Evidence map›Paper›PMID 22697249›Full record

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.

Mingjie Wang, Yuzhen Ye, Haixu Tang

Abstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

8 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Read mapping on de Bruijn graphs.BMC bioinformatics · 2016
    Article
  5. Article
  6. Article
  7. Article
  8. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Mingjie WangSchool of Informatics and Computing, Indiana University, Bloomington, IN 47405, USA.
Yuzhen Ye
Haixu Tang

Funding

Lung Microbiome and Pulmonary Inflammation/Immunity in HIV InfectionU01HL098960 · NHLBI · INDIANA UNIVERSITY INDIANAPOLIS · PI TWIGG, HOMER L, WEINSTOCK, GEORGE M · 2009 to 2013
$3.7M
Fragment assembly and metabolic/species diversity analysis for Human microbiome pR01HG004908 · NHGRI · TRUSTEES OF INDIANA UNIVERSITY · PI YE, YUZHEN · 2008 to 2010
$765k
NHGRI NIH HHS 1R01HG004908NHLBI NIH HHS 1U01HL098960
6 · The paper itself

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).

Indexed as

AlgorithmsGenome, BacterialChromosome MappingEscherichia coliGenomic Structural VariationMetagenomicsMicrobial ConsortiaPhylogenySequence AlignmentSequence Analysis, DNATreponema

Identifiers

PMID22697249
PMCPMC3375647

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.