MOTIVATION: Integrated analysis across biological databases is becoming increasingly important in life science research, leading many public databases to adopt Semantic Web technologies, also known as knowledge graphs. However, biological data possesses inherently complex and diverse structures, which makes the resulting Resource Description Framework (RDF) schemas intricate and difficult for non-expert users to master, preventing them from translating natural language questions into correct SPARQL queries. Although recent large language model (LLM)-based approaches show potential for automatic SPARQL query generation, they often suffer from structural hallucinations and require large-scale training data to capture schema-specific structures. In this study, we propose a novel framework that avoids hallucinations and requires no training data by combining LLM-based word extraction with a schema-based SPARQL query builder. RESULTS: The LLM extracts variables and parameters from the user's question based on a predefined schema, and the query builder generates a syntactically correct SPARQL query accordingly. By providing a predefined schema in prompts, our method eliminates the need for training data. Experimental results on UniProt, Rhea, and Bgee demonstrate that our method outperforms baseline LLM-based methods using fine-tuning and prompt-tuning in terms of the similarity between search results obtained from generated and expert-written queries. Furthermore, we developed a proof-of-concept chatbot system that enables users to query RDF databases via natural language input, demonstrating the practical utility of our approach in improving access to biological data resources. AVAILABILITY AND IMPLEMENTATION: Experimental environment: https://github.com/scott2121/sparql_query_generator (DOI: https://doi.org/10.5281/zenodo.18539213). Chatbot: https://github.com/scott2121/sparql_query_chatbot (DOI: https://doi.org/10.5281/zenodo.18539225).

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.

Scientific conferences are one of the most common venues for researchers and others to present and exchange new findings.In recent years, "unconferences"-i.e., meetings that promote spontaneous discussions rather than predetermined presentations-have emerged as a more collaborative approach.Unconferences differ from conferences in key ways: while conferences have a predefined set of presenters together with an audience, unconferences promote more collaborative and spontaneous interactions across participants [1].A "hackathon" is a special kind of unconference in which people come together to state, discuss, and solve problems by means of collaborative brainstorming, modeling, design, coding, testing, and documenting [2].Despite the "hack" portion in the term, hackathons welcome not only software developers but anyone involved in creating solutions that can be later consumed or exposed via software.In addition to collaboration, hackathons promote community development around a subject used as the main hackathon topic.Such a topic can correspond to a knowledge domain (e.g., semantics or genomics) or be related to a particular organization (e.g., data and data services offered).There are hackathon-like events that are called by other names, such as "codefests."In this paper, we include such events under the umbrella term "hackathon."Hackathons are especially useful in bringing together interdisciplinary sets of domain experts and specialized computer scientists with various degrees of experience and skills to "hack" solutions related to scientific topics of mutual interest.While traditional conferences focus more on transferring knowledge, hackathons are more about collaboratively generating solutions.The interactions often lead to productive collaborations, professional development opportunities, and a network of resources.Well-run hackathons are very effective at building a community among participants.Hackathons provide an opportunity for researchers and developers to interact and brainstorm with other participants in an appropriate environment to accelerate collaborations on topics of mutual benefit.In addition, they can provide a unique opportunity to think through a problem, without the usual distractions.Therefore, hackathons can be very productive and result in a major impact on the targeted topic and/or community [3].However, organizing a successful large-scale hackathon takes significant time and effort; e.g., organizing committees for the National Bioscience Database Center (NBDC) [4]/Database Center for Life Science (DBCLS) [5] and the ELIXIR Europe BioHackathons start approximately 1 year in advance.We use the term BioHackathon to refer to those hackathons addressing problems in domains related to biomedical and life sciences.BioHackathons are recognized as having a

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.

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.

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: [email protected].

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/.

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: [email protected]