Binning Of Reads Research Articles

An initial genomic blueprint of the healthy human oesophageal microbiome.

The oesophageal microbiome is thought to contribute to the pathogenesis of oesophageal cancer. However, investigations using culture and molecular barcodes have provided only a low-resolution view of this important microbial community. We therefore explored the potential of culturomics and metagenomic binning to generate a catalogue of reference genomes from the healthy human oesophageal microbiome, alongside a comparison set from saliva. Twenty-two distinct colonial morphotypes from healthy oesophageal samples were genome-sequenced. These fell into twelve species clusters, eleven of which represented previously defined species. Two isolates belonged to a novel species, which we have named Rothia gullae. We performed metagenomic binning of reads generated from UK samples from this study alongside reads generated from Australian samples in a recent study. Metagenomic binning generated 136 medium or high-quality metagenome-assembled genomes (MAGs). MAGs were assigned to 56 species clusters, eight representing novel Candidatus species, which we have named Ca. Granulicatella gullae, Ca. Streptococcus gullae, Ca. Nanosynbacter quadramensis, Ca. Nanosynbacter gullae, Ca. Nanosynbacter colneyensis, Ca. Nanosynbacter norwichensis, Ca. Nanosynococcus oralis and Ca. Haemophilus gullae. Five of these novel species belong to the recently described phylum Patescibacteria . Although members of the Patescibacteria are known to inhabit the oral cavity, this is the first report of their presence in the oesophagus. Eighteen of the metagenomic species were, until recently, identified only by hard-to-remember alphanumeric placeholder designations. Here we illustrate the utility of a set of recently published arbitrary Latinate species names in providing user-friendly taxonomic labels for microbiome analyses.Our non-redundant species catalogue contained 63 species derived from cultured isolates or MAGs. Mapping revealed that these species account for around half of the sequences in the oesophageal and saliva metagenomes. Although no species was present in all oesophageal samples, 60 species occurred in at least one oesophageal metagenome from either study, with 50 identified in both cohorts. Recovery of genomes and discovery of new species represents an important step forward in our understanding of the oesophageal microbiome. The genes and genomes that we have released into the public domain will provide a base line for future comparative, mechanistic and intervention studies.

A novel and diverse group of Candidatus Patescibacteria from bathypelagic Lake Baikal revealed through long-read metagenomics

BackgroundLake Baikal, the world’s deepest freshwater lake, contains important numbers of Candidatus Patescibacteria (formerly CPR) in its deepest reaches. However, previously obtained CPR metagenome-assembled genomes recruited very poorly indicating the potential of other groups being present. Here, we have applied for the first time a long-read (PacBio CCS) metagenomic approach to analyze in depth the Ca. Patescibacteria living in the bathypelagic water column of Lake Baikal at 1600 m.ResultsThe retrieval of nearly complete 16S rRNA genes before assembly has allowed us to detect the presence of a novel and a likely endemic group of Ca. Patescibacteria inhabiting bathypelagic Lake Baikal. This novel group seems to possess extremely high intra-clade diversity, precluding complete genomes' assembly. However, read binning and scaffolding indicate that these microbes are similar to other Ca. Patescibacteria (i.e. parasites or symbionts), although they seem to carry more anabolic pathways, likely reflecting the extremely oligotrophic habitat they inhabit. The novel bins have not been found anywhere, but one of the groups appears in small amounts in an oligotrophic and deep alpine Lake Thun. We propose this novel group be named Baikalibacteria.ConclusionThe recovery of 16S rRNA genes via long-read metagenomics plus the use of long-read binning to uncover highly diverse “hidden” groups of prokaryotes are key strategies to move forward in ecogenomic microbiology. The novel group possesses enormous intraclade diversity akin to what happens with Ca. Patescibacteria at the interclade level, which is remarkable in an environment that has changed little in the last 25 million years.

Benchmarking different approaches for Norovirus genome assembly in metagenome samples

BackgroundGenome assembly of viruses with high mutation rates, such as Norovirus and other RNA viruses, or from metagenome samples, poses a challenge for the scientific community due to the coexistence of several viral quasispecies and strains. Furthermore, there is no standard method for obtaining whole-genome sequences in non-related patients. After polyA RNA isolation and sequencing in eight patients with acute gastroenteritis, we evaluated two de Bruijn graph assemblers (SPAdes and MEGAHIT), combined with four different and common pre-assembly strategies, and compared those yielding whole genome Norovirus contigs.ResultsReference-genome guided strategies with both host and target virus did not present any advantages compared to the assembly of non-filtered data in the case of SPAdes, and in the case of MEGAHIT, only host genome filtering presented improvements. MEGAHIT performed better than SPAdes in most samples, reaching complete genome sequences in most of them for all the strategies employed. Read binning with CD-HIT improved assembly when paired with different analysis strategies, and more notably in the case of SPAdes.ConclusionsNot all metagenome assemblies are equal and the choice in the workflow depends on the species studied and the prior steps to analysis. We may need different approaches even for samples treated equally due to the presence of high intra host variability. We tested and compared different workflows for the accurate assembly of Norovirus genomes and established their assembly capacities for this purpose.

Sensitivity of shotgun metagenomics to host DNA: abundance estimates depend on bioinformatic tools and contamination is the main issue.

A recent study reported that increasing host DNA abundance and reducing read depth impairs the sensitivity of detection of low-abundance micro-organisms by shotgun metagenomics. The authors used DNA from a synthetic bacterial community with abundances varying across several orders of magnitude and added varying proportions of host DNA. However, the use of a marker-gene-based abundance estimation tool (MetaPhlAn2) requires considerable depth to detect marker genes from low-abundance organisms. Here, we reanalyse the deposited data, and place the study in the broader context of low microbial biomass metagenomics. We opted for a fast and sensitive read binning tool (Kraken 2) with abundance estimates from Bracken. With this approach all organisms are detected even when the sample comprises 99 % host DNA and similarly accurate abundance estimates are provided (mean squared error 0.45 vs. 0.3 in the original study). We show that off-target genera, whether contaminants or misidentified reads, come to represent over 10 % of reads when the sample is 99 % host DNA and exceed counts of many target genera. Therefore, we applied Decontam, a contaminant detection tool, which was able to remove 61 % of off-target species and 79 % of off-target reads. We conclude that read binning tools can remain sensitive to low-abundance organisms even with high host DNA content, but even low levels of contamination pose a significant problem due to low microbial biomass. Analytical mitigations are available, such as Decontam, although steps to reduce contamination are critical.

Investigation of the spatial structure and interactions of the genome at sub-kilobase-pair resolution using T2C.

Chromosome conformation capture (3C) and its derivatives (e.g., 4C, 5C and Hi-C) are used to analyze the 3D organization of genomes. We recently developed targeted chromatin capture (T2C), an inexpensive method for studying the 3D organization of genomes, interactomes and structural changes associated with gene regulation, the cell cycle, and cell survival and development. Here, we present the protocol for T2C based on capture, describing all experimental steps and bio-informatic tools in full detail. T2C offers high resolution, a large dynamic interaction frequency range and a high signal-to-noise ratio. Its resolution is determined by the resulting fragment size of the chosen restriction enzyme, which can lead to sub-kilobase-pair resolution. T2C's high coverage allows the identification of the interactome of each individual DNA fragment, which makes binning of reads (often used in other methods) basically unnecessary. Notably, T2C requires low sequencing efforts. T2C also allows multiplexing of samples for the direct comparison of multiple samples. It can be used to study topologically associating domains (TADs), determining their position, shape, boundaries, and intra- and inter-domain interactions, as well as the composition of aggregated loops, interactions between nucleosomes, individual transcription factor binding sites, and promoters and enhancers. T2C can be performed by any investigator with basic skills in molecular biology techniques in ∼7-8 d. Data analysis requires basic expertise in bioinformatics and in Linux and Python environments.

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

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 .

Single-Cell-Genomics-Facilitated Read Binning of Candidate Phylum EM19 Genomes from Geothermal Spring Metagenomes.

The vast majority of microbial life remains uncatalogued due to the inability to cultivate these organisms in the laboratory. This "microbial dark matter" represents a substantial portion of the tree of life and of the populations that contribute to chemical cycling in many ecosystems. In this work, we leveraged an existing single-cell genomic data set representing the candidate bacterial phylum "Calescamantes" (EM19) to calibrate machine learning algorithms and define metagenomic bins directly from pyrosequencing reads derived from Great Boiling Spring in the U.S. Great Basin. Compared to other assembly-based methods, taxonomic binning with a read-based machine learning approach yielded final assemblies with the highest predicted genome completeness of any method tested. Read-first binning subsequently was used to extract Calescamantes bins from all metagenomes with abundant Calescamantes populations, including metagenomes from Octopus Spring and Bison Pool in Yellowstone National Park and Gongxiaoshe Spring in Yunnan Province, China. Metabolic reconstruction suggests that Calescamantes are heterotrophic, facultative anaerobes, which can utilize oxidized nitrogen sources as terminal electron acceptors for respiration in the absence of oxygen and use proteins as their primary carbon source. Despite their phylogenetic divergence, the geographically separate Calescamantes populations were highly similar in their predicted metabolic capabilities and core gene content, respiring O2, or oxidized nitrogen species for energy conservation in distant but chemically similar hot springs.

MBBC: an efficient approach for metagenomic binning based on clustering.

BackgroundBinning environmental shotgun reads is one of the most fundamental tasks in metagenomic studies, in which mixed reads from different species or operational taxonomical units (OTUs) are separated into different groups. While dozens of binning methods are available, there is still room for improvement.ResultsWe developed a novel taxonomy-independent approach called MBBC (Metagenomic Binning Based on Clustering) to cluster environmental shotgun reads, by considering k-mer frequency in reads and Markov properties of the inferred OTUs. Tested on twelve simulated datasets, MBBC reliably estimated the species number, the genome size, and the relative abundance of each species, independent of whether there are errors in reads. Tested on multiple experimental datasets, MBBC outperformed two state-of-the-art taxonomy-independent methods, in terms of the accuracy of the estimated species number, genome sizes, and percentages of correctly assigned reads, among other metrics.ConclusionsWe have developed a novel method for binning metagenomic reads based on clustering. This method is demonstrated to reliably predict species numbers, genome sizes, relative species abundances, and k-mer coverage in simple datasets. Our method also has a high accuracy in read binning. The MBBC software is freely available at http://eecs.ucf.edu/~xiaoman/MBBC/MBBC.html.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-015-0473-8) contains supplementary material, which is available to authorized users.

A two-phase binning algorithm using l-mer frequency on groups of non-overlapping reads.

BackgroundMetagenomics is the study of genetic materials derived directly from complex microbial samples, instead of from culture. One of the crucial steps in metagenomic analysis, referred to as “binning”, is to separate reads into clusters that represent genomes from closely related organisms. Among the existing binning methods, unsupervised methods base the classification on features extracted from reads, and especially taking advantage in case of the limitation of reference database availability. However, their performance, under various aspects, is still being investigated by recent theoretical and empirical studies. The one addressed in this paper is among those efforts to enhance the accuracy of the classification.ResultsThis paper presents an unsupervised algorithm, called BiMeta, for binning of reads from different species in a metagenomic dataset. The algorithm consists of two phases. In the first phase of the algorithm, reads are grouped into groups based on overlap information between the reads. The second phase merges the groups by using an observation on l-mer frequency distribution of sets of non-overlapping reads. The experimental results on simulated and real datasets showed that BiMeta outperforms three state-of-the-art binning algorithms for both short and long reads (≥700 bp) datasets.ConclusionsThis paper developed a novel and efficient algorithm for binning of metagenomic reads, which does not require any reference database. The software implementing the algorithm and all test datasets mentioned in this paper can be downloaded at http://it.hcmute.edu.vn/bioinfo/bimeta/index.htm.Electronic supplementary materialThe online version of this article (doi:10.1186/s13015-014-0030-4) contains supplementary material, which is available to authorized users.

A novel \(l\)-mer counting method abundance-based binning of metagenomic reads

The binning of reads is a crucial step in metagenomic data analysis. While unsupervised methods which are based on composition features are only efficient for long reads, genome abundance-based methods are often used in the binning of short reads. Previous abundance-based binning approaches usually use fixed-length \(l\)-mer frequencies to separate reads into groups such that reads in each group belong to genomes (or species) of very similar abundances. However, their classification performances are very sensitive to the length of \(l\)-mers, and they get difficult to separate reads from low-abundance genomes due to the repeat of short length \(l\)-mers in the genomes. In this paper, a new variable-length \(l\)-mer counting method is proposed to enable dealing with the short length \(l\)-mer repetition for improving the accuracy of abundance-based binning approaches. Computational experiments demonstrate that an improved approach of AbundanceBin (a commonly used binning method) in which the proposed method is applied achieves higher accuracy than the original one. The software implementing the approach can be downloaded at http://fit.hcmute.edu.vn/bioinfo/MetaSeqBin/index.htm.

Whole-genome haplotyping approaches and genomic medicine

Genomic information reported as haplotypes rather than genotypes will be increasingly important for personalized medicine. Current technologies generate diploid sequence data that is rarely resolved into its constituent haplotypes. Furthermore, paradigms for thinking about genomic information are based on interpreting genotypes rather than haplotypes. Nevertheless, haplotypes have historically been useful in contexts ranging from population genetics to disease-gene mapping efforts. The main approaches for phasing genomic sequence data are molecular haplotyping, genetic haplotyping, and population-based inference. Long-read sequencing technologies are enabling longer molecular haplotypes, and decreases in the cost of whole-genome sequencing are enabling the sequencing of whole-chromosome genetic haplotypes. Hybrid approaches combining high-throughput short-read assembly with strategic approaches that enable physical or virtual binning of reads into haplotypes are enabling multi-gene haplotypes to be generated from single individuals. These techniques can be further combined with genetic and population approaches. Here, we review advances in whole-genome haplotyping approaches and discuss the importance of haplotypes for genomic medicine. Clinical applications include diagnosis by recognition of compound heterozygosity and by phasing regulatory variation to coding variation. Haplotypes, which are more specific than less complex variants such as single nucleotide variants, also have applications in prognostics and diagnostics, in the analysis of tumors, and in typing tissue for transplantation. Future advances will include technological innovations, the application of standard metrics for evaluating haplotype quality, and the development of databases that link haplotypes to disease.