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.

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.

Building cloud computing environments for genome analysis in Japan.

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.

Is There a Correlation Between Left Ventricular Outflow Tract Velocity Time Integral and Stroke Volume Index in Patients Undergoing Cardiac Surgery?

Introduction Left ventricular outflow tract velocity time integral (LVOT VTI) is a promising surrogate for stroke volume (SV). However, there is controversy in the literature regarding its correlation with thermodilution or newer cardiac output measurement techniques. This study was conducted to determine the correlation between LVOT VTI determined by transesophageal echocardiography (TEE) with stroke volume index (SVI) calculated by thermodilution. Methods Consecutive patients older than 17 years undergoing elective cardiac surgery with pulmonary artery catheter (PAC) and TEE monitoring between September 2021 and February 2022 were included in this prospective, descriptive, single-center study. LVOT VTI was measured using TEE after induction of anesthesia but before skin incision and at least four hours after initial LVOT VTI measurement. SVI was simultaneously measured using the continuous thermodilution technique with a PAC. The correlation between LVOT VTI and SVI was determined with Pearson's correlation index. Results Twelve patients were included and 21 paired measurements were compared. Mean SVI was 31.62 ± 10.71 mL/m2 and mean LVOT VTI was 14.74 ± 4.79 cm. The Pearson's correlation index for the two measurements was r = 0.257, p = 0.262. Conclusion This prospective study demonstrated a weak correlation between LVOT VTI and SVI in patients undergoing cardiac surgery.

DNA Data Bank of Japan (DDBJ) update report 2021.

The Bioinformation and DDBJ (DNA Data Bank of Japan) Center (DDBJ Center; https://www.ddbj.nig.ac.jp) operates archival databases that collect nucleotide sequences, study and sample information, and distribute them without access restriction to progress life science research as a member of the International Nucleotide Sequence Database Collaboration (INSDC), in collaboration with the National Center for Biotechnology Information (NCBI) and the European Bioinformatics Institute. Besides the INSDC databases, the DDBJ Center also provides the Genomic Expression Archive for functional genomics data and the Japanese Genotype-phenotype Archive for human data requiring controlled access. Additionally, the DDBJ Center started a new public repository, MetaboBank, for experimental raw data and metadata from metabolomics research in October 2020. In response to the COVID-19 pandemic, the DDBJ Center openly shares SARS-CoV-2 genome sequences in collaboration with Shizuoka Prefecture and Keio University. The operation of DDBJ is based on the National Institute of Genetics (NIG) supercomputer, which is open for large-scale sequence data analysis for life science researchers. This paper reports recent updates on the archival databases and the services of DDBJ.

Sapporo: A workflow execution service that encourages the reuse of workflows in various languages in bioinformatics.

The increased demand for efficient computation in data analysis encourages researchers in biomedical science to use workflow systems. Workflow systems, or so-called workflow languages, are used for the description and execution of a set of data analysis steps. Workflow systems increase the productivity of researchers, specifically in fields that use high-throughput DNA sequencing applications, where scalable computation is required. As systems have improved the portability of data analysis workflows, research communities are able to share workflows to reduce the cost of building ordinary analysis procedures. However, having multiple workflow systems in a research field has resulted in the distribution of efforts across different workflow system communities. As each workflow system has its unique characteristics, it is not feasible to learn every single system in order to use publicly shared workflows. Thus, we developed Sapporo, an application to provide a unified layer of workflow execution upon the differences of various workflow systems. Sapporo has two components: an application programming interface (API) that receives the request of a workflow run and a browser-based client for the API. The API follows the Workflow Execution Service API standard proposed by the Global Alliance for Genomics and Health. The current implementation supports the execution of workflows in four languages: Common Workflow Language, Workflow Description Language, Snakemake, and Nextflow. With its extensible and scalable design, Sapporo can support the research community in utilizing valuable resources for data analysis.

Practical guide for managing large-scale human genome data in research.

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.

DDBJ update: streamlining submission and access of human data.

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.

DDBJ Data Analysis Challenge: a machine learning competition to predict Arabidopsis chromatin feature annotations from DNA sequences.

Recently, the prospect of applying machine learning tools for automating the process of annotation analysis of large-scale sequences from next-generation sequencers has raised the interest of researchers. However, finding research collaborators with knowledge of machine learning techniques is difficult for many experimental life scientists. One solution to this problem is to utilise the power of crowdsourcing. In this report, we describe how we investigated the potential of crowdsourced modelling for a life science task by conducting a machine learning competition, the DNA Data Bank of Japan (DDBJ) Data Analysis Challenge. In the challenge, participants predicted chromatin feature annotations from DNA sequences with competing models. The challenge engaged 38 participants, with a cumulative total of 360 model submissions. The performance of the top model resulted in an area under the curve (AUC) score of 0.95. Over the course of the competition, the overall performance of the submitted models improved by an AUC score of 0.30 from the first submitted model. Furthermore, the 1st- and 2nd-ranking models utilised external data such as genomic location and gene annotation information with specific domain knowledge. The effect of incorporating this domain knowledge led to improvements of approximately 5%-9%, as measured by the AUC scores. This report suggests that machine learning competitions will lead to the development of highly accurate machine learning models for use by experimental scientists unfamiliar with the complexities of data science.

Impact of the stress ulcer prophylactic protocol on reducing the unnecessary administration of stress ulcer medications and gastrointestinal bleeding: a single-center, retrospective pre-post study.

BACKGROUND: Clinically significant gastrointestinal bleeding from stress ulcers increases patient mortality in intensive care, and histamine type 2 receptor blockers and proton pump inhibitors as stress ulcer prophylaxes were reported to decrease the incidence of that.Although medical checklists are widely used to maintain high compliance with medications and interventions to improve patient outcome in the intensive care field, the efficacy of medical checklists regarding the incidence of gastrointestinal bleeding and the reduction of unnecessary administration of stress ulcer prophylaxis medications has not been sufficiently explored to date.This study aimed to investigate the incidence of gastrointestinal bleeding and the rate of administering stress ulcer prophylaxis medication before and after setting administration criteria for stress ulcer prophylaxis and introducing a medical checklist for critically ill adults. METHODS: This was a retrospective pre-post study at a single-center, tertiary adult and pediatric mixed ICU. Adult patients (≥ 18 years) who were admitted to the ICU for reasons other than gastrectomy, esophagectomy, pancreatoduodenectomy, and gastrointestinal bleeding were analyzed. A medical checklist and stress ulcer prophylaxis criteria were introduced on December 22, 2014, and the patients were classified into the preintervention group (from September to December 21, 2014) and the postintervention group (from December 22, 2014, to April 2015). The primary outcome was the incidence of upper gastrointestinal bleeding, and the secondary outcome was the proportion administered stress ulcer prophylaxis medications. RESULTS: One hundred adult patients were analyzed. The incidence of upper gastrointestinal bleeding in the pre- and postintervention groups was both 4.0% [95% confidence interval, 0.5-13.7%]. The proportion administered stress ulcer prophylaxis medications decreased from 100 to 38% between the pre- and post-intervention groups. CONCLUSIONS: After the checklist and the criteria were introduced, the administration of stress ulcer prophylaxis medications decreased without an increase in upper gastrointestinal bleeding in critically ill adults. Prospective studies are necessary to evaluate the causal relationship between the introduction of them and gastrointestinal adverse events in critically ill adults.

DDBJ Database updates and computational infrastructure enhancement.

The Bioinformation and DDBJ Center (https://www.ddbj.nig.ac.jp) in the National Institute of Genetics (NIG) maintains a primary nucleotide sequence database as a member of the International Nucleotide Sequence Database Collaboration (INSDC) in partnership with the US National Center for Biotechnology Information and the European Bioinformatics Institute. The NIG operates the NIG supercomputer as a computational basis for the construction of DDBJ databases and as a large-scale computational resource for Japanese biologists and medical researchers. In order to accommodate the rapidly growing amount of deoxyribonucleic acid (DNA) nucleotide sequence data, NIG replaced its supercomputer system, which is designed for big data analysis of genome data, in early 2019. The new system is equipped with 30 PB of DNA data archiving storage; large-scale parallel distributed file systems (13.8 PB in total) and 1.1 PFLOPS computation nodes and graphics processing units (GPUs). Moreover, as a starting point of developing multi-cloud infrastructure of bioinformatics, we have also installed an automatic file transfer system that allows users to prevent data lock-in and to achieve cost/performance balance by exploiting the most suitable environment from among the supercomputer and public clouds for different workloads.

Visualization of Brain Activity in a Neuropathic Pain Model Using Quantitative Activity-Dependent Manganese Magnetic Resonance Imaging.

Human brain imaging studies have revealed several regions that are activated in patients with chronic pain. In rodent brains, functional changes due to chronic pain have not been fully elucidated, as brain imaging techniques such as functional magnetic resonance imaging and positron emission tomography (PET) require the use of anesthesia to suppress movement. Consequently, conclusions derived from existing imaging studies in rodents may not accurately reflect brain activity under awake conditions. In this study, we used quantitative activation-induced manganese-enhanced magnetic resonance imaging to directly capture the previous brain activity of awake mice. We also observed and quantified the brain activity of the spared nerve injury (SNI) neuropathic pain model during awake conditions. SNI-operated mice exhibited a robust decrease of mechanical nociceptive threshold 14 days after nerve injury. Imaging on SNI-operated mice revealed increased neural activity in the limbic system and secondary somatosensory, sensory-motor, piriform, and insular cortex. We present the first study demonstrating a direct measurement of awake neural activity in a neuropathic pain mouse model.

Accumulating computational resource usage of genomic data analysis workflow to optimize cloud computing instance selection.

BACKGROUND: Container virtualization technologies such as Docker are popular in the bioinformatics domain because they improve the portability and reproducibility of software deployment. Along with software packaged in containers, the standardized workflow descriptors Common Workflow Language (CWL) enable data to be easily analyzed on multiple computing environments. These technologies accelerate the use of on-demand cloud computing platforms, which can be scaled according to the quantity of data. However, to optimize the time and budgetary restraints of cloud usage, users must select a suitable instance type that corresponds to the resource requirements of their workflows. RESULTS: We developed CWL-metrics, a utility tool for cwltool (the reference implementation of CWL), to collect runtime metrics of Docker containers and workflow metadata to analyze workflow resource requirements. To demonstrate the use of this tool, we analyzed 7 transcriptome quantification workflows on 6 instance types. The results revealed that choice of instance type can deliver lower financial costs and faster execution times using the required amount of computational resources. CONCLUSIONS: CWL-metrics can generate a summary of resource requirements for workflow executions, which can help users to optimize their use of cloud computing by selecting appropriate instances. The runtime metrics data generated by CWL-metrics can also help users to share workflows between different workflow management frameworks.

DDBJ update: the Genomic Expression Archive (GEA) for functional genomics data.

The Genomic Expression Archive (GEA) for functional genomics data from microarray and high-throughput sequencing experiments has been established at the DNA Data Bank of Japan (DDBJ) Center (https://www.ddbj.nig.ac.jp), which is a member of the International Nucleotide Sequence Database Collaboration (INSDC) with the US National Center for Biotechnology Information and the European Bioinformatics Institute. The DDBJ Center collects nucleotide sequence data and associated biological information from researchers and also services the Japanese Genotype-phenotype Archive (JGA) with the National Bioscience Database Center for collecting human data. To automate the submission process, we have implemented the DDBJ BioSample validator which checks submitted records, auto-corrects their format, and issues error messages and warnings if necessary. The DDBJ Center also operates the NIG supercomputer, prepared for analyzing large-scale genome sequences. We now offer a secure platform specifically to handle personal human genomes. This report describes database activities for INSDC and JGA over the past year, the newly launched GEA, submission, retrieval, and analysis services available in our supercomputer system and their recent developments.

ChIP-Atlas: a data-mining suite powered by full integration of public ChIP-seq data.

We have fully integrated public chromatin chromatin immunoprecipitation sequencing (ChIP-seq) and DNase-seq data (n > 70,000) derived from six representative model organisms (human, mouse, rat, fruit fly, nematode, and budding yeast), and have devised a data-mining platform-designated ChIP-Atlas (http://chip-atlas.org). ChIP-Atlas is able to show alignment and peak-call results for all public ChIP-seq and DNase-seq data archived in the NCBI Sequence Read Archive (SRA), which encompasses data derived from GEO, ArrayExpress, DDBJ, ENCODE, Roadmap Epigenomics, and the scientific literature. All peak-call data are integrated to visualize multiple histone modifications and binding sites of transcriptional regulators (TRs) at given genomic loci. The integrated data can be further analyzed to show TR-gene and TR-TR interactions, as well as to examine enrichment of protein binding for given multiple genomic coordinates or gene names. ChIP-Atlas is superior to other platforms in terms of data number and functionality for data mining across thousands of ChIP-seq experiments, and it provides insight into gene regulatory networks and epigenetic mechanisms.

MACON: a web tool for computing DNA methylation data obtained by the Illumina Infinium Human DNA methylation BeadArray.

AIM: Bioinformatics analysis for Illumina Infinium Human DNA methylation BeadArray is essential, but still remains difficult task for many experimental researchers. We here aimed to develop a browser-accessible bioinformatics tool for analyzing the BeadArray data. MATERIALS & METHODS: The tool was established as an analytical pipeline using R, Perl and Python programming languages. RESULTS: We introduced a method that groups neighboring probes into a genomic block, which facilitated efficient identification of densely methylated/unmethylated regions. The tool, MACON, provided probe filtering, ß-mixture quantile normalization, grouping into genomic blocks, annotation and production of a data subset. CONCLUSION: MACON allows researchers to analyze the BeadArray data using a web browser ( http://epigenome.ncc.go.jp/macon ).

DNA Data Bank of Japan: 30th anniversary.

The DNA Data Bank of Japan (DDBJ) Center (http://www.ddbj.nig.ac.jp) has been providing public data services for 30 years since 1987. We are collecting nucleotide sequence data and associated biological information from researchers as a member of the International Nucleotide Sequence Database Collaboration (INSDC), in collaboration with the US National Center for Biotechnology Information and the European Bioinformatics Institute. The DDBJ Center also services the Japanese Genotype-phenotype Archive (JGA) with the National Bioscience Database Center to collect genotype and phenotype data of human individuals. Here, we outline our database activities for INSDC and JGA over the past year, and introduce submission, retrieval and analysis services running on our supercomputer system and their recent developments. Furthermore, we highlight our responses to the amended Japanese rules for the protection of personal information and the launch of the DDBJ Group Cloud service for sharing pre-publication data among research groups.

RefEx, a reference gene expression dataset as a web tool for the functional analysis of genes.

Gene expression data are exponentially accumulating; thus, the functional annotation of such sequence data from metadata is urgently required. However, life scientists have difficulty utilizing the available data due to its sheer magnitude and complicated access. We have developed a web tool for browsing reference gene expression pattern of mammalian tissues and cell lines measured using different methods, which should facilitate the reuse of the precious data archived in several public databases. The web tool is called Reference Expression dataset (RefEx), and RefEx allows users to search by the gene name, various types of IDs, chromosomal regions in genetic maps, gene family based on InterPro, gene expression patterns, or biological categories based on Gene Ontology. RefEx also provides information about genes with tissue-specific expression, and the relative gene expression values are shown as choropleth maps on 3D human body images from BodyParts3D. Combined with the newly incorporated Functional Annotation of Mammals (FANTOM) dataset, RefEx provides insight regarding the functional interpretation of unfamiliar genes. RefEx is publicly available at http://refex.dbcls.jp/.

DNA Data Bank of Japan.

The DNA Data Bank of Japan (DDBJ) (http://www.ddbj.nig.ac.jp) has been providing public data services for thirty years (since 1987). We are collecting nucleotide sequence data from researchers as a member of the International Nucleotide Sequence Database Collaboration (INSDC, http://www.insdc.org), in collaboration with the US National Center for Biotechnology Information (NCBI) and European Bioinformatics Institute (EBI). The DDBJ Center also services Japanese Genotype-phenotype Archive (JGA), with the National Bioscience Database Center to collect human-subjected data from Japanese researchers. Here, we report our database activities for INSDC and JGA over the past year, and introduce retrieval and analytical services running on our supercomputer system and their recent modifications. Furthermore, with the Database Center for Life Science, the DDBJ Center improves semantic web technologies to integrate and to share biological data, for providing the RDF version of the sequence data.

DNA data bank of Japan (DDBJ) progress report.

The DNA Data Bank of Japan Center (DDBJ Center; http://www.ddbj.nig.ac.jp) maintains and provides public archival, retrieval and analytical services for biological information. The contents of the DDBJ databases are shared with the US National Center for Biotechnology Information (NCBI) and the European Bioinformatics Institute (EBI) within the framework of the International Nucleotide Sequence Database Collaboration (INSDC). Since 2013, the DDBJ Center has been operating the Japanese Genotype-phenotype Archive (JGA) in collaboration with the National Bioscience Database Center (NBDC) in Japan. In addition, the DDBJ Center develops semantic web technologies for data integration and sharing in collaboration with the Database Center for Life Science (DBCLS) in Japan. This paper briefly reports on the activities of the DDBJ Center over the past year including submissions to databases and improvements in our services for data retrieval, analysis, and integration.