately, even finding an exact occurrence of a query string as a subpath in a graph is a conditionally hard problem [12,13]: only quadratic time dynamic programming solutions are known and faster algorithms would contradict the Strong Exponential Time Hypothesis (SETH). Due to these difficulties, researchers have aimed at finding parameterized solutions to such alignment problems [3,10,9,22,21,6,24,26], and/or separating the task into finding short exact occurrences (anchors) and then chaining them into longer matches. Colinear chaining has been effectively applied as a heuristic to the original alignment problem.…”

Section: Introductionmentioning

confidence: 99%

“…For the case with two strings as input, a recent formulation of co-linear chaining [19] captures unit cost edit distance. There has been an attempt to extend the results to graphs considering gap costs [6], but it appears difficult to make such formulation fully symmetric (due to there being exponential many paths between two anchors).…”

Section: Introductionmentioning

confidence: 99%

Chaining of Maximal Exact Matches in Graphs

Rizzo¹,

Cáceres²,

Mäkinen³

2023

Preprint

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We study the problem of finding maximal exact matches (MEMs) between a query string Q and a labeled directed acyclic graph (DAG) G = (V, E, ℓ) and subsequently co-linearly chaining these matches. We show that it suffices to compute MEMs between node labels and Q (node MEMs) to encode full MEMs. Node MEMs can be computed in linear time and we show how to co-linearly chain them to solve the Longest Common Subsequence (LCS) problem between Q and G. Our chaining algorithm is the first to consider a symmetric formulation of the chaining problem in graphs and runs inwhere k is the width (minimum number of paths covering the nodes) of G, and N is the number of node MEMs.We then consider the problem of finding MEMs when the input graph is an indexable elastic founder graph (subclass of labeled DAGs studied by Equi et al., Algorithmica 2022). For arbitrary input graphs, the problem cannot be solved in truly sub-quadratic time under SETH (Equi et al., ICALP 2019). We show that we can report all MEMs between Q and an indexable elastic founder graph in time O(nH 2 + m + Mκ), where n is the total length of node labels, H is the maximum number of nodes in a block of the graph, m = |Q|, and Mκ is the number of MEMs of length at least κ.The results extend to the indexing problem, where the graph is preprocessed and a set of queries is processed as a batch.

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“…Since the time complexity of optimal sequence-to-graph alignment grows linearly with the number of edges in the graph [20,16], many approaches instead follow an approximate seed-and-extend strategy [2], which operates in four main steps: i) seed extraction , which in its simplest form involves finding all substrings with a certain length, ii) seed anchoring , finding matching nodes in the graph, iii) seed filtration , often involving clustering [9,37] or co-linear chaining [25,1,32,8] of seeds, and iv) seed extension , involving performing semi-global pairwise sequence alignment forwards and backwards from each anchored seed [28]. We will review the usage of exact seeds utilized in tools such as vg[15] and G raph A ligner [37] and discuss their limitations in a high mutation-rate setting.…”

Section: Introductionmentioning

confidence: 99%

Aligning Distant Sequences to Graphs using Long Seed Sketches

Joudaki

Meterez

Mustafa

et al. 2022

Preprint

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Sequence-to-graph alignment is an important step in applications such as variant genotyping, read error correction and genome assembly. When a query sequence requires a substantial number of edits to align, approximate alignment tools that follow the seed-and-extend approach require shorter seeds to get any matches. However, in large graphs with high variation, relying on a shorter seed length leads to an exponential increase in spurious matches. We propose a novel seeding approach relying on long inexact matches instead of short exact matches. We demonstrate experimentally that our approach achieves a better time-accuracy trade-off in settings with up to a 25% mutation rate. We achieve this by sketching a subset of graph nodes and storing them in a K-nearest neighbor index. While sketches are more robust to indels, finding the nearest neighbor of a sketch in a high-dimensional space is more computationally challenging than finding exact seeds. We demonstrate that if we store sketch vectors in a K-nearest neighbor index, we can circumvent the curse of dimensionality. Our long sketch-based seed scheme contrasts existing approaches and highlights the important role that tensor sketching can play in bioinformatics applications. Our proposed seeding method and implementation have several advantages: i) We empirically show that our method is efficient and scales to graphs with 1 billion nodes, with time and memory requirements for preprocessing growing linearly with graph size and query time growing quasi-logarithmically with query length. ii) For queries with an edit distance of 25% relative to their length, on the 1 billion node graph, longer sketch-based seeds yield a 4x increase in recall compared to exact seeds. iii) Conceptually, our seeder can be incorporated into other aligners, proposing a novel direction for sequence-to-graph alignment. The implementation is available at: https://github.com/ratschlab/tensor-sketch-alignment.

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Sequence to graph alignment using gap-sensitive co-linear chaining

Cited by 8 publications

References 59 publications

Label-guided seed-chain-extend alignment on annotated De Bruijn graphs

Label-guided seed-chain-extend alignment on annotated De Bruijn graphs

Chaining of Maximal Exact Matches in Graphs

Aligning Distant Sequences to Graphs using Long Seed Sketches

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