1
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Du Y, Zuo W, Sun F. Imputing Metagenomic Hi-C Contacts Facilitates the Integrative Contig Binning Through Constrained Random Walk with Restart. J Comput Biol 2024. [PMID: 39246231 DOI: 10.1089/cmb.2024.0663] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/10/2024] Open
Abstract
Metagenomic Hi-C (metaHi-C) has shown remarkable potential for retrieving high-quality metagenome-assembled genomes from complex microbial communities. Nevertheless, existing metaHi-C-based contig binning methods solely rely on Hi-C interactions between contigs, disregarding crucial biological information such as the presence of single-copy marker genes. To overcome this limitation, we introduce ImputeCC, an integrative contig binning tool optimized for metaHi-C datasets. ImputeCC integrates both Hi-C interactions and the discriminative power of single-copy marker genes to group marker-gene-containing contigs into preliminary bins. It also introduces a novel constrained random walk with restart algorithm to enhance Hi-C connectivity among contigs. Comprehensive assessments using both mock and real metaHi-C datasets from diverse environments demonstrate that ImputeCC consistently outperforms other Hi-C-based contig binning tools. A genus-level analysis of the sheep gut microbiota reconstructed by ImputeCC underlines its capability to recover key species from dominant genera and identify previously unknown genera.
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Affiliation(s)
- Yuxuan Du
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, California, USA
| | - Wenxuan Zuo
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, California, USA
| | - Fengzhu Sun
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, California, USA
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2
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Mallawaarachchi V, Wickramarachchi A, Xue H, Papudeshi B, Grigson SR, Bouras G, Prahl RE, Kaphle A, Verich A, Talamantes-Becerra B, Dinsdale EA, Edwards RA. Solving genomic puzzles: computational methods for metagenomic binning. Brief Bioinform 2024; 25:bbae372. [PMID: 39082646 PMCID: PMC11289683 DOI: 10.1093/bib/bbae372] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2024] [Revised: 06/05/2024] [Accepted: 07/15/2024] [Indexed: 08/03/2024] Open
Abstract
Metagenomics involves the study of genetic material obtained directly from communities of microorganisms living in natural environments. The field of metagenomics has provided valuable insights into the structure, diversity and ecology of microbial communities. Once an environmental sample is sequenced and processed, metagenomic binning clusters the sequences into bins representing different taxonomic groups such as species, genera, or higher levels. Several computational tools have been developed to automate the process of metagenomic binning. These tools have enabled the recovery of novel draft genomes of microorganisms allowing us to study their behaviors and functions within microbial communities. This review classifies and analyzes different approaches of metagenomic binning and different refinement, visualization, and evaluation techniques used by these methods. Furthermore, the review highlights the current challenges and areas of improvement present within the field of research.
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Affiliation(s)
- Vijini Mallawaarachchi
- Flinders Accelerator for Microbiome Exploration, College of Science and Engineering, Flinders University, Adelaide, SA 5042, Australia
| | - Anuradha Wickramarachchi
- Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Westmead, NSW 2145, Australia
| | - Hansheng Xue
- School of Computing, National University of Singapore, Singapore 119077, Singapore
| | - Bhavya Papudeshi
- Flinders Accelerator for Microbiome Exploration, College of Science and Engineering, Flinders University, Adelaide, SA 5042, Australia
| | - Susanna R Grigson
- Flinders Accelerator for Microbiome Exploration, College of Science and Engineering, Flinders University, Adelaide, SA 5042, Australia
| | - George Bouras
- Adelaide Medical School, Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, SA 5005, Australia
- The Department of Surgery—Otolaryngology Head and Neck Surgery, University of Adelaide and the Basil Hetzel Institute for Translational Health Research, Central Adelaide Local Health Network, Adelaide, SA 5011, Australia
| | - Rosa E Prahl
- Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Westmead, NSW 2145, Australia
| | - Anubhav Kaphle
- Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Westmead, NSW 2145, Australia
| | - Andrey Verich
- Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Westmead, NSW 2145, Australia
- The Kirby Institute, The University of New South Wales, Randwick, Sydney, NSW 2052, Australia
| | - Berenice Talamantes-Becerra
- Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Westmead, NSW 2145, Australia
| | - Elizabeth A Dinsdale
- Flinders Accelerator for Microbiome Exploration, College of Science and Engineering, Flinders University, Adelaide, SA 5042, Australia
| | - Robert A Edwards
- Flinders Accelerator for Microbiome Exploration, College of Science and Engineering, Flinders University, Adelaide, SA 5042, Australia
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3
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Pinto Y, Bhatt AS. Sequencing-based analysis of microbiomes. Nat Rev Genet 2024:10.1038/s41576-024-00746-6. [PMID: 38918544 DOI: 10.1038/s41576-024-00746-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/15/2024] [Indexed: 06/27/2024]
Abstract
Microbiomes occupy a range of niches and, in addition to having diverse compositions, they have varied functional roles that have an impact on agriculture, environmental sciences, and human health and disease. The study of microbiomes has been facilitated by recent technological and analytical advances, such as cheaper and higher-throughput DNA and RNA sequencing, improved long-read sequencing and innovative computational analysis methods. These advances are providing a deeper understanding of microbiomes at the genomic, transcriptional and translational level, generating insights into their function and composition at resolutions beyond the species level.
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Affiliation(s)
- Yishay Pinto
- Department of Genetics, Stanford University, Stanford, CA, USA
- Department of Medicine, Divisions of Hematology and Blood & Marrow Transplantation, Stanford University, Stanford, CA, USA
| | - Ami S Bhatt
- Department of Genetics, Stanford University, Stanford, CA, USA.
- Department of Medicine, Divisions of Hematology and Blood & Marrow Transplantation, Stanford University, Stanford, CA, USA.
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4
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Vancaester E, Blaxter ML. MarkerScan: Separation and assembly of cobionts sequenced alongside target species in biodiversity genomics projects. Wellcome Open Res 2024; 9:33. [PMID: 38617467 PMCID: PMC11016177 DOI: 10.12688/wellcomeopenres.20730.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/18/2023] [Indexed: 04/16/2024] Open
Abstract
Contamination of public databases by mislabelled sequences has been highlighted for many years and the avalanche of novel sequencing data now being deposited has the potential to make databases difficult to use effectively. It is therefore crucial that sequencing projects and database curators perform pre-submission checks to remove obvious contamination and avoid propagating erroneous taxonomic relationships. However, it is important also to recognise that biological contamination of a target sample with unexpected species' DNA can also lead to the discovery of fascinating biological phenomena through the identification of environmental organisms or endosymbionts. Here, we present a novel, integrated method for detection and generation of high-quality genomes of all non-target genomes co-sequenced in eukaryotic genome sequencing projects. After performing taxonomic profiling of an assembly from the raw data, and leveraging the identity of small rRNA sequences discovered therein as markers, a targeted classification approach retrieves and assembles high-quality genomes. The genomes of these cobionts are then not only removed from the target species' genome but also available for further interrogation. Source code is available from https://github.com/CobiontID/MarkerScan. MarkerScan is written in Python and is deployed as a Docker container.
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Affiliation(s)
| | - Mark L. Blaxter
- Tree of Life, Wellcome Sanger Institute, Hinxton, England, UK
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5
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Kim C, Pongpanich M, Porntaveetus T. Unraveling metagenomics through long-read sequencing: a comprehensive review. J Transl Med 2024; 22:111. [PMID: 38282030 PMCID: PMC10823668 DOI: 10.1186/s12967-024-04917-1] [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: 10/15/2023] [Accepted: 01/21/2024] [Indexed: 01/30/2024] Open
Abstract
The study of microbial communities has undergone significant advancements, starting from the initial use of 16S rRNA sequencing to the adoption of shotgun metagenomics. However, a new era has emerged with the advent of long-read sequencing (LRS), which offers substantial improvements over its predecessor, short-read sequencing (SRS). LRS produces reads that are several kilobases long, enabling researchers to obtain more complete and contiguous genomic information, characterize structural variations, and study epigenetic modifications. The current leaders in LRS technologies are Pacific Biotechnologies (PacBio) and Oxford Nanopore Technologies (ONT), each offering a distinct set of advantages. This review covers the workflow of long-read metagenomics sequencing, including sample preparation (sample collection, sample extraction, and library preparation), sequencing, processing (quality control, assembly, and binning), and analysis (taxonomic annotation and functional annotation). Each section provides a concise outline of the key concept of the methodology, presenting the original concept as well as how it is challenged or modified in the context of LRS. Additionally, the section introduces a range of tools that are compatible with LRS and can be utilized to execute the LRS process. This review aims to present the workflow of metagenomics, highlight the transformative impact of LRS, and provide researchers with a selection of tools suitable for this task.
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Affiliation(s)
- Chankyung Kim
- Center of Excellence in Genomics and Precision Dentistry, Department of Physiology, Faculty of Dentistry, Chulalongkorn University, Bangkok, Thailand
- Graduate Program in Bioinformatics and Computational Biology, Faculty of Science, Chulalongkorn University, Bangkok, Thailand
| | - Monnat Pongpanich
- Department of Mathematics and Computer Science, Faculty of Science, Chulalongkorn University, Bangkok, Thailand
- Center of Excellence for Cancer and Inflammation, Chulalongkorn University, Bangkok, Thailand
| | - Thantrira Porntaveetus
- Center of Excellence in Genomics and Precision Dentistry, Department of Physiology, Faculty of Dentistry, Chulalongkorn University, Bangkok, Thailand.
- Graduate Program in Geriatric and Special Patients Care, Faculty of Dentistry, Chulalongkorn University, Bangkok, Thailand.
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Zulfiqar M, Singh V, Steinbeck C, Sorokina M. Review on computer-assisted biosynthetic capacities elucidation to assess metabolic interactions and communication within microbial communities. Crit Rev Microbiol 2024:1-40. [PMID: 38270170 DOI: 10.1080/1040841x.2024.2306465] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Accepted: 01/12/2024] [Indexed: 01/26/2024]
Abstract
Microbial communities thrive through interactions and communication, which are challenging to study as most microorganisms are not cultivable. To address this challenge, researchers focus on the extracellular space where communication events occur. Exometabolomics and interactome analysis provide insights into the molecules involved in communication and the dynamics of their interactions. Advances in sequencing technologies and computational methods enable the reconstruction of taxonomic and functional profiles of microbial communities using high-throughput multi-omics data. Network-based approaches, including community flux balance analysis, aim to model molecular interactions within and between communities. Despite these advances, challenges remain in computer-assisted biosynthetic capacities elucidation, requiring continued innovation and collaboration among diverse scientists. This review provides insights into the current state and future directions of computer-assisted biosynthetic capacities elucidation in studying microbial communities.
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Affiliation(s)
- Mahnoor Zulfiqar
- Institute for Inorganic and Analytical Chemistry, Friedrich Schiller University, Jena, Germany
- Cluster of Excellence Balance of the Microverse, Friedrich Schiller University Jena, Jena, Germany
| | - Vinay Singh
- Institute for Inorganic and Analytical Chemistry, Friedrich Schiller University, Jena, Germany
| | - Christoph Steinbeck
- Institute for Inorganic and Analytical Chemistry, Friedrich Schiller University, Jena, Germany
- Cluster of Excellence Balance of the Microverse, Friedrich Schiller University Jena, Jena, Germany
| | - Maria Sorokina
- Institute for Inorganic and Analytical Chemistry, Friedrich Schiller University, Jena, Germany
- Data Science and Artificial Intelligence, Research and Development, Pharmaceuticals, Bayer, Berlin, Germany
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7
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Du Y, Sun F. MetaCC allows scalable and integrative analyses of both long-read and short-read metagenomic Hi-C data. Nat Commun 2023; 14:6231. [PMID: 37802989 PMCID: PMC10558524 DOI: 10.1038/s41467-023-41209-6] [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: 03/16/2023] [Accepted: 08/25/2023] [Indexed: 10/08/2023] Open
Abstract
Metagenomic Hi-C (metaHi-C) can identify contig-to-contig relationships with respect to their proximity within the same physical cell. Shotgun libraries in metaHi-C experiments can be constructed by next-generation sequencing (short-read metaHi-C) or more recent third-generation sequencing (long-read metaHi-C). However, all existing metaHi-C analysis methods are developed and benchmarked on short-read metaHi-C datasets and there exists much room for improvement in terms of more scalable and stable analyses, especially for long-read metaHi-C data. Here we report MetaCC, an efficient and integrative framework for analyzing both short-read and long-read metaHi-C datasets. MetaCC outperforms existing methods on normalization and binning. In particular, the MetaCC normalization module, named NormCC, is more than 3000 times faster than the current state-of-the-art method HiCzin on a complex wastewater dataset. When applied to one sheep gut long-read metaHi-C dataset, MetaCC binning module can retrieve 709 high-quality genomes with the largest species diversity using one single sample, including an expansion of five uncultured members from the order Erysipelotrichales, and is the only binner that can recover the genome of one important species Bacteroides vulgatus. Further plasmid analyses reveal that MetaCC binning is able to capture multi-copy plasmids.
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Affiliation(s)
- Yuxuan Du
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA
| | - Fengzhu Sun
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA.
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8
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Arikawa K, Hosokawa M. Uncultured prokaryotic genomes in the spotlight: An examination of publicly available data from metagenomics and single-cell genomics. Comput Struct Biotechnol J 2023; 21:4508-4518. [PMID: 37771751 PMCID: PMC10523443 DOI: 10.1016/j.csbj.2023.09.010] [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: 06/15/2023] [Revised: 09/10/2023] [Accepted: 09/10/2023] [Indexed: 09/30/2023] Open
Abstract
Owing to the ineffectiveness of traditional culture techniques for the vast majority of microbial species, culture-independent analyses utilizing next-generation sequencing and bioinformatics have become essential for gaining insight into microbial ecology and function. This mini-review focuses on two essential methods for obtaining genetic information from uncultured prokaryotes, metagenomics and single-cell genomics. We analyzed the registration status of uncultured prokaryotic genome data from major public databases and assessed the advantages and limitations of both the methods. Metagenomics generates a significant quantity of sequence data and multiple prokaryotic genomes using straightforward experimental procedures. However, in ecosystems with high microbial diversity, such as soil, most genes are presented as brief, disconnected contigs, and lack association of highly conserved genes and mobile genetic elements with individual species genomes. Although technically more challenging, single-cell genomics offers valuable insights into complex ecosystems by providing strain-resolved genomes, addressing issues in metagenomics. Recent technological advancements, such as long-read sequencing, machine learning algorithms, and in silico protein structure prediction, in combination with vast genomic data, have the potential to overcome the current technical challenges and facilitate a deeper understanding of uncultured microbial ecosystems and microbial dark matter genes and proteins. In light of this, it is imperative that continued innovation in both methods and technologies take place to create high-quality reference genome databases that will support future microbial research and industrial applications.
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Affiliation(s)
- Koji Arikawa
- Department of Life Science and Medical Bioscience, Waseda University, 2-2 Wakamatsu-cho, Shinjuku-ku, Tokyo 162-8480, Japan
- bitBiome, Inc., 513 Wasedatsurumaki-cho, Shinjuku-ku, Tokyo 162-0041, Japan
| | - Masahito Hosokawa
- Department of Life Science and Medical Bioscience, Waseda University, 2-2 Wakamatsu-cho, Shinjuku-ku, Tokyo 162-8480, Japan
- bitBiome, Inc., 513 Wasedatsurumaki-cho, Shinjuku-ku, Tokyo 162-0041, Japan
- Research Organization for Nano and Life Innovation, Waseda University, 513 Wasedatsurumaki-cho, Shinjuku-ku, Tokyo 162-0041, Japan
- Institute for Advanced Research of Biosystem Dynamics, Waseda Research Institute for Science and Engineering, 3-4-1 Okubo, Shinjuku-ku, Tokyo 169-8555, Japan
- Computational Bio Big-Data Open Innovation Laboratory, National Institute of Advanced Industrial Science and Technology, 3-4-1 Okubo, Shinjuku-ku, Tokyo 169-8555, Japan
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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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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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11
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Xiang B, Zhao L, Zhang M. Unitig level assembly graph based metagenome-assembled genome refiner (UGMAGrefiner): A tool to increase completeness and resolution of metagenome-assembled genomes. Comput Struct Biotechnol J 2023; 21:2394-2404. [PMID: 37066122 PMCID: PMC10091015 DOI: 10.1016/j.csbj.2023.03.030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Revised: 03/16/2023] [Accepted: 03/16/2023] [Indexed: 04/03/2023] Open
Abstract
De novo assembly of next generation metagenomic reads is widely used to provide taxonomic and functional information of genomes in a microbial community. As strains are functionally specific, recovery of strain-resolved genomes is important but still a challenge. Unitigs and assembly graphs are mid-products generated during the assembly of reads into contigs, and they provide higher resolution for sequences connection information. In this study, we propose a new approach UGMAGrefiner (a unitig level assembly graph-based metagenome-assembled Genome refiner), which uses the connection and coverage information from unitig level assembly graphs to recruit unbinned unitigs to MAGs, adjust binning result, and infer unitigs shared by multiple MAGs. In two simulated datasets (Simdata and CAMI data) and one real dataset (GD02), it outperforms two state-of-the-art assembly graph-based binning refine tools in the refinement of MAGs' quality by stably increasing the completeness of genomes. UGMAGrefiner can identify genome specific clusters of genomes with below 99% average nucleotide identity for homologous sequences. For MAGs mixed with 99% similarity genome clusters, it could distinguish 8 out of 9 genomes in Simdata and 8 out of 12 genomes in CAMI data. In GD02 data, it could identify 16 new unitig clusters representing genome specific regions of mixed genomes and 4 unitig clusters representing new genomes from total 135 MAGs for further functional analysis. UGMAGrefiner provides an efficient way to obtain more complete MAGs and study genome specific functions. It will be useful to improve taxonomic and functional information of genomes after de novo assembly.
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12
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Vuong P, Wise MJ, Whiteley AS, Kaur P. Ten simple rules for investigating (meta)genomic data from environmental ecosystems. PLoS Comput Biol 2022; 18:e1010675. [PMID: 36480496 PMCID: PMC9731419 DOI: 10.1371/journal.pcbi.1010675] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Affiliation(s)
- Paton Vuong
- UWA School of Agriculture & Environment, University of Western Australia, Perth, Australia
| | - Michael J. Wise
- School of Physics, Mathematics and Computing, University of Western Australia, Perth, Australia
- The Marshall Centre of Infectious Diseases, School of Biological Sciences, The University of Western Australia, Perth, Australia
| | - Andrew S. Whiteley
- Centre for Environment & Life Sciences, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Floreat, Australia
| | - Parwinder Kaur
- UWA School of Agriculture & Environment, University of Western Australia, Perth, Australia
- * E-mail:
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13
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Pillay S, Calderón-Franco D, Urhan A, Abeel T. Metagenomic-based surveillance systems for antibiotic resistance in non-clinical settings. Front Microbiol 2022; 13:1066995. [PMID: 36532424 PMCID: PMC9755710 DOI: 10.3389/fmicb.2022.1066995] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Accepted: 11/09/2022] [Indexed: 08/12/2023] Open
Abstract
The success of antibiotics as a therapeutic agent has led to their ineffectiveness. The continuous use and misuse in clinical and non-clinical areas have led to the emergence and spread of antibiotic-resistant bacteria and its genetic determinants. This is a multi-dimensional problem that has now become a global health crisis. Antibiotic resistance research has primarily focused on the clinical healthcare sectors while overlooking the non-clinical sectors. The increasing antibiotic usage in the environment - including animals, plants, soil, and water - are drivers of antibiotic resistance and function as a transmission route for antibiotic resistant pathogens and is a source for resistance genes. These natural compartments are interconnected with each other and humans, allowing the spread of antibiotic resistance via horizontal gene transfer between commensal and pathogenic bacteria. Identifying and understanding genetic exchange within and between natural compartments can provide insight into the transmission, dissemination, and emergence mechanisms. The development of high-throughput DNA sequencing technologies has made antibiotic resistance research more accessible and feasible. In particular, the combination of metagenomics and powerful bioinformatic tools and platforms have facilitated the identification of microbial communities and has allowed access to genomic data by bypassing the need for isolating and culturing microorganisms. This review aimed to reflect on the different sequencing techniques, metagenomic approaches, and bioinformatics tools and pipelines with their respective advantages and limitations for antibiotic resistance research. These approaches can provide insight into resistance mechanisms, the microbial population, emerging pathogens, resistance genes, and their dissemination. This information can influence policies, develop preventative measures and alleviate the burden caused by antibiotic resistance.
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Affiliation(s)
- Stephanie Pillay
- Delft Bioinformatics Lab, Delft University of Technology, Delft, Netherlands
| | | | - Aysun Urhan
- Delft Bioinformatics Lab, Delft University of Technology, Delft, Netherlands
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, United States
| | - Thomas Abeel
- Delft Bioinformatics Lab, Delft University of Technology, Delft, Netherlands
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, United States
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14
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Tolstoganov I, Kamenev Y, Kruglikov R, Ochkalova S, Korobeynikov A. BinSPreader: Refine binning results for fuller MAG reconstruction. iScience 2022; 25:104770. [PMID: 35992057 PMCID: PMC9386100 DOI: 10.1016/j.isci.2022.104770] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2022] [Revised: 06/20/2022] [Accepted: 07/12/2022] [Indexed: 11/02/2022] Open
Abstract
Despite the recent advances in high-throughput sequencing, metagenome analysis of microbial populations still remains a challenge. In particular, the metagenome-assembled genomes (MAGs) are often fragmented due to interspecies repeats, uneven coverage, and varying strain abundance. MAGs are constructed via a binning process that uses features of input data in order to cluster long contigs presumably belonging to the same species. In this work, we present BinSPreader-a binning refiner tool that exploits the assembly graph topology and other connectivity information to refine binning, correct binning errors, and propagate binning to shorter contigs. We show that BinSPreader could increase the completeness of the bins without sacrificing the purity and could predict contigs belonging to several MAGs. BinSPreader is effective in binning shorter contigs that often contain important conservative sequences that might be of great interest to researchers.
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Affiliation(s)
- Ivan Tolstoganov
- Center for Algorithmic Biotechnology, Saint Petersburg State University, Saint Petersburg, 199004, Russia
| | - Yuri Kamenev
- ITMO University, Saint Petersburg 197101, Russia
| | | | - Sofia Ochkalova
- Applied Genomics Laboratory, SCAMT Institute, ITMO University, Saint Petersburg 197101, Russia
| | - Anton Korobeynikov
- Center for Algorithmic Biotechnology, Saint Petersburg State University, Saint Petersburg, 199004, Russia
- Department of Statistical Modelling, Saint Petersburg State University, Saint Petersburg, 198504, Russia
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15
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Goussarov G, Mysara M, Vandamme P, Van Houdt R. Introduction to the principles and methods underlying the recovery of metagenome-assembled genomes from metagenomic data. Microbiologyopen 2022; 11:e1298. [PMID: 35765182 PMCID: PMC9179125 DOI: 10.1002/mbo3.1298] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Revised: 05/19/2022] [Accepted: 05/19/2022] [Indexed: 11/18/2022] Open
Abstract
The rise of metagenomics offers a leap forward for understanding the genetic diversity of microorganisms in many different complex environments by providing a platform that can identify potentially unlimited numbers of known and novel microorganisms. As such, it is impossible to imagine new major initiatives without metagenomics. Nevertheless, it represents a relatively new discipline with various levels of complexity and demands on bioinformatics. The underlying principles and methods used in metagenomics are often seen as common knowledge and often not detailed or fragmented. Therefore, we reviewed these to guide microbiologists in taking the first steps into metagenomics. We specifically focus on a workflow aimed at reconstructing individual genomes, that is, metagenome-assembled genomes, integrating DNA sequencing, assembly, binning, identification and annotation.
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Affiliation(s)
- Gleb Goussarov
- Microbiology Unit, Belgian Nuclear Research Centre (SCK CEN)MolBelgium
- Laboratory of Microbiology and BCCM/LMG Bacteria Collection, Faculty of SciencesGhent UniversityGhentBelgium
| | - Mohamed Mysara
- Microbiology Unit, Belgian Nuclear Research Centre (SCK CEN)MolBelgium
| | - Peter Vandamme
- Laboratory of Microbiology and BCCM/LMG Bacteria Collection, Faculty of SciencesGhent UniversityGhentBelgium
| | - Rob Van Houdt
- Microbiology Unit, Belgian Nuclear Research Centre (SCK CEN)MolBelgium
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16
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Du Y, Sun F. HiFine: integrating Hi-c-based and shotgun-based methods to reFine binning of metagenomic contigs. Bioinformatics 2022; 38:2973-2979. [PMID: 35482530 PMCID: PMC9154269 DOI: 10.1093/bioinformatics/btac295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Revised: 03/28/2022] [Accepted: 04/21/2022] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION Metagenomic binning aims to retrieve microbial genomes directly from ecosystems by clustering metagenomic contigs assembled from short reads into draft genomic bins. Traditional shotgun-based binning methods depend on the contigs' composition and abundance profiles and are impaired by the paucity of enough samples to construct reliable co-abundance profiles. When applied to a single sample, shotgun-based binning methods struggle to distinguish closely related species only using composition information. As an alternative binning approach, Hi-C-based binning employs metagenomic Hi-C technique to measure the proximity contacts between metagenomic fragments. However, spurious inter-species Hi-C contacts inevitably generated by incorrect ligations of DNA fragments between species link the contigs from varying genomes, weakening the purity of final draft genomic bins. Therefore, it is imperative to develop a binning pipeline to overcome the shortcomings of both types of binning methods on a single sample. RESULTS We develop HiFine, a novel binning pipeline to refine the binning results of metagenomic contigs by integrating both Hi-C-based and shotgun-based binning tools. HiFine designs a strategy of fragmentation for the original bin sets derived from the Hi-C-based and shotgun-based binning methods, which considerably increases the purity of initial bins, followed by merging fragmented bins and recruiting unbinned contigs. We demonstrate that HiFine significantly improves the existing binning results of both types of binning methods and achieves better performance in constructing species genomes on publicly available datasets. To the best of our knowledge, HiFine is the first pipeline to integrate different types of tools for the binning of metagenomic contigs. AVAILABILITY HiFine is available at https://github.com/dyxstat/HiFine. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Yuxuan Du
- Department of Quantitative and Computational Biology, University of Southern California, USA
| | - Fengzhu Sun
- Department of Quantitative and Computational Biology, University of Southern California, USA
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Segal-Kischinevzky C, Romero-Aguilar L, Alcaraz LD, López-Ortiz G, Martínez-Castillo B, Torres-Ramírez N, Sandoval G, González J. Yeasts Inhabiting Extreme Environments and Their Biotechnological Applications. Microorganisms 2022; 10:794. [PMID: 35456844 PMCID: PMC9028089 DOI: 10.3390/microorganisms10040794] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Revised: 04/06/2022] [Accepted: 04/06/2022] [Indexed: 11/17/2022] Open
Abstract
Yeasts are microscopic fungi inhabiting all Earth environments, including those inhospitable for most life forms, considered extreme environments. According to their habitats, yeasts could be extremotolerant or extremophiles. Some are polyextremophiles, depending on their growth capacity, tolerance, and survival in the face of their habitat's physical and chemical constitution. The extreme yeasts are relevant for the industrial production of value-added compounds, such as biofuels, lipids, carotenoids, recombinant proteins, enzymes, among others. This review calls attention to the importance of yeasts inhabiting extreme environments, including metabolic and adaptive aspects to tolerate conditions of cold, heat, water availability, pH, salinity, osmolarity, UV radiation, and metal toxicity, which are relevant for biotechnological applications. We explore the habitats of extreme yeasts, highlighting key species, physiology, adaptations, and molecular identification. Finally, we summarize several findings related to the industrially-important extremophilic yeasts and describe current trends in biotechnological applications that will impact the bioeconomy.
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Affiliation(s)
- Claudia Segal-Kischinevzky
- Departamento de Biología Celular, Facultad de Ciencias, Universidad Nacional Autónoma de México, Avenida Universidad 3000, Coyoacán, Mexico City 04510, Mexico; (C.S.-K.); (L.D.A.); (B.M.-C.); (N.T.-R.)
| | - Lucero Romero-Aguilar
- Departamento de Bioquímica, Facultad de Medicina, Universidad Nacional Autónoma de México, Avenida Universidad 3000, Coyoacán, Mexico City 04510, Mexico;
| | - Luis D. Alcaraz
- Departamento de Biología Celular, Facultad de Ciencias, Universidad Nacional Autónoma de México, Avenida Universidad 3000, Coyoacán, Mexico City 04510, Mexico; (C.S.-K.); (L.D.A.); (B.M.-C.); (N.T.-R.)
| | - Geovani López-Ortiz
- Subdivisión de Medicina Familiar, Facultad de Medicina, Universidad Nacional Autónoma de México, Avenida Universidad 3000, Coyoacán, Mexico City 04510, Mexico;
| | - Blanca Martínez-Castillo
- Departamento de Biología Celular, Facultad de Ciencias, Universidad Nacional Autónoma de México, Avenida Universidad 3000, Coyoacán, Mexico City 04510, Mexico; (C.S.-K.); (L.D.A.); (B.M.-C.); (N.T.-R.)
| | - Nayeli Torres-Ramírez
- Departamento de Biología Celular, Facultad de Ciencias, Universidad Nacional Autónoma de México, Avenida Universidad 3000, Coyoacán, Mexico City 04510, Mexico; (C.S.-K.); (L.D.A.); (B.M.-C.); (N.T.-R.)
| | - Georgina Sandoval
- Laboratorio de Innovación en Bioenergéticos y Bioprocesos Avanzados (LIBBA), Unidad de Biotecnología Industrial, Centro de Investigación y Asistencia en Tecnología y Diseño del Estado de Jalisco AC (CIATEJ), Av. Normalistas No. 800 Col. Colinas de la Normal, Guadalajara 44270, Mexico;
| | - James González
- Departamento de Biología Celular, Facultad de Ciencias, Universidad Nacional Autónoma de México, Avenida Universidad 3000, Coyoacán, Mexico City 04510, Mexico; (C.S.-K.); (L.D.A.); (B.M.-C.); (N.T.-R.)
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