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Current Opinion in Microbiology Dec 2022While they are the most abundant biological entities on the planet, the role of bacteriophages (phages) in the microbiome remains enigmatic and understudied. With a rise... (Review)
Review
While they are the most abundant biological entities on the planet, the role of bacteriophages (phages) in the microbiome remains enigmatic and understudied. With a rise in the number of metagenomics studies and the publication of highly efficient phage mining programmes, we now have extensive data on the genomic and taxonomic diversity of (mainly) DNA bacteriophages in a wide range of environments. In addition, the higher throughput and quality of sequencing is allowing for strain-level reconstructions of phage genomes from metagenomes. These factors will ultimately help us to understand the role these phages play as part of specific microbial communities, enabling the tracking of individual virus genomes through space and time. Using lessons learned from the latest metagenomic studies, we focus on two explicit aspects of the role bacteriophages play within the microbiome, their ecological role in structuring bacterial populations, and their contribution to microbiome functioning by encoding auxiliary metabolism genes.
Topics: Humans; Bacteriophages; Metagenomics; Metagenome; Genome, Viral; Bacteria
PubMed: 36347213
DOI: 10.1016/j.mib.2022.102229 -
Clinical Microbiology and Infection :... Sep 2022The diagnosis of bacterial infections continues to rely on culture, a slow process in which antibiotic susceptibility profiles of potential pathogens are made available... (Review)
Review
BACKGROUND
The diagnosis of bacterial infections continues to rely on culture, a slow process in which antibiotic susceptibility profiles of potential pathogens are made available to clinicians 48 hours after sampling, at best. Recently, clinical metagenomics, the metagenomic sequencing of samples with the purpose of identifying microorganisms and determining their susceptibility to antimicrobials, has emerged as a potential diagnostic tool that could prove faster than culture. Clinical metagenomics indeed has the potential to detect antibiotic resistance genes (ARGs) and mutations associated with resistance. Nevertheless, many challenges have yet to be overcome in order to make rapid phenotypic inference of antibiotic susceptibility from metagenomic data a reality.
OBJECTIVES
The objective of this narrative review is to discuss the challenges underlying the phenotypic inference of antibiotic susceptibility from metagenomic data.
SOURCES
We conducted a narrative review using published articles available in the National Center for Biotechnology Information PubMed database.
CONTENT
We review the current ARG databases with a specific emphasis on those which now provide associations with phenotypic data. Next, we discuss the bioinformatic tools designed to identify ARGs in metagenomes. We then report on the performance of phenotypic inference from genomic data and the issue predicting the expression of ARGs. Finally, we address the challenge of linking an ARG to this host.
IMPLICATIONS
Significant improvements have recently been made in associating ARG and phenotype, and the inference of susceptibility from genomic data has been demonstrated in pathogenic bacteria such as Staphylococci and Enterobacterales. Resistance involving gene expression is more challenging however, and inferring susceptibility from species such as Pseudomonas aeruginosa remains difficult. Future research directions include the consideration of gene expression via RNA sequencing and machine learning.
Topics: Anti-Bacterial Agents; Drug Resistance, Microbial; Genes, Bacterial; Metagenome; Metagenomics
PubMed: 35551982
DOI: 10.1016/j.cmi.2022.04.017 -
Bioresource Technology Feb 2022Traditionally, lipid-producing microorganisms have been obtained via conventional bioprospecting based on isolation and screening techniques, demanding time and effort.... (Review)
Review
Traditionally, lipid-producing microorganisms have been obtained via conventional bioprospecting based on isolation and screening techniques, demanding time and effort. Thus, high-throughput sequencing combined with conventional microbiological approaches has emerged as an advanced and rapid strategy for recovering novel oleaginous microorganisms from target environments. This review highlights recent developments in lipid-producing microorganism bioprospecting, following (i) from traditional cultivation techniques to state-of-the-art metagenomics approaches; (ii) related topics on workflow, next-generation sequencing platforms, and knowledge bioinformatics; and (iii) biotechnological potential of the production of docosahexaenoic acid (DHA) by Aurantiochytrium limacinum, arachidonic acid (ARA) by Mortierella alpina and biodiesel by Rhodosporidium toruloides. These three species have been shown to be highly promising and studied in research articles, patents and commercialized products. Trends, innovations and future perspectives of these microorganisms are also addressed. Thus, these microbial lipids allow the development of food, feed and biofuels as alternative solutions to animal and vegetable oils.
Topics: Animals; Arachidonic Acid; Biofuels; Bioprospecting; Metagenome; Metagenomics
PubMed: 34863851
DOI: 10.1016/j.biortech.2021.126455 -
Chinese Medical Journal Oct 2022
Topics: Humans; Virome; Bacteriophages; Feces; Metagenome; Metagenomics
PubMed: 36583859
DOI: 10.1097/CM9.0000000000002382 -
Molecular Ecology Resources Aug 2022Many metagenomic and environmental DNA studies require the taxonomic assignment of individual reads or sequences by aligning reads to a reference database, known as...
Many metagenomic and environmental DNA studies require the taxonomic assignment of individual reads or sequences by aligning reads to a reference database, known as taxonomic binning. When a read aligns to more than one reference sequence, it is often classified based on sequence similarity. This step can assign reads to incorrect taxa, at a rate which depends both on the assignment algorithm and on underlying population genetic and database parameters. In particular, as we move towards using environmental DNA to study eukaryotic taxa subject to regular recombination, we must take into account issues concerning gene tree discordance. Though accuracy is often compared across algorithms using a fixed data set, the relative impact of these population genetic and database parameters on accuracy has not yet been quantified. Here, we develop both a theoretical and simulation framework in the simplified case of two reference species, and compute binning accuracy over a wide range of parameters, including sequence length, species-query divergence time, divergence times of the reference species, reference database completeness, sample age and effective population size. We consider two assignment methods and contextualize our results using parameters from a recent ancient environmental DNA study, comparing them to the commonly used discriminative k-mer-based method Clark (Current Biology, 31, 2021, 2728; BMC Genomics, 16, 2015, 1). Our results quantify the degradation in assignment accuracy as the samples diverge from their closest reference sequence, and with incompleteness of reference sequences. We also provide a framework in which others can compute expected accuracy for their particular method or parameter set. Code is available at https://github.com/bdesanctis/binning-accuracy.
Topics: Algorithms; DNA, Environmental; Metagenome; Metagenomics; Sequence Analysis, DNA; Software
PubMed: 35285150
DOI: 10.1111/1755-0998.13608 -
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 -
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 -
Microbiological Research Jul 2022Reference genomes are essential for analyzing the metabolic and functional potentials of microbiomes. However, microbial genome resources are limited because most of... (Review)
Review
Reference genomes are essential for analyzing the metabolic and functional potentials of microbiomes. However, microbial genome resources are limited because most of microorganisms are difficult to culture. Genome binning is a culture-independent approach that can recover a vast number of microbial genomes from short-read high throughput shotgun metagenomic sequencing data. In this review, we summarize methods commonly used for reconstructing metagenome-assembled genomes (MAGs) to provide a reference for researchers to choose propriate software programs among the numerous and complicated tools and pipelines that are available for these analyses. In addition, we discuss application prospects, challenges, and opportunities for recovering MAGs from metagenomic sequencing data.
Topics: High-Throughput Nucleotide Sequencing; Metagenome; Metagenomics; Microbiota
PubMed: 35430490
DOI: 10.1016/j.micres.2022.127023 -
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