International Nucleotide Sequence Database Research Articles

Guidelines for Gene and Genome Assembly Nomenclature.

The rapid increase in the number of reference-quality genome assemblies presents significant new opportunities for genomic research. However, the absence of standardized naming conventions for genome assemblies and annotations across datasets creates substantial challenges. Inconsistent naming hinders the identification of correct assemblies, complicates the integration of bioinformatics pipelines, and makes it difficult to link assemblies across multiple resources. To address this, we developed a specification for standardizing the naming of reference genome assemblies, to improve consistency across datasets and facilitate interoperability. This specification was created with FAIR (Findable, Accessible, Interoperable, and Reusable) practices in mind, ensuring that reference assemblies are easier to locate, access, and reuse across research communities. Additionally, it has been designed to comply with primary genomic data repositories, including members of the International Nucleotide Sequence Database Collaboration (INSDC) consortium, ensuring compatibility with widely used databases. While initially tailored to the agricultural genomics community, the specification is adaptable for use across different taxa. Widespread adoption of this standardized nomenclature would streamline assembly management, better enable cross-species analyses, and improve the reproducibility of research. It would also enhance natural language processing applications that depend on consistent reference assembly names in genomic literature, promoting greater integration and automated analysis of genomic data. This is a good time to consider more consistent genomic data nomenclature as many research communities and data resources are now finding themselves juggling multiple datasets from multiple data providers.

The international nucleotide sequence database collaboration (INSDC): enhancing global participation.

The members of the International Nucleotide Sequence Database Collaboration (INSDC; https://insdc.org) have built systems to collect, archive and disseminate sequence data for more than four decades. The three collaborating organizations, the National Library of Medicine, National Center for Biotechnology Information (NLM-NCBI) in the UnitedStates, Research Organization of Information and Systems, National Institute of Genetics (ROIS-NIG) in Japan; and the European Molecular Biology Laboratory-European Bioinformatics Institute (EMBL-EBI) formalized their relationship through the adoption of an arrangement which documents their commitment to free and open access to genomic sequences. The INSDC is committed to expand the collaboration to be more representative of the global community of sequences and users. Diversifying participation through new membership will advance open science and data sharing and, in turn, drive innovation. This expansion will additionally benefit the INSDC and its broad user base by providing additional diverse perspectives as it explores emerging areas of data management, including federation, attribution and management.

Infection status of Baylisascaris transfuga in captive Asiatic black bears (Ursus thibetanus) from three bile farms in South Korea, with molecular analyses

Bears harbor various endoparasites that pose risks to humans and other animals. However, information on parasitic infections in Asiatic black bears in South Korea (Korea) is limited, and potential hazards still exist because of the close contact between bears and humans on bile farms. The present study was conducted to assess the gastrointestinal parasite infection status in captive Asiatic black bears (Ursus thibetanus) at bile farms in Korea. Additionally, we aimed to characterize the molecular aspects of Baylisascaris transfuga (Nematoda: Ascarididae), the sole species investigated throughout the study. Fecal examinations were performed on a total of 84 fecal samples collected from three bear farms in Hwacheon-gun (gun = County) and Donghae-si (si = City), Gangwon-do (do = Province), and Dangjin-si, Chungcheongnam-do. Eggs were identified morphologically, and an adult worm expelled in the feces was analyzed molecularly using the partial cytochrome c oxidase subunit 1 and 2 (cox1 and cox2) gene markers. The prevalence of helminthic eggs was 10.7 %, identified as B. transfuga based on morphological characteristics. The eggs, which were oval and brown with thick shells, had a mean length of 87.3 ± 8.9 μm on the long axis and 70.7 ± 6.1 μm on the short axis (n = 30). An adult female roundworm found in the feces of one individual, measuring 27 cm in length and 0.4 cm in width, was also identified as same species through genetic methods. Herein, we described the prevalence of B. transfuga in captive bears in Korea and its molecular characteristics for the first time. Additionally, we reviewed the sequences of the genus Baylisascaris available in the International Nucleotide Sequence Database Collection, noting that many of these sequences often have ambiguous or potentially incorrect identifications, complicating the understanding of their phylogenetic status. This investigation is part of disease screening efforts aimed at enhancing the welfare of captive bears prior to their imminent relocation to sanctuaries. Health monitoring and endoparasite control in farmed bears should be carried out in both veterinary and public health contexts.

Ruminant microbiome data are skewed and unFAIR, undermining their usefulness for sustainable production improvement

The ruminant microbiome plays a key role in the health, feed utilization and environmental impact of ruminant production systems. Microbiome research provides insights to reduce the environmental footprint and improve meat and milk production from ruminants. However, the microbiome composition depends on the ruminant species, habitat and diet, highlighting the importance of having a good representation of ruminant microbiomes in their local environment to translate research findings into beneficial approaches. This information is currently lacking. In this study, we examined the metadata of farmed ruminant microbiome studies to determine global representativeness and summarized information by ruminant species, geographic location, body site, and host information. We accessed data from the International Nucleotide Sequence Database Collaboration via the National Center for Biotechnology Information database. We retrieved 47,628 sample metadata, with cattle accounting for more than two-thirds of the samples. In contrast, goats, which have a similar global population to cattle, were underrepresented with less than 4% of the total samples. Most samples originated in Western Europe, North America, Australasia and China but countries with large ruminant populations in South America, Africa, Asia, and Eastern Europe were underrepresented. Microbiomes from the gastrointestinal tract were the most frequently studied, comprising about 87% of all samples. Additionally, the number of samples from other body sites such as the respiratory tract, milk, skin, reproductive tract, and fetal tissue, has markedly increased over the past decade. More than 40% of the samples lacked basic information and many were retrieved from generic taxonomic classifications where the ruminant species was manually recovered. The lack of basic information such as age, breed or sex can limit the reusability of the data for further analysis and follow-up studies. This requires correct taxonomic assignment of the ruminant host and basic metadata information using accepted ontologies adapted to host-associated microbiomes. Repositories should require this information as a condition of acceptance. The results of this survey highlight the need to encourage studies of the ruminant microbiome from underrepresented ruminant species and countries worldwide. This shortfall in information poses a challenge for the development of microbiome-based strategies to meet sustainability requirements, particularly in areas with expanding livestock production systems.

Taxonomy Identifiers (TaxId) for Biodiversity Genomics: a guide to getting TaxId for submission of data to public databases.

Biodiversity genomics critically depends on correct taxonomic identification of the sample from which data are derived. Tracking of that taxonomic information through systems that archive data and report on genome sequencing efforts. For submission of data to the International Nucleotide Sequence Database Collaboration (INSDC) databases (DNA DataBank of Japan [DDBJ], European Nucleotide Archive [ENA] and National Center for Biotechnology Information [NCBI]), samples and data derived from them must be assigned a species-level NCBI Taxonomy taxonomic identifier (TaxId, sometimes referred to as taxId or txid). We thus need to be able to identify the TaxId for a target species efficiently. Because the NCBI Taxonomy does not include all known species and cannot preemptively represent unknown taxa, we also need an efficient process for generating new TaxIds for species not yet listed. This document provides workflows for different kinds of TaxId acquisition scenarios and was created to guide users in these processes. Although developed for European projects such as Darwin Tree of Life and the European Reference Genome Atlas, the workflows are universally applicable and describe the use of ENA in resolving taxonomic issues. Too Long: Didn't Read (TL;DR): Use the ENA REST API programmatically to retrieve TaxIds for target species and confirm that sequence data can be submitted to those TaxIds.Use the NCBI Web interface to NCBI Taxonomy to identify potential homotypic synonyms.Request a new TaxId from ENA for a species not yet in NCBI Taxonomy, and for species-like entries for which the full Linnaean binomen is not determined (see https://ena-docs.readthedocs.io/en/latest/faq/taxonomy_requests.html#creating-taxon-requests).Discuss directly with the NCBI Taxonomy curators or the curators at ENA and NCBI whenever you think there is an opportunity to improve their database.

Best Practice for Publishing Environmental DNA (eDNA) Data According to FAIR Principles

Reversing global biodiversity loss will require transformational human actions and robust measurements of their effectiveness. Diversity assessment using environmental DNA (eDNA) has emerged as a cutting-edge technique with the potential to address the challenges of measuring biodiversity. Vast amounts of eDNA sequences and eDNA-based species detections are generated in scientific studies. These datasets are typically stored in a variety of different repositories in multiple formats, hindering their reuse (Berry et al. 2020). Ensuring the publication of eDNA data following the FAIR (Findable, Accessible, Interoperable, Reusable) principles (Wilkinson et al. 2016) would revolutionise environmental assessment, including monitoring of biodiversity, individual species, and interactions across extensive spatial and temporal scales, and generate critical knowledge for evidence-based management. Archiving FAIR eDNA data requires standardising data formats and vocabularies, cyberinfrastructures, guidelines, data sharing policy, and collaboration among scientists and institutions. Some of these requirements are addressed by existing data standards and infrastructures, including Darwin Core (DwC) (Wieczorek et al. 2012), Minimum Information about any (x) Sequence (MIxS) (Yilmaz et al. 2011), the Global Biodiversity Information Facility (GBIF) network, and International Nucleotide Sequence Database Collaboration (INSDC) partners (Arita et al. 2020). However, multiple challenges remain and FAIR data practices have yet to be established among the eDNA community. This is partly because critical attributes unique to eDNA data are not adequately accommodated by existing standards. For example, monitoring contamination and excluding non-target taxa, and the parameters used for quality filtering and species detection vary greatly between studies, depending on the study scopes and the associated financial and ecological costs of incorrectly inferring presence or absence. Making such information FAIR is needed for future studies reusing data and requiring high confidence levels in species detection and taxonomic assignment. Furthermore, the procedures of targeted-taxon detection approach (e.g., interpretations of quantitative polymerase chain reaction (qPCR) results in detecting the presence of DNA from individual taxa) have not yet been fully captured by existing standards. Increasing efforts have been made to establish minimum reporting requirements to validate eDNA study methods and data (Klymus et al. 2020, Thalinger et al. 2021). These requirements need further development to be translated into data standards and formats to enhance machine readability and reusability, and to support and guide the eDNA community, so that they are effectively utilised. In this talk, we share our best practice guide for formatting and publishing eDNA data, developed by an international multidisciplinary working-group comprising eDNA researchers, journal editors, and biodiversity and omics data scientists. We identified required data types, formats and metadata checklists through reviewing and integrating existing data standards, devising subject-specific vocabularies, and introducing additional terms to accommodate the distinctive properties of eDNA data. Implementing the FAIR eDNA data best practice guide, offers a pivotal step towards standardising and enhancing the publication and re-use of eDNA data.

DDBJ update in 2024: the DDBJ Group Cloud service for sharing pre-publication data.

The Bioinformation and DNA Data Bank of Japan Center (DDBJ Center, https://www.ddbj.nig.ac.jp) provides public databases that cover a wide range of fields in life sciences. As a founding member of the International Nucleotide Sequence Database Collaboration (INSDC), the DDBJ Center accepts and distributes nucleotide sequence data ranging from raw reads to assembled and annotated sequences with the National Center for Biotechnology Information and the European Bioinformatics Institute. Besides INSDC databases, the DDBJ Center provides databases for functional genomics (Genomic Expression Archive), metabolomics (MetaboBank), human genetic variations (TogoVar-repository) and human genetic and phenotypic data (Japanese Genotype-phenotype Archive). These database systems have been built on the National Institute of Genetics supercomputer, which is also a platform for the DDBJ Group Cloud (DGC) services for sharing and analysis of pre-publication data among research groups. This paper reports recent updates on the databases and the services of the DDBJ Center, highlighting the DGC service.

Aspects of NCBI GenBank as a Biodiversity Information Resource

DNA sequencing of museum specimens, also known as museomics, provides new insights into the study of biodiversity, including taxonomy, phylogeny, and environmental studies. Also, sequencing specimens have led to the rediscovery of extinct species (Suzuki et al. 2016), identification of related species (Waku et al. 2016), and analysis of ancient DNA (Kanzawa-Kiriyama et al. 2016). Nucleotide sequence data have been collected for more than 30 years under the framework of the International Nucleotide Sequence Database Collaboration (INSDC) by three institutes, namely, National Center for Biotechnology Information, US (NCBI), European Bioinformatics Institute (EBI), and DNA Data Bank of Japan (DDBJ) (Arita et al. 2020). NCBI has collated a database of sequence data, GenBank, which contains approximately 494 million sequences as of April 2022 (Sayers et al. 2021). In fact, GenBank is designed with qualifiers to describe various types of biodiversity information such as "/specimen_voucher", "/lat_lon" (latitude and longitude) and "/collection_date". Also, INSDC now requires that all submissions include the sampling location and date (INSDC 2023). I surveyed the biodiversity information assigned to GenBank records to determine the potential of GenBank as a biodiversity resource. I downloaded all GenBank data as of August 2023 from the FTP site. The “/specimen_voucher” qualifier was introduced to describe specimen ID in Release 104 in December 1997. This qualifier was designed to fill the value in free text: for example, /specimen_voucher="Smith s. n. 4-IV-1995 (U. S. Natl. Herbarium)". After Release 162 in October 2007, a method of writing with a structured value of "[<institution-code>: [<collection-code>:]] <specimen_id>" was added (institution-code and collection-code are optional). There are 527,215 records (37.8%) with "/specimen_voucher" qualifier for fish, 3,096,112 records (40.3%) for insects, 1,505,556 records (39.0%) for flowering plants. But fewer than 10% of records have specimen IDs listed using this structured description. To utilize these ambiguous specimen IDs in GenBank, these IDs may need to be cleansed using databases such as NCBI BioCollections, GRSciColl (Global Registry of Scientific Collections) or AI to map them to IDs in databases rich in specimen information such as those of the Global Biodiveristy Information Facility (GBIF) and Barcode of Life System (BOLD). In GenBank, the BOLD ID is listed in the /db_xref qualifier in the “Features” field as the ID of the external database. The 70% of insect sequence data with a specimen ID in the /specimen_voucher qualifier are also assigned a BOLD ID (Nakazato and Jinbo 2022). The correspondence between specimen IDs in biodiversity information databases such as GBIF and specimen IDs in GenBank is expected to further enhance the value of museum specimens. In addition, GenBank provides the /type_material qualifier for describing the type of voucher (e.g., holotype of Asphondylia bursicola). In GenBank insect data, there were over 2,000 records for type material, and approximately 450 species were mentioned, including 269 for holotypes. We found approximately 3,000 records with type information by including “/notes” and “/specimens_voucher” qualifiers in addition to “/type_material”. Thus, GenBank has potential as a biodiversity information resource, but for more effective use, data mining and linkage with other specimen-based biodiversity databases are essential.

Bioinformatics Goes Viral: I. Databases, Phylogenetics and Phylodynamics Tools for Boosting Virus Research.

Computer-aided analysis of proteins or nucleic acids seems like a matter of course nowadays; however, the history of Bioinformatics and Computational Biology is quite recent. The advent of high-throughput sequencing has led to the production of "big data", which has also affected the field of virology. The collaboration between the communities of bioinformaticians and virologists already started a few decades ago and it was strongly enhanced by the recent SARS-CoV-2 pandemics. In this article, which is the first in a series on how bioinformatics can enhance virus research, we show that highly useful information is retrievable from selected general and dedicated databases. Indeed, an enormous amount of information-both in terms of nucleotide/protein sequences and their annotation-is deposited in the general databases of international organisations participating in the International Nucleotide Sequence Database Collaboration (INSDC). However, more and more virus-specific databases have been established and are progressively enriched with the contents and features reported in this article. Since viruses are intracellular obligate parasites, a special focus is given to host-pathogen protein-protein interaction databases. Finally, we illustrate several phylogenetic and phylodynamic tools, combining information on algorithms and features with practical information on how to use them and case studies that validate their usefulness. Databases and tools for functional inference will be covered in the next article of this series: Bioinformatics goes viral: II. Sequence-based and structure-based functional analyses for boosting virus research.

METAPLANTCODE: Harmonizing Plant Metabarcoding Pipelines in Europe

The METAPLANTCODE project is dedicated to advancing and optimizing pan-European case studies on metabarcoding. The project's objectives include providing best practice recommendations, optimizing analysis pipelines for species identification, and creating user-friendly reference databases. To accomplish these objectives, METAPLANTCODE will identify and address gaps in current methodologies, publish best practice documents on FAIR (Findable, Accessible, Interoperable, Reusable) data publishing for plant metabarcode data to GBIF (Global Biodiversity Information Facility) and the INSDC (International Nucleotide Sequence Database Collaboration), and implement ELIXIR-compatible multimodal deep learning (DL) models in novel tools for standalone metabarcoding analyses using various data sources. A significant focus of the project is enhancing species identification accuracy through GBIF records and metadata. This involves mapping regional, national, and international botanical taxonomic checklists, red lists, and floras to the Catalogue of Life (COL) via the COL ChecklistBank. Additionally, taxonomic and floristic literature will be semantically enriched with new entity recognition and relationship extraction modules, supporting the enhanced identification of species through domain-specific descriptive and phenotypic features. An interface will link taxonomic names to treatments, identify homonyms and synonyms, and facilitate the conversion and annotation of floras, red lists, and ecological treatments. All METAPLANTCODE products will adhere to FAIR standards by the project's end. The project emphasizes knowledge transfer from the outset, engaging with associated partners and stakeholders. Key stakeholders will be identified, priorities set, and communication channels established, monitored, and adjusted as necessary. Efforts to enhance stakeholder engagement, training, and outreach will ensure that plant metabarcoding becomes a routine standard for biodiversity monitoring in Europe and beyond. Deep Learning for Plant Metabarcoding Within the METAPLANTCODE project, our team is tasked with improving taxonomic precision by integrating deep learning on metabarcoding data and metadata. Previous studies have demonstrated the applicability of deep learning to non-plant barcoding data and its computational efficiency compared to traditional bioinformatics approaches (Flück et al. 2022). Deep Learning Models for Metabarcoding Data Our approach involves evaluating the efficacy of several deep learning models—such as Convolutional Neural Networks (CNN)(LeCun et al. 2015), Transformer models (Vaswani et al. 2017), Hyena (Poli et al. 2023), and Mamba architectures (Gu et al. 2023)—on plant barcoding datasets. Preliminary results will be presented, highlighting the application of these models and the proposed ensemble method (Mohammed and Kora 2023), which combines multiple barcode sequence representations and learning strategies. The ensemble approach, when integrated with classical machine learning models such as logistic regression and Support Vector Machines (SVM) (Noble 2006), is anticipated to offer improved precision and robustness compared to individual model applications (Fig. 1). Multimodal Refinement of Predictions In the subsequent phase, we aim to refine genetic sequence classifications by employing a multimodal strategy. This approach will integrate genetic information with traditional botanical knowledge. We will utilize biological interaction lists (e.g., species-species, species-habitat) provided by the METAPLANTCODE project to train a large language model (LLM) on relevant scientific literature. This LLM, specifically tailored for plant biodiversity, will incorporate metadata associated with genetic samples (including location, temporality, and climatic conditions). By merging embeddings of both metadata and genetic data, we aim to enhance the accuracy of taxonomic predictions (Fig. 2). Conclusion Through this research, we aim to develop an effective method for integrating genetic data with textual information from various sources. We anticipate that this approach will not only enhance plant metabarcoding but also be applicable to other barcoding fields, such as bacteria, fish, fungi, and more. Additionally, we expect this methodology to find broader applications in genomic research, providing valuable insights and improvements across diverse biological disciplines.

EDNAqua-Plan—Standardisation Overview for eDNA Sequencing of Aquatic Organisms and the Downstream Data Ecosystem

The eDNAqua-Plan project stands as a beacon of innovation in the biomonitoring of marine and freshwater ecosystems, propelled by the urgent need to integrate DNA-based approaches in aquatic bioassessment and monitoring frameworks. The broad utilisation of cutting-edge environmental DNA (eDNA) and DNA barcoding methodologies is dependent on complete, reliable, and accessible reference DNA sequence data (Rimet et al. 2021). Complete and interoperable metadata is crucial to allow a broad reuse of (e)DNA data and analysis outputs, and for a broader uptake of results by end users. The eDNAqua-Plan project aims to address key limitations to the routine implementation of eDNA-based monitoring methods in Europe by developing plans for federated DNA barcode reference libraries and eDNA data repositories to support DNA-based environmental monitoring. This will ensure a sustainable and reliable infrastructure to underpin its broad use, thereby paving the way for more effective conservation and management strategies. The project is working towards creating a comprehensive overview of standardisation efforts and data workflows, through collaborations with other projects, initiatives and infrastructures for aquatic monitoring across the European Union (EU) and associated countries. We are analysing existing archives (e.g., International Nucleotide Sequence Database Collaboration (INSDC), Barcode of Life Data System (BOLD), Global Biodiversity Information Facility (GBIF), Ocean Biodiversity Information System (OBIS)), portals, and papers to determine current and best practices through the use of questionnaires, manual evaluation of repositories, and machine learning methods (LLMs). This includes an overview of the usage of existing metadata and data standards (e.g., Minimum Information about any (X) Sequence Specifications from the Genomics Standards Consortium (GSC), Darwin Core standard). The results are being integrated by a team of experts in marine and freshwater biomonitoring. With a diverse consortium comprising 18 partner institutions from 11 countries and one international institute, eDNAqua-Plan brings together experts in marine and freshwater monitoring, eDNA analysis, and data science. The collective effort by this consortium will lay the groundwork for the creation of a digital ecosystem of eDNA repositories and an integrated reference library of marine and freshwater species, adhering to FAIR (Findable, Accessible, Interoperable, and Reusable) principles.

Shared clusters between phylogenetic trees for genomic segments of Rotavirus A with distinct genotype constellations

Rotavirus A (RVA) possesses a genome of 11-segmented, double-stranded RNAs, each of which is classified into genotypes. A variety of genotype constellations have been generated through reassortment, but reassortment does not appear to occur randomly. Here genomic sequences for 281 RVA strains with distinct genotype constellations retrieved from the International Nucleotide Sequence Database were analyzed to identify shared clusters between phylogenetic trees for genomic segments. The largest numbers of shared clusters were observed between the genomic segments encoding viral protein 1 (VP1) and VP2 as well as VP2 and VP3, suggesting that not only VP1 but also VP3 may interact with VP2 inside the core. Larger numbers were also associated with the segment encoding non-structural protein 5/6 (NSP5/6), which may be a hub for bundling genomic segments. Although VP7 and VP4 segments were associated with smaller numbers, reassortments between these segments as well as VP7 and VP6 segments appeared to be restricted due to interactions in constructing the virion. For VP4 segment, the number with NSP1 segment was significantly greater than those with other segments, possibly reflecting that specificities in receptor binding and interferon antagonism may define host range restriction. Overall, no cluster was shared by all genomic segments, supporting that RVA constituted a single species.

Molnupiravir increases SARS-CoV-2 genome diversity and complexity: A case-control cohort study.

Molnupiravir, an oral direct-acting antiviral effective in vitro against SARS-CoV-2, has been largely employed during the COVID-19 pandemic, since December 2021. After marketing and widespread usage, a progressive increase in SARS-CoV-2 lineages characterized by a higher transition/transversion ratio, a characteristic signature of molnupiravir action, appeared in the Global Initiative on Sharing All Influenza Data(GISAID) and International Nucleotide Sequence Database Collaboration(INSDC) databases. Here, we assessed the drug effects by SARS-CoV-2 whole-genome sequencing on 38 molnupiravir-treated persistently positive COVID-19 outpatients tested before and after treatment. Seventeen tixagevimab/cilgavimab-treated outpatients served as controls. Mutational analyses confirmed that SARS-CoV-2 exhibits an increased transition/transversion ratio seven days after initiation of molnupiravir. Moreover we observed an increased G->A ratio compared to controls, which was not related to apolipoprotein B mRNAediting enzyme, catalytic polypeptide-like (APOBEC) activity. In addition, we demonstrated for the first time an increased diversity and complexity of the viral quasispecies.

KoNA: Korean Nucleotide Archive as A New Data Repository for Nucleotide Sequence Data.

During the last decade, the generation and accumulation of petabase-scale high-throughput sequencing data have resulted in great challenges, including access to human data, as well as transfer, storage, and sharing of enormous amounts of data. To promote data-driven biological research, the Korean government announced that all biological data generated from government-funded research projects should be deposited at the Korea BioData Station (K-BDS), which consists of multiple databases for individual data types. Here, we introduce the Korean Nucleotide Archive (KoNA), a repository of nucleotide sequence data. As of July 2022, the Korean Read Archive in KoNA has collected over 477 TB of raw next-generation sequencing data from national genome projects. To ensure data quality and prepare for international alignment, a standard operating procedure was adopted, which is similar to that of the International Nucleotide Sequence Database Collaboration. The standard operating procedure includes quality control processes for submitted data and metadata using an automated pipeline, followed by manual examination. To ensure fast and stable data transfer, a high-speed transmission system called GBox is used in KoNA. Furthermore, the data uploaded to or downloaded from KoNA through GBox can be readily processed using a cloud computing service called Bio-Express. This seamless coupling of KoNA, GBox, and Bio-Express enhances the data experience, including submission, access, and analysis of raw nucleotide sequences. KoNA not only satisfies the unmet needs for a national sequence repository in Korea but also provides datasets to researchers globally and contributes to advances in genomics. The KoNA is available at https://www.kobic.re.kr/kona/.

Classification of sapoviruses based on comparison of phylogenetic trees for structural and non-structural proteins

Sapoviruses (SaVs) compose the Sapovirus genus in the Caliciviridae family. In the genome of SaVs, the junction of coding regions for the non-structural proteins (NSs) and the major structural protein (viral protein 1: VP1) is known as the recombination hotspot. SaVs have been divided into genogroups GI–GXIX based on the phylogenetic relationship of amino acid sequences for VP1. Here, phylogenetic analyses were conducted for NSs and VP1 of 326 SaV strains retrieved from the International Nucleotide Sequence Database. Shared clusters were identified between the phylogenetic trees, and genogroups of SaVs were classified into 10 shared cluster groups, which may represent the monophyletic recombining groups of SaVs. All of human GI, GII, GIV, and GV strains were classified into the same shared cluster group. Continuous surveillance may be warranted for monitoring emergence of intra-genogroup and inter-genogroup recombinants in human SaVs.

Putting everything in its place: using the INSDC compliant Pathogen Data Object Model to better structure genomic data submitted for public health applications.

Fast, efficient public health actions require well-organized and coordinated systems that can supply timely and accurate knowledge. Public databases of pathogen genomic data, such as the International Nucleotide Sequence Database Collaboration (INSDC), have become essential tools for efficient public health decisions. However, these international resources began primarily for academic purposes, rather than for surveillance or interventions. Now, queries need to access not only the whole genomes of multiple pathogens but also make connections using robust contextual metadata to identify issues of public health relevance. Databases that over time developed a patchwork of submission formats and requirements need to be consistently organized and coordinated internationally to allow effective searches.To help resolve these issues, we propose a common pathogen data structure called the Pathogen Data Object Model (DOM) that will formalize the minimum pieces of sequence data and contextual data necessary for general public health uses, while recognizing that submitters will likely withhold a wide range of non-public contextual data. Further, we propose contributors use the Pathogen DOM for all pathogen submissions (bacterial, viral, fungal, and parasites), which will simplify data submissions and provide a consistent and transparent data structure for downstream data analyses. We also highlight how improved submission tools can support the Pathogen DOM, offering users additional easy-to-use methods to ensure this structure is followed.

DDBJ update in 2023: the MetaboBank for metabolomics data and associated metadata.

The Bioinformation and DNA Data Bank of Japan (DDBJ) Center (https://www.ddbj.nig.ac.jp) provides database archives that cover a wide range of fields in life sciences. As a founding member of the International Nucleotide Sequence Database Collaboration (INSDC), DDBJ accepts and distributes nucleotide sequence data as well as their study and sample information along with the National Center for Biotechnology Information in the United States and the European Bioinformatics Institute (EBI). Besides INSDC databases, the DDBJ Center provides databases for functional genomics (GEA: Genomic Expression Archive), metabolomics (MetaboBank) and human genetic and phenotypic data (JGA: Japanese Genotype-phenotype Archive). These database systems have been built on the National Institute of Genetics (NIG) supercomputer, which is also open for domestic life science researchers to analyze large-scale sequence data. This paper reports recent updates on the archival databases and the services of the DDBJ Center, highlighting the newly redesigned MetaboBank. MetaboBank uses BioProject and BioSample in its metadata description making it suitable for multi-omics large studies. Its collaboration with MetaboLights at EBI brings synergy in locating and reusing public data.

Improving the gold standard in NCBI GenBank and related databases: DNA sequences from type specimens and type strains.

Scientific names permit humans and search engines to access knowledge about the biodiversity that surrounds us, and names linked to DNA sequences are playing an ever-greater role in search-and-match identification procedures. Here, we analyze how users and curators of the National Center for Biotechnology Information (NCBI) are flagging and curating sequences derived from nomenclatural type material, which is the only way to improve the quality of DNA-based identification in the long run. For prokaryotes, 18,281 genome assemblies from type strains have been curated by NCBI staff and improve the quality of prokaryote naming. For Fungi, type-derived sequences representing over 21,000 species are now essential for fungus naming and identification. For the remaining eukaryotes, however, the numbers of sequences identifiable as type-derived are minuscule, representing only 739 species of arthropods, 1542 vertebrates, and 125 embryophytes. An increase in the production and curation of such sequences will come from (i) sequencing of types or topotypic specimens in museum collections, (ii) the March 2023 rule changes at the International Nucleotide Sequence Database Collaboration requiring more metadata for specimens, and (iii) efforts by data submitters to facilitate curation, including informing NCBI curators about a specimen's type status. We illustrate different type-data submission journeys and provide best-practice examples from a range of organisms. Expanding the number of type-derived sequences in DNA databases, especially of eukaryotes, is crucial for capturing, documenting, and protecting biodiversity.

The European Nucleotide Archive in 2023.

The European Nucleotide Archive (ENA; https://www.ebi.ac.uk/ena) is maintained by the European Molecular Biology Laboratory's European Bioinformatics Institute (EMBL-EBI). The ENA is one of the three members of the International Nucleotide Sequence Database Collaboration (INSDC). It serves the bioinformatics community worldwide via the submission, processing, archiving and dissemination of sequence data. The ENA supports data types ranging from raw reads, through alignments and assemblies to functional annotation. The data is enriched with contextual information relating to samples and experimental configurations. In this article, we describe recent progress and improvements to ENA services. In particular, we focus upon three areas of work in 2023: FAIRness of ENA data, pandemic preparedness and foundational technology. For FAIRness, we have introduced minimal requirements for spatiotemporal annotation, created a metadata-based classification system, incorporated third party metadata curations with archived records, and developed a new rapid visualisation platform, the ENA Notebooks. For foundational enhancements, we have improved the INSDC data exchange and synchronisation pipelines, and invested in site reliability engineering for ENA infrastructure. In order to support genomic surveillance efforts, we have continued to provide ENA services in support of SARS-CoV-2 data mobilisation and have adapted these for broader pathogen surveillance efforts.

Improving FAIRness of eDNA and Metabarcoding Data: Standards and tools for European Nucleotide Archive data deposition

The advancements in sequencing technologies have promoted the generation of molecular data for cataloguing and describing biodiversity. The analysis of environmental DNA (eDNA) through the application of metabarcoding techniques enables comprehensive descriptions of communities and their function, being fundamental for understanding and preserving biodiversity. Metabarcoding is becoming widely used and standard methods are being generated for a growing range of applications with high scalability. The generated data can be made available in its unprocessed form, as raw data (the sequenced reads) or as interpreted data, including sets of sequences derived after bioinformatics processing (Amplicon Sequence Variants (ASVs) or Operational Taxonomic Units (OTUs)) and occurrence tables (tables that describe the occurrences and abundances of species or OTUs/ASVs). However, for this data to be Findable, Accessible, Interoperable and Reusable (FAIR), and therefore fully available for meaningful interpretation, it needs to be deposited in public repositories together with enriched sample metadata, protocols and analysis workflows (ten Hoopen et al. 2017). Metabarcoding raw data and associated sample metadata is often stored and made available through the International Nucleotide Sequence Database Collaboration (INSDC) archives (Arita et al. 2020), of which the European Nucleotide Archive (ENA, Burgin et al. 2022) is its European database, but it is often deposited with minimal information, which hinders data reusability. Within the scope of the Horizon 2020 project, Biodiversity Community Integrated Knowledge Library (BiCIKL), which is building a community of interconnected data for biodiversity research (Penev et al. 2022), we are working towards improving the standards for molecular ecology data sharing, developing tools to facilitate data deposition and retrieval, and linking between data types. Here we will present the ENA data model, showcasing how metabarcoding data can be shared, while providing enriched metadata, and how this data is linked with existing data in other research infrastructures in the biodiversity domain, such as the Global Biodiversity Information Facility (GBIF), where data is deposited following the guidelines published in Abarenkov et al. (2023). We will also present the results of our recent discussions on standards for this data type and discuss future plans towards continuing to improve data sharing and interoperability for molecular ecology.