bin3C : Exploiting Hi-C sequencing data to accurately resolve metagenome-assembled genomes (MAGs)

Abstract

Most microbes inhabiting the planet cannot be easily grown in the lab. Metagenomic techniques provide a means to study these organisms, and recent advances in the field have enabled the resolution of individual genomes from metagenomes, so-called Metagenome Assembled Genomes (MAGs). In addition to expanding the catalog of known microbial diversity, the systematic retrieval of MAGs stands as a tenable divide and conquer reduction of metagenome analysis to the simpler problem of single genome analysis. Many leading approaches to MAG retrieval depend upon time-series or transect data, whose effectiveness is a function of community complexity, target abundance and depth of sequencing. Without the need for time-series data, promising alternative methods are based upon the high-throughput sequencing technique called Hi-C.The Hi-C technique produces read-pairs which capture in-vivo DNA-DNA proximity interactions (contacts). The physical structure of the community modulates the signal derived from these interactions and a hierarchy of interaction rates exists ( Intra-chromosomal > Inter-chromosomal > Inter-cellular ). . CC-BY-NC-ND 4.0 International license It is made available under awas not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.The copyright holder for this preprint (which . http://dx.doi.org/10.1101/388355 doi: bioRxiv preprint first posted online Aug. 9, 2018; We describe an unsupervised method that exploits the hierarchical nature of Hi-C interaction rates to resolve MAGs from a single time-point. As a quantitative demonstration, next, we validate the method against the ground truth of a simulated human faecal microbiome. Lastly, we directly compare our method against a recently announced proprietary service ProxiMeta, which also performs MAG retrieval using Hi-C data. bin3C has been implemented as a simple open-source pipeline and makes use of the unsupervised community detection algorithm Infomap ( https://github.com/cerebis/bin3C ).

Viruses interact with hosts that span distantly related microbial domains in dense hydrothermal mats

“…Noise-to-signal ratios were calculated using two methods: (1) raw noise: (# inter-mMAG contacts)/(# intra-mMAG contacts) and ( 2) relaxed noise: (# inter-order contacts)/(# intra-order contacts), where # inter-mMAG contacts = # Hi-C read pairs mapping to different mMAGs, # intra-mMAG contacts = # Hi-C read pairs mapping to the same mMAG or contig, # inter-order contacts = # Hi-C read pairs mapping to different mMAGs belonging to different taxonomic orders, # intra-order contacts = # Hi-C read pairs mapping to the same contig or contigs binned to the same taxonomic order. Log-transformed contact matrices were visualized using a modified bin3C 78 mkmap function with flag max_image_size = 5,000.…”

Inference of phylogenetic trees directly from raw sequencing reads using Read2Tree

“…Nevertheless, Read2Tree could be further developed to process metagenomic samples-by combining it with a genome binning preprocessing step. In recent years, a number of different approaches for genome binning have been proposed, be it through 'differential coverage' approaches, which exploit correlated abundance across samples to identify reads coming from the same species [53][54][55] , using Hi-C protocols, which make it possible to identify parts of DNA in close physical proximity 56,57 , or single-cell technologies 58 .…”

Read2Tree: scalable and accurate phylogenetic trees from raw reads

“…Nevertheless, Read2Tree could be further developed to process metagenomic samples-by combining it with a genome binning pre-processing step. In recent years, a number of different approaches for genome binning have been proposed, be it through "differential coverage" approaches, which exploit correlated abundance across samples to identify reads coming from the same species [53][54][55] , using Hi-C protocols, which make it possible to identify parts of DNA in close physical proximity 56,57 , or single-cell technologies 58 .…”