Meetings
BioHackSWAT4HCLS 2025
BioHackathon Europe 2024
3rd BioHackathon Germany
DBCLS BioHackathon 2024
ELIXIR INTOXICOM
Recent preprints
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BioHackEU22 Report for Project 16: Make your own or favourite software available on your cluster with EasyBuild/EESSI
EasyBuild is a community effort to develop a software build and installation framework that allows you to manage (scientific) software on High Performance Computing (HPC) systems in an efficient way. As its name suggests, EasyBuild makes software installation easy by automating builds, making previous builds reproducible, resolving dependencies, and retaining logs for traceability. It is also one of the components of the European Environment for Scientific Software Installations (EESSI), a collaboration between different European HPC sites and industry partners, with the common goal to set up a shared repository of scientific software installations that can be used on a variety of operating systems and computer architectures. It can be applied in a full size HPC cluster, a cloud environment, a container or a personal workstation.With the deluge of data in the genomics field (e.g., clinical data) and the concomitant development of new technologies, the number of data analysis software has exploded in recent years. The fields of bioinformatics and cheminformatics follow this same trend with ever more developments to optimize and parallelize analyses. The bioinformatics field is now the main provider of new software in EasyBuild. Developers of those tools are not always professional developers, and they do therefore not always follow best practices when releasing their software. As a result, many tools are complicated to install, making them ideal candidates for porting their installation to EasyBuild so that they become more easily accessible to end users.We propose to introduce users to EasyBuild and EESSI, and to port new software to EasyBuild/EESSI (e.g., the participant’s own or favourite software), thereby making it available and discoverable to the entire EasyBuild community. In parallel we would like to build bridges between EESSI and Galaxy to make the scientific software more accessible to researchers in the domain. -
Validating Subtype Specific Oncology Drug Predictions
There is an impressive number of data and code reproducibility initiatives, both within Europe and across the world. To motivate researchers to use this amazing infrastructure, we must show the translational research community that the aforementioned initiatives are able to drive change in translational science. Here we demonstrate that using public datasets, it is reasonable to build a pipeline for proposal and validation of driver mutation and subtype-specific colorectal cancer medications. While all three molecular, clinical and chemical name harmonization were necessary, open data and code initiatives, while varied in their approaches, made this project possible. -
BioHackEU22 Project 22: Plant data exchange and standard interoperability
Status of discussions around the topic of data standard formats for plant sciences and their interoperability at the BioHackathon Europe 2022 in Paris. -
Metadata for BioHackrXiv Markdown publications
biohackrxiv.org is a scholarly publication service forBioHackathons and Codefests where papers are generated from Markdowntemplates where the header is a YAML/JSON record that includes thetitle, authors, affiliations and tags. Many projects in BioHackathons are about using FAIR data. Because the current setup is lacking in the findable (F) andaccessible (A) of FAIR, for the ELIXIR BioHackathon 2020, we decidedto add an additional service that provides a SPARQL endpoint forqueries and some simple HTML output that can be embedded in aBioHackathon website. -
Mapping OHDSI OMOP Common Data Model and GA4GH Phenopackets for COVID-19 disease epidemics and analytics
The COVID-19 crisis demonstrates a critical requirement for rapid and efficient sharing of data to facilitate the global response to this and future pandemics. Our project aims are to enhance interoperability between health and research data by mapping Phenopackets and OMOP schemas, and representing COVID-19 metadata using the FAIR principles to enable discovery, integration and analysis of genotypic and phenotypic data. Here, we present our outcomes after one week of BioHacking together 17 participants (10 new to the project), from different countries (CH, US and in EU), and continents. -
An ETL pipeline to construct the Intrinsically Disordered Proteins Knowledge Graph (IDP-KG) using Bioschemas JSON-LD data dumps
Schema.org and Bioschemas are lightweight vocabularies that aim at making the contents of web pages machine-readable so that software agents can consume that content and understand it in an actionable way. Due to the time needed to process each page, extracting markup by visiting each page of a site is not practical for huge sites. This approach imposes processing requirements on the publisher and the consumer. The Schema.org community proposed a method for exchanging markup from various pages as a DataFeed published at a recognized address in February 2022. This would ease publisher and customer processing requirements and accelerate data collection. In this work, we report on the implementation of a JSON-LD consumer ETL (Extract-Transform-Load) pipeline that enables data dumps to be ingested into knowledge graphs (KG). The pipeline loads scraped JSON-LD from the three sources, converts it to RDF, applies SPARQL construct queries to map the source RDF to a unified Bioschemas-based model and stores the resulting KG as a turtle file. This work was conducted during the one-week Biohackathion Europe 2022 in Paris France, under Project 23 titled, “Publishing and Consuming Schema.org DataFeeds.” -
Enhancement and Reusage of Biomedical Knowledge Graph Subsets
Knowledge Graphs (KGs) such as Wikidata act as a hub of information from multiple domains and disciplines, and is crowdsourced by multiple stakeholders. The vast amount of available information makes it difficult for researchers to manage the entire KG, which is also continually being edited. It is necessary to develop tools that extract subsets for domains of interest. These subsets will help researchers to reduce costs and time, making data of interest more accessible. In the last two BioHackathons (BH20, BH21), we have created prototypes to extract subsets easily applicable to Wikidata, as well as to define a map of the different approaches used to tackle this problem. Building on those outcomes, we aim to enhance subsetting in both definitions using Entity schemas based on Shape Expressions (ShEx) and extraction algorithms, with a special focus on the biomedical domain. Our first aim is to develop complex subsetting patterns based on qualifiers and references for enhancing credibility of datasets. Our second aim is to establish a faster subsetting extraction platform applying new algorithms based on Apache Spark and new tools like a document-oriented DBMS platform.