BEGIN:VCALENDAR CALNAME:Nextflow Summit 2024 - Barcelona NAME:Nextflow Summit 2024 - Barcelona PRODID:-//github.com/ical-org/ical.net//NONSGML ical.net 4.0//EN VERSION:2.0 X-WR-CALNAME:Nextflow Summit 2024 - Barcelona BEGIN:VTIMEZONE TZID:Romance Standard Time X-LIC-LOCATION:Europe/Paris BEGIN:STANDARD DTSTART:20241027T030000 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=10 TZNAME:CET TZOFFSETFROM:+0200 TZOFFSETTO:+0100 END:STANDARD BEGIN:DAYLIGHT DTSTART:20250330T020000 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=3 TZNAME:CEST TZOFFSETFROM:+0100 TZOFFSETTO:+0200 END:DAYLIGHT END:VTIMEZONE BEGIN:VEVENT DESCRIPTION:World Trade Center\, 1ª planta Edif. Este\, Moll de Barcelona\ , s/n\, 08039 Barcelona DTEND:20241028T100000 DTSTAMP:20260416T133018Z DTSTART:20241028T090000 LOCATION:Hackathon SEQUENCE:330849 STATUS:CONFIRMED SUMMARY:Hackathon and Training: Registration and arrivals UID:SZSESSIONef86f047-a41c-4740-92f9-1b95ba705d6e END:VEVENT BEGIN:VEVENT DESCRIPTION: DTEND:20241028T103000 DTSTAMP:20260416T133018Z DTSTART:20241028T100000 LOCATION:Hackathon SEQUENCE:330849 STATUS:CONFIRMED SUMMARY:Hackathon welcome UID:SZSESSION791160db-240c-47fa-9cba-ddd7bc9e44a9 END:VEVENT BEGIN:VEVENT DESCRIPTION: DTEND:20241028T103000 DTSTAMP:20260416T133018Z DTSTART:20241028T100000 LOCATION:Training SEQUENCE:330849 STATUS:CONFIRMED SUMMARY:Training welcome UID:SZSESSIONb0b1139b-8534-4ea8-87bc-fe5a6284d683 END:VEVENT BEGIN:VEVENT DESCRIPTION: DTEND:20241028T130000 DTSTAMP:20260416T133018Z DTSTART:20241028T103000 LOCATION:Hackathon SEQUENCE:330849 STATUS:CONFIRMED SUMMARY:Hackathon: Session 1 UID:SZSESSIONdd4da12c-fe9b-4693-9920-ab93bcce19b2 END:VEVENT BEGIN:VEVENT DESCRIPTION: DTEND:20241028T130000 DTSTAMP:20260416T133018Z DTSTART:20241028T103000 LOCATION:Training SEQUENCE:330849 STATUS:CONFIRMED SUMMARY:Training: Session 1 UID:SZSESSION2beb3369-2fff-4804-bb33-14b2043ee904 END:VEVENT BEGIN:VEVENT DESCRIPTION: DTEND:20241028T131500 DTSTAMP:20260416T133018Z DTSTART:20241028T130000 LOCATION:Hackathon SEQUENCE:330849 STATUS:CONFIRMED SUMMARY:Group photo UID:SZSESSIONec2c12e3-6415-4761-8ac7-9984877ec1ce END:VEVENT BEGIN:VEVENT DESCRIPTION: DTEND:20241028T140000 DTSTAMP:20260416T133018Z DTSTART:20241028T131500 LOCATION:Hackathon SEQUENCE:330849 STATUS:CONFIRMED SUMMARY:Hackathon and Training: Lunch UID:SZSESSION4a221b60-f85b-4632-8204-5cdb4b31e08c END:VEVENT BEGIN:VEVENT DESCRIPTION: DTEND:20241028T164500 DTSTAMP:20260416T133018Z DTSTART:20241028T140000 LOCATION:Hackathon SEQUENCE:330849 STATUS:CONFIRMED SUMMARY:Hackathon: Session 2 UID:SZSESSIONedc9f78b-89bd-4f0b-8d58-4dc478815f98 END:VEVENT BEGIN:VEVENT DESCRIPTION: DTEND:20241028T164500 DTSTAMP:20260416T133018Z DTSTART:20241028T140000 LOCATION:Training SEQUENCE:330849 STATUS:CONFIRMED SUMMARY:Training: Session 2 UID:SZSESSION3bce755e-a726-4780-b8b2-61cf1bfe870c END:VEVENT BEGIN:VEVENT DESCRIPTION:Ends 5:30PM DTEND:20241028T172000 DTSTAMP:20260416T133018Z DTSTART:20241028T164500 LOCATION:Hackathon SEQUENCE:330849 STATUS:CONFIRMED SUMMARY:Hackathon: Day 1 sync UID:SZSESSIONbefb127a-8779-4bf7-8e67-425d5aa78e93 END:VEVENT BEGIN:VEVENT DESCRIPTION:Ends 5:30PM DTEND:20241028T172000 DTSTAMP:20260416T133018Z DTSTART:20241028T164500 LOCATION:Training SEQUENCE:330849 STATUS:CONFIRMED SUMMARY:Training: Day 1 review UID:SZSESSION13bad2af-24ab-416a-9642-020132095756 END:VEVENT BEGIN:VEVENT DESCRIPTION:Hard Rock Cafè\, 6:30 - 9:00 PM. See the [travel page](https:/ /summit.nextflow.io/2024/barcelona/travel/) for details. DTEND:20241028T213000 DTSTAMP:20260416T133018Z DTSTART:20241028T183000 LOCATION:Hackathon SEQUENCE:330849 STATUS:CONFIRMED SUMMARY:Hackathon and Training: Social event at Hard Rock Café UID:SZSESSION366cd5a1-5cd1-41d9-8afa-5db052107700 END:VEVENT BEGIN:VEVENT DESCRIPTION:World Trade Center\, 1ª planta Edif. Este\, Moll de Barcelona\ , s/n\, 08039 Barcelona DTEND:20241029T100000 DTSTAMP:20260416T133018Z DTSTART:20241029T093000 LOCATION:Hackathon SEQUENCE:330849 STATUS:CONFIRMED SUMMARY:Hackathon and Training: Arrivals UID:SZSESSION121354ab-e876-4c3d-8144-2a3690d3a42b END:VEVENT BEGIN:VEVENT DESCRIPTION: DTEND:20241029T130000 DTSTAMP:20260416T133018Z DTSTART:20241029T100000 LOCATION:Hackathon SEQUENCE:330849 STATUS:CONFIRMED SUMMARY:Hackathon: Session 3 UID:SZSESSIONdf4ffbec-03ca-4b7e-9a60-c1cf82b8c937 END:VEVENT BEGIN:VEVENT DESCRIPTION: DTEND:20241029T130000 DTSTAMP:20260416T133018Z DTSTART:20241029T100000 LOCATION:Training SEQUENCE:330849 STATUS:CONFIRMED SUMMARY:Training: Session 3 UID:SZSESSIONe4f94082-5fcb-4ace-8bb9-08551929fa3f END:VEVENT BEGIN:VEVENT DESCRIPTION: DTEND:20241029T140000 DTSTAMP:20260416T133018Z DTSTART:20241029T130000 LOCATION:Hackathon SEQUENCE:330849 STATUS:CONFIRMED SUMMARY:Hackathon and Training: Lunch UID:SZSESSION1f97277e-00d5-47e4-8d53-cb205de2e99c END:VEVENT BEGIN:VEVENT DESCRIPTION: DTEND:20241029T141500 DTSTAMP:20260416T133018Z DTSTART:20241029T140000 LOCATION:Hackathon SEQUENCE:330849 STATUS:CONFIRMED SUMMARY:Hackathon and Training: Quiz UID:SZSESSION34d9ddee-5559-4df8-bb5d-d2f154f9be64 END:VEVENT BEGIN:VEVENT DESCRIPTION: DTEND:20241029T170000 DTSTAMP:20260416T133018Z DTSTART:20241029T141500 LOCATION:Hackathon SEQUENCE:330849 STATUS:CONFIRMED SUMMARY:Hackathon: Session 4 UID:SZSESSIONfed866f9-1d16-429e-bad8-bdd48cf97e9b END:VEVENT BEGIN:VEVENT DESCRIPTION: DTEND:20241029T170000 DTSTAMP:20260416T133018Z DTSTART:20241029T141500 LOCATION:Training SEQUENCE:330849 STATUS:CONFIRMED SUMMARY:Training: Session 4 UID:SZSESSIONc7ba6e66-35fb-4a9f-88ed-9af74961f881 END:VEVENT BEGIN:VEVENT DESCRIPTION: DTEND:20241029T173000 DTSTAMP:20260416T133018Z DTSTART:20241029T170000 LOCATION:Hackathon SEQUENCE:330849 STATUS:CONFIRMED SUMMARY:Hackathon: Day 2 sync UID:SZSESSION5538eb7b-9e1f-4d45-900b-34e291fff358 END:VEVENT BEGIN:VEVENT DESCRIPTION: DTEND:20241029T173000 DTSTAMP:20260416T133018Z DTSTART:20241029T170000 LOCATION:Training SEQUENCE:330849 STATUS:CONFIRMED SUMMARY:Training: Day 2 review UID:SZSESSION6b4148df-465b-4789-89cd-5aa7ccc2985d END:VEVENT BEGIN:VEVENT DESCRIPTION:World Trade Center\, 1ª planta Edif. Este\, Moll de Barcelona\ , s/n\, 08039 Barcelona DTEND:20241030T100000 DTSTAMP:20260416T133018Z DTSTART:20241030T093000 LOCATION:Hackathon SEQUENCE:330849 STATUS:CONFIRMED SUMMARY:Hackathon and Training: Arrivals UID:SZSESSIONd9d8e2d8-c712-41cc-b7e3-dcac85541381 END:VEVENT BEGIN:VEVENT DESCRIPTION: DTEND:20241030T123000 DTSTAMP:20260416T133018Z DTSTART:20241030T100000 LOCATION:Hackathon SEQUENCE:330849 STATUS:CONFIRMED SUMMARY:Hackathon: Session 5 UID:SZSESSION828383c8-9955-400b-80d4-da8a153ffe21 END:VEVENT BEGIN:VEVENT DESCRIPTION: DTEND:20241030T123000 DTSTAMP:20260416T133018Z DTSTART:20241030T100000 LOCATION:Training SEQUENCE:330849 STATUS:CONFIRMED SUMMARY:Training: Session 5 UID:SZSESSION765290ea-48c4-4353-80df-7c94754f737c END:VEVENT BEGIN:VEVENT DESCRIPTION: DTEND:20241030T130000 DTSTAMP:20260416T133018Z DTSTART:20241030T123000 LOCATION:Hackathon SEQUENCE:330849 STATUS:CONFIRMED SUMMARY:Hackathon: Wrap-up UID:SZSESSION3d792591-c23b-45c9-8bf8-462e8c004f5c END:VEVENT BEGIN:VEVENT DESCRIPTION: DTEND:20241030T130000 DTSTAMP:20260416T133018Z DTSTART:20241030T123000 LOCATION:Training SEQUENCE:330849 STATUS:CONFIRMED SUMMARY:Training: Wrap-up UID:SZSESSION96d3c188-0fcf-4cfe-87c8-5befb440686e END:VEVENT BEGIN:VEVENT DESCRIPTION: DTEND:20241030T140000 DTSTAMP:20260416T133018Z DTSTART:20241030T130000 LOCATION:Hackathon SEQUENCE:330849 STATUS:CONFIRMED SUMMARY:Hackathon and Training: Lunch UID:SZSESSION21ab4023-bfc5-454d-82f1-89e285444311 END:VEVENT BEGIN:VEVENT DESCRIPTION:World Trade Center\, 1ª planta Edif. Este\, Moll de Barcelona\ , s/n\, 08039 Barcelona DTEND:20241030T150000 DTSTAMP:20260416T133018Z DTSTART:20241030T140000 LOCATION:Summit SEQUENCE:330849 STATUS:CONFIRMED SUMMARY:Summit: Arrivals and registration UID:SZSESSION346e1958-10ef-4b10-a154-1b27ab8fecf7 END:VEVENT BEGIN:VEVENT DESCRIPTION:Speaker: Evan Floden DTEND:20241030T151500 DTSTAMP:20260416T133018Z DTSTART:20241030T150000 LOCATION:Summit SEQUENCE:330849 STATUS:CONFIRMED SUMMARY:Summit welcome UID:SZSESSION720692 END:VEVENT BEGIN:VEVENT DESCRIPTION:Speaker: James A. Fellows Yates\n\nMetagenomics refers to tech niques that aim to simultaneously analyze the DNA from all organisms in a sample. Given the ever-growing size of DNA datasets needed to address ec ological questions\, and the need to compare against a large number of ge nomes\, Nextflow is an ideal framework for building scalable and computat ionally efficient pipelines.\n\nIn this talk\, I will introduce the nf-co re offerings of community-developed pipelines for metagenomics. Of these\ , I will focus on pipelines for metagenomic taxonomic profiling (nf-core/ taxprofiler)\, metagenomic de novo assembly (nf-core/mag)\, and screening for antimicrobial resistance and natural product genes (nf-core/funcscan ). I will also discuss how these pipelines fit in the wider nf-core ecosy stem\, and how they can be utilised together for scientists in microbial ecology\, clinical metagenomics\, and even ancient DNA. Finally\, I will briefly introduce the new 'meta-omics' nf-core special interest group and how we plan to further deepen the integration of the various nf-core met agenomics-related pipelines with upstream (e.g. nf-core/fetchngs) and dow nstream pipelines (e.g. nf-core/differentialabundance) to streamline prim ary metagenomic analysis. DTEND:20241030T153000 DTSTAMP:20260416T133018Z DTSTART:20241030T151500 LOCATION:Summit SEQUENCE:330849 STATUS:CONFIRMED SUMMARY:Integrated metagenomic data processing with nf-core UID:SZSESSION703035 END:VEVENT BEGIN:VEVENT DESCRIPTION:Speaker: Lukas Forer\n\nThe workflow management system Nextflo w\, together with the nf-core community\, builds an essential ecosystem i n Bioinformatics. However\, ensuring the correctness and reliability of l arge and complex pipelines is challenging. To provide this crucial compon ent to the community\, we developed the testing framework nf-test. It int roduces a modular approach that enables pipeline developers to test indiv idual process blocks\, workflow patterns\, and entire pipelines in isolat ion. nf-test is based on a syntax similar to Nextflow DSL2 and provides u nique features such as snapshot testing and smart testing to save resourc es by testing only changed modules. Already adopted by dozens of pipeline s and serving as the new standard testing framework for nf-core\, nf-test improves robustness and reliability in pipeline development.\n\nIn this talk\, I will give an overview of nf-test's capabilities\, the improvemen ts it has brought to the Nextflow community\, and will highlight new feat ures from the past year. This includes optimizations to minimize executio n time and to implement continuous integration. Moreover\, I will introdu ce nf-test's plugin system and provide an overview of available plugins\, especially for Bioinformatics. I'll also demonstrate through various exa mples how the community has adapted it to their own needs and outline fut ure plans for the framework. DTEND:20241030T154500 DTSTAMP:20260416T133018Z DTSTART:20241030T153000 LOCATION:Summit SEQUENCE:330849 STATUS:CONFIRMED SUMMARY:Improving the reliability and quality of Nextflow pipelines with n f-test UID:SZSESSION702656 END:VEVENT BEGIN:VEVENT DESCRIPTION:Speaker: Annick Renevey\n\nnf-core/rnafusion is designed for d etecting gene fusions\, which are increasingly observed across various ca ncer types. Identifying these fusions can significantly impact cancer dia gnoses and therapy selection.\nThe pipeline emphasizes reproducibility\, robustness\, and ease of operation. For approximately a year\, Clinical G enomics Stockholm has used this pipeline to provide crucial data to genet icists at Karolinska University Hospital\, aiding in clinical decision-ma king. Although still in its early days in the clinic\, the standard of ca re for specific cancer types\, such as sarcomas\, is being updated to inc lude gene fusion analysis.\nThis presentation will focus on pipeline deve lopment strategies that facilitate clinical implementation\, technical se tups\, and insights gained from our use of nf-core and the Seqera Platfor m frameworks. Importantly\, steps towards obtaining an accredited and IVD R-compliant community-based pipeline will be described. DTEND:20241030T160000 DTSTAMP:20260416T133018Z DTSTART:20241030T154500 LOCATION:Summit SEQUENCE:330849 STATUS:CONFIRMED SUMMARY:Implementing nf-core/rnafusion in a clinical setting: Key insights and lessons learned UID:SZSESSION710965 END:VEVENT BEGIN:VEVENT DESCRIPTION:Speaker: Ziad Al Bkhetan\n\nThe advanced developments in AI ap plication to the protein-structure prediction challenge have introduced t ools that can predict protein structures efficiently and accurately\, suc h as AlphaFold2 and 3\, ColabFold\, ESMFold and RoseTTAFold. Such tools a re compute-intensive and require powerful GPUs for efficient utilization which makes them not readily available for all researchers.\n\nTo support Australian researchers interested in utilizing protein structure predict ors\, the Australian BioCommons in collaboration with the Australian stru ctural biology community is working on establishing a national protein st ructure prediction service based on the Australian Nextflow Seqera Servic e. The service greatly simplifies these complex workflows to a ‘bring you r own data’ approach and is delivered via an easy-to-use web interface to Australian researchers.\n\nImprovements on the ProteinFold workflow (ori ginally available through nf-core) have been carried out to allow the exe cution of different models including AlphaFold2\, Colabfold\, and ESMFold on the GADI supercomputer at the National Computational Infrastructure ( NCI). We have developed and integrated an interactive visualization into the reporting functionality of Seqera platform\, allowing users to invest igate and interact with predicted structures through 3D interactive model s and plots.\n\nWe aim to integrate several GPGPU infrastructures from di fferent research institutions to distribute the compute required for such analysis. We also aim to include a search functionality through Foldseek to retrieve known protein structures with a high degree of similarity to the predicted structures. The modifications are planned to be contribute d to the nf-core workflow and will be available for researchers to reuse. DTEND:20241030T161500 DTSTAMP:20260416T133018Z DTSTART:20241030T160000 LOCATION:Summit SEQUENCE:330849 STATUS:CONFIRMED SUMMARY:The Australian ProteinFold service: Interactive prediction and vis ualisation of protein structure UID:SZSESSION718055 END:VEVENT BEGIN:VEVENT DESCRIPTION:Speaker: Geraldine Van der Auwera\n\nThe Nextflow community ha s been growing rapidly in size\, global reach and breadth of scientific a pplications. Join us for a whistlestop tour of some of the most exciting new developments we’re seeing across the community! We’ll highlight the e xciting contributions of Nextflow Ambassadors around the world\, a networ k of dedicated experts promoting best practices and facilitating knowledg e sharing and local engagement. Additionally\, we’ll present newly develo ped training materials designed to improve onboarding for newcomers to Ne xtflow and we’ll preview upcoming events aimed at further engaging and ed ucating the community. This comprehensive overview will underscore the dy namic developments within the Nextflow community and its commitment to co ntinuous innovation and collaboration. DTEND:20241030T163000 DTSTAMP:20260416T133018Z DTSTART:20241030T161500 LOCATION:Summit SEQUENCE:330849 STATUS:CONFIRMED SUMMARY:Updates from the Nextflow community UID:SZSESSION730869 END:VEVENT BEGIN:VEVENT DESCRIPTION: DTEND:20241030T170000 DTSTAMP:20260416T133018Z DTSTART:20241030T163000 LOCATION:Summit SEQUENCE:330849 STATUS:CONFIRMED SUMMARY:Summit: Coffee break UID:SZSESSIONa14179cf-3595-4e43-8cc0-837bd2882bc5 END:VEVENT BEGIN:VEVENT DESCRIPTION:Speaker: Alvaro Martinez Barrio\n\nThe spatial distribution of cell surface proteins governs vital processes of the immune system such as inter-cell communication and mobility. Pixelgen Technologies has devel oped an optics-free technology\, Molecular Pixelation (MPX)\, that is abl e to simultaneously quantify protein abundance\, the spatial distribution \, and co-localization of targeted cell surface proteins of thousands of individual cells in parallel. Our company vision is to drive spatial prot eomics discoveries at subcellular level by overcoming the limited scalabi lity in the multiplexing and throughput of previous technologies and with out the need of dedicated instrumentation to perform the experiment.\n\nM PX creates three-dimensional spatial maps of cells by imprinting spatial information from antibody oligonucleotide conjugates bound to protein on the cell surface\, using DNA-based nanometer sized molecular pixels. Thes e DNA-pixels form over 1\,000 connected spatial zones per single cell\, p roducing graphs that reconstruct the cell surface in-silico\; forming a s ingle cell surface map of membrane proteins. In our recent study\, we sho w how MPX can be used to monitor constellations of proteins on the cell s urface after the effects of treatment or stimulation. By applying spatial statistics on these cell surface graph representations\, we uncover both known and novel patterns of protein spatial polarization and co-localiza tion associated with vital immune processes such as intercellular communi cation\, antibody dependent cellular cytotoxicity and mobility.\n\nIn thi s talk\, I will show how MPX is a technology that allows us to represent cells as volumetric point clouds retaining information about the respecti ve cellular shape and stimulation. DTEND:20241030T171500 DTSTAMP:20260416T133018Z DTSTART:20241030T170000 LOCATION:Summit SEQUENCE:330849 STATUS:CONFIRMED SUMMARY:Molecular Pixelation: 3D representation of single-cells without a microscope UID:SZSESSION730498 END:VEVENT BEGIN:VEVENT DESCRIPTION:Speaker: Loïc Lannelongue\n\nFrom genetic studies and astrophy sics simulations to AI\, scientific computing has enabled amazing discove ries and there is no doubt it will continue to do so. However\, the corre sponding environmental impact is a growing concern in light of the urgenc y of the climate crisis\, so what can we all do about it? Tackling this i ssue and making it easier for scientists to engage with sustainable compu ting is what motivated the Green Algorithms project. Through the prism of the GREENER principles for environmentally sustainable science\, we will discuss what we learned along the way\, how to estimate the impact of ou r work and what hurdles still exist. It will also be a chance to highligh t how the new Green DiSC certification framework can support scientists a nd institutions in making their research more sustainable. DTEND:20241030T180000 DTSTAMP:20260416T133018Z DTSTART:20241030T171500 LOCATION:Summit SEQUENCE:330849 STATUS:CONFIRMED SUMMARY:Towards environmentally sustainable computational science with Gre en Algorithms UID:SZSESSION736663 END:VEVENT BEGIN:VEVENT DESCRIPTION:Departure from the Summit lobby at the World Trade Center\, 1ª planta Edif. Este\, Moll de Barcelona\, s/n\, 08039 Barcelona\n\nStart y our second day with energy and fresh air! Join our third annual Sunrise F un Run & Walk\, open to all fitness levels. The Seqera team will lead war m-ups before exploring the Barcelona waterfront. It's a great chance to e nergize\, mingle\, and enjoy the morning views. Walkers can take a leisur ely stroll along the waterfront. DTEND:20241031T081500 DTSTAMP:20260416T133018Z DTSTART:20241031T073000 LOCATION:Summit SEQUENCE:330849 STATUS:CONFIRMED SUMMARY:Summit: Run & Walk UID:SZSESSION5e61a45f-360b-4499-80c1-1e74021e587e END:VEVENT BEGIN:VEVENT DESCRIPTION:World Trade Center\, 1ª planta Edif. Este\, Moll de Barcelona\ , s/n\, 08039 Barcelona DTEND:20241031T093000 DTSTAMP:20260416T133018Z DTSTART:20241031T090000 LOCATION:Summit SEQUENCE:330849 STATUS:CONFIRMED SUMMARY:Summit: Arrivals UID:SZSESSION983ad0b9-b38c-4803-8184-69e1e236c5e0 END:VEVENT BEGIN:VEVENT DESCRIPTION:Speaker: Sam Wilkinson\n\nThe SARS-CoV-2 pandemic highlighted how crucial platforms for sharing and analysing public health data are. A t the University of Birmingham we developed a novel infrastructure - CLIM B-COVID - as a trusted research environment for academics studying and/or sequencing SARS-CoV-2\, public health professionals involved in the nati onal pandemic response\, and genomic scientists involved in sequencing ef forts to collaborate on a national scale dataset covering >3.5M genome se quences over the period 2020-2024. The accessibility of the submission me thod and availability of the dataset provided a much needed service to th e UK.\n\nBuilding on these efforts we have developed an umbrella pathogen genome surveillance infrastructure called CLIMB-TRE. This infrastructure can be broadly applicable to numerous infectious pathogens\, as well as metagenomics datasets. CLIMB-TRE utilises Nextflow pipelines to provide a continually integrating dataset from sequencing data ingested from multi ple sources (diagnostic labs\, public health agencies and public data) an d provides quality control and primary analytical functionality with gene rated outputs that can be used for downstream public health surveillance activities and academic research.\n\nIn this session I will cover some of the technical aspects of the system with a specific focus on how we util ise Nextflow workflows to process submitted data and enable CLIMB-TRE use rs to conduct their own analysis on our cloud computing infrastructure. DTEND:20241031T094500 DTSTAMP:20260416T133018Z DTSTART:20241031T093000 LOCATION:Summit SEQUENCE:330849 STATUS:CONFIRMED SUMMARY:CLIMB-TRE: Nextflow powered analysis of public health big data in a trusted research environment UID:SZSESSION703540 END:VEVENT BEGIN:VEVENT DESCRIPTION:Speaker: Kübra Narcı\n\nBackground: The National Center for Tu mor Diseases (NCT) Heidelberg and the German Cancer Research Center (DKFZ ) have developed\, maintained\, and used somatic variant calling pipeline s for high-throughput data analysis. These pipelines have played a signif icant role in large consortia\, including the International Cancer Genome Consortium (ICGC). The workflows encompass diverse software components f or calling somatic single nucleotide variations (SNVs)\, short insertions and deletions (indels)\, structural variants (SVs)\, and allele-specific somatic copy number aberrations (sCNAs). \nWhat we did: With the ultimat e aim of providing adequate resources for data sharing and harmonizing th e processing of data\, as a part of the federated European Genome-Phenome Archive (EGA)\, The German Human Genome-Phenome Archive (GHGA) is commit ted to the utilization of FAIR-compliant bioinformatic workflows which re quires efforts on secure portability of data and workflows\, flexibility\ , scalability and automation of the processes. Nextflow\, a prevalent wor kflow management language\, has emerged as a solution for such reproducib le workflows aligning with the goals of GHGA. In a fruitful collaboration with nf-core\, a community effort supporting nextflow\, GHGA is ensuring standardized and non-repetitive work in this respect. The translation of DKFZ somatic variant calling workflows to nextflow enables us to create a framework for the standardization of workflow processes. \n\nAt the end : The initial releases of standardized nf-platypusindelcalling\, nf-snvca lling\, and nf-aceseq workflows are readily available under the GHGA GitH ub repository.\n\nConclusion: GHGA collaborates with the nf-core communit y to provide FAIR-optimized versions of the DKFZ/NCT Somatic Variant call ing pipelines. \n DTEND:20241031T100000 DTSTAMP:20260416T133018Z DTSTART:20241031T094500 LOCATION:Summit SEQUENCE:330849 STATUS:CONFIRMED SUMMARY:Leveraging Nextflow for the development of FAIR-compliant somatic variant calling workflows UID:SZSESSION701830 END:VEVENT BEGIN:VEVENT DESCRIPTION:Speaker: Björn Langer\n\nStandardised analysis pipelines are a n important part of FAIR bioinformatics research. Over the last decade\, there has been a notable shift from point-and-click pipeline solutions li ke Galaxy to command-line solutions such as Nextflow and Snakemake.\n\nWe report on the process that led six European research consortia\, under t he EuroFAANG umbrella and dedicated to farmed animal genomics\, to adopt Nextflow\, and nf-core as implementation standard for Nextflow pipelines. This adoption of a common standard has been driven by recent advancement s in nf-core and Nextflow\, such as DSL2\, and the promise of faster deve lopment\, better interoperability\, and collaboration with the nf-core co mmunity. Given the general lack of dedicated resources\, a key factor in adoption was the progressive nature of the standardization process\, enab ling effective bottom-up adoption.\n\nAuthors: Björn E. Langer\, Andreia Amaral\, Marie-Odile Baudement\, Franziska Bonath\, Mathieu Charles\, Pra veen Krishna Chitneedi\, Emily L. Clark\, Paolo Di Tommaso\, Sarah Djebal i\, Philip A. Ewels\, Sonia Eynard\, James A. Fellows Yates\, Daniel Fisc her\, Evan W. Floden\, Sylvain Foissac\, Gisela Gabernet\, Maxime U. Garc ia\, Gareth Gillard\, Manu Kumar Gundappa\, Cervin Guyomar\, Christopher Hakkaart\, Friederike Hanssen\, Peter W. Harrison\, Matthias Hörtenhuber\ , Cyril Kurylo\, Christa Kühn\, Sandrine Lagarrigue\, Delphine Lallias\, Daniel J. Macqueen\, Edmund Miller\, Júlia Mir-Pedrol\, Gabriel Costa Mon teiro Moreira\, Sven Nahnsen\, Harshil Patel\, Alexander Peltzer\, Freder ique Pitel\, Yuliaxis Ramayo-Caldas\, Marcel da Câmara Ribeiro-Dantas\, D ominique Rocha\, Mazdak Salavati\, Alexey Sokolov\, Jose Espinosa-Carrasc o\, Cedric Notredame and the nf-core community DTEND:20241031T101500 DTSTAMP:20260416T133018Z DTSTART:20241031T100000 LOCATION:Summit SEQUENCE:330849 STATUS:CONFIRMED SUMMARY:Empowering bioinformatics communities with Nextflow and nf-core UID:SZSESSION703479 END:VEVENT BEGIN:VEVENT DESCRIPTION:Speaker: Stacy Gorelik\n\nWe all understand the need for a sca lable\, efficient\, and user-friendly platform for bioinformatics – but u nderstanding how to build it is much tougher! Join us for an in-depth tec hnical look at how we approach some of our biggest challenges and share l essons that we've learned in our mission to make modern software engineer ing practices and infrastructure more accessible to scientists everywhere . DTEND:20241031T103000 DTSTAMP:20260416T133018Z DTSTART:20241031T101500 LOCATION:Summit SEQUENCE:330849 STATUS:CONFIRMED SUMMARY:Scaling Seqera Platform: How we're building the modern biotech sta ck UID:SZSESSION730850 END:VEVENT BEGIN:VEVENT DESCRIPTION:Speakers: Brendan Bouffler\, David Lecomber\n\nNextflow has si gnificantly advanced in capabilities and applications\, and AWS has been instrumental in supporting this growth by collaborating with the open-sou rce community. In this talk\, we will delve into how AWS services are emp owering Nextflow users to run and scale their workflows efficiently in th e cloud. We'll highlight recent key developments\, such as supporting cos t-effective and environmentally friendly ARM-based AWS Graviton instances through Seqera Containers\, and showcase real-world use cases that demon strate enhanced performance and scalability. Additionally\, we will discu ss upcoming challenges that we're addressing and invite community engagem ent to help shape future solutions. DTEND:20241031T110000 DTSTAMP:20260416T133018Z DTSTART:20241031T103000 LOCATION:Summit SEQUENCE:330849 STATUS:CONFIRMED SUMMARY:Making Nextflow run smoother: What we’ve cooked up with Seqera UID:SZSESSION752810 END:VEVENT BEGIN:VEVENT DESCRIPTION: DTEND:20241031T113000 DTSTAMP:20260416T133018Z DTSTART:20241031T110000 LOCATION:Summit SEQUENCE:330849 STATUS:CONFIRMED SUMMARY:Summit: Coffee break UID:SZSESSIONe1348b9c-96ec-4b8f-8b39-6456104d799f END:VEVENT BEGIN:VEVENT DESCRIPTION:Speaker: Kamil Malisz\n\nRNA-seq analysis has established itse lf as a powerful tool in clinical research\, enabling the comprehensive p rofiling of gene expression. Clinical studies\, however\, present a compl ex landscape due to the multitude of clinical parameters that need to be measured\, often leading to incomplete paired-patient data sets. Limma/vo om has shown advantages in handling complex incomplete paired-patient dat a\, a common occurrence in clinical studies.\n\nThe nf-core differentiala bundance pipeline is a versatile workflow for testing differentially expr essedgenes or proteins from count or intensity-based high-throughput stud ies. Currently\, the workflow supports limma for intensity-based and DESe q2 for count-based input. In a collaborative project between Ardigen and Boehringer Ingelheim\, we have extended the nf-core differentialabundance pipeline for limma/voom analysis with mixed models which promises to enh ance the accuracy of gene expression profiling\, particularly in studies with missing data\, and could potentially lead to more reliable biomedica l research outcomes.\n\nTo ensure adherence to the highest standards of o pen-source community and regulatory compliance\, we have incorporated com prehensive testing and documentation into the pipeline. This initiative a ligns with the nf-core community’s commitment to meeting the stringent re quirements set forth by regulatory authorities. DTEND:20241031T114500 DTSTAMP:20260416T133018Z DTSTART:20241031T113000 LOCATION:Summit SEQUENCE:330849 STATUS:CONFIRMED SUMMARY:Advancing open science in big pharma: Integrating LIMMA/VOOM into the nf-core differential abundance UID:SZSESSION710865 END:VEVENT BEGIN:VEVENT DESCRIPTION:Speaker: Júlia Mir-Pedrol\n\nnf-core/tools is a suite designed to support the Nextflow pipeline ecosystem by providing tools for develo ping and running Nextflow pipelines. While central to all nf-core pipelin es\, it is also available for use outside the nf-core community.\n\nIn th is talk\, we will present the latest enhancements to nf-core/tools\, incl uding the implementation of a more customisable and minimalistic pipeline template\, as well as the\ngeneration of RO-Crate files for improved pip eline provenance and interoperability. We will also outline which project s are next in the development of nf-core/tools. DTEND:20241031T120000 DTSTAMP:20260416T133018Z DTSTART:20241031T114500 LOCATION:Summit SEQUENCE:330849 STATUS:CONFIRMED SUMMARY:Announcing nf-core/tools v3 UID:SZSESSION701885 END:VEVENT BEGIN:VEVENT DESCRIPTION:Speaker: Ken Brewer\n\nEven when developing code that can’t be contributed to open-source\, it pays great dividends for Nextflow develo pers to build their pipelines compatible with nf-core tooling. As a resul t\, many organizations maintain repositories containing a mix of open-sou rce and proprietary code. \n\nTo support this common scenario\, this talk will explore the practicalities of using nf-core pipelines and tooling i n hybrid-source environments. It will include an overview of strategies f or adding custom code and configurations\, from the lightweight (and stil l-open) option of creating an institutional pipeline configuration to the heavier lift (and fully closed) option of maintaining private pipeline f orks and private module libraries. Additionally\, the talk will discuss t he benefits of empowering bioinformatics teams with open-source contribut ion policies and guidelines that allow them to contribute code in the env ironment (in nf-core or not) that best balances the short and long-term n eeds of a commercial organization participating in a broader scientific c ommunity. DTEND:20241031T121500 DTSTAMP:20260416T133018Z DTSTART:20241031T120000 LOCATION:Summit SEQUENCE:330849 STATUS:CONFIRMED SUMMARY:How to NOT contribute to nf-core: Best practices for using nf-core tooling in closed-source contexts UID:SZSESSION748526 END:VEVENT BEGIN:VEVENT DESCRIPTION:Speaker: Evan Floden\n\nSeqera co-founder and CEO\, Evan Flode n\, will unveil the Seqera vision for addressing the 'blank page problem' in data analysis. It’s commonly understood that all science is built on top of the works of others\, yet that is often easier said than done\; Se qera aims to make it exceedingly simple. Built on the deep knowledge of h igh-throughput analysis from developing Nextflow\, Seqera makes scientifi c data analysis accessible at any scale. By providing convenient access t o the essential building blocks of your analysis—code\, data\, and enviro nments—Seqera removes the daunting barrier of starting from scratch. It c onsolidates fragmented data and diverse computing resources into a unifie d control plane\, enabling teams to rapidly develop\, test\, and deploy c ollaboratively while maintaining deep insights into resource usage and le veraging intelligent scaling tools. Seqera represents the future of moder n software engineering for science\, making research scalable\, flexible\ , and collaborative. DTEND:20241031T130000 DTSTAMP:20260416T133018Z DTSTART:20241031T121500 LOCATION:Summit SEQUENCE:330849 STATUS:CONFIRMED SUMMARY:Solving the 'blank page problem' in bioinformatics UID:SZSESSION743123 END:VEVENT BEGIN:VEVENT DESCRIPTION: DTEND:20241031T140000 DTSTAMP:20260416T133018Z DTSTART:20241031T130000 LOCATION:Summit SEQUENCE:330849 STATUS:CONFIRMED SUMMARY:Summit: Lunch UID:SZSESSION206537c5-886d-44ac-b3c3-a9f90976675d END:VEVENT BEGIN:VEVENT DESCRIPTION:Speaker: Phil Ewels\n\nThis talk will introduce the latest fea tures in MultiQC\, designed to further streamline data reporting and visu alisation in bioinformatics workflows. Alongside these updates\, we’ll ex plore different approaches running for MultiQC: default CLI functionality \, custom content files\, writing custom plugins and using MultiQC as a l ibrary within Python scripts.\n\nWe'll show how to use this newly announc ed scripting ability to tweak how parsed data is processed: filtering res ults\, adding additional columns to tables\, and generating custom plots. These additions provide even greater flexibility in tailoring MultiQC to specific project needs. DTEND:20241031T141500 DTSTAMP:20260416T133018Z DTSTART:20241031T140000 LOCATION:Summit SEQUENCE:330849 STATUS:CONFIRMED SUMMARY:MultiQC: New features and flexible data parsing UID:SZSESSION747821 END:VEVENT BEGIN:VEVENT DESCRIPTION:Speaker: Alexander Peltzer\n\nThe new nf-core "Regulatory" spe cial interest group is intended to bring people together to discuss topic s around pipeline validation and qualification for clinical applications. We hope to make key nf-core pipelines even more complete in terms of tes ting\, adherence to standards and documentation. By bringing different st akeholders with interests in validation & qualification together\, we aim to improve standards and fill gaps with respect to FDA/EMA requirements. \n\nThe Regulatory group had its first meeting in July 2024 with great in terest from many groups around the world. In this talk we will discuss th e key points identified and describe planned aims and objectives in the c oming months. It's our hope that many more people from the wider Nextflow and nf-core community will join! DTEND:20241031T143000 DTSTAMP:20260416T133018Z DTSTART:20241031T141500 LOCATION:Summit SEQUENCE:330849 STATUS:CONFIRMED SUMMARY:#regulatory - The nf-core special interest group for clinical appl ications UID:SZSESSION710944 END:VEVENT BEGIN:VEVENT DESCRIPTION:Speaker: Laurence Kuhlburger\n\nIn recent years\, CRISPR techn ology has become widely applied in scientific research\, being simpler\, cheaper\, and more precise than previous gene editing techniques. This ed iting technology can be used for various applications such as gene knock- out (KO)\, gene knock-in (KI)\, CRISPR activation (CRISPRa) and CRISPR in terference (CRISPRi)\, CRISPR screens\, base editing (BE) and prime editi ng (PE).\n\nThe share of pipelines to capture the variety of CRISPR exper imental designs is low and until now none of them caters to both gene edi ting and CRISPR-based functional genomics. Here we introduce nf-core/cris prseq\, a Nextflow DSL2 pipeline for the assessment of CRISPR gene editin g and screening assays. The workflow is written in a modularized fashion\ , to allow the easy incorporation of new steps. nf-core/crisprseq is the first generic pipeline enabling the analysis of the broad spectrum of CRI SPR designs.\n\nWe show its usability by running the pipeline on two exam ple datasets.\n DTEND:20241031T144500 DTSTAMP:20260416T133018Z DTSTART:20241031T143000 LOCATION:Summit SEQUENCE:330849 STATUS:CONFIRMED SUMMARY:Integrated CRISPR-Cas9 analysis pipeline for targeted and screenin g genomics UID:SZSESSION703095 END:VEVENT BEGIN:VEVENT DESCRIPTION:Speakers: Ben Sherman\, Paolo Di Tommaso\n\nJoin us for an exc iting talk by Paolo Di Tommaso\, co-founder and CTO of Seqera\, as he unv eils the latest features in Nextflow. This presentation will cover brand new enhancements that significantly expand Nextflow's technical capabilit ies and streamline the developer experience. Paolo will delve into cuttin g-edge updates\, showcasing how these innovations can elevate your workfl ow orchestration and data analysis projects. The talk will culminate in a practical demonstration of the new features in action\, offering attende es a hands-on glimpse into the future of Nextflow. Don't miss this opport unity to stay ahead of the curve and enhance your Nextflow proficiency. DTEND:20241031T153000 DTSTAMP:20260416T133018Z DTSTART:20241031T144500 LOCATION:Summit SEQUENCE:330849 STATUS:CONFIRMED SUMMARY:What’s next for Nextflow: New features and roadmap for the future UID:SZSESSION730848 END:VEVENT BEGIN:VEVENT DESCRIPTION: DTEND:20241031T163000 DTSTAMP:20260416T133018Z DTSTART:20241031T153000 LOCATION:Summit SEQUENCE:330849 STATUS:CONFIRMED SUMMARY:Summit: Coffee break & poster session UID:SZSESSION33cddce7-c2b4-4179-af7a-ab4041814771 END:VEVENT BEGIN:VEVENT DESCRIPTION:Speaker: John Michael Egana\n\nThe recent COVID-19 pandemic le d to the adoption of genomic biosurveillance to identify variants that ar ise due to genome variations in the process of evolution. The integration of the model of evolution and epidemiology gives rise to the field of ph ylodynamics which is normally analyzed using Bayesian approaches. These a pproaches albeit come with advantages such as summarizing phylogenetic un certainty among others\, also come with various challenges due to the sca le of a global pandemic\, complexity of the bioinformatics pipeline\, and the inherent computational intractability of Bayesian inference.\n\nTo a ddress these challenges\, we have developed a scalable and parametrizable pipeline based on the principles of nf-core to streamline the process of compiling sequence data from public databases\, setting up prior distrib utions\, performing various preliminary genomic analyses\, and finally ca lculating the posterior distributions of the epidemiologic parameters bei ng inferred using Markov chain Monte Carlo (MCMC) methods together with s imultaneously sampling the phylogeny and population size trajectory.\n\nT he main input of the pipeline are sequence and metadata files of SARS-CoV -2 either from the EpiCoV database of the Global Initiative on Sharing Al l Influenza Data (GISAID) and/or from the ones we generated in-house at t he Philippine Genome Center. The first filtering steps are subsetting the data by time period of the analysis and subsampling fraction. Outgroups are then added to stabilize the phylogenetic tree and the filtered datase t are now inputted to an Augur pipeline as preliminary genomic analyses. The multiple sequence alignment is loaded to an extensible markup languag e (XML) together with the necessary models and parameter configurations s uch as MCMC initial values\, prior distributions\, chain length\, and num ber of CPU cores to be used. The XML is then used by Bayesian evolutionar y analysis by sampling trees (BEAST2) as an input. Overall\, the pipeline provides a straightforward method of deploying the analyses to different high-performance computing cluster and configuring different combination s of parameter configurations crucial in Bayesian phylodynamic inference. DTEND:20241031T163000 DTSTAMP:20260416T133018Z DTSTART:20241031T153000 LOCATION:Poster Room SEQUENCE:330849 STATUS:CONFIRMED SUMMARY:A scalable and parametrizable pipeline for the Bayesian phylodynam ic inference of SARS-CoV-2 UID:SZSESSION703802 END:VEVENT BEGIN:VEVENT DESCRIPTION:Speaker: Urszula Smyczynska\n\nMutations in BRCA1/2 genes lead to impairment of DNA repair mechanism. In consequence\, patients are at higher risk of developing malignancies\, most often breast\, ovarian\, pr ostate\, and pancreatic cancer\, than the general population. Previously\ , we showed that the expression of miRNA is changed in sera of patients w ith BRCA1/2 mutations and selected a set of 10 miRNA as a signature of BR CA deficiency. Due to the availability of only plasma samples in some bio banks\, next we investigated if it can be used interchangeably with serum . For this purpose\, we obtained a unique dataset of 30 pairs of plasma a nd serum samples from the same patients\, both BRCA deficient and control s. MiRNA sequencing was performed in those samples and then we applied nf -core/smrnaseq pipeline v2.3.0 for mapping and counting miRNA reads. Besi des miRNAs we decided to analyze other non-coding RNAs (yRNA\, tRNA\, piR NA) as recent studies showed that their expression is changed by some pat hological conditions. To obtain alignments and counts for these RNA class es\, we used a contamination filter included in the pipeline\, modifying its output so that mappings are saved for all types of contaminations. Fi nally\, comparing serum and plasma we observed significant differences in expression of non-coding RNAs. tRNAs comprised 38.6% of reads in serum\, while they were virtually not detected in plasma (1.2%). Mature miRNAs w ere detected in both\, making 6.6% reads in serum and 12.5% in plasma\, w hile piRNA 1.1% in serum and 2.9% in plasma. In conclusion\, blood proces sing strongly affects non-coding RNA composition\, probably due to the re lease of nucleic acids from blood morphotic elements\, thus plasma and se rum are not equivalent materials in this context. DTEND:20241031T163000 DTSTAMP:20260416T133018Z DTSTART:20241031T153000 LOCATION:Poster Room SEQUENCE:330849 STATUS:CONFIRMED SUMMARY:Application of nf-core/smrnaseq pipeline for comparison of small n on-coding RNA expression in plasma UID:SZSESSION702444 END:VEVENT BEGIN:VEVENT DESCRIPTION:Speaker: Anna Delgado Tejedor\n\nThe increasing complexity and volume of data generated in sequencing facilities necessitate robust sol utions for data management and analysis. We present an integrated approac h to automating data processing\, analysis\, and management by combining a Django-based Laboratory Information Management System (LIMS) with a Nex tflow pipeline. The LIMS facilitates efficient tracking and management of samples\, projects\, and instrument runs\, while the Nextflow pipeline a utomates the execution of demultiplexing\, quality control\, and data tra nsfer. By integrating these systems\, we achieved seamless data flow\, en suring reproducibility and reducing manual errors. Furthermore\, leveragi ng containerization and HPC computing with Nextflow enhances the scalabil ity and portability of the procedures. This combined approach provides a comprehensive solution for sequencing facilities\, streamlining both data management and computational processes\, ultimately accelerating biomedi cal research and discoveries. DTEND:20241031T163000 DTSTAMP:20260416T133018Z DTSTART:20241031T153000 LOCATION:Poster Room SEQUENCE:330849 STATUS:CONFIRMED SUMMARY:Automating data management and analyses in sequencing facilities u sing a Django-based LIMS UID:SZSESSION702814 END:VEVENT BEGIN:VEVENT DESCRIPTION:Speaker: Ayorinde Afolayan\n\nThe ability to distinguish strai ns from clinical samples holds enduring medical significance. Recent adva ncements in long-read sequencing technology promise substantial improveme nts in the precision of strain-level identification from metagenomic data . In this study\, we leveraged Nextflow pipelines to elucidate the effici ency of three current bioinformatics tools—Trac'm\, Strainberry\, and Str ainy— in resolving bacterial strains from mock microbial community and au thentic metagenomes derived from long-read sequencing.\n\nMicrobial mock communities and actual microbial communities were prepared for long-read sequencing on the GridION platform. Human-DNA-free raw reads were process ed using custom Nextflow pipelines on an Ubuntu Linux distribution (v.20. 04) with ×86_64 architecture for each strain resolution tool. Trac’m alig ned these reads to a custom reference database\, while Strainberry and St rainy mapped reads to metagenome assemblies for strain resolution. We ass essed the task execution time\, physical memory usage\, and single-core C PU utilization of each tool\, utilizing pipeline information generated by each Nextflow workflow.\n\nTrac'm exhibited the highest strain completen ess in both mock and authentic metagenomes\, while Strainy demonstrated t he highest strain accuracy. Despite its higher single-core CPU usage\, Tr ac's provided faster strain resolution and better computational efficienc y compared to Strainberry and Strainy. The longer execution time and high er memory usage of Strainberry and Strainy can be attributed to their rel iance on metagenomic assembly prior to strain resolution.\nThe automated and reproducible workflows facilitated by Nextflow enable seamless integr ation of diverse bioinformatics tools\, significantly enhancing the scala bility and reliability of strain resolution processes. Of the three tools tested\, Trac'm holds the greatest potential for applications such as re al-time pathogen tracking\, outbreak identification\, transmission monito ring\, intervention evaluation\, and other public health initiatives. Mov ing forward\, we aim to integrate Trac’m into our current workflows to ex pedite pathogen tracking at the strain level\, thereby guiding actionable decisions for patient care. DTEND:20241031T163000 DTSTAMP:20260416T133018Z DTSTART:20241031T153000 LOCATION:Poster Room SEQUENCE:330849 STATUS:CONFIRMED SUMMARY:Automating reproducible workflows with Nextflow for strain resolut ion from long-read metagenomes UID:SZSESSION701862 END:VEVENT BEGIN:VEVENT DESCRIPTION:Speaker: Salomé Llabrés\n\nDesigning new medicines more effici ently and cost-effectively is closely linked to the exploration of chemic al space. On-demand chemical libraries are expanding every year\, aiming to include as much of the vastness of chemical space as possible. Current ly\, the size of these collections surpasses the throughput capabilities of standard high-throughput virtual screening (HTVS)\, requiring intellig ent solutions rather than brute-force docking.\n\nIn response\, we have d eveloped a new platform for mining these extensive libraries\, capable of rapidly screening billions of compounds to identify those with the highe st probability of binding to specific therapeutic targets and possessing favorable ADMET properties. To demonstrate proof of concept\, we have tes ted this approach on relevant oncogenic targets and experimentally valida ted the selected compounds through biological assays with therapeutically relevant readouts. The automatization of this complicated protocol into a platform is necessary to provide universal\, access to state-of-the-art drug discovery capabilities to researchers anywhere. This will incentivi ze the exploration of novel targets and mechanisms of action that are per ceived as too risky or insufficiently lucrative. DTEND:20241031T163000 DTSTAMP:20260416T133018Z DTSTART:20241031T153000 LOCATION:Poster Room SEQUENCE:330849 STATUS:CONFIRMED SUMMARY:Automatization of the bottom-up exploration of the Chemical Space for the early drug discovery UID:SZSESSION702657 END:VEVENT BEGIN:VEVENT DESCRIPTION:Speaker: Kresimir Bestak\n\nUnlocking the full potential of em erging imaging-based spatial omics technologies requires high-throughput\ , scalable\, adaptable\, and reproducible data processing. As the spatial omics field continues to grow\, the need for workflow standardization th at ensures long-term sustainability is becoming increasingly important. T he nf-core community\, tools and guidelines provide a strong foundation t o accelerate spatial omics workflow development.\nWe present two highly-m ultiplexed pipelines: Molkart\, a pipeline tailored for targeted spatial transcriptomic data (Molecular Cartography\, MERSCOPE)\, and an adaptatio n and expansion of MCMICRO\, the multiple choice microscopy pipeline for processing of large Bioformats-compatible highly-multiplexed antibody-bas ed imaging data.\n DTEND:20241031T163000 DTSTAMP:20260416T133018Z DTSTART:20241031T153000 LOCATION:Poster Room SEQUENCE:330849 STATUS:CONFIRMED SUMMARY:Bringing imaging pipelines to nf-core: MCMICRO and molkart UID:SZSESSION703527 END:VEVENT BEGIN:VEVENT DESCRIPTION:Speaker: Carolin Schwitalla\n\nThe number of newly developed r esearch software in the biomedical field has increased in the past years\ , but the reuse of this software remains a significant challenge. This pr oblem was recognised and addressed by the introduction of The FAIR princi ples (Findable\, Accessible\, Interoperable\, Reusable) for research soft ware (Barker et al.\, Scientific Data 2022\; Patel et al.\, Scientific Da ta 2023) highlighting the need for more sustainable software development . The goal is to enable the broader use of existing tools\, prevent redun dancy by each research group developing their own tools and not be depend ent on proprietary software solutions.\n\nIn this work\, we present a com putational strategy to rescue an “orphan” codebase\, a repository with no sign of developer activity\, and improve the FAIRness of published softw are. \n\nWe focus our efforts on the field of biological imaging\, where datasets can be in the terabyte range and acquisition times are lower and lower. Data analysis demonstrates special challenges\, no clear standard s or best practices can be found\, a lot of manual steps are included in the analysis when using GUIs like FIJI\, scientists often rely on proprie tary software like MATLAB or Imaris and a lack of FAIR open source end-to -end processing tools (Istrate\, Ana-Maria et al.\, 2022).\nSpecifically\ , we demonstrate the rescue of the “orphan” pipeline NuMorph ( Krupa et a l.\, Cell reports 2021) for processing and analysis of tissue-cleared who le mouse brain images. This extensive pipeline is used for processing lig ht-sheet microscopy datasets in the terabyte range\, but since its public ation\, maintenance and usage by others besides the lab of the authors co uld not be observed. \nWe implemented the existing pipeline in nextflow w ith the final goal of integrating it into the nf-core community which ult imately leads to an improvement of each of the FAIR principles. With this \, we want to contribute to a more sustainable research software developm ent process\, as well as introduce one of the first best-practice pipelin es for image processing and analysis into the nf-core community thereby m oving towards standardized and reproducible image analysis.\n DTEND:20241031T163000 DTSTAMP:20260416T133018Z DTSTART:20241031T153000 LOCATION:Poster Room SEQUENCE:330849 STATUS:CONFIRMED SUMMARY:Computational rescue strategy for an orphan codebase UID:SZSESSION703837 END:VEVENT BEGIN:VEVENT DESCRIPTION:Speaker: Adrien Coulier\n\nWith the advent of new sequencing i nstruments\, high throughput labs face unprecedented volumes of data — ex ceeding several hundred terabases per year. On top of that\, core facilit ies often have to mix different library preparations and read lengths on a single flowcell\, which is not always supported by the manufacturer's s oftware. To manage this avalanche of information\, core facilities requir e not only computational power but also a robust and flexible infrastruct ure that ensures data quality and uninterrupted flow. In other words\, an d while compute efficiency is crucial\, processing pipelines must also ad dress other critical factors\, including robustness\, reproducibility\, a nd traceability.\n\nFrom an operator’s perspective\, an ideal pipeline sh ould automatically process vanilla runs reliably while offering traceabil ity and customization options for identifying and diagnosing cases requir ing human intervention. Enter Arteria [1] — a collection of microservices built around Nextflow pipelines at the National Genomics Infrastructure (NGI) in Uppsala\, Sweden. Arteria streamlines the processing\, quality control\, and delivery of sequencing data from the moment it emerges from the sequencer until it reaches researchers.\n\nKey features of Arteria i nclude:\n- Automated Processing: Arteria decreases processing time\, enab ling bioinformaticians to focus on analyzing problematic runs.\n- Nextflo w and nf-core Integration: Arteria wraps around Nextflow and nf-core to e xecute essential steps\, including demultiplexing\, quality control\, and report generation.\n- Flagging and Prioritization: the system identifies sequencing runs that may need reprocessing or resequencing\, ensuring ef ficient resource allocation.\n\nHere\, we present Arteria’s architecture\ , its role in managing sequencing data\, and how it empowers our genomics core at NGI by automating and facilitating critical processes in our dat a workflow.\n\n[1] Dahlberg et al.\, GigaScience 2019\, https://doi.org/1 0.1093/gigascience/giz135 DTEND:20241031T163000 DTSTAMP:20260416T133018Z DTSTART:20241031T153000 LOCATION:Poster Room SEQUENCE:330849 STATUS:CONFIRMED SUMMARY:Demultiplexing at scale with Arteria UID:SZSESSION709334 END:VEVENT BEGIN:VEVENT DESCRIPTION:Speaker: Hemanoel Passarelli\n\nThe Brazilian population exhib its significant genetic diversity due to its admixed American Indigenous\ , African\, and European ancestries. This diversity makes it an ideal set ting for comprehensive genetic studies. However\, about 80% of existing g enetic knowledge is derived from studies of European populations\, result ing in missed opportunities for pioneering discoveries in non-European ge nomes that could drive healthcare innovations. Addressing this\, the 'Gen -t do Brasil' project aims to map the DNA of more than 200\,000 Brazilian s by 2027 to understand the impact of genetic factors on health\, and to develop innovative methods for disease detection and treatment. Tradition al studies often overlook such populations due to the analytical complexi ties posed by their mixed genomic heritage. We utilize the genetic divers ity of the admixed Brazilian population to conduct admixture mapping. Thi s technique is based on the hypothesis that variations in disease rates a mong different populations are due to differences in the frequency of dis ease-causing genetic variants. To tackle these challenges\, we have devel oped a series of Nextflow pipelines specifically tailored for admixture m apping. These pipelines are designed to provide robust\, scalable\, and r eproducible analyses of genetic data\, thereby enhancing our understandin g of Brazilian populations. Additionally\, integrating NFTest enhances ou r workflow management capabilities and ensures the code-safe scalability needed to establish a Brazilian biobank. This initiative sets a new stand ard for inclusivity and comprehensive research in genomic science\, highl ighting the importance of building a scalable biobank infrastructure. DTEND:20241031T163000 DTSTAMP:20260416T133018Z DTSTART:20241031T153000 LOCATION:Poster Room SEQUENCE:330849 STATUS:CONFIRMED SUMMARY:Enhancing genomic research in Brazil with Nextflow-based admixture mapping pipelines UID:SZSESSION711699 END:VEVENT BEGIN:VEVENT DESCRIPTION:Speaker: Abhinav Sharma\n\nLeveraging the recent advances in W GS analysis of Tuberculosis data to empower public health surveillance in TB endemic areas (such as Brazil and South Africa) using the MAGMA pipel ine.\n\nIn the session we will showcase how the pipeline facilitates surv eillance and how it is being integrated into national health information systems. DTEND:20241031T163000 DTSTAMP:20260416T133018Z DTSTART:20241031T153000 LOCATION:Poster Room SEQUENCE:330849 STATUS:CONFIRMED SUMMARY:From research to surveillance: Leveraging WGS for precision public health in high TB burden setting UID:SZSESSION703006 END:VEVENT BEGIN:VEVENT DESCRIPTION:Speaker: Zehra Hazal Sezer\n\nAs human omics data expands\, th e requirement for data management is to facilitate secondary use. Therefo re the German Human Genome-Phenome Archive (GHGA)\, within the federated European Genome-Phenome Archive (FEGA)\, is developing a scalable and sec ure IT infrastructure for Germany\, an ethico-legal framework to handle o mics data in a data-protection-compliant but open and FAIR manner\, a har monized metadata schema\, and standardized workflows to process all incom ing omics data. We are building upon the nf-core and nextflow community t o build NGS analysis workflows for all incoming data modalities such as n f-core/SAREK. GHGA enables the creation of harmonized and standardized NG S resources across datasets and projects by maintaining and developing wo rkflows\, creating runtime configurations for each data modality with sta ble identifies\, and continuously evaluating the performance. In collabor ation with the nf-core community and beyond\, GHGA is maintaining and co- developing six workflows. To evaluate the performance of the workflows GH GA together with the German NGS Competence Network developed a continuous benchmarking framework: NCBench. GHGA is creating a runtime configuratio n to uniformly process NGS data while guaranteeing the highest standards and quality of the workflows. By maintaining scalable\, reproducible\, an d continuously benchmarked workflows\, GHGA will create a harmonized and standardized NGS data resource ready to be used by the German research co mmunity. Such a harmonized resource will enable cross-analysis of project s and population-scale studies\, promote new collaborations and research projects\, and establish the foundation for developing a German-based var iant frequency database.\n\nGrants: German Research Foundation (DFG) 4419 14366 (NFDI 1/1) DTEND:20241031T163000 DTSTAMP:20260416T133018Z DTSTART:20241031T153000 LOCATION:Poster Room SEQUENCE:330849 STATUS:CONFIRMED SUMMARY:GHGA: Standardizing and harmonizing NGS analysis workflows to crea te a unified omics data resources UID:SZSESSION707499 END:VEVENT BEGIN:VEVENT DESCRIPTION:Speaker: Louis Le Nézet\n\nGenome imputation is a statistical technique that enhances the resolution of genotyping arrays and low-pass sequencing (<1x) by filling missing data with information from reference panels. While existing pipelines primarily focus on the imputation step and in the human species\, crucial steps such as panel preparation\, phas ing\, and imputation assessment are often overlooked. \nTo address this g ap\, we introduce nf-core/phaseimpute\, a comprehensive pipeline performi ng panel preparation\, genetic simulation\, imputation\, and tool assessm ent. Each step is designed for independent execution\, enabling users to save outputs and computational time for subsequent analysis. In addition\ , we took advantage of Nextflow’s capabilities in workflow distribution b y processing each dataset by chromosomes or chunks. This means that tasks can be processed in parallel\, reducing overall execution time. With sup port for various imputation tools like GLIMPSE1\, GLIMPSE2\, STITCH\, and QUILT\, the pipeline accommodates diverse research needs. Moreover\, it offers flexibility by allowing execution with or without reference panels \, making it invaluable for non-model species where phased haplotypes may not always be available.\nThe journey from the initial idea to the first release of the nf-core/phaseimpute pipeline in the nf-core community has been an extensive one. Starting with the aim of creating an efficient\, reproducible solution for genomic phasing and imputation\, we developed t he essential imputation and phasing processes within the nf-core modules repository before integrating them into subworkflows. Using the existing nf-core modules significantly accelerated the development process. Throug hout the implementation\, we observed that some nf-core modules required design modifications when first tested within the pipeline context. These adjustments were necessary to accommodate all required parameters and en sure the modules met the user’s specific requirements. Consequently\, we contributed back to the community by adding new functionality that was no t previously available. . Additionally\, we benefited from advancements m ade in Nextflow plugins\, such as nf-validation and nf-test. The nf-valid ation plugin enabled us to enforce schema validation\, ensuring that our pipeline configurations met predefined standards and reducing the likelih ood of errors. Continuous integration testing and the nf-test plugin were used to verify that each update maintained the pipeline's accuracy and s tability\, ensuring that no matter the changes different developers would make in the code\, the final output files would still be the same. Colla borative efforts within the bioinformatics community facilitated the inte gration of optimal tools and rigorous testing\, resulting in a reliable\, high-performance pipeline now accessible to the nf-core community for ad vanced genomic research. DTEND:20241031T163000 DTSTAMP:20260416T133018Z DTSTART:20241031T153000 LOCATION:Poster Room SEQUENCE:330849 STATUS:CONFIRMED SUMMARY:Introducing nf-core/phaseimpute -r dev from idea to release UID:SZSESSION710344 END:VEVENT BEGIN:VEVENT DESCRIPTION:Speaker: Evangelos Karatzas\n\nThe MGnify resource (https://ww w.ebi.ac.uk/metagenomics) is a platform for the assembly\, analysis\, and archiving of microbiome derived sequence data. MGnify has a repertoire o f specialized pipelines (the majority described in Nextflow) to generate detailed taxonomic and functional annotations depending on the nature of the input data (metagenomic\, metatranscriptomic\, and metabarcoding). In late 2018\, MGnify introduced metagenomic assembly as a service\, provid ing greater access to complete proteins and their genomic context. Buildi ng upon these assemblies the resource has witnessed an increasing shift t o genome-resolved metagenomics. This is demonstrated by the MGnify Genome s resource which hosts 11 biome-specific MAG (metagenome-assembled genome ) catalogs comprising hundreds of thousands of genomes\, produced by the community and the MGnify team.\n\nAt the time of writing\, MGnify has ide ntified ~2.5 billion unique protein sequences from their metagenomic asse mblies - one of the largest sequence collections in the world\, which are clustered at 90% sequence identity to produce ~720 million representativ e sequences. The proteins contained in the MGnify protein database includ e those that are members of known protein families\, as well as those tha t represent hitherto novel protein families. We have developed Nextflow p ipelines to iteratively cluster the sequences to produce metagenomics pro tein families called MGnifams. Through these pipelines\, we determine whe ther these new families represent expansions of known protein families or entirely novel protein families. MGnifams consists of various in-house d eveloped Nextflow workflows revolving around data preprocessing\, sequenc e clustering and family generation\, redundancy checking\, matching famil y profile hidden Markov models to known Pfam domains\, structural predict ion and annotation\, and data exporting to facilitate ingestion into a de dicated MGnifams database. These workflows can be executed either individ ually or in a complete end-to-end Nextflow pipeline. Workflows are compos ed of subworkflows and modules\, using both modules in-house developed (h ttps://github.com/EBI-Metagenomics/nf-modules) and nf-core ones. An alpha -version demo of MGnifams can be found online here: http://mgnifams-demo. mgnify.org DTEND:20241031T163000 DTSTAMP:20260416T133018Z DTSTART:20241031T153000 LOCATION:Poster Room SEQUENCE:330849 STATUS:CONFIRMED SUMMARY:MGnifams - Workflows for the generation and annotation of metageno mics derived protein families UID:SZSESSION709328 END:VEVENT BEGIN:VEVENT DESCRIPTION:Speaker: Victoria Cepeda Espinoza\n\nThe COVID-19 pandemic hig hlighted the need for robust genomic surveillance. Inspired by SARS-CoV-2 monitoring efforts\, we established a central data hub and an automated Nextflow bioinformatics workflow to collect\, store\, and analyze bacteri al sequencing data from clinical patients. This project aims to detect em erging pathogens\, monitor antimicrobial resistance (AMR) in bacterial pa thogens\, and is part of the Genomic Pathogen Surveillance in Germany (Ge nSurv and GenSurv+) initiatives.\n\nOur Nextflow pipeline efficiently han dles short- and long-read bacterial sequencing data\, automating genome a ssembly\, annotation\, plasmid identification\, and AMR gene identificati on. By leveraging Nextflow's capabilities\, the hub supports customizable configurations\, Docker-based deployment\, and persistent storage\, ensu ring seamless data processing and analysis. DTEND:20241031T163000 DTSTAMP:20260416T133018Z DTSTART:20241031T153000 LOCATION:Poster Room SEQUENCE:330849 STATUS:CONFIRMED SUMMARY:Monitoring Antimicrobial Resistance (AMR) in clinical bacterial pa thogens UID:SZSESSION711696 END:VEVENT BEGIN:VEVENT DESCRIPTION:Speaker: Abhinav Sharma\n\nMTBseq is one of the earliest publi cly available pipelines for TB genomics and is widely used by the researc h community. However\, its design limits the CPU usage to 8 threads. Usin g a Nextflow wrapper\, we have achieved an optimization of at least 50% f or large cohorts of TB genomics data\, without altering the baseline tool . DTEND:20241031T163000 DTSTAMP:20260416T133018Z DTSTART:20241031T153000 LOCATION:Poster Room SEQUENCE:330849 STATUS:CONFIRMED SUMMARY:MTBseq-nf: Enabling scalable Tuberculosis genomics “big data” anal ysis through a Nextflow Wrapper UID:SZSESSION703009 END:VEVENT BEGIN:VEVENT DESCRIPTION:Speaker: Praveen krishna Chitneedi\n\nWe developed a Nextflow dsl2 based pipeline to perform eQTL association studies with expression a nd genotype data. This pipeline support different input formats and can p erform cis\, trans \, ase and splicing eQTL studies. This pipeline takes the advantage of nextflow dsl2 modular concept to offer more flexibility for the users. DTEND:20241031T163000 DTSTAMP:20260416T133018Z DTSTART:20241031T153000 LOCATION:Poster Room SEQUENCE:330849 STATUS:CONFIRMED SUMMARY:Nextflow based pipeline for association studies UID:SZSESSION710983 END:VEVENT BEGIN:VEVENT DESCRIPTION:Speaker: Till Englert\n\nData analysis using computational pip elines is scaling to new heights\, allowing many fields to unlock new dep ths of interpretation. However\, pipeline optimisations tend to focus on speed rather than resources\, making CO2 emissions a neglected issue for many applications.\n\nWe aim to make Nextflow pipelines natively report o n the CO2 footprint of the current workflow\, enabling users to make info rmed decisions about the allocation of computational resources and to ado pt more eco-friendly configurations. During pipeline execution\, nf-co2fo otprint calculates the CO2 emissions for each task based on Nextflow's re source usage metrics and information about the power consumption of the u nderlying compute system\, taking into account the carbon footprint of th e respective energy production. All results are presented in a user-frien dly report similar to the Nextflow pipeline execution report.\n\nIn a pro totype version of the nf-co2footprint\, we have already demonstrated the potential of carbon footprint analysis to further optimise resource usage . We will present the latest version of nf-co2footprint\, among others wi th improved provenance tracking\, and share updates about the project. We aim to improve its accuracy and make it more widely applicable to resear chers and institutes around the world using different compute systems. DTEND:20241031T163000 DTSTAMP:20260416T133018Z DTSTART:20241031T153000 LOCATION:Poster Room SEQUENCE:330849 STATUS:CONFIRMED SUMMARY:nf-co2footprint: A Nextflow plugin to estimate the carbon footprin t of pipeline runs UID:SZSESSION711555 END:VEVENT BEGIN:VEVENT DESCRIPTION:Speaker: Igor Trujnara\n\nOrthology is a highly relevant aspec t of genomics\, as orthologous genes allow functional inference\, identif y certain evolutionary constraints and are used for reconstructing the tr ee of life. This is especially important in light of new massive sequenci ng initiatives\, most importantly the Earth Biogenome Project. There is a large variety of publicly available orthology prediction methods\, but t he results they provide are highly varied and agreement is limited. Signi ficant effort is made to assess the performance of those methods\, most n otably through the ongoing Quest for Orthologs\, which created a comprehe nsive benchmark for orthology prediction.\nWe propose nf-core/reportho\, a Nextflow pipeline for comparative analysis of ortholog predictions. The pipeline analyzes the predictions from the perspective of a single query protein. The pipeline retrieves orthology predictions from public source s\, performs comparative analysis\, calculates agreement statistics and c reates multiple visual representations. It also provides basic downstream analysis in the form of MSA and phylogenetic reconstruction. We envision that nf-core/reportho will enable and accelerate new research projects i nvolving specific proteins\, as well as systematic investigation of ortho logy databases. DTEND:20241031T163000 DTSTAMP:20260416T133018Z DTSTART:20241031T153000 LOCATION:Poster Room SEQUENCE:330849 STATUS:CONFIRMED SUMMARY:nf-core/reportho: a pipeline for comparative analysis of ortholog predictions UID:SZSESSION702064 END:VEVENT BEGIN:VEVENT DESCRIPTION:Speaker: Margherita Mutarelli\n\nOver the past decade\, the st udy of genome structural organization has progressed swiftly due to advan cements in high-throughput technologies. The role of chromatin organizati on is only starting to reveal its importance in timely regulation of gene expression as its aberrant alterations have in many pathologies. The SAM MY-seq (Sequential Analysis of Macro Molecule accessibility sequencing) i s an innovative technique based on the separation of chromatin in fractio ns\, each progressively based on their solubility and accessibility\, and extraction and sequencing of the DNA present in each of them. Unlike oth er methods\, SAMMY-seq does not require crosslinking or antibodies\, can help reconstructing active and inactive chromatin compartments\, and is r elatively cost-effective. \nHere we present nf-core/sammyseq\, the first Nextflow pipeline for SAMMY-seq analysis implemented using the community standards and best practices for reproducibility and portability. DTEND:20241031T163000 DTSTAMP:20260416T133018Z DTSTART:20241031T153000 LOCATION:Poster Room SEQUENCE:330849 STATUS:CONFIRMED SUMMARY:nf-core/sammyseq: A new pipeline for Sequential Analysis of MacroM olecules accessibilitY sequencing UID:SZSESSION711698 END:VEVENT BEGIN:VEVENT DESCRIPTION:Speakers: Alfred Kedhammar\, Franziska Bonath\n\nProviding hig h quality data from a range of different sequencing instruments to their users is in the interest of every sequencing facility. In order to monito r their sequencing quality\, performing standardized\, yet flexible quali ty controls for every sequencing project and sample that passes through t heir facilities is crucial to ensure consistent quality and dependable re sults.\n\nThe Nextflow pipeline nf-core/seqinspector is envisioned as a u nified quality control pipeline for sequencing data originating from inst ruments of various providers like Illumina\, Oxford Nanopore Technologies or Pacific Biosciences.\n\nIt will assess sequencing quality\, duplicati on levels and complexity on a per-sample basis\, in addition to highlight ing adapter contents and technical artifacts. Furthermore\, it will facil itate the detection of common biological contaminants that may have been introduced to the samples before or during library preparation.\n\nSince facilities share their flowcells and even sequencing lanes between differ ent projects\, the report generation will be particularly versatile and c ustomizable. Quality reports can be obtained with a variable granularity ranging from individual samples or projects to whole flow cells. Therefor e\, receiving one single MultiQC report that summarizes all input samples \, or having individual MultiQC reports for sample groups determined by t he sample sheet will be possible. \n\nWhile nf-core/seqinspector is devel oped by and for core facilities\, it will also be a useful QC solution fo r research groups that own or have access to a sequencer outside of facil ities. This project is still under development\, and we are happy to welc ome collaborators.\n DTEND:20241031T163000 DTSTAMP:20260416T133018Z DTSTART:20241031T153000 LOCATION:Poster Room SEQUENCE:330849 STATUS:CONFIRMED SUMMARY:nf-core/seqinspector - A basic QC pipeline for sequencing core fac ilities UID:SZSESSION706734 END:VEVENT BEGIN:VEVENT DESCRIPTION:Speaker: Mariangela Santorsola\n\nDespite advancements in gene tic research\, a considerable portion of the genetic basis for complex tr aits remains unaccounted for\, known as missing heritability. This phenom enon challenges our understanding and ability to predict genetic influenc es on such traits. Contributing factors include rare variants\, and non-l inear genetic interactions which collectively can impact a single trait. \n\nSynthetic or simulated data\, which closely mimic the real-world gene tic architectures\, i.e. preservation of linkage disequilibrium in Human populations\, play a crucial role in developing new statistical\, bioinfo rmatics\, and deep learning methods to address genetic complexities. By i njecting both rare or common loci\, as well as interacting variants into simulated data\, researchers aim to enhance the analytical power of newly developed methods.\n\nSeveral tools exist for generating synthetic datas ets tailored to specific i) study designs\, such as family-based or case/ control models\, and ii) genetic architectures\, including rare or common single loci associated variants\, or interacting loci with and without m arginal effect\, different penetrance or trait prevalence. However\, impl ementing these tools can be challenging due to the diversity in employed programming languages (i.e. Julia\, Java\, and Python)\, installation pro cedures\, and usage specifications. The lack of standardized environments like conda containers complicates the installation process across platfo rms\, hindering their widespread application. Additionally\, each tool re quires unique genetic model definitions\, including different parameter s ettings\, input formats\, and quality control steps\, adding complexity t o their use. \n\nTo address these challenges\, we are developing the nf-c ore/variantsimulator\, a comprehensive Nextflow pipeline that integrates selected tools (Epigen2\, EpiReSim3\, Gametes4\, HAPNEST5) to generate gr ound-truth phenotype and genotype datasets. It is designed to accept a si ngle standardized model definition file. Based on the desired design and genetic architecture to be simulated\, the model definition is automatica lly translated into the appropriate format for the tool being used. The p ipeline outputs genetic data in standard formats\, such as VCF and PLINK\ , to ensure downstream compatibility. Additionally\, linkage disequilibri um and GWAS analyses are incorporated into downstream QC steps to ensure the simulation meets the desired outcomes. The variantsimulator pipeline will streamline the creation of varied statistical and deep-learning solu tions in numerous research areas.\nMoreover\, coordination with other nf- core simulation (readsimulator) or genetic analysis pipelines (sarek) wil l be ensured to allow future integrations. DTEND:20241031T163000 DTSTAMP:20260416T133018Z DTSTART:20241031T153000 LOCATION:Poster Room SEQUENCE:330849 STATUS:CONFIRMED SUMMARY:nf-core/variantsimulator: A pipeline to simulate variants with dif ferent genetic architectures UID:SZSESSION702098 END:VEVENT BEGIN:VEVENT DESCRIPTION:Speaker: Darko Cucin\n\nThe nf-core/rnaseq pipeline is the mos t utilized nf-core workflow and has received a considerable amount of sup port from the community in terms of development. A number of benchmarks h ave been performed and published using the pipeline’s test_full profile\, which consists of 8 samples. To perform a benchmark that better reflects the common use case\, rnaseq was run on the SevenBridges platform using a dataset containing 78 samples of distinct human liver biopsies. The use d dataset can be found under the accession number PRJNA542148 with an ave rage input file size of ~4.2 GB (paired-end data) per sample. \n\nThe pi peline optimization was focused on enhancing both computation and storage resources to reduce the analysis cost. \n\nTo optimize performance\, sp ecific computational requirements and instances were assigned to each pro cess. Additionally\, to process multiple samples simultaneously and speed up task execution\, up to 10 parallel AWS instances were used. Running t he analysis with multiple parallel instances allowed for faster sample pr ocessing\, reducing the execution time by a significant margin. Allocated instances were chosen to best fit each job’s computational requirements. \n\nTo optimize storage\, additional disks were attached as the instance s approached their storage limit\, instead of attaching a large disk for the whole duration of execution. \n\nThe SevenBridges platform handled co mputational resource allocation and task orchestration. \n\nWhen the pipe line was run with the same optimization setup\, a cost reduction of up to 45% and a decrease in wall time by as much as 41.5% were achieved\, comp ared to the default setup. DTEND:20241031T163000 DTSTAMP:20260416T133018Z DTSTART:20241031T153000 LOCATION:Poster Room SEQUENCE:330849 STATUS:CONFIRMED SUMMARY:Optimization of nf-core/rnaseq pipeline using a large number of sa mples on the SevenBridges Platform UID:SZSESSION703577 END:VEVENT BEGIN:VEVENT DESCRIPTION:Speaker: Danilo Di Leo\n\nIn the last decade\, the study of mi crobial communities through RNA sequencing from various environments has significantly increased. Metatranscriptomics offers insights into metabol ic processes within microbial communities\, providing a snapshot of gene expression based on in situ environmental conditions. To support biologis ts in this endeavor and to promote reproducibility and standardization in data analysis\, we developed two complementary pipelines with the help o f the nf-core community: nf-core/metatdenovo and nf-core/magmap. These pi pelines\, designed to be user-friendly and reproducible\, aim to investig ate the activity of microbial communities with varying levels of genomic knowledge.\n\nThe nf-core/metatdenovo pipeline applies a de novo assembly approach to annotate metatranscriptomic data. This approach constructs t ranscriptomes directly from RNA sequencing reads without requiring a refe rence genome\, followed by quantification of active genes and the assignm ent of both taxonomy and functional annotation. This approach makes nf-co re/metatdenovo particularly advantageous for studying environments where genomic resources are scarce or incomplete. Such environments could inclu de extreme habitats like the deep sea or soils\, where many organisms rem ain uncultured and uncharacterized.\nThe nf-core/magmap pipeline instead is applicable for communities for which genomes are available – e.g. gut microbiomes or surface water – either in the form of metagenome-assembled genomes (MAGs) or reference genomes. The pipeline identifies references genomes using a kmer-based approach using Sourmash\, to continue with map ping reads to identified genomic references\, to allow quantification of expressed genes.\n\nBy developing these pipelines\, we aimed to provide b iologists with robust\, flexible tools that cater to different research n eeds and environmental contexts. To demonstrate the utility and performan ce of these pipelines\, we will show a comparative analysis using a datas et derived from a complex microbial community. Our results show the disti nct advantages of the nf-core/metatdenovo and nf-core/magmap pipelines in different ecological contexts. DTEND:20241031T163000 DTSTAMP:20260416T133018Z DTSTART:20241031T153000 LOCATION:Poster Room SEQUENCE:330849 STATUS:CONFIRMED SUMMARY:Reproducible analysis of metatranscriptomics either through nf-cor e/metatdenovo or nf-core/magmap UID:SZSESSION702808 END:VEVENT BEGIN:VEVENT DESCRIPTION:Speaker: Ira Iosub\n\nRibosome profiling is a cutting-edge met hod that provides a detailed view of protein synthesis across the entire set of RNA molecules within cells. To ensure the reliability of such stud ies\, high-quality data and the ability to replicate analyses are crucial . To address this\, we present riboseq-flow\, a new tool built with Nextf low DSL2\, tailored for analysing data from ribosome profiling experiment s. This pipeline stands out for its ease of use\, flexibility\, and commi tment to high reproducibility standards. It's designed to handle multiple samples simultaneously\, ensuring efficient analysis for large-scale stu dies. Moreover\, riboseq-flow automatically generates detailed reports an d visual representations to assess the data quality\, enhancing researche rs' understanding of their experiments and guiding future decisions. We h ope that our modules and workflow will be useful assets for integration w ith complementary pipelines (e.g. nf-core/riboseq) and community endeavou rs in developing cutting-edge ribo-seq analysis pipelines. DTEND:20241031T163000 DTSTAMP:20260416T133018Z DTSTART:20241031T153000 LOCATION:Poster Room SEQUENCE:330849 STATUS:CONFIRMED SUMMARY:riboseq-flow: a streamlined\, reliable pipeline for ribosome profi ling data analysis and QC UID:SZSESSION703376 END:VEVENT BEGIN:VEVENT DESCRIPTION:Speaker: Luca Beltrame\n\nCopy number alterations (CNAs)\, tha t is alterations of the number of copies of DNA in a cell\, occur in tumo rs (but also some genetic disorders) as a result of chromosomal instabili ty\, and are present with varying degrees in many different types of canc ers. These structural changes are not only an indication of the state of the tumor genome\, but may be exploited for diagnostic\, prognostic\, or treatment purposes. For example\, the presence of absence of specific alt erations may influence response to drugs\, or indicate the risk of a pati ent to relapse after surgical or pharmacological treatment. In addition\, copy number alterations can be tracked not only in tissues\, but also in other biological samples\, such as plasma\, or other specimens routinely used in the clinic.\n\nThe standard approaches towards detection of CNAs rely mostly on whole exome sequencing (WES) or whole genome sequencing ( WGS). Although precise and sensitive\, both WGS and WES are poorly suitab le for a clinical setting\, due to the sequencing costs and the storage a nd computing requirements. An alternative that emerged in the past decade is the use of shallow whole-genome sequencing (sWGS)\, also called ultra low-pass whole-genome sequencing (ULP-WGS). sWGS involves sequencing the entire genome at very low depths of coverage (from 0.1X to 1X)\, and all ows identification of CNAs with reasonable precision\, with much lower c omputing power requirements at a fraction of the cost of WES and WGS. The bioinformatics has developed a range of tools to handle these data\, eit her coming from sequencing of bulk tissue\, biological samples\, or singl e cells\, but dedicated\, fully reproducible pipelines are still lacking despite the maturity of the approach.\n\nTo fill this gap\, we have devel oped SAMURAI (Shallow Analysis of Copy nuMber alterations Using a Reprodu cible And Integrated bioinformatics pipeline)\, a pipeline for the analys is of sWGS data (either from tissue samples\, or other biological specime ns) that leverages both Nextflow and the tools developed by the nf-core c ommunity to ensure robustness and reproducibility of the results. Current ly SAMURAI is being used for all the sWGS analyses in the Cancer Pharmaco logy laboratory at Humanitas Research Hospital. Here I will describe the design principles of SAMURAI\, how it was developed\, and the advantages it brought over custom scripts. \n\nThis session was funded by AIRC 2019 IG\, project code 23059. DTEND:20241031T163000 DTSTAMP:20260416T133018Z DTSTART:20241031T153000 LOCATION:Poster Room SEQUENCE:330849 STATUS:CONFIRMED SUMMARY:SAMURAI: A pipeline for DNA copy number analysis of shallow whole- genome sequencing experiments UID:SZSESSION709695 END:VEVENT BEGIN:VEVENT DESCRIPTION:Speaker: Sameesh Kher\n\nSpatial omics technologies represent a transformative approach in biological research\, enabling the comprehen sive analysis of molecular profiles within their native spatial context. By preserving the spatial relationships between cells\, spatial omics tec hnologies\, such as 10x Xenium\, offer critical insights into tissue arch itecture\, cellular heterogeneity\, and the microenvironment's role in a healthy and disease context. As there is an increase in demand for unders tanding spatial patterns to study diseases\, there is a need for standard ized and reproducible workflows. The nf-core community thus presents spat ialxe\, a blueprint for the analysis of Xenium data. Spatialxe supports f eatured benchmarked tools\, such as Xenium Ranger. It generates a spatial object data that includes the cell feature matrix that can be used for f urther downstream analysis. We would also implement a number of segmentat ion algorithms like Cellpose\, Baysor and QuPath for image annotation. Sp atialxe will be an extensive pipeline to cover not only standard processi ng but also single cell and spatial omics quality control\, conversion of the data to be SpatialData ready\, and automated image annotation. The w orkflow will be deployed within the German Human Genome-Phenome Archive ( GHGA - www.ghga.de) as the default analysis workflow for incoming Xenium data. The pipeline will be used to process and analyze Xenium data from p rimary tumor and metastase samples of hard-to-treat entities of colorecta l cancer. The standardization as well as the reproducibility of the Nextf low/nf-core pipelines combined with the infrastructure for FAIR omics dat a usage and ethico-legal framework offered by GHGA will enable cross-proj ect analysis and hence promote new collaborations and research projects. DTEND:20241031T163000 DTSTAMP:20260416T133018Z DTSTART:20241031T153000 LOCATION:Poster Room SEQUENCE:330849 STATUS:CONFIRMED SUMMARY:Spatialxe - Standard processing and analysis of spatial Xenium in situ data UID:SZSESSION703117 END:VEVENT BEGIN:VEVENT DESCRIPTION:Speaker: Mark Polster\n\nStreamlining T-cell Receptor based i mmunological research by combining competitve state-of-the-art tools in n f-core/airrflow\n \nThe immune system recognizes antigens from external s ources such as viruses\, bacteria\, or cancers\, and initiates an immune response that is carrying out the required action. T-cells\, one the majo r players in adaptive immunity either eliminate infected cells or release cytokines to invoke a response from other immune cells. Each person has a unique T-cell repertoire comprising numerous clonal groups (10^6-10^8)\ , each with a distinct T-cell receptor (TCR). Gabernet et al. demonstrat ed the effectiveness of the analysis of a B-cell receptor (BCR) repertoir e study using nf-core/airrflow\, a containerized and publicly accessible computational pipeline within the nf-core community which can be used to study the adaptive immune receptor repertoire (AIRR). The pipeline alread y incorporates state-of-the-art tools such as the Immcantation framework while ensuring portability\, reproducibility\, scalability\, and adherenc e to the FAIR principles.\n\nWe present the innovations with a focus on t he T-cell component of nf-core/airrflow by incorporating new functionalit ies for analyzing both publicly available and newly generated TCR-sequenc ing data as well as raw single-cell AIRR-sequencing data and for extracti ng AIRR sequences from raw unselected RNA-seq data. This is possible thro ugh incorporation of MiXCR\, TRUST4 and the cellranger vdj frameworks int o the pipeline. Furthermore\, these tools have been smoothly integrated w ith the downstream Immcantation framework by the use of the AIRR communit y standard format\, enabling users to leverage and combine the strengths of different tools in a single pipeline run and to compare the results f rom various competing tools. Consequently\, this approach can enhance the accuracy and significance of TCR repertoire sequencing experiments by ex panding the tool repertoire of nf-core/airrflow to enable comparative ana lysis of results across tools.\n DTEND:20241031T163000 DTSTAMP:20260416T133018Z DTSTART:20241031T153000 LOCATION:Poster Room SEQUENCE:330849 STATUS:CONFIRMED SUMMARY:Streamlining T-cell receptor based research by combining competitv e tools in nf-core/airrflow UID:SZSESSION703549 END:VEVENT BEGIN:VEVENT DESCRIPTION:Speaker: Luciane Sussuchi\n\nGlioblastoma is the most aggressi ve primary malignant tumor of the central nervous system in adults. Treat ment consists of surgical resection\, followed by radiotherapy and chemot herapy with alkylating agents. Temozolomide (TMZ) is an alkylating agent that acts on the methylation of O6-Guanines\, activating the mismatch rep air mechanisms and triggering cell apoptosis. The MGMT gene produces an e nzyme that corrects the damage to DNA caused by TMZ\, leading to treatmen t resistance. Objective: This study aims to identify and characterize nov el compounds with selective inhibitory potential against MGMT using in si lico computational methodologies\, including screening natural Brazilian compounds from the Nubbe platform. The three-dimensional structure of MGM T was obtained from the RCSB Protein Data Bank and further refined for ap o and holo-models of zinc ion using Modeller. The pKa of titratable resid ues were estimated with H++ and molecular dynamics simulations of 500 ns were performed with GROMACS for both apo and holo-models. Subsequently\, molecular docking will be carried out to search for molecules with the po tential to interact with active site binding regions\, followed by analys is of selected compounds' physicochemical and pharmacological properties. This comprehensive approach aims to identify promising MGMT inhibitors f rom natural sources that could potentially enhance the therapeutic effica cy of TMZ in glioblastoma patients\, offering new avenues for overcoming treatment resistance. DTEND:20241031T163000 DTSTAMP:20260416T133018Z DTSTART:20241031T153000 LOCATION:Poster Room SEQUENCE:330849 STATUS:CONFIRMED SUMMARY:Structure-guided drug screening for mgmt inhibitors in glioblastom a resistance UID:SZSESSION739350 END:VEVENT BEGIN:VEVENT DESCRIPTION:Speaker: Pieter Moris\n\nDespite wide-scale control and elimin ation efforts\, the global decline in malaria has stagnated in recent yea rs. There is a strong need for malaria molecular surveillance in endemic regions to address the threats posed by antimalarial drug and diagnostic resistance\, and to strategically prioritize the limited resources for ma laria control programs. Meanwhile\, non-endemic regions are not only bein g faced with an increasing number of travel-related malaria cases\, in co njunction with adverse clinical outcomes\, but also with rare cases of au tochthonous transmission.\n\nTo address these issues\, we present the SUr veillance of Molecular epidemiology of Malaria In Travelers (SUMMIT) proj ect. Travelers present an as-of-yet untapped resource of diverse genomic information on the Plasmodium parasite\, which allows for close-to-real-t ime data collection and sharing\, as well as the standardization of lab a nd bioinformatics methodologies.\n\nWe aim to establish a pioneering plat form for the routine systematic surveillance of travel-related malaria\, which combines parasite whole-genome sequencing data with epidemiological patient data. The platform will be enabled through a global network of t ravel clinics (GeoSentinel) and embrace FAIR principles by providing stan dardized and reproducible bioinformatics analysis and reporting pipelines . These will be built using Nextflow and powered by nf-core's components and best practices. \n\nWe will leverage this continuously growing and gl obal-spanning resource to monitor the emergence and spread of clinically relevant markers\, develop machine learning tools to trace parasite origi ns\, and conduct studies on treatment failure. Our approach is complement ary to existing surveillance approaches and strives to increase the geogr aphic\, temporal and genetic resolution of the available genomic data and function as a sentinel system to aid preparedness efforts for reactive a nd coordinated responses to outbreaks. Our ultimate goal is to provide an up-to-date and accessible resource containing actionable data that can f acilitate further malaria research\, inform case management\, prioritize efficacy studies\, and aid in surveillance and control efforts.\n\nHere\, we give an overview of the initial setup of the SUMMIT project\, our pre liminary findings\, and future objectives\, paying particular attention t o how we will utilize Nextflow pipelines to achieve our goals. DTEND:20241031T163000 DTSTAMP:20260416T133018Z DTSTART:20241031T153000 LOCATION:Poster Room SEQUENCE:330849 STATUS:CONFIRMED SUMMARY:Surveilling the genomic epidemiology of malaria in travelers UID:SZSESSION703327 END:VEVENT BEGIN:VEVENT DESCRIPTION:Speaker: Eray Sahin\n\nIntroduction\nShotgun metagenomic seque ncing is an informative but very complex method exhibiting several challe nges. There is no gold standard established for the bioinformatic analysi s of the shotgun metagenomics data\, and existing methods and pipelines e xpose varying advantages and disadvantages. Researchers decide on the ana lysis methodology considering different aspects such as the sensitivity\, usage simplicity of the tools or the computational power and time necess ary to perform whole analysis. In this study\, shotgun metagenomics seque ncing data\, obtained from rat faecal samples to investigate the gut-brai n axis in the kainic acid-induced epilepsy development\, were used to tes t the taxonomic classification efficiencies of different analysis algorit hms and methods.\nMethods\nA total of three rat subgroups were defined as Sham controls\, kainic acid administered rats with and without seizure d evelopment as Epi and No-Epi. DNA extracted from 66 rat faecal samples we re sequenced on Illumina NovaSeq 6000 platform with 2x100 bp by CeGaT Gmb H (Tübingen/Germany). Two main approaches were employed\; assembly-free a nalysis or de novo assembly of the short reads to obtain metagenomic asse mbled genomes (MAGs).\nFor assembly-free methods\, pre-processes reads we re analyzed using three different tools\; Kraken2\, METAgenomic PHyLogene tic Analysis 4 (MetaPhlAn4)\, and Kaiju. Summary tables were prepared eit her manually or automatically using built-in functions of the tools if av ailable.\nFor the generation of MAGs\, pre-processing\, hybrid-assembly a nd binning steps were carried out using nf-core/mag analysis pipeline (ve r. 2.2.1). For assembly step\, reads of the samples belonging to same rat group were co-assembled. Independent from nf-core\, a third metagenomic binning tool\, MetaBinner was used. All the bins produced by three binnin g tools were combined\, and processed by bin refinement module of MetaWRA P using CheckM\, based on minimum completeness rate of 70% and maximum co ntamination of 10%. After obtaining first set of MAGs\, pre-processed rea ds were mapped to them\, and unmapped reads were used for second iteratio n of MAG generation by following the steps above\, except\, all the unmap ped reads were pooled and used for co-assembly. Two sets of MAGs were com bined\, and dereplicated via dRep tool with default ANI threshold paramet ers. Taxonomic annotation of the final set of MAGs was carried out by Gen ome Taxonomy Database (GTBD)-Tk (ver. 2.1.1) using reference database ver sion of R207_v2.\nResults\nWhen the annotations are aggregated into two c ategories based on classification rates\, for Kraken2\; median rates are obtained as 17.46% and 82.53% for classified and unclassified reads\, res pectively. In case of annotation using MetaPhlAn4\, the classified and un classified portions of the reads are 56.76% and 43.24%\, respectively. Ka iju classification based on phylum level annotations\, the median rates w ere 49.4% for bacteria and 39.19% for unassigned reads. \nIn case of MAG generation strategy using a hybrid workflow composed of nf-core mag pipel ine and additional customs tools and step\, three rat group-based co-asse mbly strategy in the first round yielded 176\, 179\, and 175 MAGs from Sh am\, No-Epi\, and Epi group sample reads\, respectively. In the second ro und\, all sample reads that are not mapped to those three MAG groups were pooled and assembled\, and 109 MAGs were produced. After dereplication o f all the MAGs obtained\, a final catalogue composed of 324 unique MAGs w ere obtained. When the pre-processed reads were mapped to the MAG catalog ue\, a median mapping rate of 52.42% was obtained for pre-processed reads of 66 samples based on unique and concordant mapping settings. Based on GTBD-Tk classification\, 100% of the MAGs were efficiently annotated at t he phylum\, class\, order\, and family level\, while 97.5% and 62% of the m were annotated at the genus and species levels\, respectively.\nDiscuss ion\nIn this study\, taxonomic composition of the gut microbiota in three rat groups were examined by analysis of deep shotgun metagenomics sequen cing data with different approaches including assembly-free and de novo a ssembly methods. Relatively conservative parameters were set to reduce ri sk of false-positive annotation\, and hence\, it further reduced the rate s of classification from Kraken2 and Kaiju\, and MetaPhlAn4 showed the be st performance by means of classification success. On the other hand\, me tagenomic assembly approach outperformed short-read based methods. We obt ained a catalogue composed of 324 MAGs representing 52.42% of the pre-pro cessed reads with much higher resolution obtained in different taxonomic levels. DTEND:20241031T163000 DTSTAMP:20260416T133018Z DTSTART:20241031T153000 LOCATION:Poster Room SEQUENCE:330849 STATUS:CONFIRMED SUMMARY:Testing different approaches in analysis of shotgun metagenomics d ata from epilepsy rat model UID:SZSESSION711015 END:VEVENT BEGIN:VEVENT DESCRIPTION:Speaker: Stephan Amstler\n\nLong-range nanopore sequencing all ows single-molecule sequencing but still faces challenges due to its high error rate. Umi-pipeline-nf addresses this by using unique molecular ide ntifiers (UMIs) to create highly accurate single-molecule consensus seque nces\, reducing error rates by over 100-fold.\n\nUmi-pipeline-nf is a Nex tflow-based pipeline designed to process UMI-tagged nanopore sequencing d ata. The pipeline applies quality control to input Fastq files and filter s for full-length reads by aligning them against a reference sequence. Th e terminal UMIs are extracted\, used to cluster reads\, and produce highl y accurate\, polished consensus sequences for each UMI cluster (i.e.\, ev ery tagged input molecule). It also offers optional low-frequency variant calling within the obtained consensus sequences.\nKey features include r eal-time monitoring of the number of clusters per sample during sequencin g\, optional GPU acceleration for cluster polishing\, and detailed qualit y control of the UMI clusters to remove potentially admixed clusters. Add itionally\, Umi-pipeline-nf supports multiple variant callers to provide flexibility in data analysis and Docker/Singularity containers to ensure reproducibility and portability to HPC clusters. \n\nUmi-pipeline-nf is p articularly useful in applications requiring virtually error-free sequenc ing with clonal\, respectively single-molecule resolution\, such as seque ncing repetitive genome regions\, studying intra-host viral evolution\, i nvestigating cancer clonal evolution\, or determining detailed metagenomi c profiles. In a recent preprint\, we showcase its ability to generate hi ghly accurate\, full-length haplotypes at single repeat resolution of a l ong\, complex\, and repetitive human repeat element\, the LPA KIV-2 VNTR (https://doi.org/10.1101/2024.03.01.582741). DTEND:20241031T163000 DTSTAMP:20260416T133018Z DTSTART:20241031T153000 LOCATION:Poster Room SEQUENCE:330849 STATUS:CONFIRMED SUMMARY:Umi-pipeline-nf: Accurate consensus sequence creation for UMI-tagg ed Nanopore data UID:SZSESSION710908 END:VEVENT BEGIN:VEVENT DESCRIPTION:Speaker: Arthur Gymer\n\nAt Genomics England we are migrating existing and developing new pipelines in Nextflow. We are largely alignin g our code and work with the best practices of the community as defined b y nf-core. The comprehensive tooling provided by nf-core allows us to str eamline workflows and increase developer productivity. \n\nIn parallel wi th public nf-core modules we maintain an internal remote for sharing modu les with proprietary code across pipelines. Internal tools are containeri sed in both docker and singularity and CI/CD testing is performed using a minimal datasets held in S3 storage.\n\nInternally we have adopted much of the `nf-core` specification and standards so there is a pathway to ope n-sourcing work at a later stage if appropriate and collaboration with th e community is easier\, whilst allowing us flexibility to tailor aspects to the architecture and requirements of the business. DTEND:20241031T163000 DTSTAMP:20260416T133018Z DTSTART:20241031T153000 LOCATION:Poster Room SEQUENCE:330849 STATUS:CONFIRMED SUMMARY:Utilising nf-core and associated tooling with in-house pipelines UID:SZSESSION711490 END:VEVENT BEGIN:VEVENT DESCRIPTION:Speaker: Chris Wright\n\nThe Customer Workflows team at Oxford Nanopore Technologies Plc creates software tools and Nextflow workflows for analysis of nanopore sequencing data. We strive to bridge the gap bet ween web-lab scientists and bioinformaticians. Our EPI2ME Desktop applica tion works on Windows\, macOS\, and Linux to provide an simple-to-use int erface for interacting with our library of Nextflow workflows. Our workfl ows produce reports for scientific users. The application can use either local or cloud compute. The technology underylying EPI2ME Desktop is used also to integrate analysis with Oxford Nanopore Sequencing devices.\n\nI n this talk we will demonstrate the ease-of-use of EPI2ME Desktop with lo cal and cloud compute\, showcase some of our workflows\, and perhaps one more thing. DTEND:20241031T164500 DTSTAMP:20260416T133018Z DTSTART:20241031T163000 LOCATION:Summit SEQUENCE:330849 STATUS:CONFIRMED SUMMARY:Bioinformatics beyond the basement UID:SZSESSION735330 END:VEVENT BEGIN:VEVENT DESCRIPTION:Speaker: Sebastian Schönherr\n\nThe Michigan Imputation Server (MIS) is a web-based service for genotype imputation aimed at democratiz ing access to large reference panels. Since its start in 2015\, the servi ce has imputed over 112 million genomes for more than 12\,000 users\, bee n recognized as an important resource for human disease genetics with ove r 3\,000 citations and has become the backbone for genome-wide associatio n studies (GWAS). Behind the scenes\, the server divides reference and ta rget data sets into small chromosome segments that are processed in paral lel. Originally developed for Hadoop MapReduce\, this talk outlines how w e successfully migrated the full imputation pipeline - including validati on\, quality control\, phasing and imputation - to Nextflow\, and highlig hts lessons learned. The publicly available Nextflow pipeline will enable others to setup their own imputation servers or run a proven imputation pipeline on different cluster architectures or cloud providers. DTEND:20241031T170000 DTSTAMP:20260416T133018Z DTSTART:20241031T164500 LOCATION:Summit SEQUENCE:330849 STATUS:CONFIRMED SUMMARY:Michigan Imputation Server: Migrating an important resource for hu man genetics to Nextflow UID:SZSESSION703338 END:VEVENT BEGIN:VEVENT DESCRIPTION:Speaker: Mathys Grapotte\n\nDeep learning model development in natural science is an empirical and costly process. Users must define a pre-processing pipeline\, an architecture\, find the best parameters for said architecture and iterate over this process.\n\nLeveraging the power of Nextflow (polyglotism\, container integration\, scalable on the cloud) \, we propose STIMULUS\, an open-source software built to automatize deep learning model development for genomics.\n\nSTIMULUS takes as input a us er defined PyTorch model\, a dataset\, a configuration file to describe t he pre-processing steps to be performed\, and a range of parameters for t he PyTorch model. It then transforms the data according to all possible pre-processing steps\, finds the best architecture parameters for each of the transformed datasets\, performs sanity checks on the models and trai n a minimal deep learning version for each dataset/architecture.\n\nThose experiments are then compiled into an intuitive report\, making it easie r for scientists to pick the best design choice to be sent to large scale training.\n\nStimulus is available at : https://github.com/mathysgrapott e/stimulus DTEND:20241031T171500 DTSTAMP:20260416T133018Z DTSTART:20241031T170000 LOCATION:Summit SEQUENCE:330849 STATUS:CONFIRMED SUMMARY:STIMULUS : A nextflow-based pipeline for training deep learning mo dels UID:SZSESSION706259 END:VEVENT BEGIN:VEVENT DESCRIPTION:Speakers: Abhinav Sharma\, Tom Sante\n\nNGS-driven programs re ly heavily on an established computing infrastructure\, which is a norm i n the Global North and a rarity in the Global South\, with multiple regio ns that are endemic to infectious diseases such as Tuberculosis and HIV.\ n\nWith the nf-nomad plugin\, researchers and public health authorities c an rely upon low-cost hybrid clusters built from existing\, older machine s and be able to run the standardized Nextflow pipelines for research\, d iagnostics\, and surveillance. DTEND:20241031T173000 DTSTAMP:20260416T133018Z DTSTART:20241031T171500 LOCATION:Summit SEQUENCE:330849 STATUS:CONFIRMED SUMMARY:Advancing precision medicine and infectious disease surveillance u sing nf-nomad plugin for Nextflow UID:SZSESSION703027 END:VEVENT BEGIN:VEVENT DESCRIPTION:Speaker: Phil Ewels\n\nJoin us for an exciting talk by Phil Ew els\, co-founder of nf-core\, presenting the latest updates from this col laborative initiative that develops high-quality\, reusable bioinformatic s pipelines built using Nextflow. The project has blossomed into a vibran t community\, bringing together developers\, bioinformaticians\, and rese archers from around the globe. This talk will offer a comprehensive catch -up on recent advancements\, new pipeline releases\, and ongoing initiati ves within the nf-core ecosystem. Phil will also share insights on how yo u can contribute to and benefit from this thriving community\, making it a must-attend event for anyone passionate about bioinformatics and open s cience. DTEND:20241031T180000 DTSTAMP:20260416T133018Z DTSTART:20241031T173000 LOCATION:Summit SEQUENCE:330849 STATUS:CONFIRMED SUMMARY:Updates from the nf-core ecosystem UID:SZSESSION730851 END:VEVENT BEGIN:VEVENT DESCRIPTION:Fábrica Moritz\, Rda. de Sant Antoni\, 41\, L'Eixample\, 08011 Barcelona\n\nJoin us at Fábrica Moritz\, Barcelona's historic brewery an d the ideal setting for a spooky Halloween party! Beyond beer\, we'll ser ve a variety of drinks and food for all Summit attendees to enjoy. Hallow een costumes are optional—but welcomed—and group costumes are highly enco uraged! DTEND:20241101T000000 DTSTAMP:20260416T133018Z DTSTART:20241031T203000 LOCATION:Summit SEQUENCE:330849 STATUS:CONFIRMED SUMMARY:Summit social UID:SZSESSION331508af-764d-46af-8abd-054a67610372 END:VEVENT BEGIN:VEVENT DESCRIPTION:Stops at:\n- Carrer de Pelai\, 38\, Ciutat Vella\, 08001 Barce lona\, Spain\n- H10 Marina Barcelona hotel\, Olympic Village\, Av. del Bo gatell\, 64\, 68\, Sant Martí\, 08005 Barcelona\, Spain\n- Hotel Cataloni a Port\, Carrer Ample\, 1\, Ciutat Vella\, 08002 Barcelona\, Spain\nEuros tars Grand Marina\, Moll de Barcelona\, S/N\, Ciutat Vella\, 08039 Barcel ona\, Spain\n- Plaça d'Espanya\, Sants-Montjuïc\, 08004 Barcelona\, Spain DTEND:20241101T000000 DTSTAMP:20260416T133018Z DTSTART:20241031T230000 LOCATION:Summit SEQUENCE:330849 STATUS:CONFIRMED SUMMARY:Shuttle Bus at 11PM\, 12AM and 1AM from Summit Social UID:SZSESSIONf4b9a2c6-76d9-4bed-8812-4d562fbeeee1 END:VEVENT BEGIN:VEVENT DESCRIPTION:World Trade Center\, 1ª planta Edif. Este\, Moll de Barcelona\ , s/n\, 08039 Barcelona DTEND:20241101T093000 DTSTAMP:20260416T133018Z DTSTART:20241101T090000 LOCATION:Summit SEQUENCE:330849 STATUS:CONFIRMED SUMMARY:Summit: Arrivals UID:SZSESSION3f71b8d4-87ec-40b7-8deb-75e7077f1d93 END:VEVENT BEGIN:VEVENT DESCRIPTION:Speakers: Kevin Moore\, Laura Richards\n\nBioinformatics teams face challenges aggregating results and sample metadata across experimen ts and next-generation sequencing (NGS) runs\, leading to unnecessary tim e spent record-keeping and data wrangling. Disparate data sources\, incon sistent naming conventions\, and diverse file formats complicate locating and linking NGS results with metadata. Consequently\, fragmented dataset s can obscure biological patterns and batch effects\, visible only when d ata is unified and analyzed at scale.\n\nDespite widespread use in data s cience\, SQL is underused by the bioinformatics community. Familiar relat ional databases require users to predefine tables\, slowing pipeline deve lopment. Schema-on-read databases\, like AWS Athena and Google BigQuery\, allow bioinformaticians to query directly over pipeline outputs in cloud storage\, but only if output files adhere to specific folder structures. \n\nIn our session\, we illustrate how SQL and schema-on-read databases c an unify metadata with NGS results across runs to simplify data accessibi lity. We address two main implementation bottlenecks experienced by the c ommunity: (1) a lack of familiarity and tools to create table definitions for NGS data\, and (2) the output folder structures of nf-core pipelines are typically incompatible with query-on-read databases\, or inefficient for querying.\n\nWe provide examples of constructing table definitions a nd database views from common nf-core pipeline outputs (fetchngs\, rnaseq ) alongside queries that eliminate manual file wrangling time. For exampl e\, we processed RNA-seq data with metadata across multiple runs in CCLE and performed queries revealing scientific insights\, like target gene ex pression across cancer types\, rapidly with minimal code.\n\nThese techni ques integrate into existing Nextflow infrastructures\, streamlining bioi nformaticians' access to unified datasets. DTEND:20241101T094500 DTSTAMP:20260416T133018Z DTSTART:20241101T093000 LOCATION:Summit SEQUENCE:330849 STATUS:CONFIRMED SUMMARY:Unifying Nextflow pipeline outputs and biological metadata with SQ L and schema-on-read databases UID:SZSESSION703672 END:VEVENT BEGIN:VEVENT DESCRIPTION:Speaker: Francesco Lescai\n\nThe increasing complexity of bioi nformatics analyses and the need for reproducibility and portability have made workflow management a crucial skill not only for bioinformaticians but also for molecular biologists. Nextflow\, which is already a game-cha nger in a wide range of scientific fields\, has also the potential to rev olutionize the way we teach bioinformatics and computational biology. \nI n this talk\, we will share our two-year experience in integrating Nextfl ow into academic curricula at the master's level. We will highlight the b enefits of teaching workflow management to students who are learning both molecular biology and bioinformatics. We will discuss our methodology fo r teaching the basics of Nextflow as part of an academic curriculum\, inc luding the hands-on exercises we use and the concrete examples of applica tions in statistics\, predictive modeling\, and more mainstream bioinform atics analyses\, such as RNA sequencing and variant calling. In this pres entation\, we will also cover how we implement Nextflow in the classroom using both high-performance computing (HPC) and cloud infrastructure. \nB y equipping students with Nextflow skills\, we aim to prepare them for th e challenges of modern bioinformatics and computational biology\, enablin g them to design\, execute\, and reproduce complex analyses with ease. Ou r approach has the potential to improve the overall quality of bioinforma tics education and enhance the employability of our graduates in the life sciences industry and beyond. DTEND:20241101T100000 DTSTAMP:20260416T133018Z DTSTART:20241101T094500 LOCATION:Summit SEQUENCE:330849 STATUS:CONFIRMED SUMMARY:Nextflow in the classroom: Innovating bioinformatics education in life sciences. UID:SZSESSION701626 END:VEVENT BEGIN:VEVENT DESCRIPTION:Speaker: Martin Beracochea\n\nMGnify\, the metagenomics resour ce of EMBL-EBI\, offers comprehensive analysis and integration of microbi ome data. Leveraging cutting-edge bioinformatics tools and methods\, MGni fy enables researchers to gain deep insights into microbial communities a cross diverse environments. Our mission is to support the global research community by providing high-quality\, accessible microbiome data and ana lysis services.\nOver the last 10 months we have focussed on the migratio n of the MGnify production pipelines to Nextflow\, adhering to nf-core be st practices. Adopting nf-core's modern development practices and tools h as progressively improved the robustness and versatility of the MGnify pi pelines\, allowing our team to utilize their time and development efforts more effectively. This transition has significantly enhanced our reliabi lity\, speed of development and deployment\, platform support\, community engagement\, and overall support at EMBL-EBI.\nAdditionally\, we are imp roving the automation of our services end-to-end using Prefect\, with the aim of reducing the reliance on manual input. This system coordinates th e execution of several pipelines and interfaces with multiple user-facing APIs and internal processes. This approach enhances the reliability and adaptability of the MGnify resource\, improving its capability to scale e ffectively\, thus serving the increasing needs of the research community. \nIn this work\, we share our journey of integrating Nextflow into MGnify \, highlighting the challenges we faced\, the solutions we implemented\, and the lessons we learned.\n DTEND:20241101T101500 DTSTAMP:20260416T133018Z DTSTART:20241101T100000 LOCATION:Summit SEQUENCE:330849 STATUS:CONFIRMED SUMMARY:MGnify's Nextflow journey UID:SZSESSION709925 END:VEVENT BEGIN:VEVENT DESCRIPTION:Speaker: Luisa Santus\n\nThe massive generation of biological data has compelled bioinformatics tools to increasingly rely on approxima tions to manage computational feasibility in processing and analyzing lar ge datasets. Per definition\, these approximations cannot be expected to deliver exact solutions and the natural consequence is that a variety of alternative tools now exist that address similar challenges\, each perfor ming differently across datasets\, without any one of them being universa lly recognized as the best solution. This trend is anticipated to continu e growing in the foreseeable future. This presents novel challenges for b oth users and developers. Users face the complex task of navigating a div erse array of tools to select the most suitable option\, considering fact ors such as accuracy\, speed\, and data compatibility. Meanwhile\, develo pers must ensure their tools meet diverse user needs while maintaining ro bustness and usability across various computational environments and also be able to fairly compare their tools with the state of the art impartia lly. An exemplary instance where heuristic solutions have become essentia l is in multiple sequence alignment (MSA)\, a widely utilized yet computa tionally intensive modelling tool. We introduce nf-core/multiplesequencea lign: a pipeline designed to facilitate seamless MSA computation while pr oviding rigorous performance evaluation and benchmark reporting. We also highlight the beneficial aspects of Nextflow and nf-core in the design an d implementation process\, along with the most challenging components enc ountered. Overall\, we anticipate that nf-core/multiplesequencealign will serve as a model for future benchmarking efforts and become a central re source for advancing MSA methodologies.\n DTEND:20241101T103000 DTSTAMP:20260416T133018Z DTSTART:20241101T101500 LOCATION:Summit SEQUENCE:330849 STATUS:CONFIRMED SUMMARY:Seamless deployment and benchmark of multiple sequence aligners wi th nf-core/multiplesequencealign UID:SZSESSION710765 END:VEVENT BEGIN:VEVENT DESCRIPTION:Speaker: Edwin Clark\n\nDiscover how Genomics England supports population-scale whole genome analysis with Genie\, our new bioinformati cs workflow management platform that leverages Nextflow and the Seqera Pl atform. We'll explore Genie's role in the groundbreaking Newborns Generat ion Study and its future application in NHS England's Genomics Medicine S ervice and beyond. DTEND:20241101T104500 DTSTAMP:20260416T133018Z DTSTART:20241101T103000 LOCATION:Summit SEQUENCE:330849 STATUS:CONFIRMED SUMMARY:Genie: Powering Genomics England's Newborns Generation Study UID:SZSESSION757721 END:VEVENT BEGIN:VEVENT DESCRIPTION:Speaker: Harry Clifford\n\nGenomics is a big-data field driven by the ever-improving speed and cost of genomic sequencing. This require s more and more computation to address the data deluge and improve method s for understanding and interpretation\, as the field continues to develo p novel methods for interrogating the human genome.\n\nTechnologies rangi ng from short- and long-read sequencing to single cell and spatial omics are all producing vast datasets and new challenges for computational biol ogists.\n\nJoin this session to hear about how NVIDIA is addressing these challenges by powering high throughput analysis and AI in omics. DTEND:20241101T110000 DTSTAMP:20260416T133018Z DTSTART:20241101T104500 LOCATION:Summit SEQUENCE:330849 STATUS:CONFIRMED SUMMARY:Accelerated compute and AI in genomics UID:SZSESSION757844 END:VEVENT BEGIN:VEVENT DESCRIPTION: DTEND:20241101T113000 DTSTAMP:20260416T133018Z DTSTART:20241101T110000 LOCATION:Summit SEQUENCE:330849 STATUS:CONFIRMED SUMMARY:Summit: Coffee break UID:SZSESSION98f58b16-d770-40d1-865b-0e20bcb0b44d END:VEVENT BEGIN:VEVENT DESCRIPTION:Speaker: Rob Syme\n\nIn this engaging talk\, we explore how re cent advancements in Nextflow and the Seqera Platform can revolutionize r esearch workflows across a spectrum\, from initial exploration to large-s cale production. Beginning with an overview of the burgeoning field of pr otein design—where deep learning and generative models are unlocking unpr ecedented potential—this talk presents a practical approach to building a nd maturing scientific workflows using Nextflow.\n\nThis journey\, comple te with live demos\, illustrates how researchers can seamlessly progress from “Small Nextflow” to “Big Nextflow” while leveraging Seqera’s ecosyst em to enhance both research flexibility and scalability in a rapidly adva ncing scientific landscape. DTEND:20241101T120000 DTSTAMP:20260416T133018Z DTSTART:20241101T113000 LOCATION:Summit SEQUENCE:330849 STATUS:CONFIRMED SUMMARY:Small Nextflow\, Big Nextflow UID:SZSESSION730861 END:VEVENT BEGIN:VEVENT DESCRIPTION:Speakers: Anabella Trigila\, Chipo Mashayamombe-Wolfgarten\, G eraldine Van der Auwera\, José Fernández Navarro\, Raúl Alcántara Aragón\ n\nJoin us for an engaging panel discussion on non-academic careers in Bi oinformatics\, featuring three distinguished panelists who have successfu lly transitioned into diverse roles outside academia. They will share the ir unique career journeys\, insights on the evolving landscape of bioinfo rmatics\, and practical advice for navigating this dynamic field. Whether you're considering a career shift or looking to expand your professional horizons\, this event offers valuable perspectives and actionable recomm endations to help you thrive in bioinformatics beyond the academic realm. DTEND:20241101T124500 DTSTAMP:20260416T133018Z DTSTART:20241101T120000 LOCATION:Summit SEQUENCE:330849 STATUS:CONFIRMED SUMMARY:Navigating the career graph: Paths to success in Bioinformatics ou tside academia UID:SZSESSION761715 END:VEVENT BEGIN:VEVENT DESCRIPTION:Speakers: Evan Floden\, Phil Ewels\n\nJoin us as Evan wraps up the Summit with some retrospectives of the past week and some upcoming d ates.\n\nBut wait\, there's one last thing! We launch the new Open Scienc e Software Foundation (OSSF)\, a non-profit foundation dedicated to suppo rting open source scientific tools and promoting free access to resources for the global scientific community. DTEND:20241101T130000 DTSTAMP:20260416T133018Z DTSTART:20241101T124500 LOCATION:Summit SEQUENCE:330849 STATUS:CONFIRMED SUMMARY:Summit Close / One last thing: Announcing the Open Science Softwar e Foundation UID:SZSESSION768814 END:VEVENT END:VCALENDAR