De novo assembly and genotyping of variants using colored de Bruijn graphs

By Z. Iqbal, M. Cáccamo, Isaac Turner, Paul Flicek, G. McVean. Nature Genetics, 2012

677 citations recorded by the source corpus.

Detecting genetic variants that are highly divergent from a reference sequence remains a major challenge in genome sequencing. We introduce de novo assembly algorithms using colored de Bruijn graphs for detecting and genotyping simple and complex genetic variants in an individual or population. We provide an efficient software implementation, Cortex, the first de novo assembler capable of assembling multiple eukaryotic genomes simultaneously. Four applications of Cortex are presented. First, we detect and validate both simple and complex structural variations in a high-coverage human genome. Second, we identify more than 3 Mb of sequence absent from the human reference genome, in pooled low-coverage population sequence data from the 1000 Genomes Project. Third, we show how population information from ten chimpanzees enables accurate variant calls without a reference sequence. Last, we estimate classical human leukocyte antigen (HLA) genotypes at HLA-B, the most variable gene in the human genome.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/91c964330beeed2c5c875d18633793a548a987cf

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Citations in the corpus

Producing polished prokaryotic pangenomes with the Panaroo pipeline (2020, background): “Finer scale structural variants are better called using assembly graph-based approaches such as Cortex [37].”
A review of methods and databases for metagenomic classification and assembly (2019, methodology): “Kallisto [67, 95], which was originally developed for RNA-Seq analysis, uses a colored de Bruijn graph [96] in which each edge (i.”
metaSPAdes: a new versatile metagenomic assembler. (2017, background): “It also improves on the approach in Iqbal et al. (2012) by significantly reducing the memory footprint.”
The design and construction of reference pangenome graphs with minigraph (2020, methodology): “de [12]. A common way to construct a basic pangenome graph is to generate a compacted de Bruijn graph (cDBG) [13,14,15,16,17,18,19] from a set of genomes. Basic cDBG does not keep sample information. [20] proposed colored cDBG with each color represents a sample or a population. Colored cDBG can be ”
A complete Cannabis chromosome assembly and adaptive admixture for elevated cannabidiol (CBD) content (2018, methodology): “second round of genotyping were used to further saturate the genetic map if they increased map density without increasing the map length (29).”
Haplotype-based variant detection from short-read sequencing (2012, methodology): “Current methods for haplotype-based variant detection rely on assembly methods, which can be applied globally [Iqbal et al., 2012] or locally [Albers et al.”
Computational tools for copy number variation (CNV) detection using next-generation sequencing data: features and perspectives (2013, methodology): “The Cortex assembler was designed as a complete de novo CNV calling method [57].”
The Road to Metagenomics: From Microbiology to DNA Sequencing Technologies and Bioinformatics (2015, methodology): “This computational implementation can perform a genome assembly and variant calling at the same time (Iqbal et al., 2012).”
GRIDSS: sensitive and specific genomic rearrangement detection using positional de Bruijn graph assembly (2017, positive): “To make the best use of data from related samples, sequencing libraries are tracked in the de Bruijn graph using color (Iqbal et al. 2012), and evidence supporting rearrangements is shared between libraries during assembly and variant calling.”
Scaling accurate genetic variant discovery to tens of thousands of samples (2017, methodology): “In contrast, assembly-based variant callers including Platypus [22] and the GATK’s HaplotypeCaller (HC), construct theoretical haplotypes via de Bruijn-like graphs from a consensus of the reads covering the genomic region [13, 22, 7, 10, 20].”
Structural variant calling: the long and the short of it (2019, methodology): “Cortex uses a colored de Bruijn graph (see Table 2 for definition) to simultaneously infer SVs and complex combinations of SNVs, indels, and rearrangements [31].”
Assembly of long error-prone reads using de Bruijn graphs (2016, methodology): “In addition, de Bruijn graphs have been used for sequencing by hybridization (25), repeat classification (18), de novo protein sequencing (20), synteny block construction (26), genotyping (27), and Ig classification (28).”