1
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Coppo GC, Pereira AP, Netto SA, Bernardino AF. Meiofauna at a tropical sandy beach in the SW Atlantic: the influence of seasonality on diversity. PeerJ 2024; 12:e17727. [PMID: 39011380 PMCID: PMC11249015 DOI: 10.7717/peerj.17727] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Accepted: 06/20/2024] [Indexed: 07/17/2024] Open
Abstract
Background Sandy beaches are dynamic environments housing a large diversity of organisms and providing important environmental services. Meiofaunal metazoan are small organisms that play a key role in the sediment. Their diversity, distribution and composition are driven by sedimentary and oceanographic parameters. Understanding the diversity patterns of marine meiofauna is critical in a changing world. Methods In this study, we investigate if there is seasonal difference in meiofaunal assemblage composition and diversity along 1 year and if the marine seascapes dynamics (water masses with particular biogeochemical features, characterized by temperature, salinity, absolute dynamic topography, chromophoric dissolved organic material, chlorophyll-a, and normalized fluorescent line height), rainfall, and sediment parameters (total organic matter, carbonate, carbohydrate, protein, lipids, protein-to-carbohydrate, carbohydrate-to-lipids, and biopolymeric carbon) affect significatively meiofaunal diversity at a tropical sandy beach. We tested two hypotheses here: (i) meiofaunal diversity is higher during warmer months and its composition changes significatively among seasons along a year at a tropical sandy beach, and (ii) meiofaunal diversity metrics are significantly explained by marine seascapes characteristics and sediment parameters. We used metabarcoding (V9 hypervariable region from 18S gene) from sediment samples to assess the meiofaunal assemblage composition and diversity (phylogenetic diversity and Shannon's diversity) over a period of 1 year. Results Meiofauna was dominated by Crustacea (46% of sequence reads), Annelida (28% of sequence reads) and Nematoda (12% of sequence reads) in periods of the year with high temperatures (>25 °C), high salinity (>31.5 ppt), and calm waters. Our data support our initial hypotheses revealing a higher meiofaunal diversity (phylogenetic and Shannon's Diversity) and different composition during warmer periods of the year. Meiofaunal diversity was driven by a set of multiple variables, including biological variables (biopolymeric carbon) and organic matter quality (protein content, lipid content, and carbohydrate-to-lipid ratio).
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Affiliation(s)
- Gabriel C Coppo
- Grupo de Ecologia Bentônica, Universidade Federal do Espírito Santo, Vitória, Espírito Santo, Brazil
| | - Araiene P Pereira
- Grupo de Ecologia Bentônica, Universidade Federal do Espírito Santo, Vitória, Espírito Santo, Brazil
| | - Sergio A Netto
- Marítima Estudos Bênticos, Laguna, Santa Catarina, Brazil
| | - Angelo F Bernardino
- Grupo de Ecologia Bentônica, Universidade Federal do Espírito Santo, Vitória, Espírito Santo, Brazil
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2
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Bényei ÉB, Nazeer RR, Askenasy I, Mancini L, Ho PM, Sivarajan GAC, Swain JEV, Welch M. The past, present and future of polymicrobial infection research: Modelling, eavesdropping, terraforming and other stories. Adv Microb Physiol 2024; 85:259-323. [PMID: 39059822 DOI: 10.1016/bs.ampbs.2024.04.002] [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: 07/28/2024]
Abstract
Over the last two centuries, great advances have been made in microbiology as a discipline. Much of this progress has come about as a consequence of studying the growth and physiology of individual microbial species in well-defined laboratory media; so-called "axenic growth". However, in the real world, microbes rarely live in such "splendid isolation" (to paraphrase Foster) and more often-than-not, share the niche with a plethora of co-habitants. The resulting interactions between species (and even between kingdoms) are only very poorly understood, both on a theoretical and experimental level. Nevertheless, the last few years have seen significant progress, and in this review, we assess the importance of polymicrobial infections, and show how improved experimental traction is advancing our understanding of these. A particular focus is on developments that are allowing us to capture the key features of polymicrobial infection scenarios, especially as those associated with the human airways (both healthy and diseased).
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Affiliation(s)
| | | | - Isabel Askenasy
- Department of Biochemistry, Tennis Court Road, Cambridge, United Kingdom
| | - Leonardo Mancini
- Department of Biochemistry, Tennis Court Road, Cambridge, United Kingdom
| | - Pok-Man Ho
- Department of Biochemistry, Tennis Court Road, Cambridge, United Kingdom
| | | | - Jemima E V Swain
- Department of Biochemistry, Tennis Court Road, Cambridge, United Kingdom
| | - Martin Welch
- Department of Biochemistry, Tennis Court Road, Cambridge, United Kingdom.
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3
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Kumar B, Lorusso E, Fosso B, Pesole G. A comprehensive overview of microbiome data in the light of machine learning applications: categorization, accessibility, and future directions. Front Microbiol 2024; 15:1343572. [PMID: 38419630 PMCID: PMC10900530 DOI: 10.3389/fmicb.2024.1343572] [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: 11/23/2023] [Accepted: 01/29/2024] [Indexed: 03/02/2024] Open
Abstract
Metagenomics, Metabolomics, and Metaproteomics have significantly advanced our knowledge of microbial communities by providing culture-independent insights into their composition and functional potential. However, a critical challenge in this field is the lack of standard and comprehensive metadata associated with raw data, hindering the ability to perform robust data stratifications and consider confounding factors. In this comprehensive review, we categorize publicly available microbiome data into five types: shotgun sequencing, amplicon sequencing, metatranscriptomic, metabolomic, and metaproteomic data. We explore the importance of metadata for data reuse and address the challenges in collecting standardized metadata. We also, assess the limitations in metadata collection of existing public repositories collecting metagenomic data. This review emphasizes the vital role of metadata in interpreting and comparing datasets and highlights the need for standardized metadata protocols to fully leverage metagenomic data's potential. Furthermore, we explore future directions of implementation of Machine Learning (ML) in metadata retrieval, offering promising avenues for a deeper understanding of microbial communities and their ecological roles. Leveraging these tools will enhance our insights into microbial functional capabilities and ecological dynamics in diverse ecosystems. Finally, we emphasize the crucial metadata role in ML models development.
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Affiliation(s)
- Bablu Kumar
- Università degli Studi di Milano, Milan, Italy
- Department of Biosciences, Biotechnology and Environment, University of Bari A. Moro, Bari, Italy
| | - Erika Lorusso
- Department of Biosciences, Biotechnology and Environment, University of Bari A. Moro, Bari, Italy
- National Research Council, Institute of Biomembranes, Bioenergetics and Molecular Biotechnologies, Bari, Italy
| | - Bruno Fosso
- Department of Biosciences, Biotechnology and Environment, University of Bari A. Moro, Bari, Italy
| | - Graziano Pesole
- Department of Biosciences, Biotechnology and Environment, University of Bari A. Moro, Bari, Italy
- National Research Council, Institute of Biomembranes, Bioenergetics and Molecular Biotechnologies, Bari, Italy
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4
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Invited Review: Novel methods and perspectives for modulating the rumen microbiome through selective breeding as a means to improve complex traits: implications for methane emissions in cattle. Livest Sci 2023. [DOI: 10.1016/j.livsci.2023.105171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
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5
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Liang C, Wagstaff J, Aharony N, Schmit V, Manheim D. Managing the Transition to Widespread Metagenomic Monitoring: Policy Considerations for Future Biosurveillance. Health Secur 2023; 21:34-45. [PMID: 36629860 PMCID: PMC9940815 DOI: 10.1089/hs.2022.0029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023] Open
Abstract
The technological possibilities and future public health importance of metagenomic sequencing have received extensive attention, but there has been little discussion about the policy and regulatory issues that need to be addressed if metagenomic sequencing is adopted as a key technology for biosurveillance. In this article, we introduce metagenomic monitoring as a possible path to eventually replacing current infectious disease monitoring models. Many key enablers are technological, whereas others are not. We therefore highlight key policy challenges and implementation questions that need to be addressed for "widespread metagenomic monitoring" to be possible. Policymakers must address pitfalls like fragmentation of the technological base, private capture of benefits, privacy concerns, the usefulness of the system during nonpandemic times, and how the future systems will enable better response. If these challenges are addressed, the technological and public health promise of metagenomic sequencing can be realized.
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Affiliation(s)
- Chelsea Liang
- Chelsea Liang is an Independent Researcher, University of New South Wales, School of Biotechnology and Biomolecular Sciences, Sydney, Australia
| | - James Wagstaff
- James Wagstaff, PhD, is a Research Fellow, Future of Humanity Institute, University of Oxford, Oxford, UK
| | - Noga Aharony
- Noga Aharony, MS, is a PhD Student, Department of Systems Biology, Columbia University, New York, NY
| | - Virginia Schmit
- Virginia Schmit, PhD, is Director of Research, 1DatSooner, DE, and a Policy Specialist, National Institute of Allergy and Infectious Diseases, Bethesda, MD
| | - David Manheim
- David Manheim, PhD, is Head of Policy and Research, ALTER, Rehovot, Israel; Lead Researcher, 1DaySooner, Claymont, DE,Visiting Researcher, Humanities and Arts Department, Technion – Israel Institute of Technology, Haifa, Israel.,Address correspondence to: David B. Manheim, 8734 First Avenue, Silver Spring, MD 20910
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6
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Patin NV, Goodwin KD. Capturing marine microbiomes and environmental DNA: A field sampling guide. Front Microbiol 2023; 13:1026596. [PMID: 36713215 PMCID: PMC9877356 DOI: 10.3389/fmicb.2022.1026596] [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: 08/24/2022] [Accepted: 11/22/2022] [Indexed: 01/15/2023] Open
Abstract
The expanding interest in marine microbiome and eDNA sequence data has led to a demand for sample collection and preservation standard practices to enable comparative assessments of results across studies and facilitate meta-analyses. We support this effort by providing guidelines based on a review of published methods and field sampling experiences. The major components considered here are environmental and resource considerations, sample processing strategies, sample storage options, and eDNA extraction protocols. It is impossible to provide universal recommendations considering the wide range of eDNA applications; rather, we provide information to design fit-for-purpose protocols. To manage scope, the focus here is on sampling collection and preservation of prokaryotic and microeukaryotic eDNA. Even with a focused view, the practical utility of any approach depends on multiple factors, including habitat type, available resources, and experimental goals. We broadly recommend enacting rigorous decontamination protocols, pilot studies to guide the filtration volume needed to characterize the target(s) of interest and minimize PCR inhibitor collection, and prioritizing sample freezing over (only) the addition of preservation buffer. An annotated list of studies that test these parameters is included for more detailed investigation on specific steps. To illustrate an approach that demonstrates fit-for-purpose methodologies, we provide a protocol for eDNA sampling aboard an oceanographic vessel. These guidelines can aid the decision-making process for scientists interested in sampling and sequencing marine microbiomes and/or eDNA.
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Affiliation(s)
- Nastassia Virginia Patin
- Atlantic Oceanographic and Meteorological Laboratory, Ocean Chemistry and Ecosystems Division, National Oceanic and Atmospheric Administration, Miami, FL, United States,Cooperative Institute for Marine and Atmospheric Studies, Rosenstiel School of Marine, Atmospheric, and Earth Science, University of Miami, Miami, FL, United States,Stationed at Southwest Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, La Jolla, CA, United States,*Correspondence: Nastassia Virginia Patin,
| | - Kelly D. Goodwin
- Atlantic Oceanographic and Meteorological Laboratory, Ocean Chemistry and Ecosystems Division, National Oceanic and Atmospheric Administration, Miami, FL, United States,Stationed at Southwest Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, La Jolla, CA, United States
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7
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Abdelsalam NA, Elshora H, El-Hadidi M. Interactive Web-Based Services for Metagenomic Data Analysis and Comparisons. Methods Mol Biol 2023; 2649:133-174. [PMID: 37258861 DOI: 10.1007/978-1-0716-3072-3_7] [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: 06/02/2023]
Abstract
Recently, sequencing technologies have become readily available, and scientists are more motivated to conduct metagenomic research to unveil the potential of a myriad of ecosystems and biomes. Metagenomics studies the composition and functions of microbial communities and paves the way to multiple applications in medicine, industry, and ecology. Nonetheless, the immense amount of sequencing data of metagenomics research and the few user-friendly analysis tools and pipelines carry a new challenge to the data analysis.Web-based bioinformatics tools are now being developed to facilitate the analysis of complex metagenomic data without prior knowledge of any programming languages or special installation. Specialized web tools help answer researchers' main questions on the taxonomic classification, functional capabilities, discrepancies between two ecosystems, and the probable functional correlations between the members of a specific microbial community. With an Internet connection and a few clicks, researchers can conveniently and efficiently analyze the metagenomic datasets, summarize results, and visualize key information on the composition and the functional potential of metagenomic samples under study. This chapter provides a simple guide to a few of the fundamental web-based services used for metagenomic data analyses, such as BV-BRC, RDP, MG-RAST, MicrobiomeAnalyst, METAGENassist, and MGnify.
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Affiliation(s)
- Nehal Adel Abdelsalam
- University of Science and Technology, Zewail City, Giza, Egypt
- Department of Microbiology and Immunology, Faculty of Pharmacy, Cairo University, Cairo, Egypt
| | - Hajar Elshora
- Bioinformatics Group, Center for Informatics Sciences (CIS), Nile University, Giza, Egypt
- Biomedical Informatics Program, School of Information Technology and Computer Science, Nile University, Giza, Egypt
| | - Mohamed El-Hadidi
- Bioinformatics Group, Center for Informatics Sciences (CIS), Nile University, Giza, Egypt.
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8
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Waterhouse RM, Adam-Blondon AF, Agosti D, Baldrian P, Balech B, Corre E, Davey RP, Lantz H, Pesole G, Quast C, Glöckner FO, Raes N, Sandionigi A, Santamaria M, Addink W, Vohradsky J, Nunes-Jorge A, Willassen NP, Lanfear J. Recommendations for connecting molecular sequence and biodiversity research infrastructures through ELIXIR. F1000Res 2022; 10. [PMID: 35999898 PMCID: PMC9360911 DOI: 10.12688/f1000research.73825.2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 07/27/2022] [Indexed: 12/03/2022] Open
Abstract
Threats to global biodiversity are increasingly recognised by scientists and the public as a critical challenge. Molecular sequencing technologies offer means to catalogue, explore, and monitor the richness and biogeography of life on Earth. However, exploiting their full potential requires tools that connect biodiversity infrastructures and resources. As a research infrastructure developing services and technical solutions that help integrate and coordinate life science resources across Europe, ELIXIR is a key player. To identify opportunities, highlight priorities, and aid strategic thinking, here we survey approaches by which molecular technologies help inform understanding of biodiversity. We detail example use cases to highlight how DNA sequencing is: resolving taxonomic issues; Increasing knowledge of marine biodiversity; helping understand how agriculture and biodiversity are critically linked; and playing an essential role in ecological studies. Together with examples of national biodiversity programmes, the use cases show where progress is being made but also highlight common challenges and opportunities for future enhancement of underlying technologies and services that connect molecular and wider biodiversity domains. Based on emerging themes, we propose key recommendations to guide future funding for biodiversity research: biodiversity and bioinformatic infrastructures need to collaborate closely and strategically; taxonomic efforts need to be aligned and harmonised across domains; metadata needs to be standardised and common data management approaches widely adopted; current approaches need to be scaled up dramatically to address the anticipated explosion of molecular data; bioinformatics support for biodiversity research needs to be enabled and sustained; training for end users of biodiversity research infrastructures needs to be prioritised; and community initiatives need to be proactive and focused on enabling solutions. For sequencing data to deliver their full potential they must be connected to knowledge: together, molecular sequence data collection initiatives and biodiversity research infrastructures can advance global efforts to prevent further decline of Earth’s biodiversity.
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Affiliation(s)
- Robert M. Waterhouse
- Department of Ecology and Evolution and Swiss Institute of Bioinformatics, University of Lausanne, Lausanne, Vaud, 1015, Switzerland
| | | | | | - Petr Baldrian
- Institute of Microbiology of the Czech Academy of Sciences, Praha, 142 20, Czech Republic
| | - Bachir Balech
- Institute of Biomembranes, Bioenergetics and Molecular Biotechnologies, CNR, Bari, 70126, Italy
| | - Erwan Corre
- CNRS/Sorbonne Université, Station Biologique de Roscoff, Roscoff, 29680, France
| | | | - Henrik Lantz
- Department of Medical Biochemistry and Microbiology/NBIS, Uppsala University, Uppsala, Sweden
| | - Graziano Pesole
- Institute of Biomembranes, Bioenergetics and Molecular Biotechnologies, CNR, Bari, 70126, Italy
- Department of Biosciences. Biotechnology and Biopharmaceutics, University of Bari “A. Moro”, Bari, 70126, Italy
| | - Christian Quast
- Life Sciences & Chemistry, Jacobs University Bremen gGmbH, Bremen, Germany
| | - Frank Oliver Glöckner
- MARUM - Center for Marine Environmental Sciences, University of Bremen, Bremerhaven, 27570, Germany
- Alfred Wegener Institute, Helmholtz Center for Polar- and Marine Research, Bremerhaven, 27570, Germany
| | - Niels Raes
- NLBIF - Netherlands Biodiversity Information Facility, Naturalis Biodiversity Center, Leiden, 2300 RA, The Netherlands
| | | | - Monica Santamaria
- Institute of Biomembranes, Bioenergetics and Molecular Biotechnologies, CNR, Bari, 70126, Italy
| | - Wouter Addink
- DiSSCo - Distributed System of Scientific Collections, Naturalis Biodiversity Center, Leiden, 2300 RA, The Netherlands
| | - Jiri Vohradsky
- Laboratory of Bioinformatics, Institute of Microbiology, Prague, 142 20, Czech Republic
| | | | | | - Jerry Lanfear
- ELIXIR Hub, Wellcome Genome Campus, Cambridge, CB10 1SD, UK
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9
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Ko KKK, Chng KR, Nagarajan N. Metagenomics-enabled microbial surveillance. Nat Microbiol 2022; 7:486-496. [PMID: 35365786 DOI: 10.1038/s41564-022-01089-w] [Citation(s) in RCA: 71] [Impact Index Per Article: 35.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2021] [Accepted: 02/22/2022] [Indexed: 12/13/2022]
Abstract
Lessons learnt from the COVID-19 pandemic include increased awareness of the potential for zoonoses and emerging infectious diseases that can adversely affect human health. Although emergent viruses are currently in the spotlight, we must not forget the ongoing toll of morbidity and mortality owing to antimicrobial resistance in bacterial pathogens and to vector-borne, foodborne and waterborne diseases. Population growth, planetary change, international travel and medical tourism all contribute to the increasing frequency of infectious disease outbreaks. Surveillance is therefore of crucial importance, but the diversity of microbial pathogens, coupled with resource-intensive methods, compromises our ability to scale-up such efforts. Innovative technologies that are both easy to use and able to simultaneously identify diverse microorganisms (viral, bacterial or fungal) with precision are necessary to enable informed public health decisions. Metagenomics-enabled surveillance methods offer the opportunity to improve detection of both known and yet-to-emerge pathogens.
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Affiliation(s)
- Karrie K K Ko
- Laboratory of Metagenomic Technologies and Microbial Systems, Genome Institute of Singapore, Singapore, Singapore.,Department of Microbiology, Singapore General Hospital, Singapore, Singapore.,Department of Molecular Pathology, Singapore General Hospital, Singapore, Singapore.,Duke-NUS Medical School, Singapore, Singapore.,Yong Loo Lin School of Medicine, National Univerisity of Singapore, Singapore, Singapore
| | - Kern Rei Chng
- Laboratory of Metagenomic Technologies and Microbial Systems, Genome Institute of Singapore, Singapore, Singapore.,National Centre for Food Science, Singapore Food Agency, Singapore, Singapore
| | - Niranjan Nagarajan
- Laboratory of Metagenomic Technologies and Microbial Systems, Genome Institute of Singapore, Singapore, Singapore. .,Yong Loo Lin School of Medicine, National Univerisity of Singapore, Singapore, Singapore.
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10
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Penev L, Koureas D, Groom Q, Lanfear J, Agosti D, Casino A, Miller J, Arvanitidis C, Cochrane G, Hobern D, Banki O, Addink W, Kõljalg U, Copas K, Mergen P, Güntsch A, Benichou L, Benito Gonzalez Lopez J, Ruch P, Martin C, Barov B, Hristova K. Biodiversity Community Integrated Knowledge Library (BiCIKL). RESEARCH IDEAS AND OUTCOMES 2022. [DOI: 10.3897/rio.8.e81136] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
BiCIKL is an European Union Horizon 2020 project that will initiate and build a new European starting community of key research infrastructures, establishing open science practices in the domain of biodiversity through provision of access to data, associated tools and services at each separate stage of and along the entire research cycle. BiCIKL will provide new methods and workflows for an integrated access to harvesting, liberating, linking, accessing and re-using of subarticle-level data (specimens, material citations, samples, sequences, taxonomic names, taxonomic treatments, figures, tables) extracted from literature. BiCIKL will provide for the first time access and tools for seamless linking and usage tracking of data along the line: specimens > sequences > species > analytics > publications > biodiversity knowledge graph > re-use.
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11
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Pryor J, Eslick GD, Talley NJ, Duncanson K, Keely S, Hoedt EC. Clinical medicine journals lag behind science journals with regards to "microbiota sequence" data availability. Clin Transl Med 2021; 11:e656. [PMID: 34870904 PMCID: PMC8647683 DOI: 10.1002/ctm2.656] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Accepted: 11/10/2021] [Indexed: 11/08/2022] Open
Affiliation(s)
- Jennifer Pryor
- School of Biomedical Sciences and PharmacyCollege of HealthMedicine and WellbeingUniversity of NewcastleNewcastleAustralia
- NHMRC Centre for Research Excellence in Digestive HealthUniversity of NewcastleNewcastleAustralia
- Hunter Medical Research InstituteNewcastleAustralia
| | - Guy D. Eslick
- NHMRC Centre for Research Excellence in Digestive HealthUniversity of NewcastleNewcastleAustralia
- School of Medicine and Public HealthCollege of HealthMedicine and WellbeingUniversity of NewcastleAustralia
- Hunter Medical Research InstituteNewcastleAustralia
| | - Nicholas J. Talley
- NHMRC Centre for Research Excellence in Digestive HealthUniversity of NewcastleNewcastleAustralia
- School of Medicine and Public HealthCollege of HealthMedicine and WellbeingUniversity of NewcastleAustralia
- Hunter Medical Research InstituteNewcastleAustralia
| | - Kerith Duncanson
- NHMRC Centre for Research Excellence in Digestive HealthUniversity of NewcastleNewcastleAustralia
- School of Medicine and Public HealthCollege of HealthMedicine and WellbeingUniversity of NewcastleAustralia
- Hunter Medical Research InstituteNewcastleAustralia
| | - Simon Keely
- School of Biomedical Sciences and PharmacyCollege of HealthMedicine and WellbeingUniversity of NewcastleNewcastleAustralia
- NHMRC Centre for Research Excellence in Digestive HealthUniversity of NewcastleNewcastleAustralia
- Hunter Medical Research InstituteNewcastleAustralia
| | - Emily C. Hoedt
- NHMRC Centre for Research Excellence in Digestive HealthUniversity of NewcastleNewcastleAustralia
- School of Medicine and Public HealthCollege of HealthMedicine and WellbeingUniversity of NewcastleAustralia
- Hunter Medical Research InstituteNewcastleAustralia
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12
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Reporting guidelines for human microbiome research: the STORMS checklist. Nat Med 2021; 27:1885-1892. [PMID: 34789871 PMCID: PMC9105086 DOI: 10.1038/s41591-021-01552-x] [Citation(s) in RCA: 170] [Impact Index Per Article: 56.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Accepted: 09/23/2021] [Indexed: 12/18/2022]
Abstract
The particularly interdisciplinary nature of human microbiome research makes the organization and reporting of results spanning epidemiology, biology, bioinformatics, translational medicine and statistics a challenge. Commonly used reporting guidelines for observational or genetic epidemiology studies lack key features specific to microbiome studies. Therefore, a multidisciplinary group of microbiome epidemiology researchers adapted guidelines for observational and genetic studies to culture-independent human microbiome studies, and also developed new reporting elements for laboratory, bioinformatics and statistical analyses tailored to microbiome studies. The resulting tool, called 'Strengthening The Organization and Reporting of Microbiome Studies' (STORMS), is composed of a 17-item checklist organized into six sections that correspond to the typical sections of a scientific publication, presented as an editable table for inclusion in supplementary materials. The STORMS checklist provides guidance for concise and complete reporting of microbiome studies that will facilitate manuscript preparation, peer review, and reader comprehension of publications and comparative analysis of published results.
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13
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Young RB, Marcelino VR, Chonwerawong M, Gulliver EL, Forster SC. Key Technologies for Progressing Discovery of Microbiome-Based Medicines. Front Microbiol 2021; 12:685935. [PMID: 34239510 PMCID: PMC8258393 DOI: 10.3389/fmicb.2021.685935] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Accepted: 05/25/2021] [Indexed: 12/22/2022] Open
Abstract
A growing number of experimental and computational approaches are illuminating the “microbial dark matter” and uncovering the integral role of commensal microbes in human health. Through this work, it is now clear that the human microbiome presents great potential as a therapeutic target for a plethora of diseases, including inflammatory bowel disease, diabetes and obesity. The development of more efficacious and targeted treatments relies on identification of causal links between the microbiome and disease; with future progress dependent on effective links between state-of-the-art sequencing approaches, computational analyses and experimental assays. We argue determining causation is essential, which can be attained by generating hypotheses using multi-omic functional analyses and validating these hypotheses in complex, biologically relevant experimental models. In this review we discuss existing analysis and validation methods, and propose best-practice approaches required to enable the next phase of microbiome research.
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Affiliation(s)
- Remy B Young
- Centre for Innate Immunity and Infectious Diseases, Hudson Institute of Medical Research, Clayton, VIC, Australia.,Infection and Immunity Program, Monash Biomedicine Discovery Institute and Department of Microbiology, Monash University, Clayton, VIC, Australia
| | - Vanessa R Marcelino
- Centre for Innate Immunity and Infectious Diseases, Hudson Institute of Medical Research, Clayton, VIC, Australia.,Department of Molecular and Translational Sciences, Monash University, Clayton, VIC, Australia
| | - Michelle Chonwerawong
- Centre for Innate Immunity and Infectious Diseases, Hudson Institute of Medical Research, Clayton, VIC, Australia.,Department of Molecular and Translational Sciences, Monash University, Clayton, VIC, Australia
| | - Emily L Gulliver
- Centre for Innate Immunity and Infectious Diseases, Hudson Institute of Medical Research, Clayton, VIC, Australia.,Infection and Immunity Program, Monash Biomedicine Discovery Institute and Department of Microbiology, Monash University, Clayton, VIC, Australia.,Department of Molecular and Translational Sciences, Monash University, Clayton, VIC, Australia
| | - Samuel C Forster
- Centre for Innate Immunity and Infectious Diseases, Hudson Institute of Medical Research, Clayton, VIC, Australia.,Infection and Immunity Program, Monash Biomedicine Discovery Institute and Department of Microbiology, Monash University, Clayton, VIC, Australia.,Department of Molecular and Translational Sciences, Monash University, Clayton, VIC, Australia
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14
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Alam I, Kamau AA, Ngugi DK, Gojobori T, Duarte CM, Bajic VB. KAUST Metagenomic Analysis Platform (KMAP), enabling access to massive analytics of re-annotated metagenomic data. Sci Rep 2021; 11:11511. [PMID: 34075103 PMCID: PMC8169707 DOI: 10.1038/s41598-021-90799-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Accepted: 05/18/2021] [Indexed: 11/09/2022] Open
Abstract
Exponential rise of metagenomics sequencing is delivering massive functional environmental genomics data. However, this also generates a procedural bottleneck for on-going re-analysis as reference databases grow and methods improve, and analyses need be updated for consistency, which require acceess to increasingly demanding bioinformatic and computational resources. Here, we present the KAUST Metagenomic Analysis Platform (KMAP), a new integrated open web-based tool for the comprehensive exploration of shotgun metagenomic data. We illustrate the capacities KMAP provides through the re-assembly of ~ 27,000 public metagenomic samples captured in ~ 450 studies sampled across ~ 77 diverse habitats. A small subset of these metagenomic assemblies is used in this pilot study grouped into 36 new habitat-specific gene catalogs, all based on full-length (complete) genes. Extensive taxonomic and gene annotations are stored in Gene Information Tables (GITs), a simple tractable data integration format useful for analysis through command line or for database management. KMAP pilot study provides the exploration and comparison of microbial GITs across different habitats with over 275 million genes. KMAP access to data and analyses is available at https://www.cbrc.kaust.edu.sa/aamg/kmap.start .
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Affiliation(s)
- Intikhab Alam
- Computational Bioscience Research Center, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia.
| | - Allan Anthony Kamau
- Computational Bioscience Research Center, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - David Kamanda Ngugi
- Leibniz Institute DSMZ-German Collection of Microorganisms and Cell Cultures, Inhoffenstraße 7B, 38124, Brunswick, Germany
| | - Takashi Gojobori
- Computational Bioscience Research Center, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Carlos M Duarte
- Computational Bioscience Research Center, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia.,Red Sea Bioscience Research Center, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Vladimir B Bajic
- Computational Bioscience Research Center, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
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15
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Jégousse C, Vannier P, Groben R, Glöckner FO, Marteinsson V. A total of 219 metagenome-assembled genomes of microorganisms from Icelandic marine waters. PeerJ 2021; 9:e11112. [PMID: 33859876 PMCID: PMC8020865 DOI: 10.7717/peerj.11112] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Accepted: 02/23/2021] [Indexed: 12/02/2022] Open
Abstract
Marine microorganisms contribute to the health of the global ocean by supporting the marine food web and regulating biogeochemical cycles. Assessing marine microbial diversity is a crucial step towards understanding the global ocean. The waters surrounding Iceland are a complex environment where relatively warm salty waters from the Atlantic cool down and sink down to the deep. Microbial studies in this area have focused on photosynthetic micro- and nanoplankton mainly using microscopy and chlorophyll measurements. However, the diversity and function of the bacterial and archaeal picoplankton remains unknown. Here, we used a co-assembly approach supported by a marine mock community to reconstruct metagenome-assembled genomes (MAGs) from 31 metagenomes from the sea surface and seafloor of four oceanographic sampling stations sampled between 2015 and 2018. The resulting 219 MAGs include 191 bacterial, 26 archaeal and two eukaryotic MAGs to bridge the gap in our current knowledge of the global marine microbiome.
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Affiliation(s)
- Clara Jégousse
- School of Health Sciences, University of Iceland, Reykjavik, Iceland.,Microbiology Group, Matís ohf., Reykjavik, Iceland
| | | | - René Groben
- Microbiology Group, Matís ohf., Reykjavik, Iceland
| | - Frank Oliver Glöckner
- Data at the Computing Center, Alfred Wegener Institute, Bremenhaven, Germany.,MARUM - Center for Marine Environmental Sciences,University of Bremen, Bremen, Germany
| | - Viggó Marteinsson
- School of Health Sciences, University of Iceland, Reykjavik, Iceland.,Microbiology Group, Matís ohf., Reykjavik, Iceland
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16
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Moreno-Indias I, Lahti L, Nedyalkova M, Elbere I, Roshchupkin G, Adilovic M, Aydemir O, Bakir-Gungor B, Santa Pau ECD, D’Elia D, Desai MS, Falquet L, Gundogdu A, Hron K, Klammsteiner T, Lopes MB, Marcos-Zambrano LJ, Marques C, Mason M, May P, Pašić L, Pio G, Pongor S, Promponas VJ, Przymus P, Saez-Rodriguez J, Sampri A, Shigdel R, Stres B, Suharoschi R, Truu J, Truică CO, Vilne B, Vlachakis D, Yilmaz E, Zeller G, Zomer AL, Gómez-Cabrero D, Claesson MJ. Statistical and Machine Learning Techniques in Human Microbiome Studies: Contemporary Challenges and Solutions. Front Microbiol 2021; 12:635781. [PMID: 33692771 PMCID: PMC7937616 DOI: 10.3389/fmicb.2021.635781] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Accepted: 01/28/2021] [Indexed: 12/23/2022] Open
Abstract
The human microbiome has emerged as a central research topic in human biology and biomedicine. Current microbiome studies generate high-throughput omics data across different body sites, populations, and life stages. Many of the challenges in microbiome research are similar to other high-throughput studies, the quantitative analyses need to address the heterogeneity of data, specific statistical properties, and the remarkable variation in microbiome composition across individuals and body sites. This has led to a broad spectrum of statistical and machine learning challenges that range from study design, data processing, and standardization to analysis, modeling, cross-study comparison, prediction, data science ecosystems, and reproducible reporting. Nevertheless, although many statistics and machine learning approaches and tools have been developed, new techniques are needed to deal with emerging applications and the vast heterogeneity of microbiome data. We review and discuss emerging applications of statistical and machine learning techniques in human microbiome studies and introduce the COST Action CA18131 "ML4Microbiome" that brings together microbiome researchers and machine learning experts to address current challenges such as standardization of analysis pipelines for reproducibility of data analysis results, benchmarking, improvement, or development of existing and new tools and ontologies.
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Affiliation(s)
- Isabel Moreno-Indias
- Instituto de Investigación Biomédica de Málaga (IBIMA), Unidad de Gestión Clìnica de Endocrinologìa y Nutrición, Hospital Clìnico Universitario Virgen de la Victoria, Universidad de Málaga, Málaga, Spain
- Centro de Investigación Biomeìdica en Red de Fisiopatologtìa de la Obesidad y la Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain
| | - Leo Lahti
- Department of Computing, University of Turku, Turku, Finland
| | - Miroslava Nedyalkova
- Human Genetics and Disease Mechanisms, Latvian Biomedical Research and Study Centre, Riga, Latvia
| | - Ilze Elbere
- Latvian Biomedical Research and Study Centre, Riga, Latvia
| | | | - Muhamed Adilovic
- Department of Genetics and Bioengineering, International University of Sarajevo, Sarajevo, Bosnia and Herzegovina
| | - Onder Aydemir
- Department of Electrical and Electronics Engineering, Karadeniz Technical University, Trabzon, Turkey
| | - Burcu Bakir-Gungor
- Department of Computer Engineering, Abdullah Gul University, Kayseri, Turkey
| | | | - Domenica D’Elia
- Department for Biomedical Sciences, Institute for Biomedical Technologies, National Research Council, Bari, Italy
| | - Mahesh S. Desai
- Department of Infection and Immunity, Luxembourg Institute of Health, Esch-sur-Alzette, Luxembourg
- Odense Research Center for Anaphylaxis, Department of Dermatology and Allergy Center, Odense University Hospital, University of Southern Denmark, Odense, Denmark
| | - Laurent Falquet
- Department of Biology, University of Fribourg, Fribourg, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Aycan Gundogdu
- Department of Microbiology and Clinical Microbiology, Faculty of Medicine, Erciyes University, Kayseri, Turkey
- Metagenomics Laboratory, Genome and Stem Cell Center (GenKök), Erciyes University, Kayseri, Turkey
| | - Karel Hron
- Department of Mathematical Analysis and Applications of Mathematics, Palacký University, Olomouc, Czechia
| | | | - Marta B. Lopes
- NOVA Laboratory for Computer Science and Informatics (NOVA LINCS), FCT, UNL, Caparica, Portugal
- Centro de Matemática e Aplicações (CMA), FCT, UNL, Caparica, Portugal
| | - Laura Judith Marcos-Zambrano
- Computational Biology Group, Precision Nutrition and Cancer Research Program, IMDEA Food Institute, Madrid, Spain
| | - Cláudia Marques
- CINTESIS, NOVA Medical School, NMS, Universidade Nova de Lisboa, Lisbon, Portugal
| | - Michael Mason
- Computational Oncology, Sage Bionetworks, Seattle, WA, United States
| | - Patrick May
- Bioinformatics Core, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Lejla Pašić
- Sarajevo Medical School, University Sarajevo School of Science and Technology, Sarajevo, Bosnia and Herzegovina
| | - Gianvito Pio
- Department of Computer Science, University of Bari Aldo Moro, Bari, Italy
| | - Sándor Pongor
- Faculty of Information Tehnology and Bionics, Pázmány University, Budapest, Hungary
| | - Vasilis J. Promponas
- Bioinformatics Research Laboratory, Department of Biological Sciences, University of Cyprus, Nicosia, Cyprus
| | - Piotr Przymus
- Faculty of Mathematics and Computer Science, Nicolaus Copernicus University, Toruñ, Poland
| | - Julio Saez-Rodriguez
- Institute of Computational Biomedicine, Heidelberg University, Faculty of Medicine and Heidelberg University Hospital, Heidelberg, Germany
| | - Alexia Sampri
- Division of Informatics, Imaging and Data Sciences, School of Health Sciences, University of Manchester, Manchester, United Kingdom
| | - Rajesh Shigdel
- Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Blaz Stres
- Jozef Stefan Institute, Ljubljana, Slovenia
- Biotechnical Faculty, University of Ljubljana, Ljubljana, Slovenia
- Faculty of Civil and Geodetic Engineering, University of Ljubljana, Ljubljana, Slovenia
| | - Ramona Suharoschi
- Molecular Nutrition and Proteomics Lab, Faculty of the Food Science and Technology, Institute of Life Sciences, University of Agricultural Sciences and Veterinary Medicine of Cluj-Napoca, Cluj-Napoca, Romania
| | - Jaak Truu
- Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia
| | - Ciprian-Octavian Truică
- Department of Computer Science and Engineering, Faculty of Automatic Control and Computers, University Politehnica of Bucharest, Bucharest, Romania
| | - Baiba Vilne
- Bioinformatics Research Unit, Riga Stradins University, Riga, Latvia
| | - Dimitrios Vlachakis
- Laboratory of Genetics, Department of Biotechnology, School of Applied Biology and Biotechnology, Agricultural University of Athens, Athens, Greece
| | - Ercument Yilmaz
- Department of Computer Technologies, Karadeniz Technical University, Trabzon, Turkey
| | - Georg Zeller
- European Molecular Biology Laboratory, Structural and Computational Biology Unit, Heidelberg, Germany
| | - Aldert L. Zomer
- Department of Infectious Diseases and Immunology, Faculty of Veterinary Medicine, Utrecht University, Utrecht, Netherlands
| | - David Gómez-Cabrero
- Navarrabiomed, Complejo Hospitalario de Navarra (CHN), IdiSNA, Universidad Pública de Navarra (UPNA), Pamplona, Spain
| | - Marcus J. Claesson
- School of Microbiology and APC Microbiome Ireland, University College Cork, Cork, Ireland
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17
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Francioli D, Lentendu G, Lewin S, Kolb S. DNA Metabarcoding for the Characterization of Terrestrial Microbiota-Pitfalls and Solutions. Microorganisms 2021; 9:361. [PMID: 33673098 PMCID: PMC7918050 DOI: 10.3390/microorganisms9020361] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Revised: 02/04/2021] [Accepted: 02/09/2021] [Indexed: 02/06/2023] Open
Abstract
Soil-borne microbes are major ecological players in terrestrial environments since they cycle organic matter, channel nutrients across trophic levels and influence plant growth and health. Therefore, the identification, taxonomic characterization and determination of the ecological role of members of soil microbial communities have become major topics of interest. The development and continuous improvement of high-throughput sequencing platforms have further stimulated the study of complex microbiota in soils and plants. The most frequently used approach to study microbiota composition, diversity and dynamics is polymerase chain reaction (PCR), amplifying specific taxonomically informative gene markers with the subsequent sequencing of the amplicons. This methodological approach is called DNA metabarcoding. Over the last decade, DNA metabarcoding has rapidly emerged as a powerful and cost-effective method for the description of microbiota in environmental samples. However, this approach involves several processing steps, each of which might introduce significant biases that can considerably compromise the reliability of the metabarcoding output. The aim of this review is to provide state-of-the-art background knowledge needed to make appropriate decisions at each step of a DNA metabarcoding workflow, highlighting crucial steps that, if considered, ensures an accurate and standardized characterization of microbiota in environmental studies.
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Affiliation(s)
- Davide Francioli
- Microbial Biogeochemistry, Research Area Landscape Functioning, Leibniz Centre for Agricultural Landscape Research (ZALF), Eberswalder Str. 84, 15374 Müncheberg, Germany; (S.L.); (S.K.)
| | - Guillaume Lentendu
- Laboratory of Soil Biodiversity, University of Neuchâtel, Rue Emile-Argand 11, 2000 Neuchâtel, Switzerland;
| | - Simon Lewin
- Microbial Biogeochemistry, Research Area Landscape Functioning, Leibniz Centre for Agricultural Landscape Research (ZALF), Eberswalder Str. 84, 15374 Müncheberg, Germany; (S.L.); (S.K.)
| | - Steffen Kolb
- Microbial Biogeochemistry, Research Area Landscape Functioning, Leibniz Centre for Agricultural Landscape Research (ZALF), Eberswalder Str. 84, 15374 Müncheberg, Germany; (S.L.); (S.K.)
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18
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Harrow J, Hancock J, Blomberg N. ELIXIR-EXCELERATE: establishing Europe's data infrastructure for the life science research of the future. EMBO J 2021; 40:e107409. [PMID: 33565128 PMCID: PMC7957415 DOI: 10.15252/embj.2020107409] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2020] [Revised: 12/14/2020] [Accepted: 12/16/2020] [Indexed: 02/06/2023] Open
Abstract
A new inter-governmental research infrastructure, ELIXIR, aims to unify bioinformatics resources and life science data across Europe, thereby facilitating their mining and (re-)use.
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Affiliation(s)
| | - John Hancock
- ELIXIR Hub, Wellcome Genome Campus, Hinxton, Cambridge, UK
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19
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Durazzi F, Sala C, Castellani G, Manfreda G, Remondini D, De Cesare A. Comparison between 16S rRNA and shotgun sequencing data for the taxonomic characterization of the gut microbiota. Sci Rep 2021; 11:3030. [PMID: 33542369 PMCID: PMC7862389 DOI: 10.1038/s41598-021-82726-y] [Citation(s) in RCA: 186] [Impact Index Per Article: 62.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2020] [Accepted: 12/29/2020] [Indexed: 02/07/2023] Open
Abstract
In this paper we compared taxonomic results obtained by metataxonomics (16S rRNA gene sequencing) and metagenomics (whole shotgun metagenomic sequencing) to investigate their reliability for bacteria profiling, studying the chicken gut as a model system. The experimental conditions included two compartments of gastrointestinal tracts and two sampling times. We compared the relative abundance distributions obtained with the two sequencing strategies and then tested their capability to distinguish the experimental conditions. The results showed that 16S rRNA gene sequencing detects only part of the gut microbiota community revealed by shotgun sequencing. Specifically, when a sufficient number of reads is available, Shotgun sequencing has more power to identify less abundant taxa than 16S sequencing. Finally, we showed that the less abundant genera detected only by shotgun sequencing are biologically meaningful, being able to discriminate between the experimental conditions as much as the more abundant genera detected by both sequencing strategies.
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Affiliation(s)
- Francesco Durazzi
- Department of Physics and Astronomy, University of Bologna, 40127, Bologna, Italy
| | - Claudia Sala
- Department of Physics and Astronomy, University of Bologna, 40127, Bologna, Italy
| | - Gastone Castellani
- Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, 40127, Bologna, Italy
| | - Gerardo Manfreda
- Department of Agricultural and Food Sciences, University of Bologna, 40064, Ozzano dell'Emilia, Italy
| | - Daniel Remondini
- Department of Physics and Astronomy, University of Bologna, 40127, Bologna, Italy.
| | - Alessandra De Cesare
- Department of Veterinary Medical Sciences, University of Bologna, 40064, Ozzano dell'Emilia, Italy
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20
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Sielemann K, Hafner A, Pucker B. The reuse of public datasets in the life sciences: potential risks and rewards. PeerJ 2020; 8:e9954. [PMID: 33024631 PMCID: PMC7518187 DOI: 10.7717/peerj.9954] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Accepted: 08/25/2020] [Indexed: 12/13/2022] Open
Abstract
The 'big data' revolution has enabled novel types of analyses in the life sciences, facilitated by public sharing and reuse of datasets. Here, we review the prodigious potential of reusing publicly available datasets and the associated challenges, limitations and risks. Possible solutions to issues and research integrity considerations are also discussed. Due to the prominence, abundance and wide distribution of sequencing data, we focus on the reuse of publicly available sequence datasets. We define 'successful reuse' as the use of previously published data to enable novel scientific findings. By using selected examples of successful reuse from different disciplines, we illustrate the enormous potential of the practice, while acknowledging the respective limitations and risks. A checklist to determine the reuse value and potential of a particular dataset is also provided. The open discussion of data reuse and the establishment of this practice as a norm has the potential to benefit all stakeholders in the life sciences.
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Affiliation(s)
- Katharina Sielemann
- Genetics and Genomics of Plants, Center for Biotechnology (CeBiTec) & Faculty of Biology, Bielefeld University, Bielefeld, Germany
- Graduate School DILS, Bielefeld Institute for Bioinformatics Infrastructure (BIBI), Bielefeld University, Bielefeld, Germany
| | - Alenka Hafner
- Genetics and Genomics of Plants, Center for Biotechnology (CeBiTec) & Faculty of Biology, Bielefeld University, Bielefeld, Germany
- Current Affiliation: Intercollege Graduate Degree Program in Plant Biology, Penn State University, University Park, State College, PA, United States of America
| | - Boas Pucker
- Genetics and Genomics of Plants, Center for Biotechnology (CeBiTec) & Faculty of Biology, Bielefeld University, Bielefeld, Germany
- Evolution and Diversity, Department of Plant Sciences, University of Cambridge, Cambridge, United Kingdom
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21
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Su X, Jing G, Zhang Y, Wu S. Method development for cross-study microbiome data mining: Challenges and opportunities. Comput Struct Biotechnol J 2020; 18:2075-2080. [PMID: 32802279 PMCID: PMC7419250 DOI: 10.1016/j.csbj.2020.07.020] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2020] [Revised: 07/22/2020] [Accepted: 07/24/2020] [Indexed: 01/26/2023] Open
Abstract
During the past decade, tremendous amount of microbiome sequencing data has been generated to study on the dynamic associations between microbial profiles and environments. How to precisely and efficiently decipher large-scale of microbiome data and furtherly take advantages from it has become one of the most essential bottlenecks for microbiome research at present. In this mini-review, we focus on the three key steps of analyzing cross-study microbiome datasets, including microbiome profiling, data integrating and data mining. By introducing the current bioinformatics approaches and discussing their limitations, we prospect the opportunities in development of computational methods for the three steps, and propose the promising solutions to multi-omics data analysis for comprehensive understanding and rapid investigation of microbiome from different angles, which could potentially promote the data-driven research by providing a broader view of the "microbiome data space".
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Affiliation(s)
- Xiaoquan Su
- College of Computer Science and Technology, Qingdao University, Qingdao, Shandong 266071 China
- Single-Cell Center, Qingdao Institute of BioEnergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, Shandong 266101 China
| | - Gongchao Jing
- Single-Cell Center, Qingdao Institute of BioEnergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, Shandong 266101 China
| | - Yufeng Zhang
- College of Computer Science and Technology, Qingdao University, Qingdao, Shandong 266071 China
- Single-Cell Center, Qingdao Institute of BioEnergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, Shandong 266101 China
| | - Shunyao Wu
- College of Computer Science and Technology, Qingdao University, Qingdao, Shandong 266071 China
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22
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Darling JA, Pochon X, Abbott CL, Inglis GJ, Zaiko A. The risks of using molecular biodiversity data for incidental detection of species of concern. DIVERS DISTRIB 2020; 26:1116-1121. [PMID: 34121910 PMCID: PMC8193820 DOI: 10.1111/ddi.13108] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
Incidental detection of species of concern (e.g., invasive species, pathogens, threatened and endangered species) during biodiversity assessments based on high-throughput DNA sequencing holds significant risks in the absence of rigorous, fit-for-purpose data quality and reporting standards. Molecular biodiversity data are predominantly collected for ecological studies and thus are generated to common quality assurance standards. However, the detection of certain species of concern in these data would likely elicit interest from end users working in biosecurity or other surveillance contexts (e.g., pathogen detection in health-related fields), for which more stringent quality control standards are essential to ensure that data are suitable for informing decision-making and can withstand legal or political challenges. We suggest here that data quality and reporting criteria are urgently needed to enable clear identification of those studies that may be appropriately applied to surveillance contexts. In the interim, more pointed disclaimers on uncertainties associated with the detection and identification of species of concern may be warranted in published studies. This is not only to ensure the utility of molecular biodiversity data for consumers, but also to protect data generators from uncritical and potentially ill-advised application of their science in decision-making.
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Affiliation(s)
- John A Darling
- Center for Environmental Measurement & Modeling, United States Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Xavier Pochon
- Coastal and Freshwater Group, Cawthron Institute, Nelson, New Zealand.,Institute of Marine Science, University of Auckland, Warkworth, New Zealand
| | - Cathryn L Abbott
- Department of Fisheries and Oceans, Pacific Biological Station, Nanaimo, British Columbia, Canada
| | - Graeme J Inglis
- National Institute of Water & Atmospheric Research Ltd., Christchurch, New Zealand
| | - Anastasija Zaiko
- Coastal and Freshwater Group, Cawthron Institute, Nelson, New Zealand.,Institute of Marine Science, University of Auckland, Warkworth, New Zealand.,Marine Research Institute, Klaipeda University, Klaipeda, Lithuania
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23
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Berg G, Rybakova D, Fischer D, Cernava T, Vergès MCC, Charles T, Chen X, Cocolin L, Eversole K, Corral GH, Kazou M, Kinkel L, Lange L, Lima N, Loy A, Macklin JA, Maguin E, Mauchline T, McClure R, Mitter B, Ryan M, Sarand I, Smidt H, Schelkle B, Roume H, Kiran GS, Selvin J, Souza RSCD, van Overbeek L, Singh BK, Wagner M, Walsh A, Sessitsch A, Schloter M. Microbiome definition re-visited: old concepts and new challenges. MICROBIOME 2020; 8:103. [PMID: 32605663 PMCID: PMC7329523 DOI: 10.1186/s40168-020-00875-0] [Citation(s) in RCA: 706] [Impact Index Per Article: 176.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/05/2020] [Accepted: 05/22/2020] [Indexed: 05/03/2023]
Abstract
The field of microbiome research has evolved rapidly over the past few decades and has become a topic of great scientific and public interest. As a result of this rapid growth in interest covering different fields, we are lacking a clear commonly agreed definition of the term "microbiome." Moreover, a consensus on best practices in microbiome research is missing. Recently, a panel of international experts discussed the current gaps in the frame of the European-funded MicrobiomeSupport project. The meeting brought together about 40 leaders from diverse microbiome areas, while more than a hundred experts from all over the world took part in an online survey accompanying the workshop. This article excerpts the outcomes of the workshop and the corresponding online survey embedded in a short historical introduction and future outlook. We propose a definition of microbiome based on the compact, clear, and comprehensive description of the term provided by Whipps et al. in 1988, amended with a set of novel recommendations considering the latest technological developments and research findings. We clearly separate the terms microbiome and microbiota and provide a comprehensive discussion considering the composition of microbiota, the heterogeneity and dynamics of microbiomes in time and space, the stability and resilience of microbial networks, the definition of core microbiomes, and functionally relevant keystone species as well as co-evolutionary principles of microbe-host and inter-species interactions within the microbiome. These broad definitions together with the suggested unifying concepts will help to improve standardization of microbiome studies in the future, and could be the starting point for an integrated assessment of data resulting in a more rapid transfer of knowledge from basic science into practice. Furthermore, microbiome standards are important for solving new challenges associated with anthropogenic-driven changes in the field of planetary health, for which the understanding of microbiomes might play a key role. Video Abstract.
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Affiliation(s)
- Gabriele Berg
- Environmental Biotechnology, Graz University of Technology, Graz, Austria.
| | - Daria Rybakova
- Environmental Biotechnology, Graz University of Technology, Graz, Austria
| | | | - Tomislav Cernava
- Environmental Biotechnology, Graz University of Technology, Graz, Austria
| | | | - Trevor Charles
- Waterloo Centre for Microbial Research, University of Waterloo, 200 University Avenue West, Waterloo, ON, N2L 3G1, Canada
- Metagenom Bio, 550 Parkside Drive, Unit A9, Waterloo, ON, N2L 5 V4, Canada
| | - Xiaoyulong Chen
- Guizhou Provincial Key Laboratory for Agricultural Pest Management of the Mountainous Region, Guizhou University, Guiyang, 550025, Guizhou, China
| | - Luca Cocolin
- European Food Information Council, Brussels, Belgium
| | - Kellye Eversole
- International Alliance for Phytobiomes Research, Summit, Lee, MO, 's, USA
| | | | - Maria Kazou
- Laboratory of Dairy Research, Department of Food Science and Human Nutrition, Agricultural University of Athens, Athens, Greece
| | - Linda Kinkel
- Department of Plant Pathology, University of Minnesota, St. Paul, MN, 55108, USA
| | - Lene Lange
- BioEconomy, Research, & Advisory, Valby, Denmark
| | - Nelson Lima
- CEB-Centre of Biological Engineering, University of Minho, Campus de Gualtar, 4710-057, Braga, Portugal
| | - Alexander Loy
- Department of Microbial Ecology and Ecosystem Science, University of Vienna, Vienna, Austria
| | | | - Emmanuelle Maguin
- MICALIS, INRA, AgroParisTech, Université Paris-Saclay, 78350, Jouy-en-Josas, France
| | - Tim Mauchline
- Sustainable Agriculture Sciences, Rothamsted Research, Harpenden, UK
| | - Ryan McClure
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, 99352, USA
| | - Birgit Mitter
- Bioresources Unit, AIT Austrian Institute of Technology, Tulln, Austria
| | | | - Inga Sarand
- Department of Chemistry and Biotechnology, Tallinn University of Technology, Tallinn, Estonia
| | - Hauke Smidt
- Laboratory of Microbiology, Wageningen University & Research, Wageningen, the Netherlands
| | | | | | - G Seghal Kiran
- Dept of Food Science and Technology, Pondicherry University, Puducherry, India
| | - Joseph Selvin
- Department of Microbiology, Pondicherry University, Puducherry, India
| | - Rafael Soares Correa de Souza
- Genomics for Climate Change Research Center (GCCRC), Universidade Estadual de Campinas (UNICAMP), Campinas, SP, Brazil
| | - Leo van Overbeek
- Laboratory of Microbiology, Wageningen University & Research, Wageningen, the Netherlands
| | - Brajesh K Singh
- Hawkesbury Institute for the Environment, Western Sydney University, Penrith, NSW, Australia
- Global Centre for Land-Based Innovation, Western Sydney University, Penrith, NSW, Australia
| | - Michael Wagner
- Department of Microbial Ecology and Ecosystem Science, University of Vienna, Vienna, Austria
| | - Aaron Walsh
- Teagasc Food Research Centre, Moorepark, Fermoy, Co. Cork, Ireland
| | - Angela Sessitsch
- Bioresources Unit, AIT Austrian Institute of Technology, Tulln, Austria
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24
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COVID-19 pandemic reveals the peril of ignoring metadata standards. Sci Data 2020; 7:188. [PMID: 32561801 PMCID: PMC7305141 DOI: 10.1038/s41597-020-0524-5] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2020] [Accepted: 05/28/2020] [Indexed: 01/04/2023] Open
Abstract
Efficient response to the pandemic through the mobilization of the larger scientific community is challenged by the limited reusability of the available primary genomic data. Here, the Genomic Standards Consortium board highlights the essential need for contextual genomic data FAIRness, for empowering key data-driven biological questions.
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25
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Mitchell AL, Almeida A, Beracochea M, Boland M, Burgin J, Cochrane G, Crusoe MR, Kale V, Potter SC, Richardson LJ, Sakharova E, Scheremetjew M, Korobeynikov A, Shlemov A, Kunyavskaya O, Lapidus A, Finn RD. MGnify: the microbiome analysis resource in 2020. Nucleic Acids Res 2020; 48:D570-D578. [PMID: 31696235 PMCID: PMC7145632 DOI: 10.1093/nar/gkz1035] [Citation(s) in RCA: 193] [Impact Index Per Article: 48.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Accepted: 10/23/2019] [Indexed: 12/16/2022] Open
Abstract
MGnify (http://www.ebi.ac.uk/metagenomics) provides a free to use platform for the assembly, analysis and archiving of microbiome data derived from sequencing microbial populations that are present in particular environments. Over the past 2 years, MGnify (formerly EBI Metagenomics) has more than doubled the number of publicly available analysed datasets held within the resource. Recently, an updated approach to data analysis has been unveiled (version 5.0), replacing the previous single pipeline with multiple analysis pipelines that are tailored according to the input data, and that are formally described using the Common Workflow Language, enabling greater provenance, reusability, and reproducibility. MGnify's new analysis pipelines offer additional approaches for taxonomic assertions based on ribosomal internal transcribed spacer regions (ITS1/2) and expanded protein functional annotations. Biochemical pathways and systems predictions have also been added for assembled contigs. MGnify's growing focus on the assembly of metagenomic data has also seen the number of datasets it has assembled and analysed increase six-fold. The non-redundant protein database constructed from the proteins encoded by these assemblies now exceeds 1 billion sequences. Meanwhile, a newly developed contig viewer provides fine-grained visualisation of the assembled contigs and their enriched annotations.
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Affiliation(s)
- Alex L Mitchell
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Alexandre Almeida
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK.,Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SA, UK
| | - Martin Beracochea
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Miguel Boland
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Josephine Burgin
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Guy Cochrane
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Michael R Crusoe
- Common Workflow Language, a project of the Software Freedom Conservancy, Inc. 137 Montague Street, Suite 380, Brooklyn, NY 11201-3548, USA
| | - Varsha Kale
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Simon C Potter
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Lorna J Richardson
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Ekaterina Sakharova
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Maxim Scheremetjew
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Anton Korobeynikov
- Center for Algorithmic Biotechnologies, Saint Petersburg State University, Russia
| | - Alex Shlemov
- Center for Algorithmic Biotechnologies, Saint Petersburg State University, Russia
| | - Olga Kunyavskaya
- Center for Algorithmic Biotechnologies, Saint Petersburg State University, Russia
| | - Alla Lapidus
- Center for Algorithmic Biotechnologies, Saint Petersburg State University, Russia
| | - Robert D Finn
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
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26
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Courtot M, Cherubin L, Faulconbridge A, Vaughan D, Green M, Richardson D, Harrison P, Whetzel PL, Parkinson H, Burdett T. BioSamples database: an updated sample metadata hub. Nucleic Acids Res 2020; 47:D1172-D1178. [PMID: 30407529 PMCID: PMC6323949 DOI: 10.1093/nar/gky1061] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2018] [Accepted: 10/18/2018] [Indexed: 12/23/2022] Open
Abstract
The BioSamples database at EMBL-EBI provides a central hub for sample metadata storage and linkage to other EMBL-EBI resources. BioSamples has recently undergone major changes, both in terms of data content and supporting infrastructure. The data content has more than doubled from around 2 million samples in 2014 to just over 5 million samples in 2018. Fast, reciprocal data exchange was fully established between sister Biosample databases and other INSDC partners, enabling a worldwide common representation and centralization of sample metadata. The BioSamples platform has been upgraded to accommodate anticipated increases in the number of submissions via GA4GH driver projects such as the Human Cell Atlas and the EGA, as well as from mirroring of NCBI dbGaP data. The BioSamples database is now the authoritative repository for all INSDC sample metadata, an ELIXIR Deposition Database for Biomolecular Data and the EMBL-EBI sample metadata hub. To support faster turnaround for sample submission, and to increase scalability and resilience, we have upgraded the BioSamples database backend storage, APIs and user interface. Finally, the website has been redesigned to allow search and retrieval of records based on specific filters, such as ‘disease’ or ‘organism’. These changes are targeted at answering current use cases as well as providing functionalities for future emerging and anticipated developments. Availability: The BioSamples database is freely available at http://www.ebi.ac.uk/biosamples. Content is distributed under the EMBL-EBI Terms of Use available at https://www.ebi.ac.uk/about/terms-of-use.
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Affiliation(s)
| | - Luca Cherubin
- EMBL-EBI, Wellcome Genome Campus, Hinxton CB10 1SD, UK
| | | | | | - Matthew Green
- EMBL-EBI, Wellcome Genome Campus, Hinxton CB10 1SD, UK
| | | | | | | | | | - Tony Burdett
- EMBL-EBI, Wellcome Genome Campus, Hinxton CB10 1SD, UK
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27
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Shi W, Qi H, Sun Q, Fan G, Liu S, Wang J, Zhu B, Liu H, Zhao F, Wang X, Hu X, Li W, Liu J, Tian Y, Wu L, Ma J. gcMeta: a Global Catalogue of Metagenomics platform to support the archiving, standardization and analysis of microbiome data. Nucleic Acids Res 2020; 47:D637-D648. [PMID: 30365027 PMCID: PMC6324004 DOI: 10.1093/nar/gky1008] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2018] [Accepted: 10/13/2018] [Indexed: 11/26/2022] Open
Abstract
Meta-omics approaches have been increasingly used to study the structure and function of the microbial communities. A variety of large-scale collaborative projects are being conducted to encompass samples from diverse environments and habitats. This change has resulted in enormous demands for long-term data maintenance and capacity for data analysis. The Global Catalogue of Metagenomics (gcMeta) is a part of the ‘Chinese Academy of Sciences Initiative of Microbiome (CAS-CMI)’, which focuses on studying the human and environmental microbiome, establishing depositories of samples, strains and data, as well as promoting international collaboration. To accommodate and rationally organize massive datasets derived from several thousands of human and environmental microbiome samples, gcMeta features a database management system for archiving and publishing data in a standardized way. Another main feature is the integration of more than ninety web-based data analysis tools and workflows through a Docker platform which enables data analysis by using various operating systems. This platform has been rapidly expanding, and now hosts data from the CAS-CMI and a number of other ongoing research projects. In conclusion, this platform presents a powerful and user-friendly service to support worldwide collaborative efforts in the field of meta-omics research. This platform is freely accessible at https://gcmeta.wdcm.org/.
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Affiliation(s)
- Wenyu Shi
- Microbial Resource and Big Data Center, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China
| | - Heyuan Qi
- Microbial Resource and Big Data Center, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China
| | - Qinglan Sun
- Microbial Resource and Big Data Center, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China
| | - Guomei Fan
- Microbial Resource and Big Data Center, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China
| | - Shuangjiang Liu
- Microbial Resource and Big Data Center, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China.,State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China
| | - Jun Wang
- CAS Key Laboratory of Pathogenic Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Science, Beijing 100101, China
| | - Baoli Zhu
- CAS Key Laboratory of Pathogenic Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Science, Beijing 100101, China.,University of Chinese Academy of Sciences, Beijing 100049, China.,Collaborative Innovation Centre for Diagnosis and Treatment of Infectious Diseases First Attainted Hospital, College of Medicine, Zhejiang University, Hangzhou 310058, China.,Beijing Key Laboratory of Antimicrobial Resistance and Pathogen Genomics, Beijing 100101, China
| | - Hongwei Liu
- State Key Laboratory of Mycology, Institute of Microbiology, Chinese Academy of Science, Beijing 100101, China
| | - Fangqing Zhao
- Computational Genomics Lab, Beijing Institutes of Life Science, Chinese Academy of Sciences, Beijing 100101, China
| | - Xiaochen Wang
- Microbial Resource and Big Data Center, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China
| | - Xiaoxuan Hu
- Microbial Resource and Big Data Center, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China
| | - Wei Li
- Microbial Resource and Big Data Center, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China
| | - Jia Liu
- Internet of Things Information Technology and Application Laboratory, Computer Network Information Center, Chinese Academy of Sciences. Beijing 100101, China
| | - Ye Tian
- Internet of Things Information Technology and Application Laboratory, Computer Network Information Center, Chinese Academy of Sciences. Beijing 100101, China
| | - Linhuan Wu
- Microbial Resource and Big Data Center, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China.,State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China
| | - Juncai Ma
- Microbial Resource and Big Data Center, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China.,State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China
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28
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Gut microbiota and human NAFLD: disentangling microbial signatures from metabolic disorders. Nat Rev Gastroenterol Hepatol 2020; 17:279-297. [PMID: 32152478 DOI: 10.1038/s41575-020-0269-9] [Citation(s) in RCA: 511] [Impact Index Per Article: 127.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 01/21/2020] [Indexed: 02/07/2023]
Abstract
Gut microbiota dysbiosis has been repeatedly observed in obesity and type 2 diabetes mellitus, two metabolic diseases strongly intertwined with non-alcoholic fatty liver disease (NAFLD). Animal studies have demonstrated a potential causal role of gut microbiota in NAFLD. Human studies have started to describe microbiota alterations in NAFLD and have found a few consistent microbiome signatures discriminating healthy individuals from those with NAFLD, non-alcoholic steatohepatitis or cirrhosis. However, patients with NAFLD often present with obesity and/or insulin resistance and type 2 diabetes mellitus, and these metabolic confounding factors for dysbiosis have not always been considered. Patients with different NAFLD severity stages often present with heterogeneous lesions and variable demographic characteristics (including age, sex and ethnicity), which are known to affect the gut microbiome and have been overlooked in most studies. Finally, multiple gut microbiome sequencing tools and NAFLD diagnostic methods have been used across studies that could account for discrepant microbiome signatures. This Review provides a broad insight into microbiome signatures for human NAFLD and explores issues with disentangling these signatures from underlying metabolic disorders. More advanced metagenomics and multi-omics studies using system biology approaches are needed to improve microbiome biomarkers.
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29
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Abstract
OPINION STATEMENT There are approximately 1.2 million new hematologic malignancy cases resulting in ~ 690,000 deaths each year worldwide, and hematologic malignancies remain the most commonly occurring cancer in children. Even though advances in anticancer treatment regimens in recent decades have considerably improved survival rates, their cytotoxic effects and the resulting long-term complications pose a significant burden on the patients and the health care system. Therefore, non-toxic treatment modalities are needed to decrease side effects. The human body is the host to approximately 40 trillion microbes, known as the human microbiota. The large majority of the microbiota is located in the gastrointestinal tract, and is primarily composed of bacteria. The microbiota plays several important physiological roles, ranging from digestive functions to immunological and neural development. Investigating the microbiota in patients with hematologic malignancies has several important implications. The microbiota affects hematopoiesis, and influences the efficacies of chemotherapy and antimicrobial treatments. Determination of the microbiota composition and diversity could be an important part of risk stratification in the future, and may also take part to personalize antimicrobial treatments. Modulation of the microbiota via probiotics or fecal transplant can potentially be involved in reducing side effects of chemotherapy, and eliminating multiple drug resistant strains in patients with hematologic malignancies.
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30
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Klemetsen T, Raknes IA, Fu J, Agafonov A, Balasundaram SV, Tartari G, Robertsen E, Willassen NP. The MAR databases: development and implementation of databases specific for marine metagenomics. Nucleic Acids Res 2019; 46:D692-D699. [PMID: 29106641 PMCID: PMC5753341 DOI: 10.1093/nar/gkx1036] [Citation(s) in RCA: 67] [Impact Index Per Article: 13.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2017] [Accepted: 10/18/2017] [Indexed: 12/03/2022] Open
Abstract
We introduce the marine databases; MarRef, MarDB and MarCat (https://mmp.sfb.uit.no/databases/), which are publicly available resources that promote marine research and innovation. These data resources, which have been implemented in the Marine Metagenomics Portal (MMP) (https://mmp.sfb.uit.no/), are collections of richly annotated and manually curated contextual (metadata) and sequence databases representing three tiers of accuracy. While MarRef is a database for completely sequenced marine prokaryotic genomes, which represent a marine prokaryote reference genome database, MarDB includes all incomplete sequenced prokaryotic genomes regardless level of completeness. The last database, MarCat, represents a gene (protein) catalog of uncultivable (and cultivable) marine genes and proteins derived from marine metagenomics samples. The first versions of MarRef and MarDB contain 612 and 3726 records, respectively. Each record is built up of 106 metadata fields including attributes for sampling, sequencing, assembly and annotation in addition to the organism and taxonomic information. Currently, MarCat contains 1227 records with 55 metadata fields. Ontologies and controlled vocabularies are used in the contextual databases to enhance consistency. The user-friendly web interface lets the visitors browse, filter and search in the contextual databases and perform BLAST searches against the corresponding sequence databases. All contextual and sequence databases are freely accessible and downloadable from https://s1.sfb.uit.no/public/mar/.
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Affiliation(s)
- Terje Klemetsen
- Centre for Bioinformatics, Faculty of science and technology, UiT The Arctic University of Norway, PO Box 6050 Langnes, TromsøN-9037, Norway
| | - Inge A Raknes
- Centre for Bioinformatics, Faculty of science and technology, UiT The Arctic University of Norway, PO Box 6050 Langnes, TromsøN-9037, Norway
| | - Juan Fu
- Centre for Bioinformatics, Faculty of science and technology, UiT The Arctic University of Norway, PO Box 6050 Langnes, TromsøN-9037, Norway
| | - Alexander Agafonov
- Centre for Bioinformatics, Faculty of science and technology, UiT The Arctic University of Norway, PO Box 6050 Langnes, TromsøN-9037, Norway
| | - Sudhagar V Balasundaram
- Centre for Bioinformatics, Faculty of science and technology, UiT The Arctic University of Norway, PO Box 6050 Langnes, TromsøN-9037, Norway
| | - Giacomo Tartari
- Centre for Bioinformatics, Faculty of science and technology, UiT The Arctic University of Norway, PO Box 6050 Langnes, TromsøN-9037, Norway.,Department of Information Technology, UiT The Arctic University of Norway, PO Box 6050 Langnes, TromsøN-9037, Norway
| | - Espen Robertsen
- Centre for Bioinformatics, Faculty of science and technology, UiT The Arctic University of Norway, PO Box 6050 Langnes, TromsøN-9037, Norway
| | - Nils P Willassen
- Centre for Bioinformatics, Faculty of science and technology, UiT The Arctic University of Norway, PO Box 6050 Langnes, TromsøN-9037, Norway
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31
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Rambold G, Yilmaz P, Harjes J, Klaster S, Sanz V, Link A, Glöckner FO, Triebel D. Meta-omics data and collection objects (MOD-CO): a conceptual schema and data model for processing sample data in meta-omics research. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2019; 2019:5303972. [PMID: 30715273 PMCID: PMC6354027 DOI: 10.1093/database/baz002] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/09/2018] [Accepted: 01/07/2019] [Indexed: 12/16/2022]
Abstract
With the advent of advanced molecular meta-omics techniques and methods, a new era commenced for analysing and characterizing historic collection specimens, as well as recently collected environmental samples. Nucleic acid and protein sequencing-based analyses are increasingly applied to determine the origin, identity and traits of environmental (biological) objects and organisms. In this context, the need for new data structures is evident and former approaches for data processing need to be expanded according to the new meta-omics techniques and operational standards. Existing schemas and community standards in the biodiversity and molecular domain concentrate on terms important for data exchange and publication. Detailed operational aspects of origin and laboratory as well as object and data management issues are frequently neglected. Meta-omics Data and Collection Objects (MOD-CO) has therefore been set up as a new schema for meta-omics research, with a hierarchical organization of the concepts describing collection samples, as well as products and data objects being generated during operational workflows. It is focussed on object trait descriptions as well as on operational aspects and thereby may serve as a backbone for R&D laboratory information management systems with functions of an electronic laboratory notebook. The schema in its current version 1.0 includes 653 concepts and 1810 predefined concept values, being equivalent to descriptors and descriptor states, respectively. It is published in several representations, like a Semantic Media Wiki publication with 2463 interlinked Wiki pages for concepts and concept values, being grouped in 37 concept collections and subcollections. The SQL database application DiversityDescriptions, a generic tool for maintaining descriptive data and schemas, has been applied for setting up and testing MOD-CO and for concept mapping on elements of corresponding schemas.
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Affiliation(s)
- Gerhard Rambold
- University of Bayreuth, Universitätsstraße 30, Bayreuth, Germany
| | - Pelin Yilmaz
- Max Planck Institute for Marine Microbiology, Celsiusstraße 1, Bremen, Germany
| | - Janno Harjes
- University of Bayreuth, Universitätsstraße 30, Bayreuth, Germany
| | - Sabrina Klaster
- University of Bayreuth, Universitätsstraße 30, Bayreuth, Germany
| | - Veronica Sanz
- University of Bayreuth, Universitätsstraße 30, Bayreuth, Germany.,SNSB IT Center, Menzinger Straße 67, München, Germany
| | - Anton Link
- SNSB IT Center, Menzinger Straße 67, München, Germany
| | - Frank Oliver Glöckner
- Max Planck Institute for Marine Microbiology, Celsiusstraße 1, Bremen, Germany.,Jacobs University, Campus Ring 1, Bremen, Germany
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32
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Tedersoo L, Drenkhan R, Anslan S, Morales‐Rodriguez C, Cleary M. High-throughput identification and diagnostics of pathogens and pests: Overview and practical recommendations. Mol Ecol Resour 2019; 19:47-76. [PMID: 30358140 PMCID: PMC7379260 DOI: 10.1111/1755-0998.12959] [Citation(s) in RCA: 61] [Impact Index Per Article: 12.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2018] [Revised: 08/01/2018] [Accepted: 08/28/2018] [Indexed: 12/26/2022]
Abstract
High-throughput identification technologies provide efficient tools for understanding the ecology and functioning of microorganisms. Yet, these methods have been only rarely used for monitoring and testing ecological hypotheses in plant pathogens and pests in spite of their immense importance in agriculture, forestry and plant community dynamics. The main objectives of this manuscript are the following: (a) to provide a comprehensive overview about the state-of-the-art high-throughput quantification and molecular identification methods used to address population dynamics, community ecology and host associations of microorganisms, with a specific focus on antagonists such as pathogens, viruses and pests; (b) to compile available information and provide recommendations about specific protocols and workable primers for bacteria, fungi, oomycetes and insect pests; and (c) to provide examples of novel methods used in other microbiological disciplines that are of great potential use for testing specific biological hypotheses related to pathology. Finally, we evaluate the overall perspectives of the state-of-the-art and still evolving methods for diagnostics and population- and community-level ecological research of pathogens and pests.
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Affiliation(s)
- Leho Tedersoo
- Natural History Museum and Institute of Ecology and Earth SciencesUniversity of TartuTartuEstonia
| | - Rein Drenkhan
- Institute of Forestry and Rural EngineeringEstonian University of Life SciencesTartuEstonia
| | - Sten Anslan
- Natural History Museum and Institute of Ecology and Earth SciencesUniversity of TartuTartuEstonia
| | | | - Michelle Cleary
- Southern Swedish Forest Research CentreSwedish University of Agricultural SciencesAlnarpSweden
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