The UniFrac significance test generates different outputs given semantically equivalent inputs

With the advance of new sequencing technologies producing massive short reads data, metagenomics is rapidly growing, especially in the fields of environmental biology and medical science. The metagenomic data are not only high dimensional with large number of features and limited number of samples but also complex with a large number of zeros and skewed distribution. Efficient computational and statistical tools are needed to deal with these unique characteristics of metagenomic sequencing data. In metagenomic studies, one main objective is to assess whether and how multiple microbial communities differ under various environmental conditions. We propose a two-stage statistical procedure for selecting informative features and identifying differentially abundant features between two or more groups of microbial communities. In the functional analysis of metagenomes, the features may refer to the pathways, subsystems, functional roles and so on. In the first stage of the proposed procedure, the informative features are selected using elastic net as reducing the dimension of metagenomic data. In the second stage, the differentially abundant features are detected using generalized linear models with a negative binomial distribution. Compared with other available methods, the proposed approach demonstrates better performance for most of the comprehensive simulation studies. The new method is also applied to two real metagenomic datasets related to human health. Our findings are consistent with those in previous reports. R code and two example datasets are available at http://cals.arizona.edu/∼anling/software.htm. Supplementary file is available at Bioinformatics online.

As more than 90% of microbial community could not be isolated and cultivated, the metagenomic methods have been commonly used to analyze the microbial community as a whole. With the fast acumination of metagenomic samples, it is now intriguing to find simple biomarkers, especially functional biomarkers, which could distinguish different metagenomic samples. Next-generation sequencing techniques have enabled the detection of very accurate gene-presence (abundance) values in metagenomic studies. And the presence/absence or different abundance values for a set of genes could be used as appropriate biomarker for identification of the corresponding microbial community's phenotype. However, it is not yet clear how to select such a set of genes (features), and how accurate would it be for such a set of selected genes on prediction of microbial community's phenotype. In this study, we have evaluated different machine learning methods, including feature selection methods and classification methods, for selection of biomarkers that could distinguish different samples. Then we proposed a machine learning framework, which could discover biomarkers for different microbial communities from the mining of metagenomic data. Given a set of features (genes) and their presence values in multiple samples, we first selected discriminative features as candidate by feature selection, and then selected the feature sets with low error rate and classification accuracies as biomarkers by classification method. We have selected whole genome sequencing data from simulation, public domain and in-house metagenomic data generation facilities. We tested the framework on prediction and evaluation of the biomarkers. Results have shown that the framework could select functional biomarkers with very high accuracy. Therefore, this framework would be a suitable tool to discover functional biomarkers to distinguish different microbial communities.

Microorganisms are everywhere. Recent studies showed that the mixture of microbes or the microbiome on the human body plays important roles in human physiology and diseases. Metagenomic sequencing is a key technology for studying microbiomes. It produces massive amounts of data in the form of short sequencing reads. A single metagenomic sample can contain 10 7 to 10 8 reads of about 100-nucleotide (nt) length each in a typical shotgun metagenomic sequencing study. They contain rich information about microbiomes and their functions, but reading out those information from the huge highly fragmented data has multiple challenges for mathematical models, bioinformatics methods, and computer algorithms. In this paper, we review the basic bioinformatics tasks and existing methods in processing and analyzing metagenomic data, and discuss remaining open challenges and practical observations. The aim of the paper is to provide readers a whole picture of metagenomic data processing and analysis, and a reference and perspective to start with for computational scientists who are interested in this exciting field.

Slim sets of binary trees

BackgroundThe accuracy of metagenomic assembly is usually compromised by high levels of polymorphism due to divergent reads from the same genomic region recognized as different loci when sequenced and assembled together. A viral quasispecies is a group of abundant and diversified genetically related viruses found in a single carrier. Current mainstream assembly methods, such as Velvet and SOAPdenovo, were not originally intended for the assembly of such metagenomics data, and therefore demands for new methods to provide accurate and informative assembly results for metagenomic data.ResultsIn this study, we present a hybrid method for assembling highly polymorphic data combining the partial de novo-reference assembly (PDR) strategy and the BLAST-based assembly pipeline (BBAP). The PDR strategy generates in situ reference sequences through de novo assembly of a randomly extracted partial data set which is subsequently used for the reference assembly for the full data set. BBAP employs a greedy algorithm to assemble polymorphic reads. We used 12 hepatitis B virus quasispecies NGS data sets from a previous study to assess and compare the performance of both PDR and BBAP. Analyses suggest the high polymorphism of a full metagenomic data set leads to fragmentized de novo assembly results, whereas the biased or limited representation of external reference sequences included fewer reads into the assembly with lower assembly accuracy and variation sensitivity. In comparison, the PDR generated in situ reference sequence incorporated more reads into the final PDR assembly of the full metagenomics data set along with greater accuracy and higher variation sensitivity. BBAP assembly results also suggest higher assembly efficiency and accuracy compared to other assembly methods. Additionally, BBAP assembly recovered HBV structural variants that were not observed amongst assembly results of other methods. Together, PDR/BBAP assembly results were significantly better than other compared methods.ConclusionsBoth PDR and BBAP independently increased the assembly efficiency and accuracy of highly polymorphic data, and assembly performances were further improved when used together. BBAP also provides nucleotide frequency information. Together, PDR and BBAP provide powerful tools for metagenomic data studies.

An information and statistical analysis pipeline for microbial metagenomic sequencing data

and jellyfish models, and ecology of cyanophages and bathypelagic viruses.Scanning electron microscopy and scanning transmission electron microscopy are important approaches to visualize and image viruses and virus-like particles (VLPs). Kakol et al. developed and tested three different protocols for sample preparation and imaging. They found that these three different protocols have distinct advantages and disadvantages for different viral samples, highlighting that the choice of the analytical procedure significantly influences the resolution and preservation state of the observed phages and VLPs. To this end, the authors suggested that the appropriate imaging technique should be carefully selected based on the specific objectives of the project and the nature of the samples being investigated to obtain the best images of the viruses.In the era of metagenomics, it remains crucial to isolate new viruses and to characterize the interactions between virus-host model systems. Wang et al. isolated and characterized a novel T4-like cyanophage Nanhaivirus ms29 from the ocean basin in the South China Sea.Bioinformatic analysis showed that this cyanophage is mainly distributed in temperate and tropical epipelagic waters. Abundant auxiliary metabolic genes were identified in the phage genome, possibly reflecting a genomic adaption to the oligotrophic environment. Further phylogenetic analysis demonstrated that Nanhaivirus ms29 is distinct from other known T4-like cyanophages and belongs to a novel genus within the family Kyanoviridae. Viruses and hosts are in an ongoing evolutionary 'arms race'. To survive the constant infection of viruses, hosts have evolved mechanistically diverse defense strategies. Zeng et al.explored the evolutionary adaption of Vibrio parahaemolyticus against two kinds of marine phages. They found that V. parahaemolyticus inhibited phage adsorption to its surface by mutating the host flaG gene. However, this anti-phage strategy also led to the reduced growth competitiveness of the anti-phage mutant strain. These results suggested that selection pressure on different anti-phage strategies depends on the trade-off between mortality imposed by phages and the fitness cost of the defense strategy under the given environmental conditions.Focusing on the antiviral response in marine crustaceans, Hu et al. evaluated the role of apoptosis inhibitor 5 (API5) in the immune response of mud crab (Scylla paramamosain) against White Spot Syndrome Virus (WSSV) infection. The authors found that API5 was upregulated upon WSSV infection, and the silencing of API5 led to increased WSSV copy numbers and apoptotic rate of hemocytes, highlighting its important role in the immune response. Further investigation showed that API5 interacted with Heat Shock Protein 20 (Hsp20), which could promote cell apoptosis of hemocytes and reduce viral copy numbers.Focusing on the role of phage-bacteria interaction in metaorganism biology, Stante et al.exposed moon jellyfish Aurelia aurita to individual phages and a phage cocktail, and monitored polyp survival and morphology, as well as microbiome changes. The results showed that phage exposure altered the microbiota associated with A. aurita, thereby leading to recoverable malformations in polyps without affecting their survival. Moreover, this study also demonstrates the overall resilience of the A. aurita metaorganism facing phage challenges, since the main colonizer of A. aurita, likely a novel Mycoplasma species, showed resilience upon phage exposure.Over the past decades, the application of metagenomics has greatly expanded our knowledge of marine viruses. However, metagenomic studies on bathypelagic viruses remain limited. Sun et al. analyzed the 16S rRNA sequencing and viral metagenomic sequencing data of 25 samples collected from five different bathypelagic ecosystems. The low phage/host ratios in these abyssal ecosystems were different from shallow ecosystems, indicating the prevalence of lysogeny among bathypelagic viruses. Moreover, the correlation analysis revealed several phage-bacteria interaction networks of potential ecological relevance, providing novel insights into the interactions between bathypelagic bacteria and their phages.Lastly, viral lysis of host cells releases organic matter and nutrients that affect the surrounding microbial community. Man et al. explored how viral lysis of Synechococcus affected the co-existing bacteria and nutrients in the culture. The bacterial community structure was altered after cyanophage infection, and increased bacterial diversity and richness were observed. The nutrients in the cyanophage-added culture featured decreased nitrate and increased ammonium and phosphate, which is coupled with the viral progeny production and increased substance transport and metabolism potentials of the bacterial community. Furthermore, cyanophage infections contributed to the bacterial production of methane-related compounds and refractory organic matter in the culture. This study helps to deepen our understanding of the impact of viral lysis of cyanobacteria on the surrounding marine microbial community.

MotivationBacteriophages are viruses infecting bacteria. Being key players in microbial communities, they can regulate the composition/function of microbiome by infecting their bacterial hosts and mediating gene transfer. Recently, metagenomic sequencing, which can sequence all genetic materials from various microbiome, has become a popular means for new phage discovery. However, accurate and comprehensive detection of phages from the metagenomic data remains difficult. High diversity/abundance, and limited reference genomes pose major challenges for recruiting phage fragments from metagenomic data. Existing alignment-based or learning-based models have either low recall or precision on metagenomic data.ResultsIn this work, we adopt the state-of-the-art language model, Transformer, to conduct contextual embedding for phage contigs. By constructing a protein-cluster vocabulary, we can feed both the protein composition and the proteins’ positions from each contig into the Transformer. The Transformer can learn the protein organization and associations using the self-attention mechanism and predicts the label for test contigs. We rigorously tested our developed tool named PhaMer on multiple datasets with increasing difficulty, including quality RefSeq genomes, short contigs, simulated metagenomic data, mock metagenomic data and the public IMG/VR dataset. All the experimental results show that PhaMer outperforms the state-of-the-art tools. In the real metagenomic data experiment, PhaMer improves the F1-score of phage detection by 27%.

Metagenomic binning is an essential technique for genome-resolved characterization of uncultured microorganisms in various ecosystems but hampered by the low efficiency of binning tools in adequately recovering metagenome-assembled genomes (MAGs). Here, we introduce BASALT (Binning Across a Series of Assemblies Toolkit) for binning and refinement of short- and long-read sequencing data. BASALT employs multiple binners with multiple thresholds to produce initial bins, then utilizes neural networks to identify core sequences to remove redundant bins and refine non-redundant bins. Using the same assemblies generated from Critical Assessment of Metagenome Interpretation (CAMI) datasets, BASALT produces up to twice as many MAGs as VAMB, DASTool, or metaWRAP. Processing assemblies from a lake sediment dataset, BASALT produces ~30% more MAGs than metaWRAP, including 21 unique class-level prokaryotic lineages. Functional annotations reveal that BASALT can retrieve 47.6% more non-redundant opening-reading frames than metaWRAP. These results highlight the robust handling of metagenomic sequencing data of BASALT.

Engineered microorganisms in biotechnology present biosafety and environmental management challenges. As the synthetic biology market develops and deploys new technologies, these engineered organisms may escape into unintended environments. Improved predictive computational tools are necessary to assess the potential establishment risk and environmental location of these escaped engineered microorganisms, assisting their design and management. Here, we present EnCen, a risk assessment Python software package that predicts the environmental range of engineered microorganisms through annotated functional one-hot-encoded similarity between the engineered microorganism and resident microorganisms of a given environment. EnCen utilizes publicly available composite metagenomes as representatives of microbial environments that occur along an agriculture-water cycle and can be customized for any additional target environment. This tool was deployed against case studies reported in the literature and to reassess commercially available bacterial biopesticides, highlighting both the successful recapture of previously reported dynamics and the identification of select commercial products that pose a wider establishment risk in multiple environments. When further utilizing EnCen to investigate the receiving environments comprising the central database, key enzyme classes are mapped as characteristics to select environments, prioritizing certain modifications likely leading to a greater risk (or effectiveness) of establishment. The results demonstrate that EnCen meaningfully summarizes publicly available metagenomic data, prioritizes environments to monitor for adverse effects, and analyzes potential impacts on microbial community composition and functioning. Overall, this study demonstrates a computational approach to managing engineered microorganisms, aiding in the safe deployment and benefit of industrial synthetic biology.

Microbial diversity is typically characterized by clustering ribosomal RNA (SSU-rRNA) sequences into operational taxonomic units (OTUs). Targeted sequencing of environmental SSU-rRNA markers via PCR may fail to detect OTUs due to biases in priming and amplification. Analysis of shotgun sequenced environmental DNA, known as metagenomics, avoids amplification bias but generates fragmentary, non-overlapping sequence reads that cannot be clustered by existing OTU-finding methods. To circumvent these limitations, we developed PhylOTU, a computational workflow that identifies OTUs from metagenomic SSU-rRNA sequence data through the use of phylogenetic principles and probabilistic sequence profiles. Using simulated metagenomic data, we quantified the accuracy with which PhylOTU clusters reads into OTUs. Comparisons of PCR and shotgun sequenced SSU-rRNA markers derived from the global open ocean revealed that while PCR libraries identify more OTUs per sequenced residue, metagenomic libraries recover a greater taxonomic diversity of OTUs. In addition, we discover novel species, genera and families in the metagenomic libraries, including OTUs from phyla missed by analysis of PCR sequences. Taken together, these results suggest that PhylOTU enables characterization of part of the biosphere currently hidden from PCR-based surveys of diversity?

Cyanobacteria bloom is a serious public health threat and a global challenge. Literature on the bloom prediction and forecasting has been accumulating and the emphasis appears to have been on the relation between the blooms and environmental factors, whilst the complexity of the bloom mechanism makes it difficult to reach adequate output of the models. Rapid development of next generation sequencing techniques provides a way in which comprehensive and quick examination of the microbial community can be achieved, especially for the bloom community structure. This facilitates using of merely the sequence data along with the machine learning techniques to predict and forecast the bloom occurrence. But there has been rare report on this theme in the literature. In this case study, machine learning approaches were applied with the metagenomic data as the only input (rather than with environmental data) to predict the Cyanobacteria blooms. k-NN classification, SVM classification and k-means clustering were applied and their efficiencies were evaluated using relevant indices. Feature selection was performed and the yielded sub datasets were worked on seriatim. In the predicting experiment with k-NN approach, the final year's data among the 8 years OTU time series were used as target data and various combination of the preceding years' data were used as predictor data; the output came with the best values of 1.00 and 100% for the evaluation indices F1 score and sensitivity, specificity, precision, and accuracy, for the 7 preceding years' predictor input, among the experiment results. This case study demonstrated the feasibility of using machine learning approaches in the Cyanobacteria bloom prediction with only metagenomic sequence data, and the importance of feature selection processing in obtaining better output of the machine learning approaches. The metagenomic data based machine learning approaches are efficient, economic, and faster, possessing the advantage and potential for being adopted as a promising means in the bloom prediction practice.

Given a set $\mathcal{T}= \{T_{1}, T_{2}, . . .\, T_{m}\}$ of phylogenetic trees with the same leaf-label set X, we wish to remove some leaves from the trees so that there is a tree T with leaf-label set X displaying all the resulting trees. Note that different leaves may be removed from different input trees. One objective is to minimize the total number of leaves removed from the trees, while the other is to minimize the maximum number of leaves removed from an input tree. Chauve et at. [2] refer to the problem with the first (respectively, second) objective as AST-LR (respectively, AST-LR-d), and show that both problems are NP-hard. They further present algorithms for the parameterized versions of both problems. In this paper, we point out that their algorithm for the parameterized version of AST-LR is flawed. We then design integer-linear programming (ILP) models for AST-LR and AST-LR-d, and discuss speedup issues when using popular ILP solvers (say, GUROBI or CPLEX) to solve the models. Our experimental results show that our ILP approach is relatively efficient.

In the past decade, metagenomics studies have become widespread due to the arrival of second-generation sequencing platforms characterized by low costs, high throughput and short read lengths. Today, although benchtop sequencers are considered to be accurate platforms to deliver data for targeted metagenomics studies, the limiting factor has become the analysis of these data. In a previous paper, we performed an Ion Torrent PGM 16S rDNA gene sequencing of faecal DNAs from 48 Blastocystis-colonized patients and 48 Blastocystis-negative subjects, in order to decipher the impact of this widespread protist on gut microbiota composition and diversity. We report here on the Ion Torrent targeted metagenomic sequencing and analysis of these 96 human faecal samples, and the complete datasets from raw to analysed data. We also provide the key steps of the bioinformatic analyses, from library preparation to data filtering and OTUs tables generation. This data represents a valuable resource for the scientific community, enabling re-processing of these targeted metagenomic datasets through various pipelines and a comparative evaluation of microbiota analysis methods.

Metagenomics studies have revealed tremendous viral diversity in aquatic environments. Yet, while the genomic data they have provided is extensive, it is unannotated. For example, most phage sequences lack accurate information about their bacterial host, which prevents reliable phage identification and the investigation of phage–host interactions. This study aimed to take this knowledge further, using a viral metagenomic framework to decipher the composition and diversity of phage communities and to predict their bacterial hosts. To this end, we used water and sediment samples collected from seven sites with varying contamination levels in the Ebrié Lagoon in Abidjan, Ivory Coast. The bacterial communities were characterized using the 16S rRNA metabarcoding approach, and a framework was developed to investigate the virome datasets that: (1) identified phage contigs with VirSorter and VIBRANT; (2) classified these contigs with MetaPhinder using the phage database (taxonomic annotation); and (3) predicted the phages’ bacterial hosts with a machine learning-based tool: the Prokaryotic Virus-Host Predictor. The findings showed that the taxonomic profiles of phages and bacteria were specific to sediment or water samples. Phage sequences assigned to the Microviridae family were widespread in sediment samples, whereas phage sequences assigned to the Siphoviridae, Myoviridae and Podoviridae families were predominant in water samples. In terms of bacterial communities, the phyla Latescibacteria, Zixibacteria, Bacteroidetes, Acidobacteria, Calditrichaeota, Gemmatimonadetes, Cyanobacteria and Patescibacteria were most widespread in sediment samples, while the phyla Epsilonbacteraeota, Tenericutes, Margulisbacteria, Proteobacteria, Actinobacteria, Planctomycetes and Marinimicrobia were most prevalent in water samples. Significantly, the relative abundance of bacterial communities (at major phylum level) estimated by 16S rRNA metabarcoding and phage-host prediction were significantly similar. These results demonstrate the reliability of this novel approach for predicting the bacterial hosts of phages from shotgun metagenomic sequencing data.