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Clinical Microbiology and Infection :... Jul 2012The human organism is a complex structure composed of cells belonging to all three domains of life on Earth, Eukarya, Bacteria and Archaea, as well as their viruses....
The human organism is a complex structure composed of cells belonging to all three domains of life on Earth, Eukarya, Bacteria and Archaea, as well as their viruses. Bacterial cells of more than a thousand taxonomic units are condensed in a particular functional collective domain, the intestinal microbiome. The microbiome constitutes the last human organ under active research. Like other organs, and despite its intrinsic complexity, the microbiome is readily inherited, in a process probably involving 'small world' power law dynamics of construction in newborns. Like any other organ, the microbiome has physiology and pathology, and the individual (and collective?) health might be damaged when its collective population structure is altered. The diagnostic of microbiomic diseases involves metagenomic studies. The therapeutics of microbiome-induced pathology include microbiota transplantation, a technique increasingly available. Perhaps a new medical specialty, microbiomology, is being born.
Topics: Biota; Host-Pathogen Interactions; Humans; Metagenome; Metagenomics; Probiotics
PubMed: 22647038
DOI: 10.1111/j.1469-0691.2012.03916.x -
Bioinformatics (Oxford, England) Dec 2022Recovery of metagenome-assembled genomes (MAGs) from shotgun metagenomic data is an important task for the comprehensive analysis of microbial communities from variable...
MOTIVATION
Recovery of metagenome-assembled genomes (MAGs) from shotgun metagenomic data is an important task for the comprehensive analysis of microbial communities from variable sources. Single binning tools differ in their ability to leverage specific aspects in MAG reconstruction, the use of ensemble binning refinement tools is often time consuming and computational demand increases with community complexity. We introduce MAGScoT, a fast, lightweight and accurate implementation for the reconstruction of highest-quality MAGs from the output of multiple genome-binning tools.
RESULTS
MAGScoT outperforms popular bin-refinement solutions in terms of quality and quantity of MAGs as well as computation time and resource consumption.
AVAILABILITY AND IMPLEMENTATION
MAGScoT is available via GitHub (https://github.com/ikmb/MAGScoT) and as an easy-to-use Docker container (https://hub.docker.com/repository/docker/ikmb/magscot).
SUPPLEMENTARY INFORMATION
Supplementary data are available at Bioinformatics online.
Topics: Algorithms; Metagenomics; Metagenome; Microbiota
PubMed: 36264141
DOI: 10.1093/bioinformatics/btac694 -
Genome Biology Feb 2022Recovering high-quality metagenome-assembled genomes (MAGs) from complex microbial ecosystems remains challenging. Recently, high-throughput chromosome conformation...
Recovering high-quality metagenome-assembled genomes (MAGs) from complex microbial ecosystems remains challenging. Recently, high-throughput chromosome conformation capture (Hi-C) has been applied to simultaneously study multiple genomes in natural microbial communities. We develop HiCBin, a novel open-source pipeline, to resolve high-quality MAGs utilizing Hi-C contact maps. HiCBin employs the HiCzin normalization method and the Leiden clustering algorithm and includes the spurious contact detection into binning pipelines for the first time. HiCBin is validated on one synthetic and two real metagenomic samples and is shown to outperform the existing Hi-C-based binning methods. HiCBin is available at https://github.com/dyxstat/HiCBin .
Topics: Algorithms; Cluster Analysis; Metagenome; Metagenomics; Microbiota
PubMed: 35227283
DOI: 10.1186/s13059-022-02626-w -
BMC Bioinformatics May 2022FragGeneScan is currently the most accurate and popular tool for gene prediction in short and error-prone reads, but its execution speed is insufficient for use on...
BACKGROUND
FragGeneScan is currently the most accurate and popular tool for gene prediction in short and error-prone reads, but its execution speed is insufficient for use on larger data sets. The parallelization which should have addressed this is inefficient. Its alternative implementation FragGeneScan+ is faster, but introduced a number of bugs related to memory management, race conditions and even output accuracy.
RESULTS
This paper introduces FragGeneScanRs, a faster Rust implementation of the FragGeneScan gene prediction model. Its command line interface is backward compatible and adds extra features for more flexible usage. Its output is equivalent to the original FragGeneScan implementation.
CONCLUSIONS
Compared to the current C implementation, shotgun metagenomic reads are processed up to 22 times faster using a single thread, with better scaling for multithreaded execution. The Rust code of FragGeneScanRs is freely available from GitHub under the GPL-3.0 license with instructions for installation, usage and other documentation ( https://github.com/unipept/FragGeneScanRs ).
Topics: Algorithms; Metagenome; Metagenomics; Software
PubMed: 35643462
DOI: 10.1186/s12859-022-04736-5 -
Bioinformatics (Oxford, England) May 2021The microbes that live in an environment can be identified from the combined genomic material, also referred to as the metagenome. Sequencing a metagenome can result in...
MOTIVATION
The microbes that live in an environment can be identified from the combined genomic material, also referred to as the metagenome. Sequencing a metagenome can result in large volumes of sequencing reads. A promising approach to reduce the size of metagenomic datasets is by clustering reads into groups based on their overlaps. Clustering reads are valuable to facilitate downstream analyses, including computationally intensive strain-aware assembly. As current read clustering approaches cannot handle the large datasets arising from high-throughput metagenome sequencing, a novel read clustering approach is needed. In this article, we propose OGRE, an Overlap Graph-based Read clustEring procedure for high-throughput sequencing data, with a focus on shotgun metagenomes.
RESULTS
We show that for small datasets OGRE outperforms other read binners in terms of the number of species included in a cluster, also referred to as cluster purity, and the fraction of all reads that is placed in one of the clusters. Furthermore, OGRE is able to process metagenomic datasets that are too large for other read binners into clusters with high cluster purity.
CONCLUSION
OGRE is the only method that can successfully cluster reads in species-specific clusters for large metagenomic datasets without running into computation time- or memory issues.
AVAILABILITYAND IMPLEMENTATION
Code is made available on Github (https://github.com/Marleen1/OGRE).
SUPPLEMENTARY INFORMATION
Supplementary data are available at Bioinformatics online.
Topics: Algorithms; Cluster Analysis; High-Throughput Nucleotide Sequencing; Metagenome; Metagenomics; Sequence Analysis, DNA; Software
PubMed: 32871010
DOI: 10.1093/bioinformatics/btaa760 -
Microbial Biotechnology Jan 2024Building models is essential for understanding the functions and dynamics of microbial communities. Metabolic models built on genome-scale metabolic network... (Review)
Review
Building models is essential for understanding the functions and dynamics of microbial communities. Metabolic models built on genome-scale metabolic network reconstructions (GENREs) are especially relevant as a means to decipher the complex interactions occurring among species. Model reconstruction increasingly relies on metagenomics, which permits direct characterisation of naturally occurring communities that may contain organisms that cannot be isolated or cultured. In this review, we provide an overview of the field of metabolic modelling and its increasing reliance on and synergy with metagenomics and bioinformatics. We survey the means of assigning functions and reconstructing metabolic networks from (meta-)genomes, and present the variety and mathematical fundamentals of metabolic models that foster the understanding of microbial dynamics. We emphasise the characterisation of interactions and the scaling of model construction to large communities, two important bottlenecks in the applicability of these models. We give an overview of the current state of the art in metagenome sequencing and bioinformatics analysis, focusing on the reconstruction of genomes in microbial communities. Metagenomics benefits tremendously from third-generation sequencing, and we discuss the opportunities of long-read sequencing, strain-level characterisation and eukaryotic metagenomics. We aim at providing algorithmic and mathematical support, together with tool and application resources, that permit bridging the gap between metagenomics and metabolic modelling.
Topics: Metagenome; Microbiota; Metagenomics; Sequence Analysis, DNA; Computational Biology
PubMed: 38243750
DOI: 10.1111/1751-7915.14396 -
Bioinformatics (Oxford, England) Oct 2022Shotgun metagenomic sequencing provides the capacity to understand microbial community structure and function at unprecedented resolution; however, the current...
SUMMARY
Shotgun metagenomic sequencing provides the capacity to understand microbial community structure and function at unprecedented resolution; however, the current analytical methods are constrained by a focus on taxonomic classifications that may obfuscate functional relationships. Here, we present expam, a tree-based, taxonomy agnostic tool for the identification of biologically relevant clades from shotgun metagenomic sequencing.
AVAILABILITY AND IMPLEMENTATION
expam is an open-source Python application released under the GNU General Public Licence v3.0. expam installation instructions, source code and tutorials can be found at https://github.com/seansolari/expam.
SUPPLEMENTARY INFORMATION
Supplementary data are available at Bioinformatics online.
Topics: Metagenome; Metagenomics; Microbiota; Software
PubMed: 36029242
DOI: 10.1093/bioinformatics/btac591 -
GigaScience May 2022Metagenomic assembly using high-throughput sequencing data is a powerful method to construct microbial genomes in environmental samples without cultivation. However,...
BACKGROUND
Metagenomic assembly using high-throughput sequencing data is a powerful method to construct microbial genomes in environmental samples without cultivation. However, metagenomic assembly, especially when only short reads are available, is a complex and challenging task because mixed genomes of multiple microorganisms constitute the metagenome. Although long read sequencing technologies have been developed and have begun to be used for metagenomic assembly, many metagenomic studies have been performed based on short reads because the generation of long reads requires higher sequencing cost than short reads.
RESULTS
In this study, we present a new method called PLR-GEN. It creates pseudo-long reads from metagenomic short reads based on given reference genome sequences by considering small sequence variations existing in individual genomes of the same or different species. When applied to a mock community data set in the Human Microbiome Project, PLR-GEN dramatically extended short reads in length of 101 bp to pseudo-long reads with N50 of 33 Kbp and 0.4% error rate. The use of these pseudo-long reads generated by PLR-GEN resulted in an obvious improvement of metagenomic assembly in terms of the number of sequences, assembly contiguity, and prediction of species and genes.
CONCLUSIONS
PLR-GEN can be used to generate artificial long read sequences without spending extra sequencing cost, thus aiding various studies using metagenomes.
Topics: High-Throughput Nucleotide Sequencing; Humans; Metagenome; Metagenomics; Microbiota; Sequence Analysis, DNA
PubMed: 35579554
DOI: 10.1093/gigascience/giac044 -
F1000Research 2021Metagenomic sequencing allows large-scale identification and genomic characterization. Binning is the process of recovering genomes from complex mixtures of sequence...
Metagenomic sequencing allows large-scale identification and genomic characterization. Binning is the process of recovering genomes from complex mixtures of sequence fragments (metagenome contigs) of unknown bacteria and archaeal species. Assessing the quality of genomes recovered from metagenomes requires the use of complex pipelines involving many independent steps, often difficult to reproduce and maintain. A comprehensive, automated and easy-to-use computational workflow for the quality assessment of draft prokaryotic genomes, based on container technology, would greatly improve reproducibility and reusability of published results. We present metashot/prok-quality, a container-enabled Nextflow pipeline for quality assessment and genome dereplication. The metashot/prok-quality tool produces genome quality reports that are compliant with the Minimum Information about a Metagenome-Assembled Genome (MIMAG) standard, and can run out-of-the-box on any platform that supports Nextflow, Docker or Singularity, including computing clusters or batch infrastructures in the cloud. metashot/prok-quality is part of the metashot collection of analysis pipelines. Workflow and documentation are available under GPL3 licence on GitHub.
Topics: Archaea; Metagenome; Metagenomics; Prokaryotic Cells; Reproducibility of Results
PubMed: 35136576
DOI: 10.12688/f1000research.54418.1 -
Microbiology Spectrum Dec 2022Antibiotic resistance genes (ARGs) pose a serious threat to public health and ecological security in the 21st century. However, the resistome only accounts for a tiny...
Antibiotic resistance genes (ARGs) pose a serious threat to public health and ecological security in the 21st century. However, the resistome only accounts for a tiny fraction of metagenomic content, which makes it difficult to investigate low-abundance ARGs in various environmental settings. Thus, a highly sensitive, accurate, and comprehensive method is needed to describe ARG profiles in complex metagenomic samples. In this study, we established a high-throughput sequencing method based on targeted amplification, which could simultaneously detect ARGs ( = 251), mobile genetic element genes ( = 8), and metal resistance genes ( = 19) in metagenomes. The performance of amplicon sequencing was compared with traditional metagenomic shotgun sequencing (MetaSeq). A total of 1421 primer pairs were designed, achieving extremely high coverage of target genes. The amplicon sequencing significantly improved the recovery of target ARGs (~9 × 10-fold), with higher sensitivity and diversity, less cost, and computation burden. Furthermore, targeted enrichment allows deep scanning of single nucleotide polymorphisms (SNPs), and elevated SNPs detection was shown in this study. We further performed this approach for 48 environmental samples (37 feces, 20 soils, and 7 sewage) and 16 clinical samples. All samples tested in this study showed high diversity and recovery of targeted genes. Our results demonstrated that the approach could be applied to various metagenomic samples and served as an efficient tool in the surveillance and evolution assessment of ARGs. Access to the resistome using the enrichment method validated in this study enabled the capture of low-abundance resistomes while being less costly and time-consuming, which can greatly advance our understanding of local and global resistome dynamics. ARGs, an increasing global threat to human health, can be transferred into health-related microorganisms in the environment by horizontal gene transfer, posing a serious threat to public health. Advancing profiling methods are needed for monitoring and predicting the potential risks of ARGs in metagenomes. Our study described a customized amplicon sequencing assay that could enable a high-throughput, targeted, in-depth analysis of ARGs and detect a low-abundance portion of resistomes. This method could serve as an efficient tool to assess the variation and evolution of specific ARGs in the clinical and natural environment.
Topics: Humans; Metagenome; Genes, Bacterial; Anti-Bacterial Agents; Drug Resistance, Microbial; Sewage; Metagenomics
PubMed: 36287061
DOI: 10.1128/spectrum.02297-22