Population-scale detection of non-reference sequence variants using colored de Bruijn graphs

Krannich, Thomas; White, W. Timothy J.; Niehus, Sebastian; Holley, Guillaume; Halldórsson, Bjarni V.; Kehr, Birte

doi:10.1093/bioinformatics/btab749

Cited by 8 publications

(9 citation statements)

References 61 publications

(85 reference statements)

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“…The PopIns2 workflow ( Krannich et al 2022 ) was applied to detect the nonreference sequence of the data set from 898 animals. First, the assemble submodule was used to identify reads without high-quality alignment to the reference genome using default parameters.…”

Section: Methodsmentioning

confidence: 99%

Assembly of a pangenome for global cattle reveals missing sequences and novel structural variations, providing new insights into their diversity and evolutionary history

Zhou

Han

et al. 2022

Genome Res.

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A cattle pangenome representation was created based on the genome sequences of 898 cattle representing 57 breeds. The pangenome identified 83 Mb of sequence not found in the cattle reference genome, representing 3.1% novel sequence compared with the 2.71-Gb reference. A catalog of structural variants developed from this cattle population identified 3.3 million deletions, 0.12 million inversions, and 0.18 million duplications. Estimates of breed ancestry and hybridization between cattle breeds using insertion/deletions as markers were similar to those produced by single nucleotide polymorphism–based analysis. Hundreds of deletions were observed to have stratification based on subspecies and breed. For example, an insertion of a Bov-tA1 repeat element was identified in the first intron of the APPL2 gene and correlated with cattle breed geographic distribution. This insertion falls within a segment overlapping predicted enhancer and promoter regions of the gene, and could affect important traits such as immune response, olfactory functions, cell proliferation, and glucose metabolism in muscle. The results indicate that pangenomes are a valuable resource for studying diversity and evolutionary history, and help to delineate how domestication, trait-based breeding, and adaptive introgression have shaped the cattle genome.

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Section: Methodsmentioning

confidence: 99%

Assembly of a pangenome for global cattle reveals missing sequences and novel structural variations, providing new insights into their diversity and evolutionary history

Zhou

Han

et al. 2022

Genome Res.

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“…Synthetic long reads were simulated using LRSIM ( 38 ) with 65× sequence coverage. We evaluated Novel-X, PopIns2 ( 30 ), NUI ( 20 ) and Pamir ( 31 ) on these data. Because Pamir is not able to work directly with synthetic long-read data, we simulated matching standard short-read data of the same coverage from the same genome using ART ( 39 ).…”

Section: Resultsmentioning

confidence: 99%

“…This algorithm was applied to the high coverage samples in the 1000 Genomes pilot phase, and a total of 128 NovelSeq calls were reported and validated ( 26 , 27 ). Subsequent short-read methods for this problem such as MindTheGap ( 28 ), ANISE and BASIL ( 29 ), PopIns2 ( 30 ) or Pamir ( 31 ) appended population-based techniques (e.g. pooling multiple low-coverage samples from the same population) or used additional whole-genome signals such as split reads for better breakpoint resolution.…”

Section: Introductionmentioning

confidence: 99%

“…MindTheGap ( 28 ) uses a novel k -mer-based signature to find insertion sites on the reference genome, while ANISE ( 29 ) employs a new algorithm for resolving certain repeat copies. PopIns2 ( 30 ) is an alternative algorithm that uses information from several whole-genome samples to find NRS insertions common to an ancestral population. Pamir ( 31 ) also generalizes NovelSeq’s approach for handling several low-coverage genomes from the same population.…”

Section: Introductionmentioning

confidence: 99%

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Efficient detection and assembly of non-reference DNA sequences with synthetic long reads

Meleshko

Yang

Marks

et al. 2022

Nucleic Acids Research

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Recent pan-genome studies have revealed an abundance of DNA sequences in human genomes that are not present in the reference genome. A lion’s share of these non-reference sequences (NRSs) cannot be reliably assembled or placed on the reference genome. Improvements in long-read and synthetic long-read (aka linked-read) technologies have great potential for the characterization of NRSs. While synthetic long reads require less input DNA than long-read datasets, they are algorithmically more challenging to use. Except for computationally expensive whole-genome assembly methods, there is no synthetic long-read method for NRS detection. We propose a novel integrated alignment-based and local assembly-based algorithm, Novel-X, that uses the barcode information encoded in synthetic long reads to improve the detection of such events without a whole-genome de novo assembly. Our evaluations demonstrate that Novel-X finds many non-reference sequences that cannot be found by state-of-the-art short-read methods. We applied Novel-X to a diverse set of 68 samples from the Polaris HiSeq 4000 PGx cohort. Novel-X discovered 16 691 NRS insertions of size > 300 bp (total length 18.2 Mb). Many of them are population specific or may have a functional impact.

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“…Thus, this wealth of data is, in practice, inaccessible to most researchers, and standard tools need to be established for indexing these data. Sequence graphs are an increasingly prominent model for representing and indexing large collections of sequencing data [43,18], enabling improvements in both the scale and accuracy of many biological analysis tasks (e.g., genotyping [57,25], variant calling [36,13], sequence search [32,57]). We construct a separate De Bruijn graph for each input sample, with nodes weighted by k-mer abundance.…”

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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Population-scale detection of non-reference sequence variants using colored de Bruijn graphs

Cited by 8 publications

References 61 publications

Assembly of a pangenome for global cattle reveals missing sequences and novel structural variations, providing new insights into their diversity and evolutionary history

Assembly of a pangenome for global cattle reveals missing sequences and novel structural variations, providing new insights into their diversity and evolutionary history

Efficient detection and assembly of non-reference DNA sequences with synthetic long reads

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

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