Binning Of Contigs Research Articles

Imputing Metagenomic Hi-C Contacts Facilitates the Integrative Contig Binning Through Constrained Random Walk with Restart.

Metagenomic Hi-C (metaHi-C) has shown remarkable potential for retrieving high-quality metagenome-assembled genomes from complex microbial communities. Nevertheless, existing metaHi-C-based contig binning methods solely rely on Hi-C interactions between contigs, disregarding crucial biological information such as the presence of single-copy marker genes. To overcome this limitation, we introduce ImputeCC, an integrative contig binning tool optimized for metaHi-C datasets. ImputeCC integrates both Hi-C interactions and the discriminative power of single-copy marker genes to group marker-gene-containing contigs into preliminary bins. It also introduces a novel constrained random walk with restart algorithm to enhance Hi-C connectivity among contigs. Comprehensive assessments using both mock and real metaHi-C datasets from diverse environments demonstrate that ImputeCC consistently outperforms other Hi-C-based contig binning tools. A genus-level analysis of the sheep gut microbiota reconstructed by ImputeCC underlines its capability to recover key species from dominant genera and identify previously unknown genera.

AFITbin: a metagenomic contig binning method using aggregate l-mer frequency based on initial and terminal nucleotides

BackgroundUsing next-generation sequencing technologies, scientists can sequence complex microbial communities directly from the environment. Significant insights into the structure, diversity, and ecology of microbial communities have resulted from the study of metagenomics. The assembly of reads into longer contigs, which are then binned into groups of contigs that correspond to different species in the metagenomic sample, is a crucial step in the analysis of metagenomics. It is necessary to organize these contigs into operational taxonomic units (OTUs) for further taxonomic profiling and functional analysis. For binning, which is synonymous with the clustering of OTUs, the tetra-nucleotide frequency (TNF) is typically utilized as a compositional feature for each OTU.ResultsIn this paper, we present AFIT, a new l-mer statistic vector for each contig, and AFITBin, a novel method for metagenomic binning based on AFIT and a matrix factorization method. To evaluate the performance of the AFIT vector, the t-SNE algorithm is used to compare species clustering based on AFIT and TNF information. In addition, the efficacy of AFITBin is demonstrated on both simulated and real datasets in comparison to state-of-the-art binning methods such as MetaBAT 2, MaxBin 2.0, CONCOT, MetaCon, SolidBin, BusyBee Web, and MetaBinner. To further analyze the performance of the purposed AFIT vector, we compare the barcodes of the AFIT vector and the TNF vector.ConclusionThe results demonstrate that AFITBin shows superior performance in taxonomic identification compared to existing methods, leveraging the AFIT vector for improved results in metagenomic binning. This approach holds promise for advancing the analysis of metagenomic data, providing more reliable insights into microbial community composition and function.AvailabilityA python package is available at: https://github.com/SayehSobhani/AFITBin.

Metagenomic profiling of viral and microbial communities from the pox lesions of lumpy skin disease virus and sheeppox virus-infected hosts.

It has been recognized that capripoxvirus infections have a strong cutaneous tropism with the manifestation of skin lesions in the form of nodules and scabs in the respective hosts, followed by necrosis and sloughing off. Considering that the skin microbiota is a complex community of commensal bacteria, fungi and viruses that are influenced by infections leading to pathological states, there is no evidence on how the skin microbiome is affected during capripoxvirus pathogenesis. In this study, shotgun metagenomic sequencing was used to investigate the microbiome in pox lesions from hosts infected with lumpy skin disease virus and sheep pox virus. The analysis revealed a high degree of variability in bacterial community structures across affected skin samples, indicating the importance of specific commensal microorganisms colonizing individual hosts. The most common and abundant bacteria found in scab samples were Fusobacterium necrophorum, Streptococcus dysgalactiae, Helcococcus ovis and Trueperella pyogenes, irrespective of host. Bacterial reads belonging to the genera Moraxella, Mannheimia, Corynebacterium, Staphylococcus and Micrococcus were identified. This study is the first to investigate capripox virus-associated changes in the skin microbiome using whole-genome metagenomic profiling. The findings will provide a basis for further investigation into capripoxvirus pathogenesis. In addition, this study highlights the challenge of selecting an optimal bioinformatics approach for the analysis of metagenomic data in clinical and veterinary practice. For example, direct classification of reads using a kmer-based algorithm resulted in a significant number of systematic false positives, which may be attributed to the peculiarities of the algorithm and database selection. On the contrary, the process of de novo assembly requires a large number of target reads from the symbiotic microbial community. In this work, the obtained sequencing data were processed by three different approaches, including direct classification of reads based on k-mers, mapping of reads to a marker gene database, and de novo assembly and binning of metagenomic contigs. The advantages and disadvantages of these techniques and their practicality in veterinary settings are discussed in relation to the results obtained.

Effective binning of metagenomic contigs using contrastive multi-view representation learning

Contig binning plays a crucial role in metagenomic data analysis by grouping contigs from the same or closely related genomes. However, existing binning methods face challenges in practical applications due to the diversity of data types and the difficulties in efficiently integrating heterogeneous information. Here, we introduce COMEBin, a binning method based on contrastive multi-view representation learning. COMEBin utilizes data augmentation to generate multiple fragments (views) of each contig and obtains high-quality embeddings of heterogeneous features (sequence coverage and k-mer distribution) through contrastive learning. Experimental results on multiple simulated and real datasets demonstrate that COMEBin outperforms state-of-the-art binning methods, particularly in recovering near-complete genomes from real environmental samples. COMEBin outperforms other binning methods remarkably when integrated into metagenomic analysis pipelines, including the recovery of potentially pathogenic antibiotic-resistant bacteria (PARB) and moderate or higher quality bins containing potential biosynthetic gene clusters (BGCs).

PlasBin-flow: a flow-based MILP algorithm for plasmid contigs binning.

The analysis of bacterial isolates to detect plasmids is important due to their role in the propagation of antimicrobial resistance. In short-read sequence assemblies, both plasmids and bacterial chromosomes are typically split into several contigs of various lengths, making identification of plasmids a challenging problem. In plasmid contig binning, the goal is to distinguish short-read assembly contigs based on their origin into plasmid and chromosomal contigs and subsequently sort plasmid contigs into bins, each bin corresponding to a single plasmid. Previous works on this problem consist of de novo approaches and reference-based approaches. De novo methods rely on contig features such as length, circularity, read coverage, or GC content. Reference-based approaches compare contigs to databases of known plasmids or plasmid markers from finished bacterial genomes. Recent developments suggest that leveraging information contained in the assembly graph improves the accuracy of plasmid binning. We present PlasBin-flow, a hybrid method that defines contig bins as subgraphs of the assembly graph. PlasBin-flow identifies such plasmid subgraphs through a mixed integer linear programming model that relies on the concept of network flow to account for sequencing coverage, while also accounting for the presence of plasmid genes and the GC content that often distinguishes plasmids from chromosomes. We demonstrate the performance of PlasBin-flow on a real dataset of bacterial samples. https://github.com/cchauve/PlasBin-flow.

Accurate Binning of Metagenomic Contigs Using Composition, Coverage, and Assembly Graphs.

Metagenomics enables the recovery of various genetic materials from different species, thus providing valuable insights into microbial communities. Metagenomic binning group sequences belong to different organisms, which is an important step in the early stages of metagenomic analysis pipelines. The classic pipeline followed in metagenomic binning is to assemble short reads into longer contigs and then bin these resulting contigs into groups representing different taxonomic groups in the metagenomic sample. Most of the currently available binning tools are designed to bin metagenomic contigs, but they do not make use of the assembly graphs that produce such assemblies. In this study, we propose MetaCoAG, a metagenomic binning tool that uses assembly graphs with the composition and coverage information of contigs. MetaCoAG estimates the number of initial bins using single-copy marker genes, assigns contigs into bins iteratively, and adjusts the number of bins dynamically throughout the binning process. We show that MetaCoAG significantly outperforms state-of-the-art binning tools by producing similar or more high-quality bins than the second-best binning tool on both simulated and real datasets. To the best of our knowledge, MetaCoAG is the first stand-alone contig-binning tool that directly makes use of the assembly graph information along with other features of the contigs.

Binning Metagenomic Contigs Using Unsupervised Clustering and Reference Databases.

Metagenomics can directly extract the genetic material of all microorganisms from the environment, and obtain metagenomic samples with a large number of unknown DNA sequences. Binning of metagenomic contigs is a hot topic in metagenomics research. There are two key challenges for the current unsupervised metagenomic clustering algorithms. First, unsupervised metagenomic clustering methods rarely use reference databases, causing a certain waste of resources. Second, unsupervised metagenomic clustering methods are restricted by the characteristics of the sequences and the clustering algorithms, and the binning effect is limited. Therefore, a new binning method for metagenomic contigs using unsupervised clustering methods and reference databases is proposed to address these challenges, to make full use of the advantages of unsupervised clustering methods and reference databases constructed by scientists to improve the overall binning effect. This method uses the integrated SVM classification model to further bin the unsupervised clustering parts that do not perform well. Our proposed method was tested on simulated datasets and a real dataset and compared with other state-of-the-art metagenomic clustering methods including CONCOCT, Metabin2.0, Autometa, and MetaBAT. The results show that our method can achieve higher precision rate and improve the binning effect.

HiFine: integrating Hi-C-based and shotgun-based methods to refine binning of metagenomic contigs.

Metagenomic binning aims to retrieve microbial genomes directly from ecosystems by clustering metagenomic contigs assembled from short reads into draft genomic bins. Traditional shotgun-based binning methods depend on the contigs' composition and abundance profiles and are impaired by the paucity of enough samples to construct reliable co-abundance profiles. When applied to a single sample, shotgun-based binning methods struggle to distinguish closely related species only using composition information. As an alternative binning approach, Hi-C-based binning employs metagenomic Hi-C technique to measure the proximity contacts between metagenomic fragments. However, spurious inter-species Hi-C contacts inevitably generated by incorrect ligations of DNA fragments between species link the contigs from varying genomes, weakening the purity of final draft genomic bins. Therefore, it is imperative to develop a binning pipeline to overcome the shortcomings of both types of binning methods on a single sample. We develop HiFine, a novel binning pipeline to refine the binning results of metagenomic contigs by integrating both Hi-C-based and shotgun-based binning tools. HiFine designs a strategy of fragmentation for the original bin sets derived from the Hi-C-based and shotgun-based binning methods, which considerably increases the purity of initial bins, followed by merging fragmented bins and recruiting unbinned contigs. We demonstrate that HiFine significantly improves the existing binning results of both types of binning methods and achieves better performance in constructing species genomes on publicly available datasets. To the best of our knowledge, HiFine is the first pipeline to integrate different types of tools for the binning of metagenomic contigs. HiFine is available at https://github.com/dyxstat/HiFine. Supplementary data are available at Bioinformatics online.

Disentangle genus microdiversity within a complex microbial community by using a multi-distance long-read binning method: example of Candidatus Accumulibacter.

SummaryComplete genomes can be recovered from metagenomes by assembling and binning DNA sequences into metagenome assembled genomes (MAGs). Yet, the presence of microdiversity can hamper the assembly and binning processes, possibly yielding chimeric, highly fragmented and incomplete genomes. Here, the metagenomes of four samples of aerobic granular sludge bioreactors containing Candidatus (Ca.) Accumulibacter, a phosphate‐accumulating organism of interest for wastewater treatment, were sequenced with both PacBio and Illumina. Different strategies of genome assembly and binning were investigated, including published protocols and a binning procedure adapted to the binning of long contigs (MuLoBiSC). Multiple criteria were considered to select the best strategy for Ca. Accumulibacter, whose multiple strains in every sample represent a challenging microdiversity. In this case, the best strategy relies on long‐read only assembly and a custom binning procedure including MuLoBiSC in metaWRAP. Several high‐quality Ca. Accumulibacter MAGs, including a novel species, were obtained independently from different samples. Comparative genomic analysis showed that MAGs retrieved in different samples harbour genomic rearrangements in addition to accumulation of point mutations. The microdiversity of Ca. Accumulibacter, likely driven by mobile genetic elements, causes major difficulties in recovering MAGs, but it is also a hallmark of the panmictic lifestyle of these bacteria.

Recovery of strain-resolved genomes from human microbiome through an integration framework of single-cell genomics and metagenomics

BackgroundObtaining high-quality (HQ) reference genomes from microbial communities is crucial for understanding the phylogeny and function of uncultured microbes in complex microbial ecosystems. Despite improvements in bioinformatic approaches to generate curated metagenome-assembled genomes (MAGs), existing metagenome binners obtain population consensus genomes but they are nowhere comparable to genomes sequenced from isolates in terms of strain level resolution. Here, we present a framework for the integration of single-cell genomics and metagenomics, referred to as single-cell (sc) metagenomics, to reconstruct strain-resolved genomes from microbial communities at once.ResultsOur sc-metagenomics integration framework, termed SMAGLinker, uses single-cell amplified genomes (SAGs) generated using microfluidic technology as binning guides and integrates them with metagenome-assembled genomes (MAGs) to recover improved draft genomes. We compared sc-metagenomics with the metagenomics-alone approach using conventional metagenome binners. The sc-metagenomics approach showed precise contig binning and higher recovery rates (>97%) of rRNA and plasmids than conventional metagenomics in genome reconstruction from the cell mock community. In human microbiota samples, sc-metagenomics recovered the largest number of genomes with a total of 103 gut microbial genomes (21 HQ, with 65 showing >90% completeness) and 45 skin microbial genomes (10 HQ, with 40 showing >90% completeness), respectively. Conventional metagenomics recovered one Staphylococcus hominis genome, whereas sc-metagenomics recovered two S. hominis genomes from identical skin microbiota sample. Single-cell sequencing revealed that these S. hominis genomes were derived from two distinct strains harboring specifically different plasmids. We found that all conventional S. hominis MAGs had a substantial lack or excess of genome sequences and contamination from other Staphylococcus species (S. epidermidis).ConclusionsSMAGLinker enabled us to obtain strain-resolved genomes in the mock community and human microbiota samples by assigning metagenomic sequences correctly and covering both highly conserved genes such as rRNA genes and unique extrachromosomal elements, including plasmids. SMAGLinker will provide HQ genomes that are difficult to obtain using metagenomics alone and will facilitate the understanding of microbial ecosystems by elucidating detailed metabolic pathways and horizontal gene transfer networks. SMAGLinker is available at https://github.com/kojiari/smaglinker.3Xqp2i4EXkShdVYdkpPFdnVideo abstract

METAMVGL: a multi-view graph-based metagenomic contig binning algorithm by integrating assembly and paired-end graphs

BackgroundDue to the complexity of microbial communities, de novo assembly on next generation sequencing data is commonly unable to produce complete microbial genomes. Metagenome assembly binning becomes an essential step that could group the fragmented contigs into clusters to represent microbial genomes based on contigs’ nucleotide compositions and read depths. These features work well on the long contigs, but are not stable for the short ones. Contigs can be linked by sequence overlap (assembly graph) or by the paired-end reads aligned to them (PE graph), where the linked contigs have high chance to be derived from the same clusters.ResultsWe developed METAMVGL, a multi-view graph-based metagenomic contig binning algorithm by integrating both assembly and PE graphs. It could strikingly rescue the short contigs and correct the binning errors from dead ends. METAMVGL learns the two graphs’ weights automatically and predicts the contig labels in a uniform multi-view label propagation framework. In experiments, we observed METAMVGL made use of significantly more high-confidence edges from the combined graph and linked dead ends to the main graph. It also outperformed many state-of-the-art contig binning algorithms, including MaxBin2, MetaBAT2, MyCC, CONCOCT, SolidBin and GraphBin on the metagenomic sequencing data from simulation, two mock communities and Sharon infant fecal samples.ConclusionsOur findings demonstrate METAMVGL outstandingly improves the short contig binning and outperforms the other existing contig binning tools on the metagenomic sequencing data from simulation, mock communities and infant fecal samples.

Application of computational approaches to analyze metagenomic data

Microorganisms play a vital role in living systems in numerous ways. In the soil or ocean environment, microbes are involved in diverse processes, such as carbon and nitrogen cycle, nutrient recycling, and energy acquisition. The relation between microbial dysbiosis and disease developments has been extensively studied. In particular, microbial communities in the human gut are associated with the pathophysiology of several chronic diseases such as inflammatory bowel disease and diabetes. Therefore, analyzing the distribution of microorganisms and their associations with the environment is a key step in understanding nature. With the advent of next-generation sequencing technology, a vast amount of metagenomic data on unculturable microbes in addition to culturable microbes has been produced. To reconstruct microbial genomes, several assembly algorithms have been developed by incorporating metagenomic features, such as uneven depth. Since it is difficult to reconstruct complete microbial genomes from metagenomic reads, contig binning approaches were suggested to collect contigs that originate from the same genome. To estimate the microbial composition in the environment, various methods have been developed to classify individual reads or contigs and profile bacterial proportions. Since microbial communities affect their hosts and environments through metabolites, metabolic profiles from metagenomic or metatranscriptomic data have been estimated. Here, we provide a comprehensive review of computational methods that can be applied to investigate microbiomes using metagenomic and metatranscriptomic sequencing data. The limitations of metagenomic studies and the key approaches to overcome such problems are discussed.

GraphBin: refined binning of metagenomic contigs using assembly graphs.

The field of metagenomics has provided valuable insights into the structure, diversity and ecology within microbial communities. One key step in metagenomics analysis is to assemble reads into longer contigs which are then binned into groups of contigs that belong to different species present in the metagenomic sample. Binning of contigs plays an important role in metagenomics and most available binning algorithms bin contigs using genomic features such as oligonucleotide/k-mer composition and contig coverage. As metagenomic contigs are derived from the assembly process, they are output from the underlying assembly graph which contains valuable connectivity information between contigs that can be used for binning. We propose GraphBin, a new binning method that makes use of the assembly graph and applies a label propagation algorithm to refine the binning result of existing tools. We show that GraphBin can make use of the assembly graphs constructed from both the de Bruijn graph and the overlap-layout-consensus approach. Moreover, we demonstrate improved experimental results from GraphBin in terms of identifying mis-binned contigs and binning of contigs discarded by existing binning tools. To the best of our knowledge, this is the first time that the information from the assembly graph has been used in a tool for the binning of metagenomic contigs. The source code of GraphBin is available at https://github.com/Vini2/GraphBin. vijini.mallawaarachchi@anu.edu.au or yu.lin@anu.edu.au. Supplementary data are available at Bioinformatics online.

Mini-review: Advances of metagenomics research for lake microbiomes

湖泊微生物作为湖泊生态系统重要组成部分，在局域和区域的元素循环中发挥着关键作用.由于自然环境中微生物之间的复杂关系和对微生物认知的片面性，可在实验室培养的湖泊微生物比例不足1%.近10年来，宏基因组学技术在微生物生态学研究中得到了广泛应用，不仅扩展了对湖泊微生物群落组成和多样性的认识，更揭示了湖泊微生物的功能多样性和微生物之间的相互作用.特别是基于宏基因组数据的分装（Binning）手段，可以获取大量湖泊中未培养微生物的基因组信息，用于后续的比较基因组、生态进化和培养组学等研究.随着宏基因组学相关学科和技术的不断发展，其将在湖泊微生物生态学基础理论研究和环境生物监测应用中发挥更为重要的作用，成为人类了解湖泊生态系统功能和维持机制的有力工具.;As one of the essential components, microorganisms are the cores of biogeochemical circulation in lakes. However, due to the complex interaction among microorganisms and the incomplete description of their habitats, less than 1% of microorganisms in lakes can be cultivated in the laboratory. In the past ten years, metagenomic methods has been widely applied to the microbial researches, which enormously contribute to the understanding of microbiomes in lake ecosystems. The obtained results not only uncovered the composition and diversity of microbial communities, but also revealed the ecological functions of microbes as well as the interactions among microorganisms. Moreover, metagenome-assembled genomes (MAGs) of uncultured microorganisms can be obtained by various contig binning strategies based on metagenomic data mining, which can be subsequently used for comparative genomics and ecological evolution studies. With the continuous development of bioinformatics discipline and the relevant sequencing technologies, metagenomics will become a more powerful tool in basic ecological principle exploration and routine environmental biomonitoring, and also become the cornerstone of understanding the ecosystem function and maintaining the ecological services of lakes.

A binning tool to reconstruct viral haplotypes from assembled contigs

BackgroundInfections by RNA viruses such as Influenza, HIV still pose a serious threat to human health despite extensive research on viral diseases. One challenge for producing effective prevention and treatment strategies is high intra-species genetic diversity. As different strains may have different biological properties, characterizing the genetic diversity is thus important to vaccine and drug design. Next-generation sequencing technology enables comprehensive characterization of both known and novel strains and has been widely adopted for sequencing viral populations. However, genome-scale reconstruction of haplotypes is still a challenging problem. In particular, haplotype assembly programs often produce contigs rather than full genomes. As a mutation in one gene can mask the phenotypic effects of a mutation at another locus, clustering these contigs into genome-scale haplotypes is still needed.ResultsWe developed a contig binning tool, VirBin, which clusters contigs into different groups so that each group represents a haplotype. Commonly used features based on sequence composition and contig coverage cannot effectively distinguish viral haplotypes because of their high sequence similarity and heterogeneous sequencing coverage for RNA viruses. VirBin applied prototype-based clustering to cluster regions that are more likely to contain mutations specific to a haplotype. The tool was tested on multiple simulated sequencing data with different haplotype abundance distributions and contig sizes, and also on mock quasispecies sequencing data. The benchmark results with other contig binning tools demonstrated the superior sensitivity and precision of VirBin in contig binning for viral haplotype reconstruction.ConclusionsIn this work, we presented VirBin, a new contig binning tool for distinguishing contigs from different viral haplotypes with high sequence similarity. It competes favorably with other tools on viral contig binning. The source codes are available at: https://github.com/chjiao/VirBin.

MetaCon: unsupervised clustering of metagenomic contigs with probabilistic k-mers statistics and coverage

MotivationSequencing technologies allow the sequencing of microbial communities directly from the environment without prior culturing. Because assembly typically produces only genome fragments, also known as contigs, it is crucial to group them into putative species for further taxonomic profiling and down-streaming functional analysis. Taxonomic analysis of microbial communities requires contig clustering, a process referred to as binning, that is still one of the most challenging tasks when analyzing metagenomic data. The major problems are the lack of taxonomically related genomes in existing reference databases, the uneven abundance ratio of species, sequencing errors, and the limitations due to binning contig of different lengths.ResultsIn this context we present MetaCon a novel tool for unsupervised metagenomic contig binning based on probabilistic k-mers statistics and coverage. MetaCon uses a signature based on k-mers statistics that accounts for the different probability of appearance of a k-mer in different species, also contigs of different length are clustered in two separate phases. The effectiveness of MetaCon is demonstrated in both simulated and real datasets in comparison with state-of-art binning approaches such as CONCOCT, MaxBin and MetaBAT.

Using QC-Blind for Quality Control and Contamination Screening of Bacteria DNA Sequencing Data Without Reference Genome.

Quality control for next generation sequencing (NGS) has become increasingly important with the ever increasing importance of sequencing data for omics studies. Tools have been developed for filtering possible contaminants from species with known reference genome. Unfortunately, reference genomes for all the species involved, including the contaminants, are required for these tools to work. This precludes many real-life samples that have no information about the complete genome of the target species, and are contaminated with unknown microbial species. In this work we proposed QC-Blind, a novel quality control pipeline for removing contaminants without any use of reference genomes. The pipeline merely requires the information about a few marker genes of the target species. The entire pipeline consists of unsupervised read assembly, contig binning, read clustering, and marker gene assignment. When evaluated on in silico, ab initio and in vivo datasets, QC-Blind proved effective in removing unknown contaminants with high specificity and accuracy, while preserving most of the genomic information of the target bacterial species. Therefore, QC-Blind could serve well in situations where limited information is available for both target and contamination species.

SolidBin: improving metagenome binning with semi-supervised normalized cut.

Metagenomic contig binning is an important computational problem in metagenomic research, which aims to cluster contigs from the same genome into the same group. Unlike classical clustering problem, contig binning can utilize known relationships among some of the contigs or the taxonomic identity of some contigs. However, the current state-of-the-art contig binning methods do not make full use of the additional biological information except the coverage and sequence composition of the contigs. We developed a novel contig binning method, Semi-supervised Spectral Normalized Cut for Binning (SolidBin), based on semi-supervised spectral clustering. Using sequence feature similarity and/or additional biological information, such as the reliable taxonomy assignments of some contigs, SolidBin constructs two types of prior information: must-link and cannot-link constraints. Must-link constraints mean that the pair of contigs should be clustered into the same group, while cannot-link constraints mean that the pair of contigs should be clustered in different groups. These constraints are then integrated into a classical spectral clustering approach, normalized cut, for improved contig binning. The performance of SolidBin is compared with five state-of-the-art genome binners, CONCOCT, COCACOLA, MaxBin, MetaBAT and BMC3C on five next-generation sequencing benchmark datasets including simulated multi- and single-sample datasets and real multi-sample datasets. The experimental results show that, SolidBin has achieved the best performance in terms of F-score, Adjusted Rand Index and Normalized Mutual Information, especially while using the real datasets and the single-sample dataset. https://github.com/sufforest/SolidBin. Supplementary data are available at Bioinformatics online.

Genomic reconstructions and potential metabolic strategies of generalist and specialist heterotrophic bacteria associated with an estuary Synechococcus culture.

Interactions between photoautotrophs and heterotrophs are central to marine microbial ecosystems. Synechococcus are dominant marine phototrophs, and they are frequently associated with heterotrophic bacteria. These co-cultures provide a useful research system to investigate photoautotroph-heterotroph interactions in marine systems. Bacteria within the Roseobacter clade and Flavobacteria are two of the main bacterial lineages that exhibit intimate associations with Synechococcus populations. We conducted metagenomic analyses of a Synechococcus culture, followed by genomic binning of metagenomic contigs, and recovered five nearly complete genomes, including members of the Roseobacter clade (i.e. Marivita sp. XM-24) and Flavobacteria (i.e. Fluviicola sp. XM-24). Marivita sp. XM-24 is an ecological generalist of the Roseobacter clade and displays diverse metabolic capacities for the acquisition of nutrients and energy sources. Specifically, the genome contained numerous gene complements involved in the uptake and metabolism of nitrogen- and phosphorus-containing inorganic and organic compounds, in addition to the potential for aerobic anoxygenic photosynthesis, oxidation of carbon monoxide, inorganic sulfur oxidation, DMSP demethylation and PHA metabolism. The genome of the Flavobacteria representative, Fluviicola sp. XM-24, contained numerous peptidases, glycoside hydrolases, adhesion-related proteins and genes involved in gliding motility. Fluviicola sp. XM-24 likely specialize in the degradation of high molecular weight compound exudates from Synechococcus cells, including polysaccharides and polypeptides via attachment to particles, surfaces or cells. The distinct metabolic strategies identified within several heterotrophic bacteria that are associated with Syneochococcus cells provide insights into their lifestyles and nutrient utilization patterns, in addition to their interactions with photoautotrophs. Biological interactions, including mutualism, competition and antagonism, shape the microbial community structure of marine environments and are critical for understanding biogeochemical cycling in the ocean. These results provide valuable insights into the nature of interactions between dominant marine photoautotrophs and associated bacterial heterotrophs.

Fiber-associated spirochetes are major agents of hemicellulose degradation in the hindgut of wood-feeding higher termites

Symbiotic digestion of lignocellulose in wood-feeding higher termites (family Termitidae) is a two-step process that involves endogenous host cellulases secreted in the midgut and a dense bacterial community in the hindgut compartment. The genomes of the bacterial gut microbiota encode diverse cellulolytic and hemicellulolytic enzymes, but the contributions of host and bacterial symbionts to lignocellulose degradation remain ambiguous. Our previous studies of Nasutitermes spp. documented that the wood fibers in the hindgut paunch are consistently colonized not only by uncultured members of Fibrobacteres, which have been implicated in cellulose degradation, but also by unique lineages of Spirochaetes. Here, we demonstrate that the degradation of xylan, the major component of hemicellulose, is restricted to the hindgut compartment, where it is preferentially hydrolyzed over cellulose. Metatranscriptomic analysis documented that the majority of glycoside hydrolase (GH) transcripts expressed by the fiber-associated bacterial community belong to family GH11, which consists exclusively of xylanases. The substrate specificity was further confirmed by heterologous expression of the gene encoding the predominant homolog. Although the most abundant transcripts of GH11 in Nasutitermes takasagoensis were phylogenetically placed among their homologs of Firmicutes, immunofluorescence microscopy, compositional binning of metagenomics contigs, and the genomic context of the homologs indicated that they are encoded by Spirochaetes and were most likely obtained by horizontal gene transfer among the intestinal microbiota. The major role of spirochetes in xylan degradation is unprecedented and assigns the fiber-associated Treponema clades in the hindgut of wood-feeding higher termites a prominent part in the breakdown of hemicelluloses.