ArticleJournal of computational biology : a journal of computational molecular cell biology2012
A de Bruijn graph approach to the quantification of closely-related genomes in a microbial community.
Article in Journal of computational biology : a journal of computational molecular cell biology, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- K2R: Tinted de Bruijn graphs implementation for efficient read extraction from sequencing datasets.Bioinformatics advances · 2025Article
- Scalable, ultra-fast, and low-memory construction of compacted de Bruijn graphs with Cuttlefish 2.Genome biology · 2022Article
- Review
- Read mapping on de Bruijn graphs.BMC bioinformatics · 2016Article
- Utilizing de Bruijn graph of metagenome assembly for metatranscriptome analysis.Bioinformatics (Oxford, England) · 2016Article
- Strand-specific community RNA-seq reveals prevalent and dynamic antisense transcription in human gut microbiota.Frontiers in microbiology · 2015Article
- Clinical and ethical considerations of massively parallel sequencing in transplantation science.World journal of transplantation · 2013Article
- Population-Sequencing as a Biomarker of Burkholderia mallei and Burkholderia pseudomallei Evolution through Microbial Forensic Analysis.Journal of nucleic acids · 2013Article
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3 authors.
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Abstract
The wide applications of next-generation sequencing (NGS) technologies in metagenomics have raised many computational challenges. One of the essential problems in metagenomics is to estimate the taxonomic composition of a microbial community, which can be approached by mapping shotgun reads acquired from the community to previously characterized microbial genomes followed by quantity profiling of these species based on the number of mapped reads. This procedure, however, is not as trivial as it appears at first glance. A shotgun metagenomic dataset often contains DNA sequences from many closely-related microbial species (e.g., within the same genus) or strains (e.g., within the same species), thus it is often difficult to determine which species/strain a specific read is sampled from when it can be mapped to a common region shared by multiple genomes at high similarity. Furthermore, high genomic variations are observed among individual genomes within the same species, which are difficult to be differentiated from the inter-species variations during reads mapping. To address these issues, a commonly used approach is to quantify taxonomic distribution only at the genus level, based on the reads mapped to all species belonging to the same genus; alternatively, reads are mapped to a set of representative genomes, each selected to represent a different genus. Here, we introduce a novel approach to the quantity estimation of closely-related species within the same genus by mapping the reads to their genomes represented by a de Bruijn graph, in which the common genomic regions among them are collapsed. Using simulated and real metagenomic datasets, we show the de Bruijn graph approach has several advantages over existing methods, including (1) it avoids redundant mapping of shotgun reads to multiple copies of the common regions in different genomes, and (2) it leads to more accurate quantification for the closely-related species (and even for strains within the same species).
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