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Lai S, Wang H, Bork P, Chen WH, Zhao XM. Long-read sequencing reveals extensive gut phageome structural variations driven by genetic exchange with bacterial hosts. SCIENCE ADVANCES 2024; 10:eadn3316. [PMID: 39141729 PMCID: PMC11323893 DOI: 10.1126/sciadv.adn3316] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Accepted: 07/10/2024] [Indexed: 08/16/2024]
Abstract
Genetic variations are instrumental for unraveling phage evolution and deciphering their functional implications. Here, we explore the underlying fine-scale genetic variations in the gut phageome, especially structural variations (SVs). By using virome-enriched long-read metagenomic sequencing across 91 individuals, we identified a total of 14,438 nonredundant phage SVs and revealed their prevalence within the human gut phageome. These SVs are mainly enriched in genes involved in recombination, DNA methylation, and antibiotic resistance. Notably, a substantial fraction of phage SV sequences share close homology with bacterial fragments, with most SVs enriched for horizontal gene transfer (HGT) mechanism. Further investigations showed that these SV sequences were genetic exchanged between specific phage-bacteria pairs, particularly between phages and their respective bacterial hosts. Temperate phages exhibit a higher frequency of genetic exchange with bacterial chromosomes and then virulent phages. Collectively, our findings provide insights into the genetic landscape of the human gut phageome.
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Affiliation(s)
- Senying Lai
- Department of Neurology, Zhongshan Hospital and Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- State Key Laboratory of Medical Neurobiology, Institutes of Brain Science, Fudan University, Shanghai, China
- MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Huarui Wang
- Key Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Key Laboratory of Bioinformatics and Molecular Imaging, Center for Artificial Intelligence Biology, Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Peer Bork
- European Molecular Biology Laboratory, Structural and Computational Biology Unit, Heidelberg, Germany
- Max Delbrück Centre for Molecular Medicine, Berlin, Germany
- Department of Bioinformatics, Biocenter, University of Würzburg, Würzburg, Germany
| | - Wei-Hua Chen
- State Key Laboratory of Medical Neurobiology, Institutes of Brain Science, Fudan University, Shanghai, China
- College of Life Science, Henan Normal University, Xinxiang, Henan, China
| | - Xing-Ming Zhao
- Department of Neurology, Zhongshan Hospital and Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- State Key Laboratory of Medical Neurobiology, Institutes of Brain Science, Fudan University, Shanghai, China
- MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
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2
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Gtari M, Maaoui R, Ghodhbane-Gtari F, Ben Slama K, Sbissi I. MAGs-centric crack: how long will, spore-positive Frankia and most Protofrankia, microsymbionts remain recalcitrant to axenic growth? Front Microbiol 2024; 15:1367490. [PMID: 39144212 PMCID: PMC11323853 DOI: 10.3389/fmicb.2024.1367490] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Accepted: 07/04/2024] [Indexed: 08/16/2024] Open
Abstract
Nearly 50 years after the ground-breaking isolation of the primary Comptonia peregrina microsymbiont under axenic conditions, efforts to isolate a substantial number of Protofrankia and Frankia strains continue with enduring challenges and complexities. This study aimed to streamline genomic insights through comparative and predictive tools to extract traits crucial for isolating specific Frankia in axenic conditions. Pangenome analysis unveiled significant genetic diversity, suggesting untapped potential for cultivation strategies. Shared metabolic strategies in cellular components, central metabolic pathways, and resource acquisition traits offered promising avenues for cultivation. Ecological trait extraction indicated that most uncultured strains exhibit no apparent barriers to axenic growth. Despite ongoing challenges, potential caveats, and errors that could bias predictive analyses, this study provides a nuanced perspective. It highlights potential breakthroughs and guides refined cultivation strategies for these yet-uncultured strains. We advocate for tailored media formulations enriched with simple carbon sources in aerobic environments, with atmospheric nitrogen optionally sufficient to minimize contamination risks. Temperature adjustments should align with strain preferences-28-29°C for Frankia and 32-35°C for Protofrankia-while maintaining an alkaline pH. Given potential extended incubation periods (predicted doubling times ranging from 3.26 to 9.60 days, possibly up to 21.98 days), patience and rigorous contamination monitoring are crucial for optimizing cultivation conditions.
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Affiliation(s)
- Maher Gtari
- Department of Biological and Chemical Engineering, USCR Molecular Bacteriology and Genomics, National Institute of Applied Sciences and Technology, University of Carthage, Tunis, Tunisia
| | - Radhi Maaoui
- Department of Biological and Chemical Engineering, USCR Molecular Bacteriology and Genomics, National Institute of Applied Sciences and Technology, University of Carthage, Tunis, Tunisia
| | - Faten Ghodhbane-Gtari
- Department of Biological and Chemical Engineering, USCR Molecular Bacteriology and Genomics, National Institute of Applied Sciences and Technology, University of Carthage, Tunis, Tunisia
- Higher Institute of Biotechnology Sidi Thabet, University of La Manouba, Tunisia
| | - Karim Ben Slama
- LR Bioresources, Environment, and Biotechnology (LR22ES04), Higher Institute of Applied Biological Sciences of Tunis, University of Tunis El Manar, Tunis, Tunisia
| | - Imed Sbissi
- LR Pastoral Ecology, Arid Regions Institute, University of Gabes, Medenine, Tunisia
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3
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Agustinho DP, Fu Y, Menon VK, Metcalf GA, Treangen TJ, Sedlazeck FJ. Unveiling microbial diversity: harnessing long-read sequencing technology. Nat Methods 2024; 21:954-966. [PMID: 38689099 DOI: 10.1038/s41592-024-02262-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Accepted: 03/29/2024] [Indexed: 05/02/2024]
Abstract
Long-read sequencing has recently transformed metagenomics, enhancing strain-level pathogen characterization, enabling accurate and complete metagenome-assembled genomes, and improving microbiome taxonomic classification and profiling. These advancements are not only due to improvements in sequencing accuracy, but also happening across rapidly changing analysis methods. In this Review, we explore long-read sequencing's profound impact on metagenomics, focusing on computational pipelines for genome assembly, taxonomic characterization and variant detection, to summarize recent advancements in the field and provide an overview of available analytical methods to fully leverage long reads. We provide insights into the advantages and disadvantages of long reads over short reads and their evolution from the early days of long-read sequencing to their recent impact on metagenomics and clinical diagnostics. We further point out remaining challenges for the field such as the integration of methylation signals in sub-strain analysis and the lack of benchmarks.
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Affiliation(s)
- Daniel P Agustinho
- Human Genome Sequencing center, Baylor College of Medicine, Houston, TX, USA
| | - Yilei Fu
- Department of Computer Science, Rice University, Houston, TX, USA
| | - Vipin K Menon
- Human Genome Sequencing center, Baylor College of Medicine, Houston, TX, USA
- Senior research project manager, Human Genetics, Genentech, South San Francisco, CA, USA
| | - Ginger A Metcalf
- Human Genome Sequencing center, Baylor College of Medicine, Houston, TX, USA
| | - Todd J Treangen
- Department of Computer Science, Rice University, Houston, TX, USA
- Department of Bioengineering, Rice University, Houston, TX, USA
| | - Fritz J Sedlazeck
- Human Genome Sequencing center, Baylor College of Medicine, Houston, TX, USA.
- Department of Computer Science, Rice University, Houston, TX, USA.
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4
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Hauptfeld E, Pappas N, van Iwaarden S, Snoek BL, Aldas-Vargas A, Dutilh BE, von Meijenfeldt FAB. Integrating taxonomic signals from MAGs and contigs improves read annotation and taxonomic profiling of metagenomes. Nat Commun 2024; 15:3373. [PMID: 38643272 PMCID: PMC11032395 DOI: 10.1038/s41467-024-47155-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Accepted: 03/20/2024] [Indexed: 04/22/2024] Open
Abstract
Metagenomic analysis typically includes read-based taxonomic profiling, assembly, and binning of metagenome-assembled genomes (MAGs). Here we integrate these steps in Read Annotation Tool (RAT), which uses robust taxonomic signals from MAGs and contigs to enhance read annotation. RAT reconstructs taxonomic profiles with high precision and sensitivity, outperforming other state-of-the-art tools. In high-diversity groundwater samples, RAT annotates a large fraction of the metagenomic reads, calling novel taxa at the appropriate, sometimes high taxonomic ranks. Thus, RAT integrative profiling provides an accurate and comprehensive view of the microbiome from shotgun metagenomics data. The package of Contig Annotation Tool (CAT), Bin Annotation Tool (BAT), and RAT is available at https://github.com/MGXlab/CAT_pack (from CAT pack v6.0). The CAT pack now also supports Genome Taxonomy Database (GTDB) annotations.
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Affiliation(s)
- Ernestina Hauptfeld
- Theoretical Biology and Bioinformatics, Science for Life, Utrecht University, Padualaan 8, 3584 CH, Utrecht, The Netherlands
| | - Nikolaos Pappas
- Theoretical Biology and Bioinformatics, Science for Life, Utrecht University, Padualaan 8, 3584 CH, Utrecht, The Netherlands
| | - Sandra van Iwaarden
- Theoretical Biology and Bioinformatics, Science for Life, Utrecht University, Padualaan 8, 3584 CH, Utrecht, The Netherlands
| | - Basten L Snoek
- Theoretical Biology and Bioinformatics, Science for Life, Utrecht University, Padualaan 8, 3584 CH, Utrecht, The Netherlands
| | - Andrea Aldas-Vargas
- Environmental Technology, Wageningen University & Research, P.O. Box 17, 6700, EV Wageningen, The Netherlands
| | - Bas E Dutilh
- Theoretical Biology and Bioinformatics, Science for Life, Utrecht University, Padualaan 8, 3584 CH, Utrecht, The Netherlands.
- Institute of Biodiversity, Faculty of Biological Sciences, Cluster of Excellence Balance of the Microverse, Friedrich Schiller University, Rosalind Franklin Strasse 1, 07743, Jena, Germany.
| | - F A Bastiaan von Meijenfeldt
- Theoretical Biology and Bioinformatics, Science for Life, Utrecht University, Padualaan 8, 3584 CH, Utrecht, The Netherlands.
- Department of Marine Microbiology and Biogeochemistry (MMB), NIOZ Royal Netherlands Institute for Sea Research, PO Box 59, 1790AB, Den Burg, The Netherlands.
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5
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Roy G, Prifti E, Belda E, Zucker JD. Deep learning methods in metagenomics: a review. Microb Genom 2024; 10. [PMID: 38630611 DOI: 10.1099/mgen.0.001231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/19/2024] Open
Abstract
The ever-decreasing cost of sequencing and the growing potential applications of metagenomics have led to an unprecedented surge in data generation. One of the most prevalent applications of metagenomics is the study of microbial environments, such as the human gut. The gut microbiome plays a crucial role in human health, providing vital information for patient diagnosis and prognosis. However, analysing metagenomic data remains challenging due to several factors, including reference catalogues, sparsity and compositionality. Deep learning (DL) enables novel and promising approaches that complement state-of-the-art microbiome pipelines. DL-based methods can address almost all aspects of microbiome analysis, including novel pathogen detection, sequence classification, patient stratification and disease prediction. Beyond generating predictive models, a key aspect of these methods is also their interpretability. This article reviews DL approaches in metagenomics, including convolutional networks, autoencoders and attention-based models. These methods aggregate contextualized data and pave the way for improved patient care and a better understanding of the microbiome's key role in our health.
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Affiliation(s)
- Gaspar Roy
- IRD, Sorbonne University, UMMISCO, 32 avenue Henry Varagnat, Bondy Cedex, France
| | - Edi Prifti
- IRD, Sorbonne University, UMMISCO, 32 avenue Henry Varagnat, Bondy Cedex, France
- Sorbonne University, INSERM, Nutriomics, 91 bvd de l'hopital, 75013 Paris, France
| | - Eugeni Belda
- IRD, Sorbonne University, UMMISCO, 32 avenue Henry Varagnat, Bondy Cedex, France
- Sorbonne University, INSERM, Nutriomics, 91 bvd de l'hopital, 75013 Paris, France
| | - Jean-Daniel Zucker
- IRD, Sorbonne University, UMMISCO, 32 avenue Henry Varagnat, Bondy Cedex, France
- Sorbonne University, INSERM, Nutriomics, 91 bvd de l'hopital, 75013 Paris, France
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6
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Bálint B, Merényi Z, Hegedüs B, Grigoriev IV, Hou Z, Földi C, Nagy LG. ContScout: sensitive detection and removal of contamination from annotated genomes. Nat Commun 2024; 15:936. [PMID: 38296951 PMCID: PMC10831095 DOI: 10.1038/s41467-024-45024-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Accepted: 01/08/2024] [Indexed: 02/02/2024] Open
Abstract
Contamination of genomes is an increasingly recognized problem affecting several downstream applications, from comparative evolutionary genomics to metagenomics. Here we introduce ContScout, a precise tool for eliminating foreign sequences from annotated genomes. It achieves high specificity and sensitivity on synthetic benchmark data even when the contaminant is a closely related species, outperforms competing tools, and can distinguish horizontal gene transfer from contamination. A screen of 844 eukaryotic genomes for contamination identified bacteria as the most common source, followed by fungi and plants. Furthermore, we show that contaminants in ancestral genome reconstructions lead to erroneous early origins of genes and inflate gene loss rates, leading to a false notion of complex ancestral genomes. Taken together, we offer here a tool for sensitive removal of foreign proteins, identify and remove contaminants from diverse eukaryotic genomes and evaluate their impact on phylogenomic analyses.
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Affiliation(s)
- Balázs Bálint
- Synthetic and Systems Biology Unit, HUN-REN Biological Research Centre, Szeged, Szeged, 6726, Hungary
| | - Zsolt Merényi
- Synthetic and Systems Biology Unit, HUN-REN Biological Research Centre, Szeged, Szeged, 6726, Hungary
| | - Botond Hegedüs
- Synthetic and Systems Biology Unit, HUN-REN Biological Research Centre, Szeged, Szeged, 6726, Hungary
| | - Igor V Grigoriev
- U.S. Department of Energy Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
- Department of Plant and Microbial Biology, University of California Berkeley, Berkeley, CA, 94720, USA
| | - Zhihao Hou
- Synthetic and Systems Biology Unit, HUN-REN Biological Research Centre, Szeged, Szeged, 6726, Hungary
- Doctoral School of Biology, Faculty of Science and Informatics, University of Szeged, Szeged, 6720, Hungary
| | - Csenge Földi
- Synthetic and Systems Biology Unit, HUN-REN Biological Research Centre, Szeged, Szeged, 6726, Hungary
- Doctoral School of Biology, Faculty of Science and Informatics, University of Szeged, Szeged, 6720, Hungary
| | - László G Nagy
- Synthetic and Systems Biology Unit, HUN-REN Biological Research Centre, Szeged, Szeged, 6726, Hungary.
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7
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Seong HJ, Kim JJ, Sul WJ. ACR: metagenome-assembled prokaryotic and eukaryotic genome refinement tool. Brief Bioinform 2023; 24:bbad381. [PMID: 37889119 DOI: 10.1093/bib/bbad381] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Revised: 09/16/2023] [Accepted: 10/03/2023] [Indexed: 10/28/2023] Open
Abstract
Microbial genome recovery from metagenomes can further explain microbial ecosystem structures, functions and dynamics. Thus, this study developed the Additional Clustering Refiner (ACR) to enhance high-purity prokaryotic and eukaryotic metagenome-assembled genome (MAGs) recovery. ACR refines low-quality MAGs by subjecting them to iterative k-means clustering predicated on contig abundance and increasing bin purity through validated universal marker genes. Synthetic and real-world metagenomic datasets, including short- and long-read sequences, evaluated ACR's effectiveness. The results demonstrated improved MAG purity and a significant increase in high- and medium-quality MAG recovery rates. In addition, ACR seamlessly integrates with various binning algorithms, augmenting their strengths without modifying core features. Furthermore, its multiple sequencing technology compatibilities expand its applicability. By efficiently recovering high-quality prokaryotic and eukaryotic genomes, ACR is a promising tool for deepening our understanding of microbial communities through genome-centric metagenomics.
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Affiliation(s)
- Hoon Je Seong
- Korean Medicine Data Division, Korea Institute of Oriental Medicine, Daejeon, Republic of Korea
| | - Jin Ju Kim
- Department of Systems Biotechnology, Chung-Ang University, Anseong, Republic of Korea
| | - Woo Jun Sul
- Department of Systems Biotechnology, Chung-Ang University, Anseong, Republic of Korea
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8
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Mineeva O, Danciu D, Schölkopf B, Ley RE, Rätsch G, Youngblut ND. ResMiCo: Increasing the quality of metagenome-assembled genomes with deep learning. PLoS Comput Biol 2023; 19:e1011001. [PMID: 37126495 PMCID: PMC10174551 DOI: 10.1371/journal.pcbi.1011001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Revised: 05/11/2023] [Accepted: 03/06/2023] [Indexed: 05/02/2023] Open
Abstract
The number of published metagenome assemblies is rapidly growing due to advances in sequencing technologies. However, sequencing errors, variable coverage, repetitive genomic regions, and other factors can produce misassemblies, which are challenging to detect for taxonomically novel genomic data. Assembly errors can affect all downstream analyses of the assemblies. Accuracy for the state of the art in reference-free misassembly prediction does not exceed an AUPRC of 0.57, and it is not clear how well these models generalize to real-world data. Here, we present the Residual neural network for Misassembled Contig identification (ResMiCo), a deep learning approach for reference-free identification of misassembled contigs. To develop ResMiCo, we first generated a training dataset of unprecedented size and complexity that can be used for further benchmarking and developments in the field. Through rigorous validation, we show that ResMiCo is substantially more accurate than the state of the art, and the model is robust to novel taxonomic diversity and varying assembly methods. ResMiCo estimated 7% misassembled contigs per metagenome across multiple real-world datasets. We demonstrate how ResMiCo can be used to optimize metagenome assembly hyperparameters to improve accuracy, instead of optimizing solely for contiguity. The accuracy, robustness, and ease-of-use of ResMiCo make the tool suitable for general quality control of metagenome assemblies and assembly methodology optimization.
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Affiliation(s)
- Olga Mineeva
- Department of Computer Science, ETH Zürich, Zürich, Switzerland
- Department of Empirical Inference, Max Planck Institute for Intelligent Systems, Tübingen, Germany
- Swiss Institute for Bioinformatics, Lausanne, Switzerland
| | - Daniel Danciu
- Department of Computer Science, ETH Zürich, Zürich, Switzerland
| | - Bernhard Schölkopf
- Department of Computer Science, ETH Zürich, Zürich, Switzerland
- Department of Empirical Inference, Max Planck Institute for Intelligent Systems, Tübingen, Germany
- ETH AI center, ETH Zürich, Zürich, Switzerland
| | - Ruth E Ley
- Department of Microbiome Science, Max Planck Institute for Biology, Tübingen, Germany
| | - Gunnar Rätsch
- Department of Computer Science, ETH Zürich, Zürich, Switzerland
- Swiss Institute for Bioinformatics, Lausanne, Switzerland
- ETH AI center, ETH Zürich, Zürich, Switzerland
- Department of Biology, ETH Zürich, Zürich, Switzerland
- Medical Informatics Unit, Zürich University Hospital, Zürich, Switzerland
| | - Nicholas D Youngblut
- Department of Microbiome Science, Max Planck Institute for Biology, Tübingen, Germany
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9
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Jia L, Wu Y, Dong Y, Chen J, Chen WH, Zhao XM. A survey on computational strategies for genome-resolved gut metagenomics. Brief Bioinform 2023; 24:7145904. [PMID: 37114640 DOI: 10.1093/bib/bbad162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Revised: 03/20/2023] [Accepted: 04/04/2023] [Indexed: 04/29/2023] Open
Abstract
Recovering high-quality metagenome-assembled genomes (HQ-MAGs) is critical for exploring microbial compositions and microbe-phenotype associations. However, multiple sequencing platforms and computational tools for this purpose may confuse researchers and thus call for extensive evaluation. Here, we systematically evaluated a total of 40 combinations of popular computational tools and sequencing platforms (i.e. strategies), involving eight assemblers, eight metagenomic binners and four sequencing technologies, including short-, long-read and metaHiC sequencing. We identified the best tools for the individual tasks (e.g. the assembly and binning) and combinations (e.g. generating more HQ-MAGs) depending on the availability of the sequencing data. We found that the combination of the hybrid assemblies and metaHiC-based binning performed best, followed by the hybrid and long-read assemblies. More importantly, both long-read and metaHiC sequencings link more mobile elements and antibiotic resistance genes to bacterial hosts and improve the quality of public human gut reference genomes with 32% (34/105) HQ-MAGs that were either of better quality than those in the Unified Human Gastrointestinal Genome catalog version 2 or novel.
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Affiliation(s)
- Longhao Jia
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China
| | - Yingjian Wu
- Key Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Key Laboratory of Bioinformatics and Molecular-imaging, Center for Artificial Intelligence Biology, Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China
| | - Yanqi Dong
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China
| | - Jingchao Chen
- Key Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Key Laboratory of Bioinformatics and Molecular-imaging, Center for Artificial Intelligence Biology, Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China
| | - Wei-Hua Chen
- Key Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Key Laboratory of Bioinformatics and Molecular-imaging, Center for Artificial Intelligence Biology, Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China
- Institution of Medical Artificial Intelligence, Binzhou Medical University, Yantai 264003, China
| | - Xing-Ming Zhao
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Ministry of Education, Ministry of Education, Shanghai 200433, China
- MOE Frontiers Center for Brain Science, Fudan University, Shanghai 200433, China
- State Key Laboratory of Medical Neurobiology, Institutes of Brain Science, Fudan University, Shanghai, China
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10
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Ibañez-Lligoña M, Colomer-Castell S, González-Sánchez A, Gregori J, Campos C, Garcia-Cehic D, Andrés C, Piñana M, Pumarola T, Rodríguez-Frias F, Antón A, Quer J. Bioinformatic Tools for NGS-Based Metagenomics to Improve the Clinical Diagnosis of Emerging, Re-Emerging and New Viruses. Viruses 2023; 15:v15020587. [PMID: 36851800 PMCID: PMC9965957 DOI: 10.3390/v15020587] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Revised: 02/16/2023] [Accepted: 02/17/2023] [Indexed: 02/24/2023] Open
Abstract
Epidemics and pandemics have occurred since the beginning of time, resulting in millions of deaths. Many such disease outbreaks are caused by viruses. Some viruses, particularly RNA viruses, are characterized by their high genetic variability, and this can affect certain phenotypic features: tropism, antigenicity, and susceptibility to antiviral drugs, vaccines, and the host immune response. The best strategy to face the emergence of new infectious genomes is prompt identification. However, currently available diagnostic tests are often limited for detecting new agents. High-throughput next-generation sequencing technologies based on metagenomics may be the solution to detect new infectious genomes and properly diagnose certain diseases. Metagenomic techniques enable the identification and characterization of disease-causing agents, but they require a large amount of genetic material and involve complex bioinformatic analyses. A wide variety of analytical tools can be used in the quality control and pre-processing of metagenomic data, filtering of untargeted sequences, assembly and quality control of reads, and taxonomic profiling of sequences to identify new viruses and ones that have been sequenced and uploaded to dedicated databases. Although there have been huge advances in the field of metagenomics, there is still a lack of consensus about which of the various approaches should be used for specific data analysis tasks. In this review, we provide some background on the study of viral infections, describe the contribution of metagenomics to this field, and place special emphasis on the bioinformatic tools (with their capabilities and limitations) available for use in metagenomic analyses of viral pathogens.
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Affiliation(s)
- Marta Ibañez-Lligoña
- Liver Diseases-Viral Hepatitis, Liver Unit, Vall d’Hebron Institut de Recerca (VHIR), Vall d’Hebron Hospital Universitari, Vall d’Hebron Barcelona Hospital Campus, Passeig Vall d’Hebron 119-129, 08035 Barcelona, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBERehd), Instituto de Salud Carlos III, Av. Monforte de Lemos, 3-5, 28029 Madrid, Spain
- Biochemistry and Molecular Biology Department, Universitat Autònoma de Barcelona (UAB), Campus de la UAB, Plaça Cívica, 08193 Bellaterra, Spain
| | - Sergi Colomer-Castell
- Liver Diseases-Viral Hepatitis, Liver Unit, Vall d’Hebron Institut de Recerca (VHIR), Vall d’Hebron Hospital Universitari, Vall d’Hebron Barcelona Hospital Campus, Passeig Vall d’Hebron 119-129, 08035 Barcelona, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBERehd), Instituto de Salud Carlos III, Av. Monforte de Lemos, 3-5, 28029 Madrid, Spain
- Biochemistry and Molecular Biology Department, Universitat Autònoma de Barcelona (UAB), Campus de la UAB, Plaça Cívica, 08193 Bellaterra, Spain
| | - Alejandra González-Sánchez
- Microbiology Department, Vall d’Hebron Institut de Recerca (VHIR), Vall d’Hebron Hospital Universitari, Vall d’Hebron Barcelona Hospital Campus, Passeig Vall d’Hebron 119-129, 08035 Barcelona, Spain
| | - Josep Gregori
- Liver Diseases-Viral Hepatitis, Liver Unit, Vall d’Hebron Institut de Recerca (VHIR), Vall d’Hebron Hospital Universitari, Vall d’Hebron Barcelona Hospital Campus, Passeig Vall d’Hebron 119-129, 08035 Barcelona, Spain
| | - Carolina Campos
- Liver Diseases-Viral Hepatitis, Liver Unit, Vall d’Hebron Institut de Recerca (VHIR), Vall d’Hebron Hospital Universitari, Vall d’Hebron Barcelona Hospital Campus, Passeig Vall d’Hebron 119-129, 08035 Barcelona, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBERehd), Instituto de Salud Carlos III, Av. Monforte de Lemos, 3-5, 28029 Madrid, Spain
- Biochemistry and Molecular Biology Department, Universitat Autònoma de Barcelona (UAB), Campus de la UAB, Plaça Cívica, 08193 Bellaterra, Spain
| | - Damir Garcia-Cehic
- Liver Diseases-Viral Hepatitis, Liver Unit, Vall d’Hebron Institut de Recerca (VHIR), Vall d’Hebron Hospital Universitari, Vall d’Hebron Barcelona Hospital Campus, Passeig Vall d’Hebron 119-129, 08035 Barcelona, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBERehd), Instituto de Salud Carlos III, Av. Monforte de Lemos, 3-5, 28029 Madrid, Spain
| | - Cristina Andrés
- Microbiology Department, Vall d’Hebron Institut de Recerca (VHIR), Vall d’Hebron Hospital Universitari, Vall d’Hebron Barcelona Hospital Campus, Passeig Vall d’Hebron 119-129, 08035 Barcelona, Spain
| | - Maria Piñana
- Microbiology Department, Vall d’Hebron Institut de Recerca (VHIR), Vall d’Hebron Hospital Universitari, Vall d’Hebron Barcelona Hospital Campus, Passeig Vall d’Hebron 119-129, 08035 Barcelona, Spain
| | - Tomàs Pumarola
- Microbiology Department, Vall d’Hebron Institut de Recerca (VHIR), Vall d’Hebron Hospital Universitari, Vall d’Hebron Barcelona Hospital Campus, Passeig Vall d’Hebron 119-129, 08035 Barcelona, Spain
- Microbiology Department, Universitat Autònoma de Barcelona (UAB), Campus de la UAB, Plaça Cívica, 08193 Bellaterra, Spain
| | - Francisco Rodríguez-Frias
- Liver Diseases-Viral Hepatitis, Liver Unit, Vall d’Hebron Institut de Recerca (VHIR), Vall d’Hebron Hospital Universitari, Vall d’Hebron Barcelona Hospital Campus, Passeig Vall d’Hebron 119-129, 08035 Barcelona, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBERehd), Instituto de Salud Carlos III, Av. Monforte de Lemos, 3-5, 28029 Madrid, Spain
- Department of Basic Sciences, Universitat Internacional de Catalunya, Sant Cugat del Vallès, 08195 Barcelona, Spain
| | - Andrés Antón
- Microbiology Department, Vall d’Hebron Institut de Recerca (VHIR), Vall d’Hebron Hospital Universitari, Vall d’Hebron Barcelona Hospital Campus, Passeig Vall d’Hebron 119-129, 08035 Barcelona, Spain
- Microbiology Department, Universitat Autònoma de Barcelona (UAB), Campus de la UAB, Plaça Cívica, 08193 Bellaterra, Spain
| | - Josep Quer
- Liver Diseases-Viral Hepatitis, Liver Unit, Vall d’Hebron Institut de Recerca (VHIR), Vall d’Hebron Hospital Universitari, Vall d’Hebron Barcelona Hospital Campus, Passeig Vall d’Hebron 119-129, 08035 Barcelona, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBERehd), Instituto de Salud Carlos III, Av. Monforte de Lemos, 3-5, 28029 Madrid, Spain
- Biochemistry and Molecular Biology Department, Universitat Autònoma de Barcelona (UAB), Campus de la UAB, Plaça Cívica, 08193 Bellaterra, Spain
- Correspondence:
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11
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Anderson BD, Bisanz JE. Challenges and opportunities of strain diversity in gut microbiome research. Front Microbiol 2023; 14:1117122. [PMID: 36876113 PMCID: PMC9981649 DOI: 10.3389/fmicb.2023.1117122] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Accepted: 01/24/2023] [Indexed: 02/19/2023] Open
Abstract
Just because two things are related does not mean they are the same. In analyzing microbiome data, we are often limited to species-level analyses, and even with the ability to resolve strains, we lack comprehensive databases and understanding of the importance of strain-level variation outside of a limited number of model organisms. The bacterial genome is highly plastic with gene gain and loss occurring at rates comparable or higher than de novo mutations. As such, the conserved portion of the genome is often a fraction of the pangenome which gives rise to significant phenotypic variation, particularly in traits which are important in host microbe interactions. In this review, we discuss the mechanisms that give rise to strain variation and methods that can be used to study it. We identify that while strain diversity can act as a major barrier in interpreting and generalizing microbiome data, it can also be a powerful tool for mechanistic research. We then highlight recent examples demonstrating the importance of strain variation in colonization, virulence, and xenobiotic metabolism. Moving past taxonomy and the species concept will be crucial for future mechanistic research to understand microbiome structure and function.
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Affiliation(s)
- Benjamin D. Anderson
- Department of Biochemistry and Molecular Biology, Pennsylvania State University, University Park, PA, United States
| | - Jordan E. Bisanz
- Department of Biochemistry and Molecular Biology, Pennsylvania State University, University Park, PA, United States
- The Penn State Microbiome Center, Huck Institutes of the Life Sciences, University Park, PA, United States
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12
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Kukkar D, Sharma PK, Kim KH. Recent advances in metagenomic analysis of different ecological niches for enhanced biodegradation of recalcitrant lignocellulosic biomass. ENVIRONMENTAL RESEARCH 2022; 215:114369. [PMID: 36165858 DOI: 10.1016/j.envres.2022.114369] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Revised: 09/06/2022] [Accepted: 09/15/2022] [Indexed: 06/16/2023]
Abstract
Lignocellulose wastes stemming from agricultural residues can offer an excellent opportunity as alternative energy solutions in addition to fossil fuels. Besides, the unrestrained burning of agricultural residues can lead to the destruction of the soil microflora and associated soil sterilization. However, the difficulties associated with the biodegradation of lignocellulose biomasses remain as a formidable challenge for their sustainable management. In this respect, metagenomics can be used as an effective option to resolve such dilemma because of its potential as the next generation sequencing technology and bioinformatics tools to harness novel microbial consortia from diverse environments (e.g., soil, alpine forests, and hypersaline/acidic/hot sulfur springs). In light of the challenges associated with the bulk-scale biodegradation of lignocellulose-rich agricultural residues, this review is organized to help delineate the fundamental aspects of metagenomics towards the assessment of the microbial consortia and novel molecules (such as biocatalysts) which are otherwise unidentifiable by conventional laboratory culturing techniques. The discussion is extended further to highlight the recent advancements (e.g., from 2011 to 2022) in metagenomic approaches for the isolation and purification of lignocellulolytic microbes from different ecosystems along with the technical challenges and prospects associated with their wide implementation and scale-up. This review should thus be one of the first comprehensive reports on the metagenomics-based analysis of different environmental samples for the isolation and purification of lignocellulose degrading enzymes.
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Affiliation(s)
- Deepak Kukkar
- Department of Biotechnology, Chandigarh University, Gharuan, Mohali - 140413, Punjab, India; University Centre for Research and Development, Chandigarh University, Gharuan, Mohali - 140413, Punjab, India.
| | | | - Ki-Hyun Kim
- Department of Civil and Environmental Engineering, Hanyang University, Seongdong-gu, Wangsimni-ro, Seoul - 04763, South Korea.
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13
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Lai S, Pan S, Sun C, Coelho LP, Chen WH, Zhao XM. metaMIC: reference-free misassembly identification and correction of de novo metagenomic assemblies. Genome Biol 2022; 23:242. [PMID: 36376928 PMCID: PMC9661791 DOI: 10.1186/s13059-022-02810-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Accepted: 11/01/2022] [Indexed: 11/16/2022] Open
Abstract
Evaluating the quality of metagenomic assemblies is important for constructing reliable metagenome-assembled genomes and downstream analyses. Here, we present metaMIC ( https://github.com/ZhaoXM-Lab/metaMIC ), a machine learning-based tool for identifying and correcting misassemblies in metagenomic assemblies. Benchmarking results on both simulated and real datasets demonstrate that metaMIC outperforms existing tools when identifying misassembled contigs. Furthermore, metaMIC is able to localize the misassembly breakpoints, and the correction of misassemblies by splitting at misassembly breakpoints can improve downstream scaffolding and binning results.
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Affiliation(s)
- Senying Lai
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Shaojun Pan
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Chuqing Sun
- Key Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Key Laboratory of Bioinformatics and Molecular-imaging, Center for Artificial Intelligence Biology, Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei China
| | - Luis Pedro Coelho
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Wei-Hua Chen
- Key Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Key Laboratory of Bioinformatics and Molecular-imaging, Center for Artificial Intelligence Biology, Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei China
- College of Life Science, Henan Normal University, Xinxiang, Henan China
| | - Xing-Ming Zhao
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
- State Key Laboratory of Medical Neurobiology, Institutes of Brain Science, Fudan University, Shanghai, China
- Research Institute of Intelligent Complex Systems, Fudan University, Shanghai, China
- International Human Phenome Institutes (Shanghai), Shanghai, China
- Zhangjiang Fudan International Innovation Center, Shanghai, China
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14
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Zhou Y, Liu M, Yang J. Recovering metagenome-assembled genomes from shotgun metagenomic sequencing data: methods, applications, challenges, and opportunities. Microbiol Res 2022; 260:127023. [DOI: 10.1016/j.micres.2022.127023] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Revised: 03/07/2022] [Accepted: 04/05/2022] [Indexed: 12/12/2022]
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15
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Martin S, Heavens D, Lan Y, Horsfield S, Clark MD, Leggett RM. Nanopore adaptive sampling: a tool for enrichment of low abundance species in metagenomic samples. Genome Biol 2022; 23:11. [PMID: 35067223 PMCID: PMC8785595 DOI: 10.1186/s13059-021-02582-x] [Citation(s) in RCA: 68] [Impact Index Per Article: 34.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2021] [Accepted: 12/20/2021] [Indexed: 12/13/2022] Open
Abstract
Adaptive sampling is a method of software-controlled enrichment unique to nanopore sequencing platforms. To test its potential for enrichment of rarer species within metagenomic samples, we create a synthetic mock community and construct sequencing libraries with a range of mean read lengths. Enrichment is up to 13.87-fold for the least abundant species in the longest read length library; factoring in reduced yields from rejecting molecules the calculated efficiency raises this to 4.93-fold. Finally, we introduce a mathematical model of enrichment based on molecule length and relative abundance, whose predictions correlate strongly with mock and complex real-world microbial communities.
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Affiliation(s)
- Samuel Martin
- Earlham Institute, Norwich Research Park, Norwich, NR4 7UZ, UK
| | - Darren Heavens
- Earlham Institute, Norwich Research Park, Norwich, NR4 7UZ, UK
| | - Yuxuan Lan
- Earlham Institute, Norwich Research Park, Norwich, NR4 7UZ, UK
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16
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Yang C, Chowdhury D, Zhang Z, Cheung WK, Lu A, Bian Z, Zhang L. A review of computational tools for generating metagenome-assembled genomes from metagenomic sequencing data. Comput Struct Biotechnol J 2021; 19:6301-6314. [PMID: 34900140 PMCID: PMC8640167 DOI: 10.1016/j.csbj.2021.11.028] [Citation(s) in RCA: 72] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Revised: 11/17/2021] [Accepted: 11/17/2021] [Indexed: 12/16/2022] Open
Abstract
Metagenomic sequencing provides a culture-independent avenue to investigate the complex microbial communities by constructing metagenome-assembled genomes (MAGs). A MAG represents a microbial genome by a group of sequences from genome assembly with similar characteristics. It enables us to identify novel species and understand their potential functions in a dynamic ecosystem. Many computational tools have been developed to construct and annotate MAGs from metagenomic sequencing, however, there is a prominent gap to comprehensively introduce their background and practical performance. In this paper, we have thoroughly investigated the computational tools designed for both upstream and downstream analyses, including metagenome assembly, metagenome binning, gene prediction, functional annotation, taxonomic classification, and profiling. We have categorized the commonly used tools into unique groups based on their functional background and introduced the underlying core algorithms and associated information to demonstrate a comparative outlook. Furthermore, we have emphasized the computational requisition and offered guidance to the users to select the most efficient tools. Finally, we have indicated current limitations, potential solutions, and future perspectives for further improving the tools of MAG construction and annotation. We believe that our work provides a consolidated resource for the current stage of MAG studies and shed light on the future development of more effective MAG analysis tools on metagenomic sequencing.
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Key Words
- CNN, convolutional neural network
- DBG, De Bruijn graph
- GTDB, Genome Taxonomy Database
- Gene functional annotation
- Gene prediction
- Genome assembly
- HMM, Hidden Markov Model
- KEGG, Kyoto Encyclopedia of Genes and Genomes
- LCA, lowest common ancestor
- LPA, label propagation algorithm
- MAGs, metagenome-assembled genomes
- Metagenome binning
- Metagenome-assembled genomes
- Metagenomic sequencing
- Microbial abundance profiling
- OLC, overlap-layout consensus
- ONT, Oxford Nanopore Technologies
- ORFs, open reading frames
- PacBio, Pacific Biosciences
- QC, quality control
- SLR, synthetic long reads
- TNFs, tetranucleotide frequencies
- Taxonomic classification
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Affiliation(s)
- Chao Yang
- Department of Computer Science, Hong Kong Baptist University, Hong Kong Special Administrative Region
| | - Debajyoti Chowdhury
- Computational Medicine Lab, Hong Kong Baptist University, Hong Kong Special Administrative Region
- Institute of Integrated Bioinformedicine and Translational Sciences, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong Special Administrative Region
| | - Zhenmiao Zhang
- Department of Computer Science, Hong Kong Baptist University, Hong Kong Special Administrative Region
| | - William K. Cheung
- Department of Computer Science, Hong Kong Baptist University, Hong Kong Special Administrative Region
| | - Aiping Lu
- Computational Medicine Lab, Hong Kong Baptist University, Hong Kong Special Administrative Region
- Institute of Integrated Bioinformedicine and Translational Sciences, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong Special Administrative Region
| | - Zhaoxiang Bian
- Institute of Brain and Gut Research, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong Special Administrative Region
- Chinese Medicine Clinical Study Center, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong Special Administrative Region
| | - Lu Zhang
- Department of Computer Science, Hong Kong Baptist University, Hong Kong Special Administrative Region
- Computational Medicine Lab, Hong Kong Baptist University, Hong Kong Special Administrative Region
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17
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Kayani MUR, Huang W, Feng R, Chen L. Genome-resolved metagenomics using environmental and clinical samples. Brief Bioinform 2021; 22:bbab030. [PMID: 33758906 PMCID: PMC8425419 DOI: 10.1093/bib/bbab030] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2020] [Revised: 11/29/2020] [Accepted: 01/20/2021] [Indexed: 12/25/2022] Open
Abstract
Recent advances in high-throughput sequencing technologies and computational methods have added a new dimension to metagenomic data analysis i.e. genome-resolved metagenomics. In general terms, it refers to the recovery of draft or high-quality microbial genomes and their taxonomic classification and functional annotation. In recent years, several studies have utilized the genome-resolved metagenome analysis approach and identified previously unknown microbial species from human and environmental metagenomes. In this review, we describe genome-resolved metagenome analysis as a series of four necessary steps: (i) preprocessing of the sequencing reads, (ii) de novo metagenome assembly, (iii) genome binning and (iv) taxonomic and functional analysis of the recovered genomes. For each of these four steps, we discuss the most commonly used tools and the currently available pipelines to guide the scientific community in the recovery and subsequent analyses of genomes from any metagenome sample. Furthermore, we also discuss the tools required for validation of assembly quality as well as for improving quality of the recovered genomes. We also highlight the currently available pipelines that can be used to automate the whole analysis without having advanced bioinformatics knowledge. Finally, we will highlight the most widely adapted and actively maintained tools and pipelines that can be helpful to the scientific community in decision making before they commence the analysis.
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Affiliation(s)
- Masood ur Rehman Kayani
- Center for Microbiota and Immunological Diseases, Shanghai General Hospital, Shanghai Institute of Immunology, Shanghai Jiao Tong University, School of Medicine, Shanghai 2,000,025, China
| | - Wanqiu Huang
- Shanghai Institute of Immunology, Shanghai Jiao Tong University, School of Medicine, Shanghai 200,000, China
| | - Ru Feng
- Center for Microbiota and Immunological Diseases, Shanghai General Hospital, Shanghai Institute of Immunology, Shanghai Jiao Tong University, School of Medicine, Shanghai 2,000,025, China
| | - Lei Chen
- Center for Microbiota and Immunological Diseases, Shanghai General Hospital, Shanghai Institute of Immunology, Shanghai Jiao Tong University, School of Medicine, Shanghai 2,000,025, China
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18
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Orakov A, Fullam A, Coelho LP, Khedkar S, Szklarczyk D, Mende DR, Schmidt TSB, Bork P. GUNC: detection of chimerism and contamination in prokaryotic genomes. Genome Biol 2021; 22:178. [PMID: 34120611 PMCID: PMC8201837 DOI: 10.1186/s13059-021-02393-0] [Citation(s) in RCA: 88] [Impact Index Per Article: 29.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Accepted: 05/27/2021] [Indexed: 01/15/2023] Open
Abstract
Genomes are critical units in microbiology, yet ascertaining quality in prokaryotic genome assemblies remains a formidable challenge. We present GUNC (the Genome UNClutterer), a tool that accurately detects and quantifies genome chimerism based on the lineage homogeneity of individual contigs using a genome's full complement of genes. GUNC complements existing approaches by targeting previously underdetected types of contamination: we conservatively estimate that 5.7% of genomes in GenBank, 5.2% in RefSeq, and 15-30% of pre-filtered "high-quality" metagenome-assembled genomes in recent studies are undetected chimeras. GUNC provides a fast and robust tool to substantially improve prokaryotic genome quality.
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Affiliation(s)
- Askarbek Orakov
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, 69117, Heidelberg, Germany
| | - Anthony Fullam
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, 69117, Heidelberg, Germany
| | - Luis Pedro Coelho
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
| | - Supriya Khedkar
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, 69117, Heidelberg, Germany
| | - Damian Szklarczyk
- Institute of Molecular Life Sciences, University of Zurich, Zurich, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Daniel R Mende
- Department of Medical Microbiology, Amsterdam University Medical Center, Amsterdam, The Netherlands
| | - Thomas S B Schmidt
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, 69117, Heidelberg, Germany.
| | - Peer Bork
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, 69117, Heidelberg, Germany.
- Max Delbrück Centre for Molecular Medicine, Berlin, Germany.
- Yonsei Frontier Lab (YFL), Yonsei University, Seoul, 03722, South Korea.
- Department of Bioinformatics, Biocenter, University of Würzburg, Würzburg, Germany.
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19
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Meyer F, Lesker TR, Koslicki D, Fritz A, Gurevich A, Darling AE, Sczyrba A, Bremges A, McHardy AC. Tutorial: assessing metagenomics software with the CAMI benchmarking toolkit. Nat Protoc 2021; 16:1785-1801. [PMID: 33649565 DOI: 10.1038/s41596-020-00480-3] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2020] [Accepted: 11/26/2020] [Indexed: 01/31/2023]
Abstract
Computational methods are key in microbiome research, and obtaining a quantitative and unbiased performance estimate is important for method developers and applied researchers. For meaningful comparisons between methods, to identify best practices and common use cases, and to reduce overhead in benchmarking, it is necessary to have standardized datasets, procedures and metrics for evaluation. In this tutorial, we describe emerging standards in computational meta-omics benchmarking derived and agreed upon by a larger community of researchers. Specifically, we outline recent efforts by the Critical Assessment of Metagenome Interpretation (CAMI) initiative, which supplies method developers and applied researchers with exhaustive quantitative data about software performance in realistic scenarios and organizes community-driven benchmarking challenges. We explain the most relevant evaluation metrics for assessing metagenome assembly, binning and profiling results, and provide step-by-step instructions on how to generate them. The instructions use simulated mouse gut metagenome data released in preparation for the second round of CAMI challenges and showcase the use of a repository of tool results for CAMI datasets. This tutorial will serve as a reference for the community and facilitate informative and reproducible benchmarking in microbiome research.
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Affiliation(s)
- Fernando Meyer
- Computational Biology of Infection Research, Helmholtz Centre for Infection Research, Braunschweig, Germany
| | - Till-Robin Lesker
- Computational Biology of Infection Research, Helmholtz Centre for Infection Research, Braunschweig, Germany.,German Center for Infection Research (DZIF), Braunschweig, Germany
| | - David Koslicki
- Computer Science and Engineering, Biology, and The Huck Institutes of the Life Sciences, Penn State University, State College, PA, USA
| | - Adrian Fritz
- Computational Biology of Infection Research, Helmholtz Centre for Infection Research, Braunschweig, Germany
| | - Alexey Gurevich
- Center for Algorithmic Biotechnology, St. Petersburg State University, St. Petersburg, Russia
| | - Aaron E Darling
- The ithree institute, University of Technology Sydney, Sydney, Australia
| | - Alexander Sczyrba
- Faculty of Technology and Center for Biotechnology, Bielefeld University, Bielefeld, Germany
| | - Andreas Bremges
- Computational Biology of Infection Research, Helmholtz Centre for Infection Research, Braunschweig, Germany.,German Center for Infection Research (DZIF), Braunschweig, Germany
| | - Alice C McHardy
- Computational Biology of Infection Research, Helmholtz Centre for Infection Research, Braunschweig, Germany.
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20
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Large-Scale Metagenome Assembly Reveals Novel Animal-Associated Microbial Genomes, Biosynthetic Gene Clusters, and Other Genetic Diversity. mSystems 2020; 5:5/6/e01045-20. [PMID: 33144315 PMCID: PMC7646530 DOI: 10.1128/msystems.01045-20] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
Large-scale metagenome assemblies of human microbiomes have produced a vast catalogue of previously unseen microbial genomes; however, comparatively few microbial genomes derive from other vertebrates. Here, we generated 5,596 metagenome-assembled genomes (MAGs) from the gut metagenomes of 180 predominantly wild animal species representing 5 classes, in addition to 14 existing animal gut metagenome data sets. The MAGs comprised 1,522 species-level genome bins (SGBs), most of which were novel at the species, genus, or family level, and the majority were enriched in host versus environment metagenomes. Many traits distinguished SGBs enriched in host or environmental biomes, including the number of antimicrobial resistance genes. We identified 1,986 diverse biosynthetic gene clusters; only 23 clustered with any MIBiG database references. Gene-based assembly revealed tremendous gene diversity, much of it host or environment specific. Our MAG and gene data sets greatly expand the microbial genome repertoire and provide a broad view of microbial adaptations to the vertebrate gut.IMPORTANCE Microbiome studies on a select few mammalian species (e.g., humans, mice, and cattle) have revealed a great deal of novel genomic diversity in the gut microbiome. However, little is known of the microbial diversity in the gut of other vertebrates. We studied the gut microbiomes of a large set of mostly wild animal species consisting of mammals, birds, reptiles, amphibians, and fish. Unfortunately, we found that existing reference databases commonly used for metagenomic analyses failed to capture the microbiome diversity among vertebrates. To increase database representation, we applied advanced metagenome assembly methods to our animal gut data and to many public gut metagenome data sets that had not been used to obtain microbial genomes. Our resulting genome and gene cluster collections comprised a great deal of novel taxonomic and genomic diversity, which we extensively characterized. Our findings substantially expand what is known of microbial genomic diversity in the vertebrate gut.
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21
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Chen LX, Anantharaman K, Shaiber A, Eren AM, Banfield JF. Accurate and complete genomes from metagenomes. Genome Res 2020; 30:315-333. [PMID: 32188701 PMCID: PMC7111523 DOI: 10.1101/gr.258640.119] [Citation(s) in RCA: 203] [Impact Index Per Article: 50.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Genomes are an integral component of the biological information about an organism; thus, the more complete the genome, the more informative it is. Historically, bacterial and archaeal genomes were reconstructed from pure (monoclonal) cultures, and the first reported sequences were manually curated to completion. However, the bottleneck imposed by the requirement for isolates precluded genomic insights for the vast majority of microbial life. Shotgun sequencing of microbial communities, referred to initially as community genomics and subsequently as genome-resolved metagenomics, can circumvent this limitation by obtaining metagenome-assembled genomes (MAGs); but gaps, local assembly errors, chimeras, and contamination by fragments from other genomes limit the value of these genomes. Here, we discuss genome curation to improve and, in some cases, achieve complete (circularized, no gaps) MAGs (CMAGs). To date, few CMAGs have been generated, although notably some are from very complex systems such as soil and sediment. Through analysis of about 7000 published complete bacterial isolate genomes, we verify the value of cumulative GC skew in combination with other metrics to establish bacterial genome sequence accuracy. The analysis of cumulative GC skew identified potential misassemblies in some reference genomes of isolated bacteria and the repeat sequences that likely gave rise to them. We discuss methods that could be implemented in bioinformatic approaches for curation to ensure that metabolic and evolutionary analyses can be based on very high-quality genomes.
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Affiliation(s)
- Lin-Xing Chen
- Department of Earth and Planetary Sciences, University of California, Berkeley, California 94720, USA
| | - Karthik Anantharaman
- Department of Earth and Planetary Sciences, University of California, Berkeley, California 94720, USA
| | - Alon Shaiber
- Graduate Program in Biophysical Sciences, University of Chicago, Chicago, Illinois 60637, USA.,Department of Medicine, University of Chicago, Chicago, Illinois 60637, USA
| | - A Murat Eren
- Department of Medicine, University of Chicago, Chicago, Illinois 60637, USA.,Bay Paul Center, Marine Biological Laboratory, Woods Hole, Massachusetts 02543, USA
| | - Jillian F Banfield
- Department of Earth and Planetary Sciences, University of California, Berkeley, California 94720, USA.,Department of Environmental Science, Policy, and Management, University of California, Berkeley, California 94720, USA.,Earth and Environmental Sciences, Lawrence Berkeley National Laboratory, University of California, Berkeley, California 94720, USA
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