Sveinung Gundersen

• Campbell, Donald R; Cezard, Timothee; Gundersen, Sveinung; Yates, Andrew D.; Davies, Robert M. & Marshall, John [Vis alle 12 forfattere av denne artikkelen] (2025). Taming the reference genome jungle: the refget sequence collection standard. bioRxiv - the preprint server for biology. doi: 10.1101/2025.10.06.680641. Fulltekst i vitenarkiv Vis sammendrag

  Reference genomes are foundational to genomics but suffer from widespread ambiguity and incompatibility due to inconsistent naming, undocumented differences, and lack of formal mechanisms for comparison. To address this, we introduce the GA4GH refget Sequence Collections (seqcol) standard. Refget seqcol is a framework for unambiguous representation, retrieval, and comparison of sequence collections such as reference genomes and transcriptomes. The seqcol standard comprises four components: a structured data schema, a canonical encoding algorithm that produces content-based, globally unique identifiers, a retrieval API, and a comparison protocol. This standard enables precise identification of sequence collections, even across decentralized or private systems, and allows compatibility assessments beyond exact identity, such as order-relaxed matches or shared coordinate systems. We applied the refget seqcol standard to 60 human and 36 mouse reference genomes sourced from major providers. Using digest-based comparisons, we quantified levels of similarity across attributes including sequence names, lengths, coordinate systems, and actual sequence content. Our analysis revealed some consistent subsets of sequences or coordinate systems, as well as substantial incompatibility among references and duplicate references under different names. To support adoption of refget seqcol, we provide a Python package implementing the full standard, a web API, and a comparison interface allowing users to assess local references against a curated database. This work offers a scalable, reproducible solution to the reference genome compatibility crisis, enabling improved transparency, reuse, and integration in genomic analyses. Refget seqcol enhances interoperability across tools and datasets, making genomic research more robust and reproducible.

• Baydar, Damla Övek; Rauluseviciute, Ieva; Aronsen, Dina Ruud; Blanc-Mathieu, Romain; Bonthuis, Ine & de Beukelaer, Herman [Vis alle 34 forfattere av denne artikkelen] (2025). JASPAR 2026: expansion of transcription factor binding profiles and integration of deep learning models. Nucleic Acids Research (NAR). ISSN 0305-1048. 54(D1), s. D184–D193. doi: 10.1093/nar/gkaf1209. Fulltekst i vitenarkiv Vis sammendrag

  Abstract JASPAR (https://jaspar.elixir.no/) is an open-access database that has provided high-quality, manually curated, and non-redundant DNA binding profiles for transcription factors (TFs) as position frequency matrices (PFMs) for over 20 years. We expanded the CORE (306 new profiles, 12% increase) and UNVALIDATED (433, 60% increase) collections with new PFMs and updated 13 existing profiles. We updated the TF binding site predictions and genome tracks for eight species. TF binding profile clusters and familial TF binding sites were updated accordingly. We integrate the inMOTIFin software to easily simulate regulatory sequences using JASPAR PFMs. To enrich TFs’ annotations, we provide scientific literature-based human TF target information. Notably, this release features a deep learning (DL) collection, providing a paradigm shift in modeling and characterizing TF–DNA interactions with 1259 BPNet models trained on Homo sapiens ENCODE chromatin immunoprecipitation followed by sequencing (ChIP-seq) datasets from 240 TFs and interpreted to reveal predictive motif patterns for the models. The motifs associated with the same TF were clustered to provide a summary of the binding properties, resulting in 240 primary and 113 alternative motif patterns in the DL collection. The JASPAR 2026 collections lay a foundation for future endeavors in genomic research, serving the scientific community in uncovering the mechanisms of gene regulation.

• Reppe, Sjur; Gundersen, Sveinung; Sandve, Geir Kjetil Ferkingstad; Wang, Yunpeng; Andreassen, Ole A. & Medina-Gomez, Carolina [Vis alle 10 forfattere av denne artikkelen] (2024). Identification of Transcripts with Shared Roles in the Pathogenesis of Postmenopausal Osteoporosis and Cardiovascular Disease. International Journal of Molecular Sciences. ISSN 1661-6596. 25(10). doi: 10.3390/ijms25105554. Fulltekst i vitenarkiv Vis sammendrag

  Epidemiological evidence suggests existing comorbidity between postmenopausal osteoporosis (OP) and cardiovascular disease (CVD), but identification of possible shared genes is lacking. The skeletal global transcriptomes were analyzed in trans-iliac bone biopsies (n=84) from clinically well-characterized postmenopausal women (50 to 86 years) using microchips and RNA sequencing. Thousand transcripts highly correlated with areal bone mineral density (aBMD) were further analyzed using bioinformatics, and common genes overlapping with CVD and their biological mechanisms, pathways and functions were identified. Fifty genes (45 mRNAs, 5 miRNAs) were discovered with established roles in oxidative stress, inflammatory response, endothelial function, fibrosis, dyslipidemia and osteoblastogenesis/calcification. These pleiotropic genes with possible CVD comorbidity functions were also present in transcriptomes of microvascular endothelial cells and cardiomyocytes and differentially expressed between healthy and osteoporotic women with fragility fractures. The results were supported by a genetic pleiotropy-informed conditional False Discovery Rate approach identifying overlap in single nucleotide polymorphisms (SNPs) within several genes encoding the aBMD and CVD associated transcripts.The study provides transcriptional and genomic evidence for genes of importance for both BMD regulation and CVD risk in a large collection of postmenopausal bone biopsies. Most of the transcripts identified in the CVD risk categories have no previously recognized roles in OP pathogenesis and provide novel avenues for exploring the mechanistic basis for the biological association between CVD and OP.

• Abueg, Linelle Ann L.; Afgan, Enis; Allart, Olivier; Awan, Ahmed H.; Bacon, Wendi A. & Baker, Dannon [Vis alle 130 forfattere av denne artikkelen] (2024). The Galaxy platform for accessible, reproducible, and collaborative data analyses: 2024 update. Nucleic Acids Research (NAR). ISSN 0305-1048. 52(W1), s. W83–W94. doi: 10.1093/nar/gkae410. Fulltekst i vitenarkiv Vis sammendrag

  Galaxy (https://galaxyproject.org) is deployed globally, predominantly through free-to-use services, supporting user-driven research that broadens in scope each year. Users are attracted to public Galaxy services by platform stability, tool and reference dataset diversity, training, support and integration, which enables complex, reproducible, shareable data analysis. Applying the principles of user experience design (UXD), has driven improvements in accessibility, tool discoverability through Galaxy Labs/subdomains, and a redesigned Galaxy ToolShed. Galaxy tool capabilities are progressing in two strategic directions: integrating general purpose graphical processing units (GPGPU) access for cutting-edge methods, and licensed tool support. Engagement with global research consortia is being increased by developing more workflows in Galaxy and by resourcing the public Galaxy services to run them. The Galaxy Training Network (GTN) portfolio has grown in both size, and accessibility, through learning paths and direct integration with Galaxy tools that feature in training courses. Code development continues in line with the Galaxy Project roadmap, with improvements to job scheduling and the user interface. Environmental impact assessment is also helping engage users and developers, reminding them of their role in sustainability, by displaying estimated CO2 emissions generated by each Galaxy job.

• Rauluseviciute, Ieva; Riudavets-Puig, Rafael; Blanc-Mathieu, Romain; Mondragon, Jaime Abraham Castro; Ferenc, Katalin Terezia & Kumar, Vipin [Vis alle 20 forfattere av denne artikkelen] (2023). JASPAR 2024: 20th anniversary of the open-access database of transcription factor binding profiles. Nucleic Acids Research (NAR). ISSN 0305-1048. 52(D1), s. D174–D182. doi: 10.1093/nar/gkad1059. Fulltekst i vitenarkiv Vis sammendrag

  JASPAR (https://jaspar.elixir.no/) is a widely-used open-access database presenting manually curated high-quality and non-redundant DNA-binding profiles for transcription factors (TFs) across taxa. In this 10th release and 20th-anniversary update, the CORE collection has expanded with 329 new profiles. We updated three existing profiles and provided orthogonal support for 72 profiles from the previous release's UNVALIDATED collection. Altogether, the JASPAR 2024 update provides a 20% increase in CORE profiles from the previous release. A trimming algorithm enhanced profiles by removing low information content flanking base pairs, which were likely uninformative (within the capacity of the PFM models) for TFBS predictions and modelling TF-DNA interactions. This release includes enhanced metadata, featuring a refined classification for plant TFs’ structural DNA-binding domains. The new JASPAR collections prompt updates to the genomic tracks of predicted TF binding sites (TFBSs) in 8 organisms, with human and mouse tracks available as native tracks in the UCSC Genome browser. All data are available through the JASPAR web interface and programmatically through its API and the updated Bioconductor and pyJASPAR packages. Finally, a new TFBS extraction tool enables users to retrieve predicted JASPAR TFBSs intersecting their genomic regions of interest.

• Nakken, Sigve; Gundersen, Sveinung; Bernal, Fabian Leonardo Martinez; Polychronopoulos, Dimitris; Hovig, Eivind & Wesche, Jørgen (2023). Comprehensive interrogation of gene lists from genome-scale cancer screens with oncoEnrichR. International Journal of Cancer. ISSN 0020-7136. 153(10), s. 1819–1828. doi: 10.1002/ijc.34666. Fulltekst i vitenarkiv Vis sammendrag

  Genome-scale screening experiments in cancer produce long lists of candidate genes that require extensive interpretation for biological insight and prioritization for follow-up studies. Interrogation of gene lists frequently represents a significant and time-consuming undertaking, in which experimental biologists typically combine results from a variety of bioinformatics resources in an attempt to portray and understand cancer relevance. As a means to simplify and strengthen the support for this endeavor, we have developed oncoEnrichR, a flexible bioinformatics tool that allows cancer researchers to comprehensively interrogate a given gene list along multiple facets of cancer relevance. oncoEnrichR differs from general gene set analysis frameworks through the integration of an extensive set of prior knowledge specifically relevant for cancer, including ranked gene-tumor type associations, literature-supported proto-oncogene and tumor suppressor gene annotations, target druggability data, regulatory interactions, synthetic lethality predictions, as well as prognostic associations, gene aberrations and co-expression patterns across tumor types. The software produces a structured and user-friendly analysis report as its main output, where versions of all underlying data resources are explicitly logged, the latter being a critical component for reproducible science. We demonstrate the usefulness of oncoEnrichR through interrogation of two candidate lists from proteomic and CRISPR screens. oncoEnrichR is freely available as a web-based service hosted by the Galaxy platform (https://oncotools.elixir.no), and can also be accessed as a stand-alone R package (https://github.com/sigven/oncoEnrichR).

• Kalyanasundaram, Sumana; Lefol, Yohan Pierre; Gundersen, Sveinung; Rognes, Torbjørn; Alsøe, Lene & Nilsen, Hilde [Vis alle 9 forfattere av denne artikkelen] (2023). hGSuite HyperBrowser: A web-based toolkit for hierarchical metadata-informed analysis of genomic tracks. PLOS ONE. 18(7). doi: 10.1371/journal.pone.0286330. Fulltekst i vitenarkiv Vis sammendrag

  Many high-throughput sequencing datasets can be represented as objects with coordinates along a reference genome. Currently, biological investigations often involve a large number of such datasets, for example representing different cell types or epigenetic factors. Drawing overall conclusions from a large collection of results for individual datasets may be challenging and time-consuming. Meaningful interpretation often requires the results to be aggregated according to metadata that represents biological characteristics of interest. In this light, we here propose the hierarchical Genomic Suite HyperBrowser (hGSuite), an open-source extension to the GSuite HyperBrowser platform, which aims to provide a means for extracting key results from an aggregated collection of high-throughput DNA sequencing data. The hGSuite utilizes a metadata-informed data cube to calculate various statistics across the multiple dimensions of the datasets. With this work, we show that the hGSuite and its associated data cube methodology offers a quick and accessible way for exploratory analysis of large genomic datasets. The web-based toolkit named hGsuite Hyperbrowser is available at https://hyperbrowser.uio.no/hgsuite under a GPLv3 license.

• D’Anna, Flora; Waheed, Zahra; Mohamed, Anliat; Gupta, Dipaya; Keyvani, Pedram A & El-Gebali, Sara [Vis alle 8 forfattere av denne artikkelen] (2023). Streamlining data brokering from Research Data Management platforms to ELIXIR Repositories. OSF Preprints. doi: 10.37044/osf.io/mwk9f. Fulltekst i vitenarkiv
• Gundersen, Sveinung; Boddu, Sanjay; Capella-Gutierrez, Salvador; Drabløs, Finn; Fernández, José M. & Kompova, Radmila [Vis alle 10 forfattere av denne artikkelen] (2021). Recommendations for the FAIRification of genomic track metadata. F1000 Research. 10. doi: 10.12688/f1000research.28449.1. Fulltekst i vitenarkiv Vis sammendrag

  Background: Many types of data from genomic analyses can be represented as genomic tracks, i.e. features linked to the genomic coordinates of a reference genome. Examples of such data are epigenetic DNA methylation data, ChIP-seq peaks, germline or somatic DNA variants, as well as RNA-seq expression levels. Researchers often face difficulties in locating, accessing and combining relevant tracks from external sources, as well as locating the raw data, reducing the value of the generated information. Description of work: We propose to advance the application of FAIR data principles (Findable, Accessible, Interoperable, and Reusable) to produce searchable metadata for genomic tracks. Findability and Accessibility of metadata can then be ensured by a track search service that integrates globally identifiable metadata from various track hubs in the Track Hub Registry and other relevant repositories. Interoperability and Reusability need to be ensured by the specification and implementation of a basic set of recommendations for metadata. We have tested this concept by developing such a specification in a JSON Schema, called FAIRtracks, and have integrated it into a novel track search service, called TrackFind. We demonstrate practical usage by importing datasets through TrackFind into existing examples of relevant analytical tools for genomic tracks: EPICO and the GSuite HyperBrowser. Conclusion: We here provide a first iteration of a draft standard for genomic track metadata, as well as the accompanying software ecosystem. It can easily be adapted or extended to future needs of the research community regarding data, methods and tools, balancing the requirements of both data submitters and analytical end-users.

• Pavlović, Milena; Scheffer, Lonneke; Motwani, Keshav; Kanduri, Chakravarthi; Kompova, Radmila & Vazov, Nikolay Aleksandrov [Vis alle 42 forfattere av denne artikkelen] (2021). The immuneML ecosystem for machine learning analysis of adaptive immune receptor repertoires. Nature Machine Intelligence. 3(11), s. 936–944. doi: 10.1038/s42256-021-00413-z. Fulltekst i vitenarkiv
• Kanduri, Srinivasa Kalyana Chakravarthi; Bock, Christoph; Gundersen, Sveinung; Hovig, Eivind & Sandve, Geir Kjetil (2019). Colocalization analyses of genomic elements: approaches, recommendations and challenges. Bioinformatics. ISSN 1367-4803. 35(9), s. 1615–1624. doi: 10.1093/bioinformatics/bty835. Fulltekst i vitenarkiv Vis sammendrag

  Motivation: Many high-throughput methods produce sets of genomic regions as one of their main outputs. Scientists often use genomic colocalization analysis to interpret such region sets, for example to identify interesting enrichments and to understand the interplay between the underlying biological processes. Although widely used, there is little standardization in how these analyses are performed. Different practices can substantially affect the conclusions of colocalization analyses. Results: Here, we describe the different approaches and provide recommendations for performing genomic colocalization analysis, while also discussing common methodological challenges that may influence the conclusions. As illustrated by concrete example cases, careful attention to analysis details is needed in order to meet these challenges and to obtain a robust and biologically meaningful interpretation of genomic region set data.

• Simovski, Boris; Kanduri, Srinivasa Kalyana Chakravarthi; Gundersen, Sveinung; Titov, Dmytro; Domanska, Diana Ewa & Bock, Christoph [Vis alle 15 forfattere av denne artikkelen] (2018). Coloc-stats: A unified web interface to perform colocalization analysis of genomic features. Nucleic Acids Research (NAR). ISSN 0305-1048. 46(1), s. W186–W193. doi: 10.1093/nar/gky474. Fulltekst i vitenarkiv Vis sammendrag

  Functional genomics assays produce sets of genomic regions as one of their main outputs. To biologically interpret such region-sets, researchers often use colocalization analysis, where the statistical significance of colocalization (overlap, spatial proximity) between two or more region-sets is tested. Existing colocalization analysis tools vary in the statistical methodology and analysis approaches, thus potentially providing different conclusions for the same research question. As the findings of colocalization analysis are often the basis for follow-up experiments, it is helpful to use several tools in parallel and to compare the results. We developed the Coloc-stats web service to facilitate such analyses. Coloc-stats provides a unified interface to perform colocalization analysis across various analytical methods and method-specific options (e.g. colocalization measures, resolution, null models). Coloc-stats helps the user to find a method that supports their experimental requirements and allows for a straightforward comparison across methods. Coloc-stats is implemented as a web server with a graphical user interface that assists users with configuring their colocalization analyses.

• Tekle, Kidane M; Gundersen, Sveinung; Klepper, Kjetil; Bongo, Lars Ailo; Raknes, Inge Alexander & Li, Xiaxi [Vis alle 22 forfattere av denne artikkelen] (2018). Norwegian e-Infrastructure for Life Sciences (NeLS). F1000 Research. 7:968. doi: 10.5256/f1000research.16472.r35600. Fulltekst i vitenarkiv Vis sammendrag

  The Norwegian e-Infrastructure for Life Sciences (NeLS) has been developed by ELIXIR Norway to provide its users with a system enabling data storage, sharing, and analysis in a project-oriented fashion. The system is available through easy-to-use web interfaces, including the Galaxy workbench for data analysis and workflow execution. Users confident with a command-line interface and programming may also access it through Secure Shell (SSH) and application programming interfaces (APIs). NeLS has been in production since 2015, with training and support provided by the help desk of ELIXIR Norway. Through collaboration with NorSeq, the national consortium for high-throughput sequencing, an integrated service is offered so that sequencing data generated in a research project is provided to the involved researchers through NeLS. Sensitive data, such as individual genomic sequencing data, are handled using the TSD (Services for Sensitive Data) platform provided by Sigma2 and the University of Oslo. NeLS integrates national e-infrastructure storage and computing resources, and is also integrated with the SEEK platform in order to store large data files produced by experiments described in SEEK. In this article, we outline the architecture of NeLS and discuss possible directions for further development

• Simovski, Boris; Vodak, Daniel; Gundersen, Sveinung; Domanska, Diana Ewa; Azab, Abdulrahman & Holden, Lars [Vis alle 25 forfattere av denne artikkelen] (2017). GSuite HyperBrowser: integrative analysis of dataset collections across the genome and epigenome. GigaScience. 6(7), s. 1–12. doi: 10.1093/gigascience/gix032. Fulltekst i vitenarkiv Vis sammendrag

  Background: Recent large-scale undertakings such as ENCODE and Roadmap Epigenomics have generated experimental data mapped to the human reference genome (as genomic tracks) representing a variety of functional elements across a large number of cell types. Despite the high potential value of these publicly available data for a broad variety of investigations, little attention has been given to the analytical methodology necessary for their widespread utilisation. Findings: We here present a first principled treatment of the analysis of collections of genomic tracks. We have developed novel computational and statistical methodology to permit comparative and confirmatory analyses across multiple and disparate data sources. We delineate a set of generic questions that are useful across a broad range of investigations and discuss the implications of choosing different statistical measures and null models. Examples include contrasting analyses across different tissues or diseases. The methodology has been implemented in a comprehensive open-source software system, the GSuite HyperBrowser. To make the functionality accessible to biologists, and to facilitate reproducible analysis, we have also developed a web-based interface providing an expertly guided and customizable way of utilizing the methodology. With this system, many novel biological questions can flexibly be posed and rapidly answered. Conclusions: Through a combination of streamlined data acquisition, interoperable representation of dataset collections and customizable statistical analysis with guided setup and interpretation, the GSuite HyperBrowser represents a first comprehensive solution for integrative analysis of track collections across the genome and epigenome. The software is available at: https://hyperbrowser.uio.no

• Bengtsen, Mads; Klepper, Kjetil; Gundersen, Sveinung; Torre, Ignacio Cuervo; Drabløs, Finn & Hovig, Johannes Eivind [Vis alle 9 forfattere av denne artikkelen] (2015). c-Myb Binding Sites in Haematopoietic Chromatin Landscapes. PLOS ONE. 10(7). doi: 10.1371/journal.pone.0133280. Fulltekst i vitenarkiv Vis sammendrag

  Strict control of tissue-specific gene expression plays a pivotal role during lineage commit- ment. The transcription factor c-Myb has an essential role in adult haematopoiesis and func- tions as an oncogene when rearranged in human cancers. Here we have exploited digital genomic footprinting analysis to obtain a global picture of c-Myb occupancy in the genome of six different haematopoietic cell-types. We have biologically validated several c-Myb foot- prints using c-Myb knockdown data, reporter assays and DamID analysis. We show that our predicted conserved c-Myb footprints are highly dependent on the haematopoietic cell type, but that there is a group of gene targets common to all cell-types analysed. Further- more, we find that c-Myb footprints co-localise with active histone mark H3K4me3 and are significantly enriched at exons. We analysed co-localisation of c-Myb footprints with 104 chromatin regulatory factors in K562 cells, and identified nine proteins that are enriched together with c-Myb footprints on genes positively regulated by c-Myb and one protein enriched on negatively regulated genes. Our data suggest that c-Myb is a transcription fac- tor with multifaceted target regulation depending on cell type.

• Paulsen, Jonas; Sandve, Geir Kjetil F.; Gundersen, Sveinung; Lien, Tonje Gulbrandsen; Trengereid, Kai & Hovig, Johannes Eivind (2014). HiBrowse: Multi-purpose statistical analysis of genome-wide chromatin 3D organization. Bioinformatics. ISSN 1367-4803. 30(11), s. 1620–1622. doi: 10.1093/bioinformatics/btu082. Fulltekst i vitenarkiv Vis sammendrag

  Recently developed methods that couple next-generation sequencing with chromosome conformation capture-based techniques, such as Hi-C and ChIA-PET, allow for characterization of genome-wide chromatin 3D structure. Understanding the organization of chromatin in three dimensions is a crucial next step in the unraveling of global gene regulation, and methods for analyzing such data are needed. We have developed HiBrowse, a user-friendly web-tool consisting of a range of hypothesis-based and descriptive statistics, using realistic assumptions in null-models.

• Sandve, Geir Kjetil; Gundersen, Sveinung; Johansen, Morten; Glad, Ingrid Kristine; Gunathasan, Krishanthi & Holden, Lars [Vis alle 21 forfattere av denne artikkelen] (2013). The Genomic HyperBrowser: an analysis web server for genome-scale data. Nucleic Acids Research (NAR). ISSN 0305-1048. 41(W1), s. W133–W141. doi: 10.1093/nar/gkt342. Fulltekst i vitenarkiv Vis sammendrag

  The immense increase in availability of genomic scale datasets, such as those provided by the ENCODE and Roadmap Epigenomics projects, presents unprecedented opportunities for individual researchers to pose novel falsifiable biological questions. With this opportunity, however, researchers are faced with the challenge of how to best analyze and interpret their genome-scale datasets. A powerful way of representing genome-scale data is as feature-specific coordinates relative to reference genome assemblies, i.e. as genomic tracks. The Genomic HyperBrowser (http://hyperbrowser.uio.no) is an open-ended web server for the analysis of genomic track data. Through the provision of several highly customizable components for processing and statistical analysis of genomic tracks, the HyperBrowser opens for a range of genomic investigations, related to, e.g., gene regulation, disease association or epigenetic modifications of the genome.

• Gundersen, Sveinung; Kalaš, Matúš; Abul, Osman; Frigessi, Arnoldo; Hovig, Eivind & Sandve, Geir Kjetil (2011). Identifying elemental genomic track types and representing them uniformly. BMC Bioinformatics. 12. doi: 10.1186/1471-2105-12-494. Fulltekst i vitenarkiv Vis sammendrag

  Background: With the recent advances and availability of various high-throughput sequencing technologies, data on many molecular aspects, such as gene regulation, chromatin dynamics, and the three-dimensional organization of DNA, are rapidly being generated in an increasing number of laboratories. The variation in biological context, and the increasingly dispersed mode of data generation, imply a need for precise, interoperable and flexible representations of genomic features through formats that are easy to parse. A host of alternative formats are currently available and in use, complicating analysis and tool development. The issue of whether and how the multitude of formats reflects varying underlying characteristics of data has to our knowledge not previously been systematically treated. Results: We here identify intrinsic distinctions between genomic features, and argue that the distinctions imply that a certain variation in the representation of features as genomic tracks is warranted. Four core informational properties of tracks are discussed: gaps, lengths, values and interconnections. From this we delineate fifteen generic track types. Based on the track type distinctions, we characterize major existing representational formats and find that the track types are not adequately supported by any single format. We also find, in contrast to the XML formats, that none of the existing tabular formats are conveniently extendable to support all track types. We thus propose two unified formats for track data, an improved XML format, BioXSD 1.1, and a new tabular format, GTrack 1.0. Conclusions: The defined track types are shown to capture relevant distinctions between genomic annotation tracks, resulting in varying representational needs and analysis possibilities. The proposed formats, GTrack 1.0 and BioXSD 1.1, cater to the identified track distinctions and emphasize preciseness, flexibility and parsing convenience.

• Sandve, Geir Kjetil; Gundersen, Sveinung; Rydbeck, Halfdan; Glad, Ingrid Kristine; Holden, Lars & Holden, Marit [Vis alle 15 forfattere av denne artikkelen] (2011). The differential disease regulome. BMC Genomics. 12, s. 353–353. doi: 10.1186/1471-2164-12-353. Fulltekst i vitenarkiv Vis sammendrag

  Background Transcription factors in disease-relevant pathways represent potential drug targets, by impacting a distinct set of pathways that may be modulated through gene regulation. The influence of transcription factors is typically studied on a per disease basis, and no current resources provide a global overview of the relations between transcription factors and disease. Furthermore, existing pipelines for related large-scale analysis are tailored for particular sources of input data, and there is a need for generic methodology for integrating complementary sources of genomic information. Results We here present a large-scale analysis of multiple diseases versus multiple transcription factors, with a global map of over-and under-representation of 446 transcription factors in 1010 diseases. This map, referred to as the differential disease regulome, provides a first global statistical overview of the complex interrelationships between diseases, genes and controlling elements. The map is visualized using the Google map engine, due to its very large size, and provides a range of detailed information in a dynamic presentation format. The analysis is achieved through a novel methodology that performs a pairwise, genome-wide comparison on the cartesian product of two distinct sets of annotation tracks, e.g. all combinations of one disease and one TF. The methodology was also used to extend with maps using alternative data sets related to transcription and disease, as well as data sets related to Gene Ontology classification and histone modifications. We provide a web-based interface that allows users to generate other custom maps, which could be based on precisely specified subsets of transcription factors and diseases, or, in general, on any categorical genome annotation tracks as they are improved or become available. Conclusion We have created a first resource that provides a global overview of the complex relations between transcription factors and disease. As the accuracy of the disease regulome depends mainly on the quality of the input data, forthcoming ChIP-seq based binding data for many TFs will provide improved maps. We further believe our approach to genome analysis could allow an advance from the current typical situation of one-time integrative efforts to reproducible and upgradable integrative analysis. The differential disease regulome and its associated methodology is available at http://hyperbrowser.uio.no

• Sandve, Geir Kjetil; Gundersen, Sveinung; Rydbeck, Halfdan; Glad, Ingrid Kristine; Holden, Lars & Holden, Marit [Vis alle 14 forfattere av denne artikkelen] (2010). The Genomic HyperBrowser: inferential genomics at the sequence level. Genome Biology. ISSN 1465-6906. 11(12). doi: 10.1186/gb-2010-11-12-r121. Fulltekst i vitenarkiv Vis sammendrag

  The immense increase in the generation of genomic scale data poses an unmet analytical challenge, due to a lack of established methodology with the required flexibility and power. We propose a first principled approach to statistical analysis of sequence-level genomic information. We provide a growing collection of generic biological investigations that query pairwise relations between tracks, represented as mathematical objects, along the genome. The Genomic HyperBrowser implements the approach and is available at http://hyperbrowser.uio.no.

Se alle arbeider i NVA

• Gundersen, Sveinung & Bianchini, Federico (2026). D3.4 (M48) - Report on developments and operations of the FAIRtracks ecosystem (an ELIXIR Norway ELIXIR3 deliverable). doi: 10.5281/ZENODO.23341212. Fulltekst i vitenarkiv
• Gundersen, Sveinung; Faci, Pavel Vazquez; Bianchini, Federico; Baskaran, Joshua; Cheneby, Jeanne Marie & Hovig, Eivind [Vis alle 7 forfattere av denne artikkelen] (2026). fairtracks/omnipy: Omnipy v0.23.1. doi: 10.5281/ZENODO.21999005. Fulltekst i vitenarkiv
• Gundersen, Sveinung; Bianchini, Federico; Baskaran, Joshua; Chèneby, Jeanne; Fatima, Nazeefa & Hovig, Eivind (2026). fairtracks/fairtracks.net: FAIRtracks.net - ELIXIR3. doi: 10.5281/ZENODO.20089254. Fulltekst i vitenarkiv
• Faci, Pavel Vazquez; Visnovska, Martina; Gundersen, Sveinung & Bianchini, Federico (2026). D5.7 (M12/M24/M36/M48) — Report of "Workflows for omics data integration". doi: 10.5281/ZENODO.20542025. Fulltekst i vitenarkiv
• Faci, Pavel Vazquez; Visnovska, Martina; Gundersen, Sveinung & Bianchini, Federico (2026). D5.6 (M12/M24) — Report of "Implementation of existing workflows and toolkits for sensitive computing". doi: 10.5281/ZENODO.20541884. Fulltekst i vitenarkiv
• Faci, Pavel Vazquez; Gundersen, Sveinung; Visnovska, Martina & Bianchini, Federico (2026). D5.5 (M24/M36) — Report of "Framework for achieving import of non-sensitive reference data and software components obtained from external sources and deployed infrastructure for ELIXIR BioContainers repository in secure compute environments". doi: 10.5281/ZENODO.20541631. Fulltekst i vitenarkiv
• Gundersen, Sveinung & Chait, null Gavin (2025). fairtracks/biohackathon-2024-project-31: Executable metadata mappings to FAIRify Biodiversity Genome Annotations - v1.0. doi: 10.5281/ZENODO.15213330. Fulltekst i vitenarkiv Vis sammendrag

  Advances in sequencing technologies and assembly algorithms have enabled an explosion in diverse reference genomes across the tree of life, together with a need to annotate functional and structural features. There is no current set of minimal metadata to support genome annotations as FAIR objects, limiting their reproducibility and reliability. The FAIRification of Genomic Annotations Working Group (FGA-WG) in the Research Data Alliance (RDA) will develop a harmonised metadata model and recommended infrastructure to improve discovery and reuse of publicly available genomic annotations/tracks, supporting harmonised metadata for GFF3 files. Such metadata exists in e.g. project-specific databases or spreadsheets, workflow systems, repositories, exchange formats, and linked data. Harmonising metadata according to a unified data model requires the extraction, transformation and integration of data sourced in different research contexts, including "messy" data, using schema mappings or "crosswalks". These operations are time-consuming and may introduce opaque errors. FAIR principles emphasise reproducibility and trust in data analyses with persisted and shared accessible, auditable and executable data transformation and validation methods. Omnipy and Whyqd (/wɪkɪd/) are independently-developed Python libraries offering general functionality for auditable and executable metadata mappings. Each is pragmatically designed to ensure transformations are executable on real-world data, with validation and feedback. They differ in scope and users, and provide complementary functionalities. In this BioHackathon 2024 project, we developed a convenience schema, based on the FAIRtracks schema, for metadata pertaining to biodiversity genome annotations. We created executable crosswalks to transform metadata from a case study on 49 diatom genomes to fit the convenience schema. Conceptually, the work can be extended with automated flows to transform metadata from the flat convenience scheme to fit the more complex, hierarchical structure of the FAIRtracks schema, automatically enhanced with augmented fields as needed. In reality, the convenience schema we have developed will inform the development of the FGA-WG harmonised schema and related convenience schema, which will be used for later implementation.

• Faci, Pavel Vazquez; Gundersen, Sveinung; Visnovska, Martina & Bianchini, Federico (2024). D5.5 (M12)- Report of "Framework for achieving import of sensitive data obtained from external sources and deployed infrastructure for ELIXIR BioContainers repository in secure compute environments". doi: 10.5281/zenodo.10697471. Fulltekst i vitenarkiv
• Gundersen, Sveinung; Baskaran, Joshua; Bianchini, Federico; Boddu, Sanjay; Cheneby, Jeanne Marie & Drabløs, Finn Sverre [Vis alle 17 forfattere av denne artikkelen] (2023). Will you help us mobilise the abundance of functional genomics data for FAIR reuse in the age of pangenomes and AI? doi: 10.5281/ZENODO.22145365. Fulltekst i vitenarkiv
• Gundersen, Sveinung (2023). FAIRification of Genome Sequence Annotations (Tracks) WG, Kickoff meetings. doi: 10.5281/ZENODO.22644287. Fulltekst i vitenarkiv
• Gundersen, Sveinung (2023). FAIRification of Genome Annotations WG – Background, Concepts, and Case Statement. doi: 10.5281/ZENODO.22654304. Fulltekst i vitenarkiv
• Gundersen, Sveinung (2023). FAIRtracks WG - in the making. doi: 10.5281/ZENODO.22146625. Fulltekst i vitenarkiv
• Simovski, Boris; Drabløs, Finn Sverre; Gundersen, Sveinung; Johansen, Morten; Domanska, Diana Ewa & Azab, Abdulrahman [Vis alle 8 forfattere av denne artikkelen] (2016). The Genomic HyperBrowser. Fulltekst i vitenarkiv
• Evensen, Jonas; Gundersen, Sveinung; Kristoffersen, Anja Bråthen & Lagesen, Karin (2016). STRAINTRACER-DB: A flexible analysis and persistent storage system for sequence and epidemiological data. Fulltekst i vitenarkiv
• Gundersen, Sveinung; Bianchini, Federico; Vázquez, Pável; Cheneby, Jeanne Marie; Kalaš, Matúš & Baskaran, Joshua [Vis alle 7 forfattere av denne artikkelen] (2024). D3.4 (M18) - Report on developments and operations of the FAIRtracks ecosystem (an ELIXIR Norway ELIXIR3 deliverable). Zenodo. doi: 10.5281/zenodo.10722734. Fulltekst i vitenarkiv Vis sammendrag

  This report serves as an update on the progress of WP3 in ELIXIR3 task 3.5: "Further development and operations of the FAIRtracks standard for minimal genomic track metadata (functional genomics) and related libraries and services in the FAIRtracks ecosystem". The report documents progress made, changes in the scope of the task, and plans for the near future as of month 18 of a 48-month timeline.

• Calhoun, Erin Elizabeth; Kalaš, Matúš; Bösl, Korbinian Michael; Fatima, Nazeefa; Gundersen, Sveinung & Klemetsen, Terje [Vis alle 8 forfattere av denne artikkelen] (2023). D3.3 (M18) - Report on curation in core ELIXIR registries (an ELIXIR Norway ELIXIR3 deliverable). Zenodo. doi: 10.5281/zenodo.10696595. Fulltekst i vitenarkiv Vis sammendrag

  This report serves as an update on the progress of WP3 Task 3.4 in ELIXIR3, in support of curation efforts on content in repositories of metadata, datasets, tools, training, workflows, and other resources, in line with the ELIXIR Platforms. The report documents progress made, methods used, and plans for the near future as of month 18 of a 48-month timeline. ELIXIR Norway extends support to numerous internal and external service providers in Norway, in cases where the services have been accepted as ELIXIR Services. Being an ELIXIR Service comes with many advantages, from gaining exposure to a broader range of potential users to a raised profile throughout ELIXIR, that can help secure future funding, among other benefits. While these Services maintain specific operational responsibilities, ELIXIR Norway offers versatile infrastructure-related support. We provide a first-line helpdesk, potential Node e-infrastructure integration (where feasible), service monitoring, and metrics collection. Our technology hub further enriches this by imparting best practices and standards, and hosting workshops and conferences. The inception of Task 3.4 is based on observations that most ELIXIR Norway Services do not promote available resources sufficiently, including relevant ontologies, essential repositories like Bio.tools and FAIRsharing, and key Services from other ELIXIR Platforms. The primary objective of the Task is to assist service providers, task forces, and other Tasks and work packages in effectively using available resources for maximum benefit. The work in this Task has been aligned with, and partially builds up on the work performed in the European context in the H2020 project ELIXIR-CONVERGE (February 2020 - July 2023) (871074).

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