Binning metagenomic contigs by coverage and composition

Alneberg, Johannes; Bjarnason, Brynjar Smári; Bruijn, Ino de; Schirmer, Melanie; Quick, Joshua; Ijaz, Umer Zeeshan; Lahti, Leo; Loman, Nicholas J.; Andersson, Anders F.; Quince, Christopher

doi:10.1038/nmeth.3103

Cited by 1,916 publications

(1,737 citation statements)

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“…The need for de novo reconstruction of microbial genomes from environmental samples through shotgun metagenomics data has given raise to advanced techniques and software platforms that can make sense of complex assemblies [52,53,14,15,16]. Our study demonstrates that these approaches can be effectively used in eukaryotic assembly projects for curation purposes.…”

Section: Resultsmentioning

confidence: 85%

“…Today, microbiologists often exploit two essential properties of bacterial and archaeal genomes to improve the "binning" step: (1) k-mer frequencies that are somewhat preserved throughout a single microbial genome [8], to identify contigs that likely originate from the same genome [9], and (2) a set of genes that occur in the vast majority of bacterial genomes as a single copy, to estimate the level of completion and contamination of genome bins [10,11,12]. These properties, along with differential coverage of contigs across multiple samples when such data exist, are routinely used to identify coherent microbial draft genomes in metagenomic assemblies [13,14,15,16].…”

Section: Introductionmentioning

confidence: 99%

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Identifying contamination with advanced visualization and analysis practices: metagenomic approaches for eukaryotic genome assemblies

Delmont

Eren

2016

Preprint

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View the peer-reviewed version (peerj.com/articles/1839), which is the preferred citable publication unless you specifically need to cite this preprint.Delmont TO, Eren AM. 2016. Identifying contamination with advanced visualization and analysis practices: metagenomic approaches for eukaryotic genome assemblies. PeerJ 4:e1839 https://doi.org/10.7717/peerj.1839Identifying contamination with advanced visualization and analysis practices: metagenomic approaches for eukaryotic genome assemblies High-throughput sequencing provides a fast and cost effective mean to recover genomes of organisms from all domains of life. However, adequate curation of the assembly results against potential contamination of non-target organisms requires advanced bioinformatics approaches and practices. Here, we re-analyzed the sequencing data generated for the tardigrade Hypsibius dujardini using approaches routinely employed by microbial ecologists who reconstruct bacterial and archaeal genomes from metagenomic data. We created a holistic display of the eukaryotic genome assembly using DNA data originating from two groups and eleven sequencing libraries. By using bacterial single-copy genes, k- AbstractHigh-throughput sequencing provides a fast and cost effective mean to recover genomes of organisms from all domains of life. However, adequate curation of the assembly results against potential contamination of non-target organisms requires advanced bioinformatics approaches and practices. Here, we re-analyzed the sequencing data generated for the tardigrade Hypsibius dujardini using approaches routinely employed by microbial ecologists who reconstruct bacterial and archaeal genomes from metagenomic data. We created a holistic display of the eukaryotic genome assembly using DNA data originating from two groups and eleven sequencing libraries. By using bacterial single-copy genes, k-mer frequencies, and coverage values of scaffolds we could identify and characterize multiple near-complete bacterial genomes, and curate a 182 Mbp draft genome for H. dujardini supported by RNA-Seq data. Our results indicate that most contaminant scaffolds were assembled from Moleculo long-read libraries, and most of these contaminants have differed between library preparations. Our re-analysis shows that visualization and curation of eukaryotic genome assemblies can benefit from tools designed to address the needs of today's microbiologists, who are constantly challenged by the difficulties associated with the identification of distinct microbial genomes in complex environmental metagenomes.

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Section: Resultsmentioning

confidence: 85%

Section: Introductionmentioning

confidence: 99%

Identifying contamination with advanced visualization and analysis practices: metagenomic approaches for eukaryotic genome assemblies

Delmont

Eren

2016

Preprint

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“…Two HMP real datasets, one human gut dataset, and HiSeq/MiSeq datasets were used because such datasets were widely used in assessing other metagenomic tools (Alneberg et al, 2014;Kultima et al, 2012;Namiki et al, 2012;Wang et al, 2012;Wood and Salzberg, 2014). The HMP mock dataset was used because it was different from other three datasets and contained only single-end read.…”

Section: Mbmc Was Superior To Other Methods On Experimental Datasetsmentioning

confidence: 99%

MBMC: An Effective Markov Chain Approach for Binning Metagenomic Reads from Environmental Shotgun Sequencing Projects

Wang

2016

OMICS: A Journal of Integrative Biology

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Metagenomics is a next-generation omics field currently impacting postgenomic life sciences and medicine. Binning metagenomic reads is essential for the understanding of microbial function, compositions, and interactions in given environments. Despite the existence of dozens of computational methods for metagenomic read binning, it is still very challenging to bin reads. This is especially true for reads from unknown species, from species with similar abundance, and/or from low-abundance species in environmental samples. In this study, we developed a novel taxonomy-dependent and alignment-free approach called MBMC (Metagenomic Binning by Markov Chains). Different from all existing methods, MBMC bins reads by measuring the similarity of reads to the trained Markov chains for different taxa instead of directly comparing reads with known genomic sequences. By testing on more than 24 simulated and experimental datasets with species of similar abundance, species of low abundance, and/or unknown species, we report here that MBMC reliably grouped reads from different species into separate bins. Compared with four existing approaches, we demonstrated that the performance of MBMC was comparable with existing approaches when binning reads from sequenced species, and superior to existing approaches when binning reads from unknown species. MBMC is a pivotal tool for binning metagenomic reads in the current era of Big Data and postgenomic integrative biology. The MBMC software can be freely downloaded at http://hulab.ucf.edu/research/projects/ metagenomics/MBMC.html.

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“…Recent techniques which repeatedly sample an environment, extracting a signal based on correlated changes in abundance to identify genomic content that is likely to belong to individual strains or populations of cells, have confidently obtained species resolution (Alneberg et al, 2013;Imelfort et al, 2014) and begun to work toward strain (genotype) resolution (Cleary et al, 2015). Inferring abundance per-sample from contig coverage (Alneberg et al, 2013;Imelfort et al, 2014) or k-mer frequencies (Cleary et al, 2015) respectively, the strength of this discriminating signal is a function of community diversity, environmental variation and sampling depth; and represents a significant computational task.…”

Section: Introductionmentioning

confidence: 99%

Deconvoluting simulated metagenomes: the performance of hard- and soft- clustering algorithms applied to metagenomic chromosome conformation capture (3C)

DeMaere

Darling

2016

Preprint

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Background. Chromosome conformation capture, coupled with high throughput DNA sequencing in protocols like Hi-C and 3C-seq, has been proposed as viable means to generate data to resolve the genomes of microorganisms living in naturally occuring environments. Metagenomic Hi-C and 3C-seq datasets have begun to emerge, but the feasibility of resolving genomes when closely related organisms (strain-level diversity) are present in the sample has not yet been systematically characterised.

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Binning metagenomic contigs by coverage and composition

Cited by 1,916 publications

References 18 publications

Identifying contamination with advanced visualization and analysis practices: metagenomic approaches for eukaryotic genome assemblies

Identifying contamination with advanced visualization and analysis practices: metagenomic approaches for eukaryotic genome assemblies

MBMC: An Effective Markov Chain Approach for Binning Metagenomic Reads from Environmental Shotgun Sequencing Projects

Deconvoluting simulated metagenomes: the performance of hard- and soft- clustering algorithms applied to metagenomic chromosome conformation capture (3C)

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