scholarly journals Quantifying point-mutations in shotgun metagenomic data

2018 ◽  
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

AbstractMetagenomics 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 from http://microbiology.se/software/mumame

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

2019 ◽  
Vol 3 ◽  
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).

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

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.

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

2018 ◽  
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.

scholarly journals Reconstructing ribosomal genes from large scale total RNA meta-transcriptomic data

2020 ◽  
Vol 36 (11) ◽  
pp. 3365-3371
Author(s):  
Yaxin Xue ◽  
Anders Lanzén ◽  
Inge Jonassen
Keyword(s):  
Microbial Communities ◽  
Real World ◽  
Structure And Function ◽  
Rrna Genes ◽  
Supplementary Information ◽  
Metagenomic Data ◽  
Total Rna ◽  
Transcriptomic Data ◽  
And Function ◽  
Complex Datasets

Abstract Motivation Technological advances in meta-transcriptomics have enabled a deeper understanding of the structure and function of microbial communities. ‘Total RNA’ meta-transcriptomics, sequencing of total reverse transcribed RNA, provides a unique opportunity to investigate both the structure and function of active microbial communities from all three domains of life simultaneously. A major step of this approach is the reconstruction of full-length taxonomic marker genes such as the small subunit ribosomal RNA. However, current tools for this purpose are mainly targeted towards analysis of amplicon and metagenomic data and thus lack the ability to handle the massive and complex datasets typically resulting from total RNA experiments. Results In this work, we introduce MetaRib, a new tool for reconstructing ribosomal gene sequences from total RNA meta-transcriptomic data. MetaRib is based on the popular rRNA assembly program EMIRGE, together with several improvements. We address the challenge posed by large complex datasets by integrating sub-assembly, dereplication and mapping in an iterative approach, with additional post-processing steps. We applied the method to both simulated and real-world datasets. Our results show that MetaRib can deal with larger datasets and recover more rRNA genes, which achieve around 60 times speedup and higher F1 score compared to EMIRGE in simulated datasets. In the real-world dataset, it shows similar trends but recovers more contigs compared with a previous analysis based on random sub-sampling, while enabling the comparison of individual contig abundances across samples for the first time. Availability and implementation The source code of MetaRib is freely available at https://github.com/yxxue/MetaRib. Contact [email protected] or [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Metagenomic Sequences of Three Drinking Water and Two Shower Hose Biofilm Samples Treated with or without Copper-Silver Ionization

2020 ◽  
Vol 9 (3) ◽  
Author(s):  
Anke Stüken ◽  
Thomas H. A. Haverkamp
Keyword(s):  
Drinking Water ◽  
Microbial Communities ◽  
Tap Water ◽  
Data Sets ◽  
Sequence Analyses ◽  
Illumina Hiseq ◽  
Shotgun Metagenomics ◽  
Premise Plumbing ◽  
Plumbing Systems ◽  
Metagenomics Data

We announce five shotgun metagenomics data sets from two Norwegian premise plumbing systems. The samples were shotgun sequenced on two lanes of an Illumina HiSeq 3000 instrument (THRUplex chemistry, 151 bp, paired-end reads), providing an extensive resource for sequence analyses of tap water and biofilm microbial communities.

scholarly journals Soil Microbial Community Responses to a Decade of Warming as Revealed by Comparative Metagenomics

2013 ◽  
Vol 80 (5) ◽  
pp. 1777-1786 ◽  
Author(s):  
Chengwei Luo ◽  
Luis M. Rodriguez-R ◽  
Eric R. Johnston ◽  
Liyou Wu ◽  
Lei Cheng ◽  
...  
Keyword(s):  
Climate Change ◽  
Microbial Communities ◽  
Molecular Mechanisms ◽  
Cellulose Degradation ◽  
Global Climate ◽  
Soil Microbial Communities ◽  
Predictive Ability ◽  
Metagenomic Data ◽  
Data Sets ◽  
Soil Microbial

ABSTRACTSoil microbial communities are extremely complex, being composed of thousands of low-abundance species (<0.1% of total). How such complex communities respond to natural or human-induced fluctuations, including major perturbations such as global climate change, remains poorly understood, severely limiting our predictive ability for soil ecosystem functioning and resilience. In this study, we compared 12 whole-community shotgun metagenomic data sets from a grassland soil in the Midwestern United States, half representing soil that had undergone infrared warming by 2°C for 10 years, which simulated the effects of climate change, and the other half representing the adjacent soil that received no warming and thus, served as controls. Our analyses revealed that the heated communities showed significant shifts in composition and predicted metabolism, and these shifts were community wide as opposed to being attributable to a few taxa. Key metabolic pathways related to carbon turnover, such as cellulose degradation (∼13%) and CO2production (∼10%), and to nitrogen cycling, including denitrification (∼12%), were enriched under warming, which was consistent with independent physicochemical measurements. These community shifts were interlinked, in part, with higher primary productivity of the aboveground plant communities stimulated by warming, revealing that most of the additional, plant-derived soil carbon was likely respired by microbial activity. Warming also enriched for a higher abundance of sporulation genes and genomes with higher G+C content. Collectively, our results indicate that microbial communities of temperate grassland soils play important roles in mediating feedback responses to climate change and advance the understanding of the molecular mechanisms of community adaptation to environmental perturbations.

scholarly journals virMine: automated detection of viral sequences from complex metagenomic samples

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e6695 ◽  
Author(s):  
Andrea Garretto ◽  
Thomas Hatzopoulos ◽  
Catherine Putonti
Keyword(s):  
Software Tool ◽  
Metagenomic Data ◽  
Data Sets ◽  
Data Repositories ◽  
Viral Genomes ◽  
Manual Curation ◽  
Public Data ◽  
Control Assembly ◽  
Signature Sequences ◽  
Viral Sequences

Metagenomics has enabled sequencing of viral communities from a myriad of different environments. Viral metagenomic studies routinely uncover sequences with no recognizable homology to known coding regions or genomes. Nevertheless, complete viral genomes have been constructed directly from complex community metagenomes, often through tedious manual curation. To address this, we developed the software tool virMine to identify viral genomes from raw reads representative of viral or mixed (viral and bacterial) communities. virMine automates sequence read quality control, assembly, and annotation. Researchers can easily refine their search for a specific study system and/or feature(s) of interest. In contrast to other viral genome detection tools that often rely on the recognition of viral signature sequences, virMine is not restricted by the insufficient representation of viral diversity in public data repositories. Rather, viral genomes are identified through an iterative approach, first omitting non-viral sequences. Thus, both relatives of previously characterized viruses and novel species can be detected, including both eukaryotic viruses and bacteriophages. Here we present virMine and its analysis of synthetic communities as well as metagenomic data sets from three distinctly different environments: the gut microbiota, the urinary microbiota, and freshwater viromes. Several new viral genomes were identified and annotated, thus contributing to our understanding of viral genetic diversity in these three environments.

scholarly journals Humans Are Selectively Exposed to Pneumocystis jirovecii

mBio ◽  
2020 ◽  
Vol 11 (2) ◽  
Author(s):  
Ousmane H. Cissé ◽  
Liang Ma ◽  
Chao Jiang ◽  
Michael Snyder ◽  
Joseph A. Kovacs
Keyword(s):  
Fungal Spore ◽  
Airborne Fungi ◽  
Pneumocystis Jirovecii ◽  
Metagenomic Data ◽  
Transmission Model ◽  
Data Sets ◽  
Shotgun Metagenomics ◽  
Content Type ◽  
Environmental Reservoir ◽  
The Impact

ABSTRACT Environmental exposure has a significant impact on human health. While some airborne fungi can cause life-threatening infections, the impact of environment on fungal spore dispersal and transmission is poorly understood. The democratization of shotgun metagenomics allows us to explore important questions about fungal propagation. We focus on Pneumocystis, a genus of host-specific fungi that infect mammals via airborne particles. In humans, Pneumocystis jirovecii causes lethal infections in immunocompromised patients if untreated, although its environmental reservoir and transmission route remain unclear. Here, we attempt to clarify, by analyzing human exposome metagenomic data sets, whether humans are exposed to different Pneumocystis species present in the air but only P. jirovecii cells are able to replicate or whether they are selectively exposed to P. jirovecii. Our analysis supports the latter hypothesis, which is consistent with a local transmission model. These data also suggest that healthy carriers are a major driver for the transmission.

scholarly journals Microbial Communities of Cladonia Lichens and Their Biosynthetic Gene Clusters Potentially Encoding Natural Products

2021 ◽  
Vol 9 (7) ◽  
pp. 1347
Author(s):  
Tânia Keiko Shishido ◽  
Matti Wahlsten ◽  
Pia Laine ◽  
Jouko Rikkinen ◽  
Taina Lundell ◽  
...  
Keyword(s):  
Natural Products ◽  
Microbial Communities ◽  
High Throughput Sequencing ◽  
Indigenous Communities ◽  
Gene Clusters ◽  
Amplicon Sequencing ◽  
Metagenomic Data ◽  
Biosynthetic Gene ◽  
Biosynthetic Gene Clusters ◽  
Shotgun Metagenomics

Lichens have been widely used in traditional medicine, especially by indigenous communities worldwide. However, their slow growth and difficulties in the isolation of lichen symbionts and associated microbes have hindered the pharmaceutical utilisation of lichen-produced compounds. Advances in high-throughput sequencing techniques now permit detailed investigations of the complex microbial communities formed by fungi, green algae, cyanobacteria, and other bacteria within the lichen thalli. Here, we used amplicon sequencing, shotgun metagenomics, and in silico metabolomics together with compound extractions to study reindeer lichens collected from Southern Finland. Our aim was to evaluate the potential of Cladonia species as sources of novel natural products. We compared the predicted biosynthetic pathways of lichen compounds from isolated genome-sequenced lichen fungi and our environmental samples. Potential biosynthetic genes could then be further used to produce secondary metabolites in more tractable hosts. Furthermore, we detected multiple compounds by metabolite analyses, which revealed connections between the identified biosynthetic gene clusters and their products. Taken together, our results contribute to metagenomic data studies from complex lichen-symbiotic communities and provide valuable new information for use in further biochemical and pharmacological studies.

scholarly journals NetCom: A Network-Based Tool for Predicting Metabolic Activities of Microbial Communities Based on Interpretation of Metagenomics Data

2021 ◽  
Vol 9 (9) ◽  
pp. 1838
Author(s):  
Ofir Tal ◽  
Rotem Bartuv ◽  
Maria Vetcos ◽  
Shlomit Medina ◽  
Jiandong Jiang ◽  
...  
Keyword(s):  
Microbial Communities ◽  
Metagenomic Data ◽  
Natural Environments ◽  
Enzymatic Reactions ◽  
Environmental Resources ◽  
Community Function ◽  
Metagenomics Data ◽  
Metabolic Activities ◽  
And Function ◽  
The Impact

The study of microbial activity can be viewed as a triangle with three sides: environment (dominant resources in a specific habitat), community (species dictating a repertoire of metabolic conversions) and function (production and/or utilization of resources and compounds). Advances in metagenomics enable a high-resolution description of complex microbial communities in their natural environments and support a systematic study of environment-community-function associations. NetCom is a web-tool for predicting metabolic activities of microbial communities based on network-based interpretation of assembled and annotated metagenomics data. The algorithm takes as an input, lists of differentially abundant enzymatic reactions and generates the following outputs: (i) pathway associations of differently abundant enzymes; (ii) prediction of environmental resources that are unique to each treatment, and their pathway associations; (iii) prediction of compounds that are produced by the microbial community, and pathway association of compounds that are treatment-specific; (iv) network visualization of enzymes, environmental resources and produced compounds, that are treatment specific (2 and 3D). The tool is demonstrated on metagenomic data from rhizosphere and bulk soil samples. By predicting root-specific activities, we illustrate the relevance of our framework for forecasting the impact of soil amendments on the corresponding microbial communities. NetCom is available online.

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