SPAligner: Alignment of Long Diverged Molecular Sequences to Assembly Graphs

Dvorkina, Tatiana; Antipov, Dmitry; Korobeynikov, Anton; Nurk, Sergey

doi:10.1101/744755

Cited by 3 publications

(4 citation statements)

References 38 publications

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“…All practical long read to DAG aligners that scale to large genomes rely on seed–filter–extend methodology [8,22,25,27,34]. The first step is to find a set of anchors which indicate short exact matches, e.g., k -mer or minimizer matches, between substrings of a sequence to subpaths in a DAG.…”

Section: Introductionmentioning

confidence: 99%

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

Chandra

Jain

2022

Preprint

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Co-linear chaining is a widely used technique in sequence alignment tools that follow seed-filter-extend methodology. It is a mathematically rigorous approach to combine small exact matches. For co-linear chaining between two sequences, efficient subquadratic-time chaining algorithms are well-known for linear, concave and convex gap cost functions [Eppstein et al. JACM'92]. However, developing extensions of chaining algorithms for DAGs (directed acyclic graphs) has been challenging. Recently, a new sparse dynamic programming framework was introduced that exploits small path cover of pangenome reference DAGs, and enables efficient chaining [Makinen et al. TALG'19, RECOMB'18]. However, the underlying problem formulation did not consider gap cost which makes chaining less effective in practice. To address this, we develop novel problem formulations and optimal chaining algorithms that support a variety of gap cost functions. We demonstrate empirically the ability of our provably-good chaining implementation to align long reads more precisely in comparison to existing aligners. For mapping simulated long reads from human genome to a pangenome DAG of 95 human haplotypes, we achieve 98.7% precision while leaving < 2% reads unmapped. Implementation: https://github.com/at-cg/minichain

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Section: Introductionmentioning

confidence: 99%

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

Chandra

Jain

2022

Preprint

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“…In particular, their ability to more scalably represent diverse collections of sequences partially addresses the reference bias issues common in current standard analysis protocols [10]. To allow for sensitive sequence search in this new domain, many tools generalize pairwise sequence-to-sequence alignment (i.e., computing the minimum edit distance or the maximum similarity score between a query and a target sequence) algorithms to align queries against sequence graphs [1,2,54,33,29,16,37,51,52,16]. These sequence-to-graph alignment tools perform a search on the graph to align a query against the spellings of one or more walks.…”

Section: Introductionmentioning

confidence: 99%

“…These sequence-to-graph alignment tools perform a search on the graph to align a query against the spellings of one or more walks. Depending on their choice of scoring model, these tools can be categorized into those that use the graph as a compact representation of disjoint reference sequences for sequence-to-sequence alignment [1,2,33], those that align to species pangenomes [20,54,50,41], or those that treat the entire graph as a single sample [30,24,40,16,29,51,52,16]. Throughout this work, we use the convention that alignment scores measure sequence similarity, and hence, higher values are better.…”

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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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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“…The two basic strategies for assembly are reference based methods that align reads to a reference genome [ 11 – 16 ] and de-novo methods that use the overlaps among the reads themselves for assembly, without the need for a reference sequence [ 17 – 22 ]. Tools that align reads to genome graphs, such as, GraphAligner [ 23 ] and SPAligner [ 24 ], are emerging and can also be used for producing assemblies. In this manuscript, we present two tools: SAUTE (Sequence Assembly Using Target Enrichment) and SAUTE_PROT .…”

Section: Introductionmentioning

confidence: 99%

SAUTE: sequence assembly using target enrichment

Souvorov

Agarwala

2021

BMC Bioinformatics

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Background Illumina is the dominant sequencing technology at this time. Short length, short insert size, some systematic biases, and low-level carryover contamination in Illumina reads continue to make assembly of repeated regions a challenging problem. Some applications also require finding multiple well supported variants for assembled regions. Results To facilitate assembly of repeat regions and to report multiple well supported variants when a user can provide target sequences to assist the assembly, we propose SAUTE and SAUTE_PROT assemblers. Both assemblers use de Bruijn graph on reads. Targets can be transcripts or proteins for RNA-seq reads and transcripts, proteins, or genomic regions for genomic reads. Target sequences are nucleotide and protein sequences for SAUTE and SAUTE_PROT, respectively. Conclusions For RNA-seq, comparisons with Trinity, rnaSPAdes, SPAligner, and SPAdes assembly of reads aligned to target proteins by DIAMOND show that SAUTE_PROT finds more coding sequences that translate to benchmark proteins. Using AMRFinderPlus calls, we find SAUTE has higher sensitivity and precision than SPAdes, plasmidSPAdes, SPAligner, and SPAdes assembly of reads aligned to target regions by HISAT2. It also has better sensitivity than SKESA but worse precision.

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SPAligner: Alignment of Long Diverged Molecular Sequences to Assembly Graphs

Cited by 3 publications

References 38 publications

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

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

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

SAUTE: sequence assembly using target enrichment

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