ELIXIR Germany

There is an ongoing explosion of scientific datasets being generated, brought on by recent technological advances in many areas of the natural sciences. As a result, the life sciences have become increasingly computational in nature, and bioinformatics has taken on a central role in research studies. However, basic computational skills, data analysis, and stewardship are still rarely taught in life science educational programs, resulting in a skills gap in many of the researchers tasked with analysing these big datasets. In order to address this skills gap and empower researchers to perform their own data analyses, the Galaxy Training Network (GTN) has previously developed the Galaxy Training Platform (https://training.galaxyproject.org), an open access, community-driven framework for the collection of FAIR (Findable, Accessible, Interoperable, Reusable) training materials for data analysis utilizing the user-friendly Galaxy framework as its primary data analysis platform. Since its inception, this training platform has thrived, with the number of tutorials and contributors growing rapidly, and the range of topics extending beyond life sciences to include topics such as climatology, cheminformatics, and machine learning. While initially aimed at supporting researchers directly, the GTN framework has proven to be an invaluable resource for educators as well. We have focused our efforts in recent years on adding increased support for this growing community of instructors. New features have been added to facilitate the use of the materials in a classroom setting, simplifying the contribution flow for new materials, and have added a set of train-the-trainer lessons. Here, we present the latest developments in the GTN project, aimed at facilitating the use of the Galaxy Training materials by educators, and its usage in different learning environments.

Document detailing ELIXIR Europe's strategy for engagement with the European Open Science Cloud (EOSC) as produced by the ELIXIR-EOSC Focus Group, 2022.

In this article, we present a data dependent acquisition (DDA) dataset which was generated as a reference and ground truth quantitative dataset. While initially used to compare samples measured with DDA and data independent acquisition (DIA) (Barkovits et al., 2020), the presented dataset holds potential value as a benchmark reference for any workflows working on DDA data. The entire dataset consists of 15 LC-MS/MS measurements composed of five distinct spike-in-states, each with three replicates. To generate the data set, a C2C12 (immortalized mouse myoblast) cell lysate was used as a complex background for five different states which were simulated by spiking 13 defined proteins at different concentrations. For this purpose, the cell lysate was used in a constant amount of 20 µg for all samples and different amounts of the 13 selected proteins ranging from 0.1 to 10 pmol were added, reflecting physiological amounts of proteins. Afterwards, all samples were tryptically digested using the same method. From each sample 200 ng tryptic peptides were measured in triplicates on a Q Exactive HF (Thermo Fisher Scientific). The mass range for MS1 was set to 350-1400 m/z with a resolution of 60,000 at 200 m/z. HCD fragmentation of the Top10 abundant precursor ions was performed at 27% NCE. The fragment analysis (MS2) was performed with a resolution of 30,000 at 200 m/z. Additionally to the raw files, the dataset contains centroided mzML files and spectrum identification results for peptide identifications performed by Mascot (Perkins et al., 1999), MS-GF+ (Kim et al., 2010) and X!Tandem (Craig and Beavis, 2004) for each separate MS analysis. The corresponding FASTA containing protein sequences as well as a combination of all identification runs performed by PIA (Uszkoreit et al., 2019, 2015) and a peptide and protein quantification performed by OpenMS (Pfeuffer et al., 2017) is included. All data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository (Perez-Riverol et al., 2018) with the dataset identifier PXD012986.

Metagenomics studies have enabled scientists to analyze the genetic information of natural habitats or even complete ecosystems, including otherwise unculturable microbes. The processing of such datasets, however, remains a challenging task requiring extensive computational resources. MGX 2.0 is a versatile solution for the analysis and interpretation of microbial community sequence data. MGX 2.0 supports the processing of raw metagenomes and metatranscriptomes, but also enables assembly-based strategies, including downstream taxonomic binning, bin quality assessment, abundance quantification, and subsequent annotation coming from a single source. Due to the modular design of MGX, users are able to choose from a wide range of different methods for microbial community sequence data analysis, allowing them to directly compare between read-based and assembly-based approaches or to evaluate different strategies to analyze their data.

MOTIVATION: RNA-protein interactions play essential roles in every living organism, with RNA transcription, processing, and translation being just a few examples. Therefore, determining the set of RNAs that are bound by individual RNA-binding proteins, as well as the precise location of the interaction, is crucial for biological understanding. CLIP (UV-cross-linking and immunoprecipitation) is a method developed to study these interactions. Several variations of the CLIP protocol have been developed, e.g. iCLIP (individual-nucleotide resolution CLIP), which offers nucleotide-precise resolution of the cross-linking event. RESULTS: PARANOiD is a versatile software for fully automated analysis of iCLIP and iCLIP2 data. It contains all steps necessary for preprocessing, the determination of cross-link locations, and several additional steps, which can be used to detect specific characteristics, e.g. definite distances between cross-link events or identify binding motifs. Additionally, results are visualized as statistical plots for a quick overview and as standardized bioinformatics file formats, which can be used for further analysis steps. AVAILABILITY AND IMPLEMENTATION: PARANOiD is published under the MIT license and is available from https://github.com/patrick-barth/PARANOiD. The documentation is available at https://paranoid.readthedocs.io/en/latest/index.html.

The ELIXIR-IT All Hands Meeting 2026, took place on February 9–10, 2026, at Villa Romanazzi Carducci in Bari (Italy). The event marked a key moment for the Italian node of ELIXIR, providing an opportunity to present ongoing activities, highlight available services, and discuss future developments. During the meeting, the achievements of the ELIXIRxNextGenIT project were showcased, highlighting its significant contribution to strengthening the ELIXIR-IT research infrastructure. The event brought together members of the ELIXIR-IT community, participants in the ELIXIRxNextGenIT project, institutional representatives, and stakeholders interested in learning more about ELIXIR Italy’s research infrastructure and services. Funded by NextGenerationEU, project PNRR IR0000010 "ELIXIRxNextGenIT". Book of Abstracts

PARANOiD is a versatile software for fully automated analysis of iCLIP and iCLIP2 data. It contains all steps necessary for preprocessing, the determination of cross-link locations and several additional steps, which can be used to detect specific characteristics, e.g. definite distances between cross-link events or identify binding motifs. The cross-link sites are presented as WIG files that can be easily visualized e.g. using IGV, for which a config file is offered. Additionally, results are offered as statistical plots for a quick overview and as standardized bioinformatics file formats or TSV files, which can be used for further analysis steps. The data provided are used as a test case for PARANOiD. The data was extracted from RVFV MP-12 virions (virion-reads-M-fragment-only.fastq) and BHK cells infected with RVFV (BHK-reads-M-fragment-only.fastq) applying the iCLIP2 method for RVFV N iCLIP. Three independent biological replicates were performed for each sample. Sequencing was performed using the MiSeq Sequencer (Illumina) with MiSeq Reagent Kit v2 Micro (Illumina) for N-iCLIP from virus particles and MiSeq Reagent Kit v3 (Illumina) for N-iCLIP from infected BHK cells. The original reads have been aligned to the RVFV MP-12 reference genome and only reads aligning to the M-fragments were extracted. The whole dataset will be publish at a later date File description: virion-reads-M-fragment-only.fastq - Reads obtained from RVFV virions BHK-reads-M-fragment-only.fastq - Reads obtained from BHK cells infected with RVFV reference_RVFV.fasta - RVFV MP-12 reference genome barcodes-RVFV.tsv - Barcodes for virion-reads-M-fragment-only.fastq barcodes-RVFV-merge-all.tsv - Barcodes for merging all samples of virion-reads-M-fragment-only.fastq barcodes-BHK.tsv - Barcodes for BHK-reads-M-fragment-only.fastq barcodes-BHK-merge-all.tsv - Barcodes for merging all relevant samples of BHK-reads-M-fragment-only.fastq

Project 6 during the 2023 BioHackathon Europe in Barcelona focused on "Enhancing the image analysis community in Galaxy." Despite Galaxy's strong presence in genomics and proteomics, its image analysis tools and workflows are currently scattered. This project aimed to gather efforts in image analysis across fields to build a robust interdisciplinary community.

The ELIXIR-IT All Hands Meeting 2026, took place on February 9–10, 2026, at Villa Romanazzi Carducci in Bari (Italy). The event marked a key moment for the Italian node of ELIXIR, providing an opportunity to present ongoing activities, highlight available services, and discuss future developments. During the meeting, the achievements of the ELIXIRxNextGenIT project were showcased, highlighting its significant contribution to strengthening the ELIXIR-IT research infrastructure. The event brought together members of the ELIXIR-IT community, participants in the ELIXIRxNextGenIT project, institutional representatives, and stakeholders interested in learning more about ELIXIR Italy’s research infrastructure and services. Funded by NextGenerationEU, project PNRR IR0000010 "ELIXIRxNextGenIT". Book of Abstracts

PARANOiD is a versatile software for fully automated analysis of iCLIP and iCLIP2 data. It contains all steps necessary for preprocessing, the determination of cross-link locations and several additional steps, which can be used to detect specific characteristics, e.g. definite distances between cross-link events or identify binding motifs. The cross-link sites are presented as WIG files that can be easily visualized e.g. using IGV, for which a config file is offered. Additionally, results are offered as statistical plots for a quick overview and as standardized bioinformatics file formats or TSV files, which can be used for further analysis steps. The data provided are used as a test case for PARANOiD. The data was extracted from RVFV MP-12 virions (virion-reads-M-fragment-only.fastq) and BHK cells infected with RVFV (BHK-reads-M-fragment-only.fastq) applying the iCLIP2 method for RVFV N iCLIP. Three independent biological replicates were performed for each sample. Sequencing was performed using the MiSeq Sequencer (Illumina) with MiSeq Reagent Kit v2 Micro (Illumina) for N-iCLIP from virus particles and MiSeq Reagent Kit v3 (Illumina) for N-iCLIP from infected BHK cells. The original reads have been aligned to the RVFV MP-12 reference genome and only reads aligning to the M-fragments were extracted. The whole dataset will be publish at a later date File description: virion-reads-M-fragment-only.fastq - Reads obtained from RVFV virions BHK-reads-M-fragment-only.fastq - Reads obtained from BHK cells infected with RVFV reference_RVFV.fasta - RVFV MP-12 reference genome barcodes-RVFV.tsv - Barcodes for virion-reads-M-fragment-only.fastq barcodes-RVFV-merge-all.tsv - Barcodes for merging all samples of virion-reads-M-fragment-only.fastq barcodes-BHK.tsv - Barcodes for BHK-reads-M-fragment-only.fastq barcodes-BHK-merge-all.tsv - Barcodes for merging all relevant samples of BHK-reads-M-fragment-only.fastq

The ELIXIR-IT All Hands Meeting 2026, took place on February 9–10, 2026, at Villa Romanazzi Carducci in Bari (Italy). The event marked a key moment for the Italian node of ELIXIR, providing an opportunity to present ongoing activities, highlight available services, and discuss future developments. During the meeting, the achievements of the ELIXIRxNextGenIT project were showcased, highlighting its significant contribution to strengthening the ELIXIR-IT research infrastructure. The event brought together members of the ELIXIR-IT community, participants in the ELIXIRxNextGenIT project, institutional representatives, and stakeholders interested in learning more about ELIXIR Italy’s research infrastructure and services. Funded by NextGenerationEU, project PNRR IR0000010 "ELIXIRxNextGenIT".

Key Performance Indicators (KPIs) are essential for evaluating project success and establishing control mechanisms to monitor development, performance, and user acceptance of services in joint projects. However, the absence of standardized frameworks and effective monitoring tools, combined with service providers' reluctance due to fears of comparability, has limited their adoption in scientific contexts. To address this gap, we developed Scorpion, a flexible tool for KPI monitoring in project management. Scorpion enables service providers to retain control over their metrics while supporting centralized reporting. It offers both web-based and programmatic access, with features for KPI submission, visualization, and user and service management. Initially created for bioinformatics and biodiversity projects, Scorpion is applicable across diverse domains. It is particularly valuable for initiatives like the German National Research Data Infrastructure (NFDI), where funding agencies require KPI reporting for evaluation. We present the Scorpion framework, highlighting its design principles, features, and potential to improve project management practices. Use cases illustrate how Scorpion enhances KPI monitoring efficiency and accuracy, contributing to better impact evaluation, quality assurance, and informed decision-making in project and service management.

Workflow engines are now widely used for genome analysis workflows.On the other hand, there are still difficulties to build and execute their workflows in various aspects.Here are examples of such difficulties:How to develop our workflows in workflow languages such as Common Workflow Language (CWL), Snakemake, Nextflow, and others?How to integrate our workflows with containers such as Docker, Singularity, and Podman?How to integrate our workflows with job schedulers such as Slurm and GridEngine?Our group solved these problems with the following activities. First, we cooperated with other groups to develop their workflows, and to make their workflows integrated with containers.Second, we developed and improved workflow ecosystems to remove the barriers to develop and execute their workflows. Ecosystems include workflow executors, specifications of workflow languages, and workflow-related tools.This paper reports what we did during the DBCLS BioHackathon 2024.