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Kuang N, Ma Q, Zheng X, Meng X, Zhai Z, Li Q, Pan J. GeTeSEPdb: A comprehensive database and online tool for the identification and analysis of gene profiles with temporal-specific expression patterns. Comput Struct Biotechnol J 2024; 23:2488-2496. [PMID: 38939556 PMCID: PMC11208770 DOI: 10.1016/j.csbj.2024.06.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Revised: 05/29/2024] [Accepted: 06/04/2024] [Indexed: 06/29/2024] Open
Abstract
Gene expression is dynamic and varies at different stages of processes. The identification of gene profiles with temporal-specific expression patterns can provide valuable insights into ongoing biological processes, such as the cell cycle, cell development, circadian rhythms, or responses to external stimuli such as drug treatments or viral infections. However, currently, no database defines, identifies or archives gene profiles with temporal-specific expression patterns. Here, using a high-throughput regression analysis approach, eight linear and nonlinear parametric models were fitted to gene expression profiles from time-series experiments to identify eight types of gene profiles with temporal-specific expression patterns. We curated 2684 time-series transcriptome datasets and identified 2644,370 gene profiles exhibiting temporal-specific expression patterns. The results were stored in the database GeTeSEPdb (gene profiles with temporal-specific expression patterns database, http://www.inbirg.com/GeTeSEPdb/). Moreover, we implemented an online tool to identify gene profiles with temporal-specific expression patterns from user-submitted data. In summary, GeTeSEPdb is a comprehensive web service that can be used to identify and analyse gene profiles with temporal-specific expression patterns. This approach facilitates the exploration of transcriptional changes and temporal patterns of responses. We firmly believe that GeTeSEPdb will become a valuable resource for biologists and bioinformaticians.
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Affiliation(s)
- Ni Kuang
- Basic Medicine Research and Innovation Center for Novel Target and Therapeutic Intervention, Ministry of Education, Institute of Life Sciences, Chongqing Medical University, Chongqing 400016, China
| | - Qinfeng Ma
- Basic Medicine Research and Innovation Center for Novel Target and Therapeutic Intervention, Ministry of Education, Institute of Life Sciences, Chongqing Medical University, Chongqing 400016, China
| | - Xiao Zheng
- Basic Medicine Research and Innovation Center for Novel Target and Therapeutic Intervention, Ministry of Education, Institute of Life Sciences, Chongqing Medical University, Chongqing 400016, China
| | - Xuehang Meng
- Basic Medicine Research and Innovation Center for Novel Target and Therapeutic Intervention, Ministry of Education, Institute of Life Sciences, Chongqing Medical University, Chongqing 400016, China
| | - Zhaoyu Zhai
- Basic Medicine Research and Innovation Center for Novel Target and Therapeutic Intervention, Ministry of Education, Institute of Life Sciences, Chongqing Medical University, Chongqing 400016, China
| | - Qiang Li
- Basic Medicine Research and Innovation Center for Novel Target and Therapeutic Intervention, Ministry of Education, Institute of Life Sciences, Chongqing Medical University, Chongqing 400016, China
| | - Jianbo Pan
- Basic Medicine Research and Innovation Center for Novel Target and Therapeutic Intervention, Ministry of Education, Institute of Life Sciences, Chongqing Medical University, Chongqing 400016, China
- Precision Medicine Center, The Second Affiliated Hospital of Chongqing Medical University, Chongqing 400010, China
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Bergström A. Improving data archiving practices in ancient genomics. Sci Data 2024; 11:754. [PMID: 38987254 PMCID: PMC11236975 DOI: 10.1038/s41597-024-03563-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2024] [Accepted: 06/21/2024] [Indexed: 07/12/2024] Open
Abstract
Ancient DNA is producing a rich record of past genetic diversity in humans and other species. However, unless the primary data is appropriately archived, its long-term value will not be fully realised. I surveyed publicly archived data from 42 recent ancient genomics studies. Half of the studies archived incomplete datasets, preventing accurate replication and representing a loss of data of potential future use. No studies met all criteria that could be considered best practice. Based on these results, I make six recommendations for data producers: (1) archive all sequencing reads, not just those that aligned to a reference genome, (2) archive read alignments too, but as secondary analysis files, (3) provide correct experiment metadata on samples, libraries and sequencing runs, (4) provide informative sample metadata, (5) archive data from low-coverage and negative experiments, and (6) document archiving choices in papers, and peer review these. Given the reliance on destructive sampling of finite material, ancient genomics studies have a particularly strong responsibility to ensure the longevity and reusability of generated data.
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Affiliation(s)
- Anders Bergström
- School of Biological Sciences, University of East Anglia, Norwich, UK.
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3
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Deng CH, Naithani S, Kumari S, Cobo-Simón I, Quezada-Rodríguez EH, Skrabisova M, Gladman N, Correll MJ, Sikiru AB, Afuwape OO, Marrano A, Rebollo I, Zhang W, Jung S. Genotype and phenotype data standardization, utilization and integration in the big data era for agricultural sciences. Database (Oxford) 2023; 2023:baad088. [PMID: 38079567 PMCID: PMC10712715 DOI: 10.1093/database/baad088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 10/17/2023] [Accepted: 11/28/2023] [Indexed: 12/18/2023]
Abstract
Large-scale genotype and phenotype data have been increasingly generated to identify genetic markers, understand gene function and evolution and facilitate genomic selection. These datasets hold immense value for both current and future studies, as they are vital for crop breeding, yield improvement and overall agricultural sustainability. However, integrating these datasets from heterogeneous sources presents significant challenges and hinders their effective utilization. We established the Genotype-Phenotype Working Group in November 2021 as a part of the AgBioData Consortium (https://www.agbiodata.org) to review current data types and resources that support archiving, analysis and visualization of genotype and phenotype data to understand the needs and challenges of the plant genomic research community. For 2021-22, we identified different types of datasets and examined metadata annotations related to experimental design/methods/sample collection, etc. Furthermore, we thoroughly reviewed publicly funded repositories for raw and processed data as well as secondary databases and knowledgebases that enable the integration of heterogeneous data in the context of the genome browser, pathway networks and tissue-specific gene expression. Based on our survey, we recommend a need for (i) additional infrastructural support for archiving many new data types, (ii) development of community standards for data annotation and formatting, (iii) resources for biocuration and (iv) analysis and visualization tools to connect genotype data with phenotype data to enhance knowledge synthesis and to foster translational research. Although this paper only covers the data and resources relevant to the plant research community, we expect that similar issues and needs are shared by researchers working on animals. Database URL: https://www.agbiodata.org.
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Affiliation(s)
- Cecilia H Deng
- Molecular and Digital Breeding, New Cultivar Innovation, The New Zealand Institute for Plant and Food Research Limited, 120 Mt Albert Road, Auckland 1025, New Zealand
| | - Sushma Naithani
- Department of Botany and Plant Pathology, Oregon State University, Corvallis, OR 97331, USA
| | - Sunita Kumari
- Cold Spring Harbor Laboratory, 1 Bungtown Rd, Cold Spring Harbor, New York, NY 11724, USA
| | - Irene Cobo-Simón
- Department of Ecology and Evolutionary Biology, University of Connecticut, Storrs, CT, USA
- Institute of Forest Science (ICIFOR-INIA, CSIC), Madrid, Spain
| | - Elsa H Quezada-Rodríguez
- Departamento de Producción Agrícola y Animal, Universidad Autónoma Metropolitana-Xochimilco, Ciudad de México, México
- Centro de Ciencias de la Complejidad, Universidad Nacional Autónoma de México, Ciudad de México, México
| | - Maria Skrabisova
- Department of Biochemistry, Faculty of Science, Palacky University, Olomouc, Czech Republic
| | - Nick Gladman
- Cold Spring Harbor Laboratory, 1 Bungtown Rd, Cold Spring Harbor, New York, NY 11724, USA
- U.S. Department of Agriculture-Agricultural Research Service, NEA Robert W. Holley Center for Agriculture and Health, Cornell University, Ithaca, NY 14853, USA
| | - Melanie J Correll
- Agricultural and Biological Engineering Department, University of Florida, 1741 Museum Rd, Gainesville, FL 32611, USA
| | | | | | - Annarita Marrano
- Phoenix Bioinformatics, 39899 Balentine Drive, Suite 200, Newark, CA 94560, USA
| | | | - Wentao Zhang
- National Research Council Canada, 110 Gymnasium Pl, Saskatoon, Saskatchewan S7N 0W9, Canada
| | - Sook Jung
- Department of Horticulture, Washington State University, 303c Plant Sciences Building, Pullman, WA 99164-6414, USA
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4
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Building cloud computing environments for genome analysis in Japan. Hum Genome Var 2022; 9:46. [PMID: 36517473 PMCID: PMC9751107 DOI: 10.1038/s41439-022-00223-8] [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: 07/22/2022] [Revised: 10/29/2022] [Accepted: 11/07/2022] [Indexed: 12/23/2022] Open
Abstract
This review article describes the current status of data archiving and computational infrastructure in the field of genomic medicine, focusing primarily on the situation in Japan. I begin by introducing the status of supercomputer operations in Japan, where a high-performance computing infrastructure (HPCI) is operated to meet the diverse computational needs of science in general. Since this HPCI consists of supercomputers of various architectures located across the nation connected via a high-speed network, including supercomputers specialized in genome science, the status of its response to the explosive increase in genomic data, including the International Nucleotide Sequence Database Collaboration (INSDC) data archive, is explored. Separately, since it is clear that the use of commercial cloud computing environments needs to be promoted, both in light of the rapid increase in computing demands and to support international data sharing and international data analysis projects, I explain how the Japanese government has established a series of guidelines for the use of cloud computing based on its cybersecurity strategy and has begun to build a government cloud for government agencies. I will also carefully consider several other issues of user concern. Finally, I will show how Japan's major cloud computing infrastructure is currently evolving toward a multicloud and hybrid cloud configuration.
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Kobayashi K, Yoshida H, Tanjo T, Aida K. Cloud service checklist for academic communities and customization for genome medical research. Hum Genome Var 2022; 9:36. [PMID: 36253343 PMCID: PMC9574824 DOI: 10.1038/s41439-022-00214-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 08/26/2022] [Accepted: 08/29/2022] [Indexed: 01/11/2023] Open
Abstract
In this paper, we present a cloud service checklist designed to help IT administrators or researchers in academic organizations select the most suitable cloud services. This checklist, which comprises items that we believe IT administrators or researchers in academic organizations should consider when they adopt cloud services, comprehensively covers the issues related to a variety of cloud services, including security, functionality, performance, and law. In response to the increasing demands for storage and computing resources in genome medical science communities, various guidelines for using resources operated by external organizations, such as cloud services, have been published by different academic funding agencies and the Japanese government. However, it is sometimes difficult to identify the checklist items that satisfy the genome medical science community's guidelines, and some of these requirements are not included in the existing checklists. This issue provided our motivation for creating a cloud service checklist customized for genome medical research communities. The resulting customized checklist is designed to help researchers easily find information about the cloud services that satisfy the guidelines in genome medical science communities. Additionally, we explore whether many cloud service providers satisfy the requirements or checklist items in the cloud service checklist for genome medical research by evaluating their survey responses.
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Affiliation(s)
- Kumiko Kobayashi
- grid.250343.30000000110185342National Institute of Informatics, Chiyoda-ku, Japan
| | - Hiroshi Yoshida
- grid.250343.30000000110185342National Institute of Informatics, Chiyoda-ku, Japan
| | - Tomoya Tanjo
- grid.288127.60000 0004 0466 9350National Institute of Informatics, National Institute of Genetics, Mishima City, Japan
| | - Kento Aida
- grid.250343.30000000110185342National Institute of Informatics, Chiyoda-ku, Japan
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Lobanov V, Gobet A, Joyce A. Ecosystem-specific microbiota and microbiome databases in the era of big data. ENVIRONMENTAL MICROBIOME 2022; 17:37. [PMID: 35842686 PMCID: PMC9287977 DOI: 10.1186/s40793-022-00433-1] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Accepted: 06/29/2022] [Indexed: 05/05/2023]
Abstract
The rapid development of sequencing methods over the past decades has accelerated both the potential scope and depth of microbiota and microbiome studies. Recent developments in the field have been marked by an expansion away from purely categorical studies towards a greater investigation of community functionality. As in-depth genomic and environmental coverage is often distributed unequally across major taxa and ecosystems, it can be difficult to identify or substantiate relationships within microbial communities. Generic databases containing datasets from diverse ecosystems have opened a new era of data accessibility despite costs in terms of data quality and heterogeneity. This challenge is readily embodied in the integration of meta-omics data alongside habitat-specific standards which help contextualise datasets both in terms of sample processing and background within the ecosystem. A special case of large genomic repositories, ecosystem-specific databases (ES-DB's), have emerged to consolidate and better standardise sample processing and analysis protocols around individual ecosystems under study, allowing independent studies to produce comparable datasets. Here, we provide a comprehensive review of this emerging tool for microbial community analysis in relation to current trends in the field. We focus on the factors leading to the formation of ES-DB's, their comparison to traditional microbial databases, the potential for ES-DB integration with meta-omics platforms, as well as inherent limitations in the applicability of ES-DB's.
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Affiliation(s)
- Victor Lobanov
- Department of Marine Sciences, University of Gothenburg, Box 461, 405 30, Gothenburg, Sweden
| | | | - Alyssa Joyce
- Department of Marine Sciences, University of Gothenburg, Box 461, 405 30, Gothenburg, Sweden.
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7
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Moco V, Cazenave D, Garnier M, Pot M, Marcelino I, Talarmin A, Guyomard-Rabenirina S, Breurec S, Ferdinand S, Dereeper A, Reynaud Y, Couvin D. getSequenceInfo: a suite of tools allowing to get genome sequence information from public repositories. BMC Bioinformatics 2022; 23:268. [PMID: 35804320 PMCID: PMC9264741 DOI: 10.1186/s12859-022-04809-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Accepted: 06/23/2022] [Indexed: 12/02/2022] Open
Abstract
BACKGROUND Biological sequences are increasing rapidly and exponentially worldwide. Nucleotide sequence databases play an important role in providing meaningful genomic information on a variety of biological organisms. RESULTS The getSequenceInfo software tool allows to access sequence information from various public repositories (GenBank, RefSeq, and the European Nucleotide Archive), and is compatible with different operating systems (Linux, MacOS, and Microsoft Windows) in a programmatic way (command line) or as a graphical user interface. getSequenceInfo or gSeqI v1.0 should help users to get some information on queried sequences that could be useful for specific studies (e.g. the country of origin/isolation or the release date of queried sequences). Queries can be made to retrieve sequence data based on a given kingdom and species, or from a given date. This program allows the separation between chromosomes and plasmids (or other genetic elements/components) by arranging each component in a given folder. Some basic statistics are also performed by the program (such as the calculation of GC content for queried assemblies). An empirically designed nucleotide ratio is calculated using nucleotide information in order to tentatively provide a "NucleScore" for studied genome assemblies. Besides the main gSeqI tool, other additional tools have been developed to perform various tasks related to sequence analysis. CONCLUSION The aim of this study is to democratize the use of public repositories in programmatic ways, and to facilitate sequence data analysis in a pedagogical perspective. Output results are available in FASTA, FASTQ, Excel/TSV or HTML formats. The program is freely available at: https://github.com/karubiotools/getSequenceInfo . getSequenceInfo and supplementary tools are partly available through the recently released Galaxy KaruBioNet platform ( http://calamar.univ-ag.fr/c3i/galaxy_karubionet.html ).
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Affiliation(s)
- Vincent Moco
- Unité Transmission, Réservoir et Diversité des Pathogènes, Institut Pasteur de Guadeloupe, Les Abymes, Guadeloupe, France
| | - Damien Cazenave
- Unité Transmission, Réservoir et Diversité des Pathogènes, Institut Pasteur de Guadeloupe, Les Abymes, Guadeloupe, France
| | - Maëlle Garnier
- Unité Transmission, Réservoir et Diversité des Pathogènes, Institut Pasteur de Guadeloupe, Les Abymes, Guadeloupe, France
| | - Matthieu Pot
- Unité Transmission, Réservoir et Diversité des Pathogènes, Institut Pasteur de Guadeloupe, Les Abymes, Guadeloupe, France
| | - Isabel Marcelino
- Unité Transmission, Réservoir et Diversité des Pathogènes, Institut Pasteur de Guadeloupe, Les Abymes, Guadeloupe, France
| | - Antoine Talarmin
- Unité Transmission, Réservoir et Diversité des Pathogènes, Institut Pasteur de Guadeloupe, Les Abymes, Guadeloupe, France
| | - Stéphanie Guyomard-Rabenirina
- Unité Transmission, Réservoir et Diversité des Pathogènes, Institut Pasteur de Guadeloupe, Les Abymes, Guadeloupe, France
| | - Sébastien Breurec
- Unité Transmission, Réservoir et Diversité des Pathogènes, Institut Pasteur de Guadeloupe, Les Abymes, Guadeloupe, France
- Faculté de Médecine Hyacinthe Bastaraud, Université des Antilles, Pointe-à-Pitre, France
- Centre d'Investigation Clinique Antilles Guyane, Inserm CIC 1424, Pointe-à-Pitre, France
| | - Séverine Ferdinand
- Unité Transmission, Réservoir et Diversité des Pathogènes, Institut Pasteur de Guadeloupe, Les Abymes, Guadeloupe, France
| | - Alexis Dereeper
- Unité Transmission, Réservoir et Diversité des Pathogènes, Institut Pasteur de Guadeloupe, Les Abymes, Guadeloupe, France
| | - Yann Reynaud
- Unité Transmission, Réservoir et Diversité des Pathogènes, Institut Pasteur de Guadeloupe, Les Abymes, Guadeloupe, France
| | - David Couvin
- Unité Transmission, Réservoir et Diversité des Pathogènes, Institut Pasteur de Guadeloupe, Les Abymes, Guadeloupe, France.
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8
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Agostinetto G, Bozzi D, Porro D, Casiraghi M, Labra M, Bruno A. SKIOME Project: a curated collection of skin microbiome datasets enriched with study-related metadata. Database (Oxford) 2022; 2022:6586378. [PMID: 35576001 PMCID: PMC9216470 DOI: 10.1093/database/baac033] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Revised: 02/25/2022] [Accepted: 05/09/2022] [Indexed: 04/07/2023]
Abstract
Large amounts of data from microbiome-related studies have been (and are currently being) deposited on international public databases. These datasets represent a valuable resource for the microbiome research community and could serve future researchers interested in integrating multiple datasets into powerful meta-analyses. However, this huge amount of data lacks harmonization and it is far from being completely exploited in its full potential to build a foundation that places microbiome research at the nexus of many subdisciplines within and beyond biology. Thus, it urges the need for data accessibility and reusability, according to findable, accessible, interoperable and reusable (FAIR) principles, as supported by National Microbiome Data Collaborative and FAIR Microbiome. To tackle the challenge of accelerating discovery and advances in skin microbiome research, we collected, integrated and organized existing microbiome data resources from human skin 16S rRNA amplicon-sequencing experiments. We generated a comprehensive collection of datasets, enriched in metadata, and organized this information into data frames ready to be integrated into microbiome research projects and advanced post-processing analyses, such as data science applications (e.g. machine learning). Furthermore, we have created a data retrieval and curation framework built on three different stages to maximize the retrieval of datasets and metadata associated with them. Lastly, we highlighted some caveats regarding metadata retrieval and suggested ways to improve future metadata submissions. Overall, our work resulted in a curated skin microbiome datasets collection accompanied by a state-of-the-art analysis of the last 10 years of the skin microbiome field. Database URL: https://github.com/giuliaago/SKIOMEMetadataRetrieval.
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Affiliation(s)
- Giulia Agostinetto
- *Corresponding author: Giulia Agostinetto. E-mail: and Antonia Bruno. Tel: +0039 0264483413; E-mail:
| | | | - Danilo Porro
- Department of Biotechnology and Biosciences, University of Milano-Bicocca, Piazza della Scienza, 2, Milan 20126, Italy
- Institute of Molecular Bioimaging and Physiology (IBFM), National Research Council (CNR), via Fratelli Cervi, 93, Segrate (MI) 20054, Italy
| | - Maurizio Casiraghi
- Department of Biotechnology and Biosciences, University of Milano-Bicocca, Piazza della Scienza, 2, Milan 20126, Italy
| | - Massimo Labra
- Department of Biotechnology and Biosciences, University of Milano-Bicocca, Piazza della Scienza, 2, Milan 20126, Italy
| | - Antonia Bruno
- *Corresponding author: Giulia Agostinetto. E-mail: and Antonia Bruno. Tel: +0039 0264483413; E-mail:
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9
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Konishi T. Continuous mutation of SARS-CoV-2 during migration via three routes at the beginning of the pandemic. PeerJ 2022; 10:e12681. [PMID: 35378929 PMCID: PMC8976469 DOI: 10.7717/peerj.12681] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Accepted: 12/02/2021] [Indexed: 01/11/2023] Open
Abstract
Background It remains unclear how severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection started, spread worldwide, and mutated to result in the present variants. This difficulty can be attributed to the limitations associated with the analytical methodology for presenting the differences among genomic sequences. In this study, we critically analysed the early data to explain the start and spread of the pandemic. Methods Objective analyses of the RNA sequences of earlier variants of SARS-CoV-2 (up to September 1, 2020, available in DDBJ and GISAID) were performed using Principal Component Analysis (PCA). The results were compared with information on the collection dates and location. The PCA was also conducted for 12 variants of interest to the WHO as of September 2021, and compared with earlier data. Results The pandemic began in Wuhan, China. This strain was suspected to be related to other reported animal viruses; however, they had a minimal similarity. The strain then spreads via three routes while accumulating mutations. Several viral subgroups were identified along the routes, each with a large number of patients reported, indicating high infectivity to humans. These routes were only confirmed by the early data analysis, because newer variants would have more mutations, and would be preferentially be examined by PCA if they were included. On the original axes found in the early variants, the newer variants revealed that they retained previously acquired mutations, which helped to reveal the viral ancestors of the newer variants. The rate of mutation was found to be comparable to that of the influenza H1N1 virus, which causes recurrent seasonal epidemics. Another threat imposed by SARS-CoV-2 is that if the pandemic cannot be contained, new variants may emerge annually, preventing herd immunity.
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Affiliation(s)
- Tomokazu Konishi
- Graduate school of Bioresource Sciences, Akita Prefectural University, Akita, Japan
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10
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Fukushima A, Takahashi M, Nagasaki H, Aono Y, Kobayashi M, Kusano M, Saito K, Kobayashi N, Arita M. Development of RIKEN Plant Metabolome MetaDatabase. PLANT & CELL PHYSIOLOGY 2022; 63:433-440. [PMID: 34918130 PMCID: PMC8917833 DOI: 10.1093/pcp/pcab173] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/05/2021] [Revised: 11/15/2021] [Accepted: 12/16/2021] [Indexed: 06/14/2023]
Abstract
The advancement of metabolomics in terms of techniques for measuring small molecules has enabled the rapid detection and quantification of numerous cellular metabolites. Metabolomic data provide new opportunities to gain a deeper understanding of plant metabolism that can improve the health of both plants and humans that consume them. Although major public repositories for general metabolomic data have been established, the community still has shortcomings related to data sharing, especially in terms of data reanalysis, reusability and reproducibility. To address these issues, we developed the RIKEN Plant Metabolome MetaDatabase (RIKEN PMM, http://metabobank.riken.jp/pmm/db/plantMetabolomics), which stores mass spectrometry-based (e.g. gas chromatography-MS-based) metabolite profiling data of plants together with their detailed, structured experimental metadata, including sampling and experimental procedures. Our metadata are described as Linked Open Data based on the Resource Description Framework using standardized and controlled vocabularies, such as the Metabolomics Standards Initiative Ontology, which are to be integrated with various life and biomedical science data using the World Wide Web. RIKEN PMM implements intuitive and interactive operations for plant metabolome data, including raw data (netCDF format), mass spectra (NIST MSP format) and metabolite annotations. The feature is suitable not only for biologists who are interested in metabolomic phenotypes, but also for researchers who would like to investigate life science in general through plant metabolomic approaches.
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Affiliation(s)
- Atsushi Fukushima
- Metabolome Informatics Research Team, RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro, Tsurumi, Yokohama, Kanagawa 230-0045, Japan
| | - Mikiko Takahashi
- Metabolome Informatics Research Team, RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro, Tsurumi, Yokohama, Kanagawa 230-0045, Japan
| | - Hideki Nagasaki
- Metabolome Informatics Research Team, RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro, Tsurumi, Yokohama, Kanagawa 230-0045, Japan
| | - Yusuke Aono
- Degree Programs in Life and Earth Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8572, Japan
| | - Makoto Kobayashi
- Metabolome Informatics Research Team, RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro, Tsurumi, Yokohama, Kanagawa 230-0045, Japan
| | - Miyako Kusano
- Metabolome Informatics Research Team, RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro, Tsurumi, Yokohama, Kanagawa 230-0045, Japan
- Faculty of Life and Environmental Science, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8572, Japan
- Tsukuba Plant Innovation Research Center, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8572, Japan
| | - Kazuki Saito
- Metabolome Informatics Research Team, RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro, Tsurumi, Yokohama, Kanagawa 230-0045, Japan
| | - Norio Kobayashi
- Metabolome Informatics Research Team, RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro, Tsurumi, Yokohama, Kanagawa 230-0045, Japan
- Data Knowledge Organization Unit, RIKEN Information R&D and Strategy Headquarters, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan
| | - Masanori Arita
- Metabolome Informatics Research Team, RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro, Tsurumi, Yokohama, Kanagawa 230-0045, Japan
- Bioinformation and DDBJ Center, National Institute of Genetics, Yata 1111, Mishima, Shizuoka 411-8540, Japan
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11
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Music of metagenomics-a review of its applications, analysis pipeline, and associated tools. Funct Integr Genomics 2021; 22:3-26. [PMID: 34657989 DOI: 10.1007/s10142-021-00810-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Revised: 09/25/2021] [Accepted: 10/03/2021] [Indexed: 10/20/2022]
Abstract
This humble effort highlights the intricate details of metagenomics in a simple, poetic, and rhythmic way. The paper enforces the significance of the research area, provides details about major analytical methods, examines the taxonomy and assembly of genomes, emphasizes some tools, and concludes by celebrating the richness of the ecosystem populated by the "metagenome."
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12
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Philip M, Chen T, Tyagi S. A Survey of Current Resources to Study lncRNA-Protein Interactions. Noncoding RNA 2021; 7:ncrna7020033. [PMID: 34201302 PMCID: PMC8293367 DOI: 10.3390/ncrna7020033] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Revised: 05/28/2021] [Accepted: 06/07/2021] [Indexed: 12/15/2022] Open
Abstract
Phenotypes are driven by regulated gene expression, which in turn are mediated by complex interactions between diverse biological molecules. Protein-DNA interactions such as histone and transcription factor binding are well studied, along with RNA-RNA interactions in short RNA silencing of genes. In contrast, lncRNA-protein interaction (LPI) mechanisms are comparatively unknown, likely directed by the difficulties in studying LPI. However, LPI are emerging as key interactions in epigenetic mechanisms, playing a role in development and disease. Their importance is further highlighted by their conservation across kingdoms. Hence, interest in LPI research is increasing. We therefore review the current state of the art in lncRNA-protein interactions. We specifically surveyed recent computational methods and databases which researchers can exploit for LPI investigation. We discovered that algorithm development is heavily reliant on a few generic databases containing curated LPI information. Additionally, these databases house information at gene-level as opposed to transcript-level annotations. We show that early methods predict LPI using molecular docking, have limited scope and are slow, creating a data processing bottleneck. Recently, machine learning has become the strategy of choice in LPI prediction, likely due to the rapid growth in machine learning infrastructure and expertise. While many of these methods have notable limitations, machine learning is expected to be the basis of modern LPI prediction algorithms.
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Affiliation(s)
- Melcy Philip
- School of Biological Sciences, Monash University, 25 Rainforest Walk, Clayton, VIC 3800, Australia; (M.P.); (T.C.)
| | - Tyrone Chen
- School of Biological Sciences, Monash University, 25 Rainforest Walk, Clayton, VIC 3800, Australia; (M.P.); (T.C.)
| | - Sonika Tyagi
- School of Biological Sciences, Monash University, 25 Rainforest Walk, Clayton, VIC 3800, Australia; (M.P.); (T.C.)
- Monash eResearch Centre, Monash University, Clayton, VIC 3800, Australia
- Department of Infectious Disease, Monash University (Alfred Campus), 85 Commercial Road, Melbourne, VIC 3004, Australia
- Correspondence:
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13
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Mukherjee P, Burgio G, Heitlinger E. Dual RNA Sequencing Meta-analysis in Plasmodium Infection Identifies Host-Parasite Interactions. mSystems 2021; 6:e00182-21. [PMID: 33879496 PMCID: PMC8546971 DOI: 10.1128/msystems.00182-21] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Accepted: 03/04/2021] [Indexed: 11/20/2022] Open
Abstract
Dual RNA sequencing (RNA-Seq) is the simultaneous transcriptomic analysis of interacting symbionts, for example, in malaria. Potential cross-species interactions identified by correlated gene expression might highlight interlinked signaling, metabolic, or gene regulatory pathways in addition to physically interacting proteins. Often, malaria studies address one of the interacting organisms-host or parasite-rendering the other "contamination." Here we perform a meta-analysis using such studies for cross-species expression analysis. We screened experiments for gene expression from host and Plasmodium. Out of 171 studies in Homo sapiens, Macaca mulatta, and Mus musculus, we identified 63 potential studies containing host and parasite data. While 16 studies (1,950 samples) explicitly performed dual RNA-Seq, 47 (1,398 samples) originally focused on one organism. We found 915 experimental replicates from 20 blood studies to be suitable for coexpression analysis and used orthologs for meta-analysis across different host-parasite systems. Centrality metrics from the derived gene expression networks correlated with gene essentiality in the parasites. We found indications of host immune response to elements of the Plasmodium protein degradation system, an antimalarial drug target. We identified well-studied immune responses in the host with our coexpression networks, as our approach recovers known broad processes interlinked between hosts and parasites in addition to individual host and parasite protein associations. The set of core interactions represents commonalities between human malaria and its model systems for prioritization in laboratory experiments. Our approach might also allow insights into the transferability of model systems for different pathways in malaria studies.IMPORTANCE Malaria still causes about 400,000 deaths a year and is one of the most studied infectious diseases. The disease is studied in mice and monkeys as lab models to derive potential therapeutic intervention in human malaria. Interactions between Plasmodium spp. and its hosts are either conserved across different host-parasite systems or idiosyncratic to those systems. Here we use correlation of gene expression from different RNA-Seq studies to infer common host-parasite interactions across human, mouse, and monkey studies. First, we find a set of very conserved interactors, worth further scrutiny in focused laboratory experiments. Second, this work might help assess to which extent experiments and knowledge on different pathways can be transferred from models to humans for potential therapy.
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Affiliation(s)
- Parnika Mukherjee
- Department of Molecular Parasitology, Humboldt University, Berlin, Germany
- Research Group Ecology and Evolution of Molecular Parasite-Host Interactions, Leibniz-Institute for Zoo and Wildlife Research (IZW), Berlin, Germany
- Department of Immunology and Infectious Diseases, John Curtin School of Medical Research, Australian National University, Canberra, ACT, Australia
| | - Gaétan Burgio
- Department of Immunology and Infectious Diseases, John Curtin School of Medical Research, Australian National University, Canberra, ACT, Australia
| | - Emanuel Heitlinger
- Department of Molecular Parasitology, Humboldt University, Berlin, Germany
- Research Group Ecology and Evolution of Molecular Parasite-Host Interactions, Leibniz-Institute for Zoo and Wildlife Research (IZW), Berlin, Germany
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14
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Dada N, Jupatanakul N, Minard G, Short SM, Akorli J, Villegas LM. Considerations for mosquito microbiome research from the Mosquito Microbiome Consortium. MICROBIOME 2021; 9:36. [PMID: 33522965 PMCID: PMC7849159 DOI: 10.1186/s40168-020-00987-7] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/03/2020] [Accepted: 12/28/2020] [Indexed: 05/17/2023]
Abstract
In the past decade, there has been increasing interest in mosquito microbiome research, leading to large amounts of data on different mosquito species, with various underlying physiological characteristics, and from diverse geographical locations. However, guidelines and standardized methods for conducting mosquito microbiome research are lacking. To streamline methods in mosquito microbiome research and optimize data quality, reproducibility, and comparability, as well as facilitate data curation in a centralized location, we are establishing the Mosquito Microbiome Consortium, a collaborative initiative for the advancement of mosquito microbiome research. Our overall goal is to collectively work on unraveling the role of the mosquito microbiome in mosquito biology, while critically evaluating its potential for mosquito-borne disease control. This perspective serves to introduce the consortium and invite broader participation. It highlights the issues we view as most pressing to the community and proposes guidelines for conducting mosquito microbiome research. We focus on four broad areas in this piece: (1) sampling/experimental design for field, semi-field, or laboratory studies; (2) metadata collection; (3) sample processing, sequencing, and use of appropriate controls; and (4) data handling and analysis. We finally summarize current challenges and highlight future directions in mosquito microbiome research. We hope that this piece will spark discussions around this area of disease vector biology, as well as encourage careful considerations in the design and implementation of mosquito microbiome research. Video Abstract.
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Affiliation(s)
- Nsa Dada
- Faculty of Science and Technology, Norwegian University of Life Sciences, Ås, Norway.
| | - Natapong Jupatanakul
- Protein-Ligand Engineering and Molecular Biology Research Team, National Center for Genetic Engineering and Biotechnology, Khlong Neung, Thailand
| | - Guillaume Minard
- Univ Lyon, Université Claude Bernard Lyon 1, CNRS, INRAE, VetAgro Sup, UMR Ecologie Microbienne, F-69622, Villeurbanne, France
| | - Sarah M Short
- Department of Entomology, The Ohio State University, Columbus, USA
| | - Jewelna Akorli
- Department of Parasitology, Noguchi Memorial Institute for Medical Research, University of Ghana, Accra, Ghana
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15
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Arita M, Karsch-Mizrachi I, Cochrane G. The international nucleotide sequence database collaboration. Nucleic Acids Res 2021; 49:D121-D124. [PMID: 33166387 PMCID: PMC7778961 DOI: 10.1093/nar/gkaa967] [Citation(s) in RCA: 64] [Impact Index Per Article: 21.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Revised: 10/07/2020] [Accepted: 10/09/2020] [Indexed: 12/20/2022] Open
Abstract
The International Nucleotide Sequence Database Collaboration (INSDC; http://www.insdc.org/) has been the core infrastructure for collecting and providing nucleotide sequence data and metadata for >30 years. Three partner organizations, the DNA Data Bank of Japan (DDBJ) at the National Institute of Genetics in Mishima, Japan; the European Nucleotide Archive (ENA) at the European Molecular Biology Laboratory's European Bioinformatics Institute (EMBL-EBI) in Hinxton, UK; and GenBank at National Center for Biotechnology Information (NCBI), National Library of Medicine, National Institutes of Health in Bethesda, Maryland, USA have been collaboratively maintaining the INSDC for the benefit of not only science but all types of community worldwide.
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Affiliation(s)
- Masanori Arita
- Bioinformation and DDBJ Center, National Institute of Genetics, Mishima, Shizuoka 411-8540, Japan
| | - Ilene Karsch-Mizrachi
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA
| | - Guy Cochrane
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
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16
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Fukuda A, Kodama Y, Mashima J, Fujisawa T, Ogasawara O. DDBJ update: streamlining submission and access of human data. Nucleic Acids Res 2021; 49:D71-D75. [PMID: 33156332 PMCID: PMC7779041 DOI: 10.1093/nar/gkaa982] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2020] [Revised: 10/09/2020] [Accepted: 10/12/2020] [Indexed: 01/25/2023] Open
Abstract
The Bioinformation and DDBJ Center (DDBJ Center, https://www.ddbj.nig.ac.jp) provides databases that capture, preserve and disseminate diverse biological data to support research in the life sciences. This center collects nucleotide sequences with annotations, raw sequencing data, and alignment information from high-throughput sequencing platforms, and study and sample information, in collaboration with the National Center for Biotechnology Information (NCBI) and the European Bioinformatics Institute (EBI). This collaborative framework is known as the International Nucleotide Sequence Database Collaboration (INSDC). In collaboration with the National Bioscience Database Center (NBDC), the DDBJ Center also provides a controlled-access database, the Japanese Genotype–phenotype Archive (JGA), which archives and distributes human genotype and phenotype data, requiring authorized access. The NBDC formulates guidelines and policies for sharing human data and reviews data submission and use applications. To streamline all of the processes at NBDC and JGA, we have integrated the two systems by introducing a unified login platform with a group structure in September 2020. In addition to the public databases, the DDBJ Center provides a computer resource, the NIG supercomputer, for domestic researchers to analyze large-scale genomic data. This report describes updates to the services of the DDBJ Center, focusing on the NBDC and JGA system enhancements.
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Affiliation(s)
- Asami Fukuda
- Bioinformation and DDBJ Center, National Institute of Genetics, Mishima, Shizuoka 411-8540, Japan
| | - Yuichi Kodama
- Bioinformation and DDBJ Center, National Institute of Genetics, Mishima, Shizuoka 411-8540, Japan
| | - Jun Mashima
- Bioinformation and DDBJ Center, National Institute of Genetics, Mishima, Shizuoka 411-8540, Japan
| | - Takatomo Fujisawa
- Bioinformation and DDBJ Center, National Institute of Genetics, Mishima, Shizuoka 411-8540, Japan
| | - Osamu Ogasawara
- Bioinformation and DDBJ Center, National Institute of Genetics, Mishima, Shizuoka 411-8540, Japan
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17
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Sayers EW, Beck J, Bolton EE, Bourexis D, Brister JR, Canese K, Comeau DC, Funk K, Kim S, Klimke W, Marchler-Bauer A, Landrum M, Lathrop S, Lu Z, Madden TL, O’Leary N, Phan L, Rangwala SH, Schneider VA, Skripchenko Y, Wang J, Ye J, Trawick BW, Pruitt KD, Sherry ST. Database resources of the National Center for Biotechnology Information. Nucleic Acids Res 2021; 49:D10-D17. [PMID: 33095870 PMCID: PMC7778943 DOI: 10.1093/nar/gkaa892] [Citation(s) in RCA: 443] [Impact Index Per Article: 147.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Revised: 09/25/2020] [Accepted: 10/08/2020] [Indexed: 11/14/2022] Open
Abstract
The National Center for Biotechnology Information (NCBI) provides a large suite of online resources for biological information and data, including the GenBank® nucleic acid sequence database and the PubMed® database of citations and abstracts published in life science journals. The Entrez system provides search and retrieval operations for most of these data from 34 distinct databases. The E-utilities serve as the programming interface for the Entrez system. Custom implementations of the BLAST program provide sequence-based searching of many specialized datasets. New resources released in the past year include a new PubMed interface and NCBI datasets. Additional resources that were updated in the past year include PMC, Bookshelf, Genome Data Viewer, SRA, ClinVar, dbSNP, dbVar, Pathogen Detection, BLAST, Primer-BLAST, IgBLAST, iCn3D and PubChem. All of these resources can be accessed through the NCBI home page at https://www.ncbi.nlm.nih.gov.
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Affiliation(s)
- Eric W Sayers
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Jeffrey Beck
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Evan E Bolton
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Devon Bourexis
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - James R Brister
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Kathi Canese
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Donald C Comeau
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Kathryn Funk
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Sunghwan Kim
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - William Klimke
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Aron Marchler-Bauer
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Melissa Landrum
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Stacy Lathrop
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Zhiyong Lu
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Thomas L Madden
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Nuala O’Leary
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Lon Phan
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Sanjida H Rangwala
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Valerie A Schneider
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Yuri Skripchenko
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Jiyao Wang
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Jian Ye
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Barton W Trawick
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Kim D Pruitt
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Stephen T Sherry
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
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18
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Tanjo T, Kawai Y, Tokunaga K, Ogasawara O, Nagasaki M. Practical guide for managing large-scale human genome data in research. J Hum Genet 2021; 66:39-52. [PMID: 33097812 PMCID: PMC7728600 DOI: 10.1038/s10038-020-00862-1] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2020] [Revised: 10/08/2020] [Accepted: 10/11/2020] [Indexed: 12/24/2022]
Abstract
Studies in human genetics deal with a plethora of human genome sequencing data that are generated from specimens as well as available on public domains. With the development of various bioinformatics applications, maintaining the productivity of research, managing human genome data, and analyzing downstream data is essential. This review aims to guide struggling researchers to process and analyze these large-scale genomic data to extract relevant information for improved downstream analyses. Here, we discuss worldwide human genome projects that could be integrated into any data for improved analysis. Obtaining human whole-genome sequencing data from both data stores and processes is costly; therefore, we focus on the development of data format and software that manipulate whole-genome sequencing. Once the sequencing is complete and its format and data processing tools are selected, a computational platform is required. For the platform, we describe a multi-cloud strategy that balances between cost, performance, and customizability. A good quality published research relies on data reproducibility to ensure quality results, reusability for applications to other datasets, as well as scalability for the future increase of datasets. To solve these, we describe several key technologies developed in computer science, including workflow engine. We also discuss the ethical guidelines inevitable for human genomic data analysis that differ from model organisms. Finally, the future ideal perspective of data processing and analysis is summarized.
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Affiliation(s)
- Tomoya Tanjo
- National Institute of Informatics, Tokyo, 101-8430, Japan
| | - Yosuke Kawai
- Genome Medical Science Project, National Center for Global Health and Medicine, Tokyo, 162-8655, Japan
| | - Katsushi Tokunaga
- Genome Medical Science Project, National Center for Global Health and Medicine, Tokyo, 162-8655, Japan
| | - Osamu Ogasawara
- The Bioinformation and DDBJ Center, National Institute of Genetics, Mishima, Shizuoka, 411-8540, Japan.
| | - Masao Nagasaki
- Center for the Promotion of Interdisciplinary Education and Research, Kyoto University, Sakyo-ku, Kyoto, 606-8507, Japan.
- Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Sakyo-ku, Kyoto, 606-8507, Japan.
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19
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Konishi T. Principal component analysis of coronaviruses reveals their diversity and seasonal and pandemic potential. PLoS One 2020; 15:e0242954. [PMID: 33270726 PMCID: PMC7714145 DOI: 10.1371/journal.pone.0242954] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Accepted: 11/12/2020] [Indexed: 11/19/2022] Open
Abstract
Coronaviruses and influenza viruses have similarities and differences. In order to comprehensively compare them, their genome sequencing data were examined by principal component analysis. Coronaviruses had fewer variations than a subclass of influenza viruses. In addition, differences among coronaviruses that infect a variety of hosts were also small. These characteristics may have facilitated the infection of different hosts. Although many of the coronaviruses were conservative, those repeatedly found among humans showed annual changes. If SARS-CoV-2 changes its genome like the Influenza H type, it will repeatedly spread every few years. In addition, the coronavirus family has many other candidates for new pandemics.
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Affiliation(s)
- Tomokazu Konishi
- Faculty of Bioresource Sciences, Akita Prefectural University, Akita, Japan
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20
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Stevens I, Mukarram AK, Hörtenhuber M, Meehan TF, Rung J, Daub CO. Ten simple rules for annotating sequencing experiments. PLoS Comput Biol 2020; 16:e1008260. [PMID: 33017400 PMCID: PMC7535046 DOI: 10.1371/journal.pcbi.1008260] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Affiliation(s)
- Irene Stevens
- Department of Biosciences and Nutrition, Karolinska Institutet, Huddinge, Sweden
- Science for Life Laboratory, Karolinska Institutet, Stockholm, Sweden
- * E-mail:
| | - Abdul Kadir Mukarram
- Department of Biosciences and Nutrition, Karolinska Institutet, Huddinge, Sweden
| | - Matthias Hörtenhuber
- Department of Biosciences and Nutrition, Karolinska Institutet, Huddinge, Sweden
| | - Terrence F. Meehan
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom
| | - Johan Rung
- Science for Life Laboratory, Karolinska Institutet, Stockholm, Sweden
- Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden
| | - Carsten O. Daub
- Department of Biosciences and Nutrition, Karolinska Institutet, Huddinge, Sweden
- Science for Life Laboratory, Karolinska Institutet, Stockholm, Sweden
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21
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Wang X, Liu Z, Li X, Li D, Cai J, Yan H. SPDB: a specialized database and web-based analysis platform for swine pathogens. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2020; 2020:5881264. [PMID: 32761141 PMCID: PMC7409514 DOI: 10.1093/database/baaa063] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/31/2020] [Revised: 05/07/2020] [Accepted: 07/10/2020] [Indexed: 11/12/2022]
Abstract
The rapid and accurate diagnosis of swine diseases is indispensable for reducing their negative impacts on the pork industry. Next-generation sequencing (NGS) is a promising diagnostic tool for swine diseases. To support the application of NGS in the diagnosis of swine disease, we established the Swine Pathogen Database (SPDB). The SPDB represents the first comprehensive and highly specialized database and analysis platform for swine pathogens. The current version features an online genome search tool, which now contains 26 148 genomes of swine, swine pathogens and phylogenetically related species. This database offers a comprehensive bioinformatics analysis pipeline for the identification of 4403 swine pathogens and their related species in clinical samples, based on targeted 16S rRNA gene sequencing and metagenomic NGS data. The SPDB provides a powerful and user-friendly service for veterinarians and researchers to support the applications of NGS in swine disease research. Database URL: http://spdatabase.com:2080/.
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Affiliation(s)
- Xiaoru Wang
- School of Food Science and Engineering, South China University of Technology, 381 Wushan Road, Tianhe District, Guangzhou, Guangdong Province 510641, China
| | - Zongbao Liu
- Institute for Advanced Study, Shenzhen University, No. 3688, Nanhai Boulevard, Nanshan District, Shenzhen, Guangdong Province 518061, China
| | - Xiaoying Li
- School of Food Science and Engineering, South China University of Technology, 381 Wushan Road, Tianhe District, Guangzhou, Guangdong Province 510641, China
| | - Danwei Li
- School of Food Science and Engineering, South China University of Technology, 381 Wushan Road, Tianhe District, Guangzhou, Guangdong Province 510641, China
| | - Jiayu Cai
- School of Food Science and Engineering, South China University of Technology, 381 Wushan Road, Tianhe District, Guangzhou, Guangdong Province 510641, China
| | - He Yan
- School of Food Science and Engineering, South China University of Technology, 381 Wushan Road, Tianhe District, Guangzhou, Guangdong Province 510641, China
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