An accurate and fast alignment-free method for profiling microbial communities

Determining abundances of microbial genomes in metagenomic samples is an important problem in analyzing metagenomic data. Although homology-based methods are popular, they have shown to be computationally expensive due to the alignment of tens of millions of reads from metagenomic samples to reference genomes of hundreds to thousands of environmental microbial species. We introduce an efficient alignment-free approach to estimate abundances of microbial genomes in metagenomic samples. The approach is based on solving linear and quadratic programs, which are represented by genome-specific markers (GSM). We compared our method against popular alignment-free and homology-based methods. Without contamination, our method was more accurate than other alignment-free methods while being much faster than a homology-based method. In more realistic settings where samples were contaminated with human DNA, our method was the most accurate method in predicting abundance at varying levels of contamination. We achieve higher accuracy than both alignment-free and homology-based methods.

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nf-core/mag: a best-practice pipeline for metagenome hybrid assembly and binning

10.1101/2021.08.29.458094 ◽

2021 ◽

Author(s):

Sabrina Krakau ◽

Daniel Straub ◽

Hadrien Gourlé ◽

Gisela Gabernet ◽

Sven Nahnsen

Keyword(s):

Microbial Communities ◽

Best Practice ◽

Metagenomic Data ◽

Hybrid Assembly ◽

Individual Genome ◽

Long Reads ◽

Metagenome Assembly ◽

Group Information ◽

Genome Level ◽

Reference Genomes

The analysis of shotgun metagenomic data provides valuable insights into microbial communities, while allowing resolution at individual genome level. In absence of complete reference genomes, this requires the reconstruction of metagenome assembled genomes (MAGs) from sequencing reads. We present the nf-core/mag pipeline for metagenome assembly, binning and taxonomic classification. It can optionally combine short and long reads to increase assembly continuity and utilize sample-wise group-information for co-assembly and genome binning. The pipeline is easy to install - all dependencies are provided within containers -, portable and reproducible. It is written in Nextflow and developed as part of the nf-core initiative for best-practice pipeline development. All code is hosted on GitHub under the nf-core organization https://github.com/nf-core/mag and released under the MIT license.

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DOE JGI Metagenome Workflow

mSystems ◽

10.1128/msystems.00804-20 ◽

2021 ◽

Vol 6 (3) ◽

Author(s):

Alicia Clum ◽

Marcel Huntemann ◽

Brian Bushnell ◽

Brian Foster ◽

Bryce Foster ◽

...

Keyword(s):

Nucleic Acids ◽

Data Processing ◽

Microbial Communities ◽

Taxonomic Composition ◽

Metagenomic Data ◽

Data Sets ◽

Microbial Genomes ◽

Link Type ◽

Analysis System ◽

Rich Data

ABSTRACT The DOE Joint Genome Institute (JGI) Metagenome Workflow performs metagenome data processing, including assembly; structural, functional, and taxonomic annotation; and binning of metagenomic data sets that are subsequently included into the Integrated Microbial Genomes and Microbiomes (IMG/M) (I.-M. A. Chen, K. Chu, K. Palaniappan, A. Ratner, et al., Nucleic Acids Res, 49:D751–D763, 2021, https://doi.org/10.1093/nar/gkaa939) comparative analysis system and provided for download via the JGI data portal (https://genome.jgi.doe.gov/portal/). This workflow scales to run on thousands of metagenome samples per year, which can vary by the complexity of microbial communities and sequencing depth. Here, we describe the different tools, databases, and parameters used at different steps of the workflow to help with the interpretation of metagenome data available in IMG and to enable researchers to apply this workflow to their own data. We use 20 publicly available sediment metagenomes to illustrate the computing requirements for the different steps and highlight the typical results of data processing. The workflow modules for read filtering and metagenome assembly are available as a workflow description language (WDL) file (https://code.jgi.doe.gov/BFoster/jgi_meta_wdl). The workflow modules for annotation and binning are provided as a service to the user community at https://img.jgi.doe.gov/submit and require filling out the project and associated metadata descriptions in the Genomes OnLine Database (GOLD) (S. Mukherjee, D. Stamatis, J. Bertsch, G. Ovchinnikova, et al., Nucleic Acids Res, 49:D723–D733, 2021, https://doi.org/10.1093/nar/gkaa983). IMPORTANCE The DOE JGI Metagenome Workflow is designed for processing metagenomic data sets starting from Illumina fastq files. It performs data preprocessing, error correction, assembly, structural and functional annotation, and binning. The results of processing are provided in several standard formats, such as fasta and gff, and can be used for subsequent integration into the Integrated Microbial Genomes and Microbiomes (IMG/M) system where they can be compared to a comprehensive set of publicly available metagenomes. As of 30 July 2020, 7,155 JGI metagenomes have been processed by the DOE JGI Metagenome Workflow. Here, we present a metagenome workflow developed at the JGI that generates rich data in standard formats and has been optimized for downstream analyses ranging from assessment of the functional and taxonomic composition of microbial communities to genome-resolved metagenomics and the identification and characterization of novel taxa. This workflow is currently being used to analyze thousands of metagenomic data sets in a consistent and standardized manner.

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Development of a time-series shotgun metagenomics database for monitoring microbial communities at the Pacific coast of Japan

Scientific Reports ◽

10.1038/s41598-021-91615-3 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Kazutoshi Yoshitake ◽

Gaku Kimura ◽

Tomoko Sakami ◽

Tsuyoshi Watanabe ◽

Yukiko Taniuchi ◽

...

Keyword(s):

Time Series ◽

Microbial Communities ◽

Pacific Coast ◽

Three Dimensional ◽

Amplicon Sequencing ◽

Metagenomic Data ◽

Shotgun Metagenomics ◽

Functional Features ◽

Pacific Coast Of Japan ◽

Marine Microbial Communities

AbstractAlthough numerous metagenome, amplicon sequencing-based studies have been conducted to date to characterize marine microbial communities, relatively few have employed full metagenome shotgun sequencing to obtain a broader picture of the functional features of these marine microbial communities. Moreover, most of these studies only performed sporadic sampling, which is insufficient to understand an ecosystem comprehensively. In this study, we regularly conducted seawater sampling along the northeastern Pacific coast of Japan between March 2012 and May 2016. We collected 213 seawater samples and prepared size-based fractions to generate 454 subsets of samples for shotgun metagenome sequencing and analysis. We also determined the sequences of 16S rRNA (n = 111) and 18S rRNA (n = 47) gene amplicons from smaller sample subsets. We thereafter developed the Ocean Monitoring Database for time-series metagenomic data (http://marine-meta.healthscience.sci.waseda.ac.jp/omd/), which provides a three-dimensional bird’s-eye view of the data. This database includes results of digital DNA chip analysis, a novel method for estimating ocean characteristics such as water temperature from metagenomic data. Furthermore, we developed a novel classification method that includes more information about viruses than that acquired using BLAST. We further report the discovery of a large number of previously overlooked (TAG)n repeat sequences in the genomes of marine microbes. We predict that the availability of this time-series database will lead to major discoveries in marine microbiome research.

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Microbial drivers of methane emissions from unrestored industrial salt ponds

The ISME Journal ◽

10.1038/s41396-021-01067-w ◽

2021 ◽

Author(s):

Jinglie Zhou ◽

Susanna M. Theroux ◽

Clifton P. Bueno de Mesquita ◽

Wyatt H. Hartman ◽

Ye Tian ◽

...

Keyword(s):

Microbial Communities ◽

Methane Flux ◽

Methane Emissions ◽

Metagenomic Data ◽

Rrna Gene ◽

Salt Ponds ◽

Salt Pond ◽

Reference Wetlands ◽

Reference Wetland ◽

The Impact

AbstractWetlands are important carbon (C) sinks, yet many have been destroyed and converted to other uses over the past few centuries, including industrial salt making. A renewed focus on wetland ecosystem services (e.g., flood control, and habitat) has resulted in numerous restoration efforts whose effect on microbial communities is largely unexplored. We investigated the impact of restoration on microbial community composition, metabolic functional potential, and methane flux by analyzing sediment cores from two unrestored former industrial salt ponds, a restored former industrial salt pond, and a reference wetland. We observed elevated methane emissions from unrestored salt ponds compared to the restored and reference wetlands, which was positively correlated with salinity and sulfate across all samples. 16S rRNA gene amplicon and shotgun metagenomic data revealed that the restored salt pond harbored communities more phylogenetically and functionally similar to the reference wetland than to unrestored ponds. Archaeal methanogenesis genes were positively correlated with methane flux, as were genes encoding enzymes for bacterial methylphosphonate degradation, suggesting methane is generated both from bacterial methylphosphonate degradation and archaeal methanogenesis in these sites. These observations demonstrate that restoration effectively converted industrial salt pond microbial communities back to compositions more similar to reference wetlands and lowered salinities, sulfate concentrations, and methane emissions.

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The Microbiome Modeling Toolbox: from microbial interactions to personalized microbial communities

Bioinformatics ◽

10.1093/bioinformatics/bty941 ◽

2018 ◽

Vol 35 (13) ◽

pp. 2332-2334 ◽

Cited By ~ 16

Author(s):

Federico Baldini ◽

Almut Heinken ◽

Laurent Heirendt ◽

Stefania Magnusdottir ◽

Ronan M T Fleming ◽

...

Keyword(s):

Microbial Communities ◽

Microbial Interactions ◽

Microbial Genome ◽

Metagenomic Data ◽

Metabolic Interactions ◽

Constraint Based Modeling ◽

Genome Scale

Abstract Motivation The application of constraint-based modeling to functionally analyze metagenomic data has been limited so far, partially due to the absence of suitable toolboxes. Results To address this gap, we created a comprehensive toolbox to model (i) microbe–microbe and host–microbe metabolic interactions, and (ii) microbial communities using microbial genome-scale metabolic reconstructions and metagenomic data. The Microbiome Modeling Toolbox extends the functionality of the constraint-based reconstruction and analysis toolbox. Availability and implementation The Microbiome Modeling Toolbox and the tutorials at https://git.io/microbiomeModelingToolbox.

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Oral Spirochetes Implicated in Dental Diseases Are Widespread in Normal Human Subjects and Carry Extremely Diverse Integron Gene Cassettes

Applied and Environmental Microbiology ◽

10.1128/aem.00564-12 ◽

2012 ◽

Vol 78 (15) ◽

pp. 5288-5296 ◽

Cited By ~ 17

Author(s):

Yu-Wei Wu ◽

Mina Rho ◽

Thomas G. Doak ◽

Yuzhen Ye

Keyword(s):

Microbial Communities ◽

De Novo ◽

Human Subjects ◽

Human Microbiome ◽

Metagenomic Data ◽

De Bruijn Graph ◽

Content Type ◽

Gene Cassettes ◽

Dental Diseases ◽

Normal Human

ABSTRACTThe NIH Human Microbiome Project (HMP) has produced several hundred metagenomic data sets, allowing studies of the many functional elements in human-associated microbial communities. Here, we survey the distribution of oral spirochetes implicated in dental diseases in normal human individuals, using recombination sites associated with the chromosomal integron inTreponemagenomes, taking advantage of the multiple copies of the integron recombination sites (repeats) in the genomes, and using a targeted assembly approach that we have developed. We find that integron-containingTreponemaspecies are present in ∼80% of the normal human subjects included in the HMP. Further, we are able tode novoassemble the integron gene cassettes using our constrained assembly approach, which employs a unique application of the de Bruijn graph assembly information; most of these cassette genes were not assembled in whole-metagenome assemblies and could not be identified by mapping sequencing reads onto the known referenceTreponemagenomes due to the dynamic nature of integron gene cassettes. Our study significantly enriches the gene pool known to be carried byTreponemachromosomal integrons, totaling 826 (598 97% nonredundant) genes. We characterize the functions of these gene cassettes: many of these genes have unknown functions. The integron gene cassette arrays found in the human microbiome are extraordinarily dynamic, with different microbial communities sharing only a small number of common genes.

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MetaDEGalaxy: Galaxy workflow for differential abundance analysis of 16s metagenomic data

F1000Research ◽

10.12688/f1000research.18866.2 ◽

2019 ◽

Vol 8 ◽

pp. 726

Author(s):

Mike W.C. Thang ◽

Xin-Yi Chua ◽

Gareth Price ◽

Dominique Gorse ◽

Matt A. Field

Keyword(s):

Microbial Communities ◽

Sequence Data ◽

Metagenomic Data ◽

Marker Genes ◽

Metagenomic Sequencing ◽

Differential Analysis ◽

Biomedical Sciences ◽

Metagenomic Sequence ◽

Differential Abundance ◽

Differential Abundance Analysis

Metagenomic sequencing is an increasingly common tool in environmental and biomedical sciences. While software for detailing the composition of microbial communities using 16S rRNA marker genes is relatively mature, increasingly researchers are interested in identifying changes exhibited within microbial communities under differing environmental conditions. In order to gain maximum value from metagenomic sequence data we must improve the existing analysis environment by providing accessible and scalable computational workflows able to generate reproducible results. Here we describe a complete end-to-end open-source metagenomics workflow running within Galaxy for 16S differential abundance analysis. The workflow accepts 454 or Illumina sequence data (either overlapping or non-overlapping paired end reads) and outputs lists of the operational taxonomic unit (OTUs) exhibiting the greatest change under differing conditions. A range of analysis steps and graphing options are available giving users a high-level of control over their data and analyses. Additionally, users are able to input complex sample-specific metadata information which can be incorporated into differential analysis and used for grouping / colouring within graphs. Detailed tutorials containing sample data and existing workflows are available for three different input types: overlapping and non-overlapping read pairs as well as for pre-generated Biological Observation Matrix (BIOM) files. Using the Galaxy platform we developed MetaDEGalaxy, a complete metagenomics differential abundance analysis workflow. MetaDEGalaxy is designed for bench scientists working with 16S data who are interested in comparative metagenomics. MetaDEGalaxy builds on momentum within the wider Galaxy metagenomics community with the hope that more tools will be added as existing methods mature.

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Bayesian Classification of Microbial Communities Based on 16S rRNA Metagenomic Data

10.1101/340653 ◽

2018 ◽

Cited By ~ 1

Author(s):

Arghavan Bahadorinejad ◽

Ivan Ivanov ◽

Johanna W Lampe ◽

Meredith AJ Hullar ◽

Robert S Chapkin ◽

...

Keyword(s):

16S Rrna ◽

Sample Size ◽

Microbial Communities ◽

State Of The Art ◽

Metagenomic Data ◽

Data Sets ◽

Sequencing Data ◽

Sample Data

AbstractWe propose a Bayesian method for the classification of 16S rRNA metagenomic profiles of bacterial abundance, by introducing a Poisson-Dirichlet-Multinomial hierarchical model for the sequencing data, constructing a prior distribution from sample data, calculating the posterior distribution in closed form; and deriving an Optimal Bayesian Classifier (OBC). The proposed algorithm is compared to state-of-the-art classification methods for 16S rRNA metagenomic data, including Random Forests and the phylogeny-based Metaphyl algorithm, for varying sample size, classification difficulty, and dimensionality (number of OTUs), using both synthetic and real metagenomic data sets. The results demonstrate that the proposed OBC method, with either noninformative or constructed priors, is competitive or superior to the other methods. In particular, in the case where the ratio of sample size to dimensionality is small, it was observed that the proposed method can vastly outperform the others.Author summaryRecent studies have highlighted the interplay between host genetics, gut microbes, and colorectal tumor initiation/progression. The characterization of microbial communities using metagenomic profiling has therefore received renewed interest. In this paper, we propose a method for classification, i.e., prediction of different outcomes, based on 16S rRNA metagenomic data. The proposed method employs a Bayesian approach, which is suitable for data sets with small ration of number of available instances to the dimensionality. Results using both synthetic and real metagenomic data show that the proposed method can outperform other state-of-the-art metagenomic classification algorithms.

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Mumame: a software tool for quantifying gene-specific point-mutations in shotgun metagenomic data

Metabarcoding and Metagenomics ◽

10.3897/mbmg.3.36236 ◽

2019 ◽

Vol 3 ◽

Cited By ~ 1

Author(s):

Shruthi Magesh ◽

Viktor Jonsson ◽

Johan Bengtsson-Palme

Keyword(s):

Microbial Communities ◽

Point Mutations ◽

Software Tool ◽

Metagenomic Data ◽

Data Sets ◽

Resistance Mutations ◽

Shotgun Metagenomics ◽

Key Factor ◽

Detection Of Mutations ◽

And Function

Metagenomics has emerged as a central technique for studying the structure and function of microbial communities. Often the functional analysis is restricted to classification into broad functional categories. However, important phenotypic differences, such as resistance to antibiotics, are often the result of just one or a few point mutations in otherwise identical sequences. Bioinformatic methods for metagenomic analysis have generally been poor at accounting for this fact, resulting in a somewhat limited picture of important aspects of microbial communities. Here, we address this problem by providing a software tool called Mumame, which can distinguish between wildtype and mutated sequences in shotgun metagenomic data and quantify their relative abundances. We demonstrate the utility of the tool by quantifying antibiotic resistance mutations in several publicly available metagenomic data sets. We also identified that sequencing depth is a key factor to detect rare mutations. Therefore, much larger numbers of sequences may be required for reliable detection of mutations than for most other applications of shotgun metagenomics. Mumame is freely available online (http://microbiology.se/software/mumame).

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MetaBMF: a scalable binning algorithm for large-scale reference-free metagenomic studies

Bioinformatics ◽

10.1093/bioinformatics/btz577 ◽

2019 ◽

Vol 36 (2) ◽

pp. 356-363 ◽

Cited By ~ 2

Author(s):

Terry Ma ◽

Di Xiao ◽

Xin Xing

Keyword(s):

Large Scale ◽

Empirical Studies ◽

Nonnegative Matrix ◽

Rapid Identification ◽

Strain Level ◽

Bacterial Strains ◽

Binary Matrix ◽

Next Generation Sequencing Technology ◽

Microbial Species ◽

Reference Genomes

Abstract Motivation Metagenomics studies microbial genomes in an ecosystem such as the gastrointestinal tract of a human. Identification of novel microbial species and quantification of their distributional variations among different samples that are sequenced using next-generation-sequencing technology hold the key to the success of most metagenomic studies. To achieve these goals, we propose a simple yet powerful metagenomic binning method, MetaBMF. The method does not require prior knowledge of reference genomes and produces highly accurate results, even at a strain level. Thus, it can be broadly used to identify disease-related microbial organisms that are not well-studied. Results Mathematically, we count the number of mapped reads on each assembled genomic fragment cross different samples as our input matrix and propose a scalable stratified angle regression algorithm to factorize this count matrix into a product of a binary matrix and a nonnegative matrix. The binary matrix can be used to separate microbial species and the nonnegative matrix quantifies the species distributions in different samples. In simulation and empirical studies, we demonstrate that MetaBMF has a high binning accuracy. It can not only bin DNA fragments accurately at a species level but also at a strain level. As shown in our example, we can accurately identify the Shiga-toxigenic Escherichia coli O104: H4 strain which led to the 2011 German E.coli outbreak. Our efforts in these areas should lead to (i) fundamental advances in metagenomic binning, (ii) development and refinement of technology for the rapid identification and quantification of microbial distributions and (iii) finding of potential probiotics or reliable pathogenic bacterial strains. Availability and implementation The software is available at https://github.com/didi10384/MetaBMF.

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