TogoID: an exploratory ID converter to bridge biological datasets.

MOTIVATION: Understanding life cannot be accomplished without making full use of biological data, which are scattered across databases of diverse categories in life sciences. To connect such data seamlessly, identifier (ID) conversion plays a key role. However, existing ID conversion services have disadvantages, such as covering only a limited range of biological categories of databases, not keeping up with the updates of the original databases and outputs being hard to interpret in the context of biological relations, especially when converting IDs in multiple steps. RESULTS: TogoID is an ID conversion service implementing unique features with an intuitive web interface and an application programming interface (API) for programmatic access. TogoID currently supports 65 datasets covering various biological categories. TogoID users can perform exploratory multistep conversions to find a path among IDs. To guide the interpretation of biological meanings in the conversions, we crafted an ontology that defines the semantics of the dataset relations. AVAILABILITY AND IMPLEMENTATION: The TogoID service is freely available on the TogoID website (https://togoid.dbcls.jp/) and the API is also provided to allow programmatic access. To encourage developers to add new dataset pairs, the system stores the configurations of pairs at the GitHub repository (https://github.com/togoid/togoid-config) and accepts the request of additional pairs. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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.

Interactive transcriptome analysis of malaria patients and infecting Plasmodium falciparum.

To understand the molecular mechanisms of parasitism in vivo, it is essential to elucidate how the transcriptomes of the human hosts and the infecting parasites affect one another. Here we report the RNA-seq analysis of 116 Indonesian patients infected with the malaria parasite Plasmodium falciparum (Pf). We extracted RNAs from their peripheral blood as a mixture of host and parasite transcripts and mapped the RNA-seq tags to the human and Pf reference genomes to separate the respective tags. We were thus able to simultaneously analyze expression patterns in both humans and parasites. We identified human and parasite genes and pathways that correlated with various clinical data, which may serve as primary targets for drug developments. Of particular importance, we revealed characteristic expression changes in the human innate immune response pathway genes including TLR2 and TICAM2 that correlated with the severity of the malaria infection. We also found a group of transcription regulatory factors, JUND, for example, and signaling molecules, TNFAIP3, for example, that were strongly correlated in the expression patterns of humans and parasites. We also identified several genetic variations in important anti-malaria drug resistance-related genes. Furthermore, we identified the genetic variations which are potentially associated with severe malaria symptoms both in humans and parasites. The newly generated data should collectively lay a unique foundation for understanding variable behaviors of the field malaria parasites, which are far more complex than those observed under laboratory conditions.

The DDBJ Japanese Genotype-phenotype Archive for genetic and phenotypic human data.

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. Since October 2013, DDBJ Center has operated the Japanese Genotype-phenotype Archive (JGA) in collaboration with our partner institute, the National Bioscience Database Center (NBDC) of the Japan Science and Technology Agency. DDBJ Center provides the JGA database system which securely stores genotype and phenotype data collected from individuals whose consent agreements authorize data release only for specific research use. NBDC has established guidelines and policies for sharing human-derived data and reviews data submission and usage requests from researchers. In addition to the JGA project, DDBJ Center develops Semantic Web technologies for data integration and sharing in collaboration with the Database Center for Life Science. This paper describes the overview of the JGA project, updates to the DDBJ databases, and services for data retrieval, analysis and integration.

SPARQL-enabled identifier conversion with Identifiers.org.

MOTIVATION: On the semantic web, in life sciences in particular, data is often distributed via multiple resources. Each of these sources is likely to use their own International Resource Identifier for conceptually the same resource or database record. The lack of correspondence between identifiers introduces a barrier when executing federated SPARQL queries across life science data. RESULTS: We introduce a novel SPARQL-based service to enable on-the-fly integration of life science data. This service uses the identifier patterns defined in the Identifiers.org Registry to generate a plurality of identifier variants, which can then be used to match source identifiers with target identifiers. We demonstrate the utility of this identifier integration approach by answering queries across major producers of life science Linked Data. AVAILABILITY AND IMPLEMENTATION: The SPARQL-based identifier conversion service is available without restriction at http://identifiers.org/services/sparql.

TogoTable: cross-database annotation system using the Resource Description Framework (RDF) data model.

TogoTable (http://togotable.dbcls.jp/) is a web tool that adds user-specified annotations to a table that a user uploads. Annotations are drawn from several biological databases that use the Resource Description Framework (RDF) data model. TogoTable uses database identifiers (IDs) in the table as a query key for searching. RDF data, which form a network called Linked Open Data (LOD), can be searched from SPARQL endpoints using a SPARQL query language. Because TogoTable uses RDF, it can integrate annotations from not only the reference database to which the IDs originally belong, but also externally linked databases via the LOD network. For example, annotations in the Protein Data Bank can be retrieved using GeneID through links provided by the UniProt RDF. Because RDF has been standardized by the World Wide Web Consortium, any database with annotations based on the RDF data model can be easily incorporated into this tool. We believe that TogoTable is a valuable Web tool, particularly for experimental biologists who need to process huge amounts of data such as high-throughput experimental output.

CyanoBase and RhizoBase: databases of manually curated annotations for cyanobacterial and rhizobial genomes.

To understand newly sequenced genomes of closely related species, comprehensively curated reference genome databases are becoming increasingly important. We have extended CyanoBase (http://genome.microbedb.jp/cyanobase), a genome database for cyanobacteria, and newly developed RhizoBase (http://genome.microbedb.jp/rhizobase), a genome database for rhizobia, nitrogen-fixing bacteria associated with leguminous plants. Both databases focus on the representation and reusability of reference genome annotations, which are continuously updated by manual curation. Domain experts have extracted names, products and functions of each gene reported in the literature. To ensure effectiveness of this procedure, we developed the TogoAnnotation system offering a web-based user interface and a uniform storage of annotations for the curators of the CyanoBase and RhizoBase databases. The number of references investigated for CyanoBase increased from 2260 in our previous report to 5285, and for RhizoBase, we perused 1216 references. The results of these intensive annotations are displayed on the GeneView pages of each database. Advanced users can also retrieve this information through the representational state transfer-based web application programming interface in an automated manner.

KEGG OC: a large-scale automatic construction of taxonomy-based ortholog clusters.

The identification of orthologous genes in an increasing number of fully sequenced genomes is a challenging issue in recent genome science. Here we present KEGG OC (http://www.genome.jp/tools/oc/), a novel database of ortholog clusters (OCs). The current version of KEGG OC contains 1 176 030 OCs, obtained by clustering 8 357 175 genes in 2112 complete genomes (153 eukaryotes, 1830 bacteria and 129 archaea). The OCs were constructed by applying the quasi-clique-based clustering method to all possible protein coding genes in all complete genomes, based on their amino acid sequence similarities. It is computationally efficient to calculate OCs, which enables to regularly update the contents. KEGG OC has the following two features: (i) It consists of all complete genomes of a wide variety of organisms from three domains of life, and the number of organisms is the largest among the existing databases; and (ii) It is compatible with the KEGG database by sharing the same sets of genes and identifiers, which leads to seamless integration of OCs with useful components in KEGG such as biological pathways, pathway modules, functional hierarchy, diseases and drugs. The KEGG OC resources are accessible via OC Viewer that provides an interactive visualization of OCs at different taxonomic levels.

Community-driven development for computational biology at Sprints, Hackathons and Codefests.

BACKGROUND: Computational biology comprises a wide range of technologies and approaches. Multiple technologies can be combined to create more powerful workflows if the individuals contributing the data or providing tools for its interpretation can find mutual understanding and consensus. Much conversation and joint investigation are required in order to identify and implement the best approaches. Traditionally, scientific conferences feature talks presenting novel technologies or insights, followed up by informal discussions during coffee breaks. In multi-institution collaborations, in order to reach agreement on implementation details or to transfer deeper insights in a technology and practical skills, a representative of one group typically visits the other. However, this does not scale well when the number of technologies or research groups is large. Conferences have responded to this issue by introducing Birds-of-a-Feather (BoF) sessions, which offer an opportunity for individuals with common interests to intensify their interaction. However, parallel BoF sessions often make it hard for participants to join multiple BoFs and find common ground between the different technologies, and BoFs are generally too short to allow time for participants to program together. RESULTS: This report summarises our experience with computational biology Codefests, Hackathons and Sprints, which are interactive developer meetings. They are structured to reduce the limitations of traditional scientific meetings described above by strengthening the interaction among peers and letting the participants determine the schedule and topics. These meetings are commonly run as loosely scheduled "unconferences" (self-organized identification of participants and topics for meetings) over at least two days, with early introductory talks to welcome and organize contributors, followed by intensive collaborative coding sessions. We summarise some prominent achievements of those meetings and describe differences in how these are organised, how their audience is addressed, and their outreach to their respective communities. CONCLUSIONS: Hackathons, Codefests and Sprints share a stimulating atmosphere that encourages participants to jointly brainstorm and tackle problems of shared interest in a self-driven proactive environment, as well as providing an opportunity for new participants to get involved in collaborative projects.

Biogem: an effective tool-based approach for scaling up open source software development in bioinformatics.

SUMMARY: Biogem provides a software development environment for the Ruby programming language, which encourages community-based software development for bioinformatics while lowering the barrier to entry and encouraging best practices. Biogem, with its targeted modular and decentralized approach, software generator, tools and tight web integration, is an improved general model for scaling up collaborative open source software development in bioinformatics. AVAILABILITY: Biogem and modules are free and are OSS. Biogem runs on all systems that support recent versions of Ruby, including Linux, Mac OS X and Windows. Further information at http://www.biogems.info. A tutorial is available at http://www.biogems.info/howto.html CONTACT: bonnal@ingm.org.

SARS-CoV-2 HaploGraph: visualization of SARS-CoV-2 haplotype spread in Japan.

Since the early phase of the coronavirus disease 2019 (COVID-19) pandemic, a number of research institutes have been sequencing and sharing high-quality severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genomes to trace the route of infection in Japan. To provide insight into the spread of COVID-19, we developed a web platform named SARS-CoV-2 HaploGraph to visualize the emergence timing and geographical transmission of SARS-CoV-2 haplotypes. Using data from the GISAID EpiCoV database as of June 4, 2022, we created a haplotype naming system by determining the ancestral haplotype for each epidemic wave and showed prefecture- or region-specific haplotypes in each of four waves in Japan. The SARS-CoV-2 HaploGraph allows for interactive tracking of virus evolution and of geographical prevalence of haplotypes, and aids in developing effective public health control strategies during the global pandemic. The code and the data used for this study are publicly available at: https://github.com/ktym/covid19/.

The Ruby UCSC API: accessing the UCSC genome database using Ruby.

BACKGROUND: The University of California, Santa Cruz (UCSC) genome database is among the most used sources of genomic annotation in human and other organisms. The database offers an excellent web-based graphical user interface (the UCSC genome browser) and several means for programmatic queries. A simple application programming interface (API) in a scripting language aimed at the biologist was however not yet available. Here, we present the Ruby UCSC API, a library to access the UCSC genome database using Ruby. RESULTS: The API is designed as a BioRuby plug-in and built on the ActiveRecord 3 framework for the object-relational mapping, making writing SQL statements unnecessary. The current version of the API supports databases of all organisms in the UCSC genome database including human, mammals, vertebrates, deuterostomes, insects, nematodes, and yeast.The API uses the bin index-if available-when querying for genomic intervals. The API also supports genomic sequence queries using locally downloaded *.2bit files that are not stored in the official MySQL database. The API is implemented in pure Ruby and is therefore available in different environments and with different Ruby interpreters (including JRuby). CONCLUSIONS: Assisted by the straightforward object-oriented design of Ruby and ActiveRecord, the Ruby UCSC API will facilitate biologists to query the UCSC genome database programmatically. The API is available through the RubyGem system. Source code and documentation are available at https://github.com/misshie/bioruby-ucsc-api/ under the Ruby license. Feedback and help is provided via the website at http://rubyucscapi.userecho.com/.

TogoWS: integrated SOAP and REST APIs for interoperable bioinformatics Web services.

Web services have become widely used in bioinformatics analysis, but there exist incompatibilities in interfaces and data types, which prevent users from making full use of a combination of these services. Therefore, we have developed the TogoWS service to provide an integrated interface with advanced features. In the TogoWS REST (REpresentative State Transfer) API (application programming interface), we introduce a unified access method for major database resources through intuitive URIs that can be used to search, retrieve, parse and convert the database entries. The TogoWS SOAP API resolves compatibility issues found on the server and client-side SOAP implementations. The TogoWS service is freely available at: http://togows.dbcls.jp/.

TogoVar: A comprehensive Japanese genetic variation database.

TogoVar ( https://togovar.org ) is a database that integrates allele frequencies derived from Japanese populations and provides annotations for variant interpretation. First, a scheme to reanalyze individual-level genome sequence data deposited in the Japanese Genotype-phenotype Archive (JGA), a controlled-access database, was established to make allele frequencies publicly available. As more Japanese individual-level genome sequence data are deposited in JGA, the sample size employed in TogoVar is expected to increase, contributing to genetic study as reference data for Japanese populations. Second, public datasets of Japanese and non-Japanese populations were integrated into TogoVar to easily compare allele frequencies in Japanese and other populations. Each variant detected in Japanese populations was assigned a TogoVar ID as a permanent identifier. Third, these variants were annotated with molecular consequence, pathogenicity, and literature information for interpreting and prioritizing variants. Here, we introduce the newly developed TogoVar database that compares allele frequencies among Japanese and non-Japanese populations and describes the integrated annotations.

BioRuby: bioinformatics software for the Ruby programming language.

SUMMARY: The BioRuby software toolkit contains a comprehensive set of free development tools and libraries for bioinformatics and molecular biology, written in the Ruby programming language. BioRuby has components for sequence analysis, pathway analysis, protein modelling and phylogenetic analysis; it supports many widely used data formats and provides easy access to databases, external programs and public web services, including BLAST, KEGG, GenBank, MEDLINE and GO. BioRuby comes with a tutorial, documentation and an interactive environment, which can be used in the shell, and in the web browser. AVAILABILITY: BioRuby is free and open source software, made available under the Ruby license. BioRuby runs on all platforms that support Ruby, including Linux, Mac OS X and Windows. And, with JRuby, BioRuby runs on the Java Virtual Machine. The source code is available from http://www.bioruby.org/. CONTACT: katayama@bioruby.org

Full-Malaria/Parasites and Full-Arthropods: databases of full-length cDNAs of parasites and arthropods, update 2009.

Full-Malaria/Parasites is a database for transcriptome studies of apicomplexa and other parasites, which is based on our original full-length cDNA sequences and physical cDNA clone resources. In this update, the database has been expanded to contain the shogun sequencing for the entire sequences of 14,818 non-redundant full-length cDNA clones from six apicomplexa parasites and 6.8 million of transcription start sites (TSS), both of which had been produced by novel protocols using the oligo-capping method and the Illumina GA sequencer. The former should be the ultimate data for exact annotation of the expressed genes, while the latter should be useful for ultra-deep expression analysis. Furthermore, we have launched Full-Arthropods, a full-length cDNA database for arthropods of medical importance. Full-Arthropods contains 50 343 one-pass sequences, 10 399 shotgun complete sequences and 22.4 million TSS tags in anopheles mosquitoes that transmit malaria, tsetse flies that transmit trypanosomiasis and dust mites that cause allergic dermatitis and bronchial asthma. By providing the largest integrated full-length cDNA data resources in the apicomplexa parasites as well as their vectors, Full-Malaria/Parasites and Full-Arthropods should help combat parasitic diseases. Full-Malaria/Parasites and Full-Arthropods are accessible from http://fullmal.hgc.jp/.

AAindex: amino acid index database, progress report 2008.

AAindex is a database of numerical indices representing various physicochemical and biochemical properties of amino acids and pairs of amino acids. We have added a collection of protein contact potentials to the AAindex as a new section. Accordingly AAindex consists of three sections now: AAindex1 for the amino acid index of 20 numerical values, AAindex2 for the amino acid substitution matrix and AAindex3 for the statistical protein contact potentials. All data are derived from published literature. The database can be accessed through the DBGET/LinkDB system at GenomeNet (http://www.genome.jp/dbget-bin/www_bfind?aaindex) or downloaded by anonymous FTP (ftp://ftp.genome.jp/pub/db/community/aaindex/).