Reading the Underlying Information From Massive Metagenomic Sequencing Data

BackgroundNext-generation sequencing (NGS) has greatly facilitated metagenomic analysis but also raised new challenges for metagenomic DNA sequence assembly, owing to its high-throughput nature and extremely short reads generated by sequencers such as Illumina. To date, how to generate a high-quality draft assembly for metagenomic sequencing projects has not been fully addressed.ResultsWe conducted a comprehensive assessment on state-of-the-art de novo assemblers and revealed that the performance of each assembler depends critically on the sequencing depth. To address this problem, we developed a pipeline named InteMAP to integrate three assemblers, ABySS, IDBA-UD and CABOG, which were found to complement each other in assembling metagenomic sequences. Making a decision of which assembling approaches to use according to the sequencing coverage estimation algorithm for each short read, the pipeline presents an automatic platform suitable to assemble real metagenomic NGS data with uneven coverage distribution of sequencing depth. By comparing the performance of InteMAP with current assemblers on both synthetic and real NGS metagenomic data, we demonstrated that InteMAP achieves better performance with a longer total contig length and higher contiguity, and contains more genes than others.ConclusionsWe developed a de novo pipeline, named InteMAP, that integrates existing tools for metagenomics assembly. The pipeline outperforms previous assembly methods on metagenomic assembly by providing a longer total contig length, a higher contiguity and covering more genes. InteMAP, therefore, could potentially be a useful tool for the research community of metagenomics.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-015-0686-x) contains supplementary material, which is available to authorized users.

BackgroundComparison and classification of metagenome samples is one of the major tasks in the study of microbial communities of natural environments or niches on human bodies. Bioinformatics methods play important roles on this task, including 16S rRNA gene analysis and some alignment-based or alignment-free methods on metagenomic data. Alignment-free methods have the advantage of not depending on known genome annotations and therefore have high potential in studying complicated microbiomes. However, the existing alignment-free methods are all based on unsupervised learning strategy (e.g., PCA or hierarchical clustering). These types of methods are powerful in revealing major similarities and grouping relations between microbiome samples, but cannot be applied for discriminating predefined classes of interest which might not be the dominating assortment in the data. Supervised classification is needed in the latter scenario, with the goal of classifying samples into predefined classes and finding the features that can discriminate the classes. The effectiveness of supervised classification with alignment-based features on metagenomic data have been shown in some recent studies. The application of alignment-free supervised classification methods on metagenome data has not been well explored yet.ResultsWe developed a method for this task using k-tuple frequencies as features counted directly from metagenome short reads and the R-SVM (Recursive SVM) for feature selection and classification. We tested our method on a simulation dataset, a real dataset composed of several known genomes, and a real metagenome NGS short reads dataset. Experiments on simulated data showed that the method can classify the classes almost perfectly and can recover major sequence signatures that distinguish the two classes. On the real human gut metagenome data, the method can discriminate samples of inflammatory bowel disease (IBD) patients from control samples with high accuracy, which cannot be separated when comparing the samples with unsupervised clustering approaches.ConclusionsThe proposed alignment-free supervised classification method can perform well in discriminating of metagenomic samples of predefined classes and in selecting characteristic sequence features for the discrimination. This study shows as an example on the feasibility of using metagenome sequence features of microbiomes on human bodies to study specific human health conditions using supervised machine learning methods.

BackgroundNext-Generation-Sequencing (NGS) enables detection of microorganisms present in biological and other matrices of various origin and nature, allowing not only the identification of known phyla and strains but also the discovery of novel ones. The large amount of metagenomic shotgun data produced by NGS require comprehensive and user-friendly pipelines for data analysis, that speed up the bioinformatics steps, relieving the users from the need to manually perform complex and time-consuming tasks.ResultsWe describe here HOME-BIO (sHOtgun MEtagenomic analysis of BIOlogical entities), an exhaustive pipeline for metagenomics data analysis, comprising three independent analytical modules designed for an inclusive analysis of large NGS datasets.ConclusionsHOME-BIO is a powerful and easy-to-use tool that can be run also by users with limited computational expertise. It allows in-depth analyses by removing low-complexity/ problematic reads, integrating the analytical steps that lead to a comprehensive taxonomy profile of each sample by querying different source databases, and it is customizable according to specific users’ needs.

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.

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%.

An information and statistical analysis pipeline for microbial metagenomic sequencing data

Metagenomic sequence classification is a procedure to assign sequences to their source genomes. It is one of the important steps for metagenomic sequence data analysis. Although many methods exist, classification of high-throughput metagenomic sequence data in a limited time is still a challenge. We present here an ultra-fast metagenomic sequence classification system (MetaBinG) using graphic processing units (GPUs). The accuracy of MetaBinG is comparable to the best existing systems and it can classify a million of 454 reads within five minutes, which is more than 2 orders of magnitude faster than existing systems. MetaBinG is publicly available at http://cbb.sjtu.edu.cn/~ccwei/pub/software/MetaBinG/MetaBinG.php.

BackgroundMicrobial communities of the mouse gut have been extensively studied; however, their functional roles and regulation are yet to be elucidated. Metagenomic and metatranscriptomic analyses may allow us a comprehensive profiling of bacterial composition and functions of the complex gut microbiota. The present study aimed to investigate the active functions of the microbial communities in the murine cecum by analyzing both metagenomic and metatranscriptomic data on specific bacterial species within the microbial communities, in addition to the whole microbiome.ResultsBacterial composition of the healthy mouse gut microbiome was profiled using the following three different approaches: 16S rRNA-based profiling based on amplicon and shotgun sequencing data, and genome-based profiling based on shotgun sequencing data. Consistently, Bacteroidetes, Firmicutes, and Deferribacteres emerged as the major phyla. Based on NCBI taxonomy, Muribaculaceae, Lachnospiraceae, and Deferribacteraceae were the predominant families identified in each phylum. The genes for carbohydrate metabolism were upregulated in Muribaculaceae, while genes for cofactors and vitamin metabolism and amino acid metabolism were upregulated in Deferribacteraceae. The genes for translation were commonly enhanced in all three families. Notably, combined analysis of metagenomic and metatranscriptomic sequencing data revealed that the functions of translation and metabolism were largely upregulated in all three families in the mouse gut environment. The ratio of the genes in the metagenome and their expression in the metatranscriptome indicated higher expression of carbohydrate metabolism in Muribaculum, Duncaniella, and Mucispirillum.ConclusionsWe demonstrated a fundamental methodology for linking genomic and transcriptomic datasets to examine functional activities of specific bacterial species in a complicated microbial environment. We investigated the normal flora of the mouse gut using three different approaches and identified Muribaculaceae, Lachnospiraceae, and Deferribacteraceae as the predominant families. The functional distribution of these families was reflected in the entire microbiome. By comparing the metagenomic and metatranscriptomic data, we found that the expression rates differed for different functional categories in the mouse gut environment. Application of these methods to track microbial transcription in individuals over time, or before and after administration of a specific stimulus will significantly facilitate future development of diagnostics and treatments.

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?

Chapter 4 - Computational and Statistical Considerations in the Analysis of Metagenomic Data

Metagenomics (MG) and Metatranscriptomics (MT) approaches open new perspectives on the interpretation of biological systems composed by complex microbial communities. Dealing with large sequencing datasets, to extract the desired information and interpret the results are big challenges associated with meta-omics studies. There are several bioinformatics pipelines for MG data analysis and less to MT. Up to date, none performs a complete analysis integrating both MG and MT data, including the assembly of reads into contigs, functional and taxonomic annotation of identified genes, differential gene expression analysis and the comparison of multiple samples. Here, we present Meta-Omics Software for Community Analysis (MOSCA) that was designed with this purpose. It integrates RNA-Seq analysis with Whole Genome Sequencing as reference. Raw sequencing reads are submitted to preprocessing for quality trimming and rRNA removal, and assembled into contigs, which afterwards are annotated by using a reference database. MOSCA performs differential gene expression and provides graphical visualization of the results and comparison of multiple samples. Validation and reproducibility of the pipeline was obtained by using simulated MG and MT datasets.

BackgroundAnaerobic digestion has been widely applied to treat the waste activated sludge from biological wastewater treatment and produce methane for biofuel, which has been one of the most efficient solutions to both energy crisis and environmental pollution challenges. Anaerobic digestion sludge contains highly complex microbial communities, which play crucial roles in sludge treatment. However, traditional approaches based on 16S rRNA amplification or fluorescent in situ hybridization cannot completely reveal the whole microbial community structure due to the extremely high complexity of the involved communities. In this sense, the next-generation high-throughput sequencing provides a powerful tool for dissecting microbial community structure and methane-producing pathways in anaerobic digestion.ResultsIn this work, the metagenomic sequencing was used to characterize microbial community structure of the anaerobic digestion sludge from a full-scale municipal wastewater treatment plant. Over 3.0 gigabases of metagenomic sequence data were generated with the Illumina HiSeq 2000 platform. Taxonomic analysis by MG-RAST server indicated that overall bacteria were dominant (~93%) whereas a considerable abundance of archaea (~6%) were also detected in the anaerobic digestion sludge. The most abundant bacterial populations were found to be Proteobacteria, Firmicutes, Bacteroidetes, and Actinobacteria. Key microorganisms and related pathways involved in methanogenesis were further revealed. The dominant proliferation of Methanosaeta and Methanosarcina, together with the functional affiliation of enzymes-encoding genes (acetate kinase (AckA), phosphate acetyltransferase (PTA), and acetyl-CoA synthetase (ACSS)), suggested that the acetoclastic methanogenesis is the dominant methanogenesis pathway in the full-scale anaerobic digester.ConclusionsIn short, the metagenomic sequencing study of this work successfully dissected the detail microbial community structure and the dominated methane-producing pathways of a full-scale anaerobic digester. The knowledge garnered would facilitate to develop more efficient full-scale anaerobic digestion systems to achieve high-rate waste sludge treatment and methane production.Electronic supplementary materialThe online version of this article (doi:10.1186/s12934-015-0218-4) contains supplementary material, which is available to authorized users.

Host DNA depletion efficiency of microbiome DNA enrichment methods in infected tissue samples

Introduction. The strategy of correcting the composition of the gut microbiota demonstrates significant prospects for the treatment of diseases of the gastrointestinal tract. There is a confirmed link between the state of the colon microbiota and human pathologies. Monkeys, rhesus macaques, are recognized as the best animal model for studying the mechanisms affecting the development of acute and chronic inflammation. The similarity of key anatomical and physiological systems in macaques and humans provides significant advantages in conducting biomedical research on these animals to assess the safety and effectiveness of the developed microbial medicines for medical purposes proposed for the normalization of intestinal microbiocenosis. Objective. Analysis of the taxonomic composition of the gut microbiota of rhesus monkeys of various ages kept in captivity. Material and methods. The study included 17 faecal samples taken from clinically healthy rhesus monkeys specimens kept in captivity. DNA was isolated from faecal samples using the MagMax Microbiome Ultra Nucleic Acid Isolation Kit. The samples were analyzed by metagenomic sequencing of the V3-V4 region of the 16S rRNA gene. Results and discussion. Analysis of 16S rRNA metagenomic sequencing data showed that the gut microbiota of rhesus macaques is dominated by bacteria of the departments Firmicutes, Bacteriodetes and Actinobacteriota and their ratio varies in different age groups, which are also dominant in humans. With age, there was a decrease in the representation of Actinobacteriota and the ratio of Firmicutes/Bacteriodetes. The alpha diversity of the gut microbiota of different age groups of rhesus macaques did not differ statistically, at the same time, the highest absolute values of alpha diversity were observed in the group of young individuals. The analysis of the differential representation showed that the gut microbiota of young individuals is enriched with butyrate-producing bacteria, which indicates the absence of violations of the state of local intestinal immunity. Older individuals show an increased representation of Streptococcus and Lachnospiraceae, which can be interpreted as markers of inflammation, reflecting the age-related features of the gutmicrobiota of monkeys. Conclusion. The gut microbiota of rhesus monkeys varies in different age groups, while variations within the groups of cubs and young are less than in the group of old ones. The results of the sequencing of the gut microbiota of monkeys, which demonstrated the similarity of its composition with the human gut microbiota, combined with the peculiarities of age-related changes in the taxonomic composition of the gut microbiota of rhesus monkeys, which to a certain extent can be used as a model age dynamics of the human gut microbiota, are promising for age-related studies of various substances, compositions and other factors affecting the human body, potentially capable of positively or negatively affecting its microbiota.

Whole genome and metagenome sequencing are powerful approaches that enable comprehensive cataloging and profiling of antibiotic resistance genes at scales ranging from a single clinical isolate to ecosystems. Recent studies deal with genomic and metagenomic data sets at larger scales; therefore, designing computational workflows that provide high efficiency and accuracy is becoming more important. In this review, we summarize the computational workflows used in the research field of antibiotic resistome based on genome or metagenome sequencing. We introduce workflows, software tools, and data resources that have been successfully employed in this rapidly developing field. The workflow described in this review can be used to list the known antibiotic resistance genes from genomes and metagenomes, quantitatively profile them, and investigate the epidemiological and evolutionary contexts behind their emergence and transmission. We also discuss how novel antibiotic resistance genes can be discovered and how the association between the resistome and mobilome can be explored.