Metagenome Analysis Exploiting High-Throughput Chromosome Conformation Capture (3C) Data - PubMed (original) (raw)
Review
. 2015 Dec;31(12):673-682.
doi: 10.1016/j.tig.2015.10.003. Epub 2015 Nov 19.
Affiliations
- PMID: 26608779
- PMCID: PMC6831814
- DOI: 10.1016/j.tig.2015.10.003
Free PMC article
Review
Metagenome Analysis Exploiting High-Throughput Chromosome Conformation Capture (3C) Data
Martial Marbouty et al. Trends Genet. 2015 Dec.
Free PMC article
Abstract
Microbial communities are complex and constitute important parts of our environment. Genomic analysis of these populations is a dynamic research area but remains limited by the difficulty in assembling full genomes of individual species. Recently, a new method for metagenome assembly/analysis based on chromosome conformation capture has emerged (meta3C). This approach quantifies the collisions experienced by DNA molecules to identify those sharing the same cellular compartments, allowing the characterization of genomes present within complex mixes of species. The exploitation of these chromosome 3D signatures holds promising perspectives for genome sequencing of discrete species in complex populations. It also has the potential to assign correctly extra-chromosomal elements, such as plasmids, mobile elements and phages, to their host cells.
Copyright © 2015 Elsevier Ltd. All rights reserved.
Figures
Figure 1. Principle of a meta3C experiment
(A) Starting from a mix of species (metagenomicssample), a shotgun library is generated and used to generate a preliminary assembly (a 3C library can also be used here). (B) Starting from the same sample, a 3C/HiC library is generated (see Box 1). (C) Pair-end reads from the 3C library, some of which reflect the collision frequencies between all the pairs of DNA restriction fragments present within the population, is then mapped on the contigs. (D) Representation of the complex network resulting from the step C). Left panel: contact map representation of the contigs. Right panel: 3D representation of the data using a clustering visualization tool such as Gephi [63]. (E) The disordered contigs are then clustered and reordered based on their frequencies of interactions, unveiling the genomic sequences of the organisms present within the mix.
Figure 2. meta3C experiment on different mix of species [34]
(A) Chromosomal contact map of a mixture of three bacteria (Bacillus subtilis, Escherichia coli, Vibrio cholerae), with the color code representing contacts between DNA regions from low (white) to high (red) frequencies (a.u.). Frequencies of inter-species (chimeric) pairs of reads are directly reported on the matrix. (B) Contact frequencies plotted as a function of genomic distance (for all 3 bacterial genomes together). The score shows a clear decrease at the genome size of these bacteria (i.e. 4Mb). (C) Meta3C contact map of the largest 11 communities of contigs found by analyzing a river sediment sample. Each square in the matrix corresponds to a community grouping contigs that exhibit significantly more contact with each other than with other communities. Red squares indicate signal outside the main diagonal due to contigs exhibiting important contacts between several communities (i.e. overlapping communities). (D) Illustration of the interactions between the 11 largest communities of contigs using the force-directed graph-drawing algorithm Force Atlas 2 [64]. Each node corresponds to a contig and each link represents at least one meta3C interaction. The colors correspond to the communities identified by the Louvain algorithm and described in (A). The red square highlights the overlapping communities described in (A).
References
- Cryan JF, Dinan TG. Mind-altering microorganisms: the impact of the gut microbiota on brain and behaviour. Nat Rev Neurosci. 2012;13:701–712. - PubMed
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