Ivar Grytten

• Alwash, Mostafa; Hajj, Ghadi al; Grytten, Ivar & Sandve, Geir Kjetil Ferkingstad (2025). Meta simulation approach for evaluating machine learning method selection in data limited settings. Scientific Reports. 15. doi: 10.1038/s41598-025-24627-y. Fulltekst i vitenarkiv Vis sammendrag

  Selecting appropriate machine learning (ML) methods for domain-specific tasks remains a persistent challenge, particularly in medicine where datasets are often small, heterogeneous, and incomplete. Traditional benchmarking strategies rely on limited observational samples, which may not capture the complexity of the underlying data-generating process (DGP). As a result, methods that perform well on available data may generalise poorly in real-world practice. We present SimCalibration, a meta-simulation framework that leverages structural learners (SLs) to infer an approximated data-generating process from limited data and generate synthetic datasets for large-scale benchmarking. This framework enables systematic evaluation of machine learning method selection strategies in settings where the true data-generating process is either known or can be approximated, allowing both validation against the ground truth and the generation of synthetic observations inferred from sparse samples. In rare disease research for example, where patient cohorts are inherently small, causal relationships are often conceptualised as directed acyclic graphs (DAGs). In this work, such structures are approximated directly from observational data, extending the utility of small datasets by enabling investigators to benchmark ML methods in a controlled simulation setting before deploying them in practice. This reduces the risk of selecting models that generalise poorly and supports more reliable decision-making in sensitive healthcare contexts. Experiments demonstrate that (a) structural learners vary in their ability to recover representative simulations for benchmarking, (b) structural learner-based benchmarking reduces variance in performance estimates compared to traditional validation, and (c) in some cases, structural learner-based approaches yield rankings that more closely match true relative performance than those derived from limited datasets. These findings highlight the value of simulation-based benchmarking for domains where drawing generalisable conclusions is critical, such as medicine, and offer greater transparency into the assumptions underlying predictive decisions.

• Grytten, Ivar; Rand, Knut Dagestad & Sandve, Geir Kjetil Ferkingstad (2022). KAGE: fast alignment-free graph-based genotyping of SNPs and short indels. Genome Biology. ISSN 1465-6906. 23(1). doi: 10.1186/s13059-022-02771-2. Fulltekst i vitenarkiv Vis sammendrag

  Genotyping is a core application of high-throughput sequencing. We present KAGE, a genotyper for SNPs and short indels that is inspired by recent developments within graph-based genome representations and alignment-free methods. KAGE uses a pan-genome representation of the population to efficiently and accurately predict genotypes. Two novel ideas improve both the speed and accuracy: a Bayesian model incorporates genotypes from thousands of individuals to improve prediction accuracy, and a computationally efficient method leverages correlation between variants. We show that the accuracy of KAGE is at par with the best existing alignment-free genotypers, while being an order of magnitude faster.

• 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
• Grytten, Ivar; Rand, Knut Dagestad; Nederbragt, Alexander Johan & Sandve, Geir Kjetil (2020). Assessing graph-based read mappers against a baseline approach highlights strengths and weaknesses of current methods. BMC Genomics. 21. doi: 10.1186/s12864-020-6685-y. Fulltekst i vitenarkiv Vis sammendrag

  Background Graph-based reference genomes have become popular as they allow read mapping and follow-up analyses in settings where the exact haplotypes underlying a high-throughput sequencing experiment are not precisely known. Two recent papers show that mapping to graph-based reference genomes can improve accuracy as compared to methods using linear references. Both of these methods index the sequences for most paths up to a certain length in the graph in order to enable direct mapping of reads containing common variants. However, the combinatorial explosion of possible paths through nearby variants also leads to a huge search space and an increased chance of false positive alignments to highly variable regions. Results We here assess three prominent graph-based read mappers against a hybrid baseline approach that combines an initial path determination with a tuned linear read mapping method. We show, using a previously proposed benchmark, that this simple approach is able to improve overall accuracy of read-mapping to graph-based reference genomes. Conclusions Our method is implemented in a tool Two-step Graph Mapper, which is available at https://github.com/uio-bmi/two_step_graph_mapperalong with data and scripts for reproducing the experiments. Our method highlights characteristics of the current generation of graph-based read mappers and shows potential for improvement for future graph-based read mappers.

• Fan, Qiong; Nørgaard, Rikke Christine; Grytten, Ivar; Ness, Cecilie; Lucas, Christin & Vekterud, Kristin [Vis alle 15 forfattere av denne artikkelen] (2020). LXRα Regulates ChREBPα Transactivity in a Target Gene-Specific Manner through an Agonist-Modulated LBD-LID Interaction. Cells. 9(5), s. 1–26. doi: 10.3390/cells9051214. Fulltekst i vitenarkiv Vis sammendrag

  The cholesterol-sensing nuclear receptor liver X receptor (LXR) and the glucose-sensing transcription factor carbohydrate responsive element-binding protein (ChREBP) are central players in regulating glucose and lipid metabolism in the liver. More knowledge of their mechanistic interplay is needed to understand their role in pathological conditions like fatty liver disease and insulin resistance. In the current study, LXR and ChREBP co-occupancy was examined by analyzing ChIP-seq datasets from mice livers. LXR and ChREBP interaction was determined by Co-immunoprecipitation (CoIP) and their transactivity was assessed by real-time quantitative polymerase chain reaction (qPCR) of target genes and gene reporter assays. Chromatin binding capacity was determined by ChIP-qPCR assays. Our data show that LXRα and ChREBPα interact physically and show a high co-occupancy at regulatory regions in the mouse genome. LXRα co-activates ChREBPα and regulates ChREBP-specific target genes in vitro and in vivo. This co-activation is dependent on functional recognition elements for ChREBP but not for LXR, indicating that ChREBPα recruits LXRα to chromatin in trans. The two factors interact via their key activation domains; the low glucose inhibitory domain (LID) of ChREBPα and the ligand-binding domain (LBD) of LXRα. While unliganded LXRα co-activates ChREBPα, ligand-bound LXRα surprisingly represses ChREBPα activity on ChREBP-specific target genes. Mechanistically, this is due to a destabilized LXRα:ChREBPα interaction, leading to reduced ChREBP-binding to chromatin and restricted activation of glycolytic and lipogenic target genes. This ligand-driven molecular switch highlights an unappreciated role of LXRα in responding to nutritional cues that was overlooked due to LXR lipogenesis-promoting function.

• Salvatore, Stefania; Rand, Knut Dagestad; Grytten, Ivar; Ferkingstad, Egil; Domanska, Diana & Holden, Lars [Vis alle 10 forfattere av denne artikkelen] (2019). Beware the Jaccard: the choice of similarity measure is important and non-trivial in genomic colocalisation analysis. Briefings in Bioinformatics. ISSN 1467-5463. s. 1–8. doi: 10.1093/bib/bbz083. Fulltekst i vitenarkiv
• Fan, Qiong; Nørgaard, Rikke Christine; Grytten, Ivar; Ness, Cecilie Maria; Lucas, Christin & Vekterud, Kristin [Vis alle 15 forfattere av denne artikkelen] (2019). Open the LID: LXRα regulates ChREBPα transactivity in a target gene-specific manner through an agonist-modulated LBD-LID interaction. BioRxiv. ISSN 0362-4331. doi: 10.1101/2019.12.20.869974. Fulltekst i vitenarkiv Vis sammendrag

  The cholesterol-sensing nuclear receptor liver X receptor (LXR) and the glucose-sensing transcription factor carbohydrate responsive element-binding protein (ChREBP) are central players in regulating glucose and lipid metabolism in liver. We have previously shown that LXR regulates ChREBP transcription and activity; however, the underlying mechanisms are unclear. In the current study, we demonstrate that LXRα and ChREBPα interact physically, and show a high co-occupancy at regulatory regions in the mouse genome. LXRα co-activates ChREBPα, and regulates ChREBP-specific target genes in vitro and in vivo. This co-activation is dependent on functional recognition elements for ChREBP, but not for LXR, indicating that ChREBPα recruits LXRα to chromatin in trans. The two factors interact via their key activation domains; ChREBPα’s low glucose inhibitory domain (LID) and the ligand-binding domain (LBD) of LXRα. While unliganded LXRα co-activates ChREBPα, ligand-bound LXRα surprisingly represses ChREBPα activity on ChREBP-specific target genes. Mechanistically, this is due to a destabilized LXRα:ChREBPα interaction, leading to reduced ChREBP-binding to chromatin and restricted activation of glycolytic and lipogenic target genes. This ligand-driven molecular switch highlights an unappreciated role of LXRα that was overlooked due to LXR lipogenesis-promoting function.

• Grytten, Ivar; Rand, Knut Dagestad; Nederbragt, Alexander Johan; Storvik, Geir Olve; Glad, Ingrid Kristine & Sandve, Geir Kjetil (2019). Graph Peak Caller: Calling ChIP-seq peaks on graph-based reference genomes. PLoS Computational Biology. ISSN 1553-734X. 15(2), s. 1–13. doi: 10.1371/journal.pcbi.1006731. Fulltekst i vitenarkiv Vis sammendrag

  Graph-based representations are considered to be the future for reference genomes, as they allow integrated representation of the steadily increasing data on individual variation. Currently available tools allow de novo assembly of graph-based reference genomes, alignment of new read sets to the graph representation as well as certain analyses like variant calling and haplotyping. We here present a first method for calling ChIP-Seq peaks on read data aligned to a graph-based reference genome. The method is a graph generalization of the peak caller MACS2, and is implemented in an open source tool, Graph Peak Caller. By using the existing tool vg to build a pan-genome of Arabidopsis thaliana, we validate our approach by showing that Graph Peak Caller with a pan-genome reference graph can trace variants within peaks that are not part of the linear reference genome, and find peaks that in general are more motif-enriched than those found by MACS2.

• 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

• Rand, Knut Dagestad; Grytten, Ivar; Nederbragt, Alexander Johan; Storvik, Geir Olve; Glad, Ingrid Kristine & Sandve, Geir Kjetil (2017). Coordinates and intervals in graph-based reference genomes. BMC Bioinformatics. 18:263, s. 1–8. doi: 10.1186/s12859-017-1678-9. Fulltekst i vitenarkiv Vis sammendrag

  Background It has been proposed that future reference genomes should be graph structures in order to better represent the sequence diversity present in a species. However, there is currently no standard method to represent genomic intervals, such as the positions of genes or transcription factor binding sites, on graph-based reference genomes. Results We formalize offset-based coordinate systems on graph-based reference genomes and introduce methods for representing intervals on these reference structures. We show the advantage of our methods by representing genes on a graph-based representation of the newest assembly of the human genome (GRCh38) and its alternative loci for regions that are highly variable. Conclusion More complex reference genomes, containing alternative loci, require methods to represent genomic data on these structures. Our proposed notation for genomic intervals makes it possible to fully utilize the alternative loci of the GRCh38 assembly and potential future graph-based reference genomes. We have made a Python package for representing such intervals on offset-based coordinate systems, available at https://github.com/uio-cels/offsetbasedgraph . An interactive web-tool using this Python package to visualize genes on a graph created from GRCh38 is available at https://github.com/uio-cels/genomicgraphcoords .

• Børnich, Claus; Grytten, Ivar; Hovig, Johannes Eivind; Paulsen, Jonas; Cech, Martin & Sandve, Geir Kjetil (2016). Galaxy Portal: Interacting with the galaxy platform through mobile devices. Bioinformatics. ISSN 1367-4803. 32(11), s. 1743–1745. doi: 10.1093/bioinformatics/btw042. Fulltekst i vitenarkiv

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• Rand, Knut Dagestad; Grytten, Ivar; Pavlović, Milena; Kanduri, Chakravarthi & Sandve, Geir Kjetil Ferkingstad (2024). BioNumPy: array programming for biology. Nature Methods. ISSN 1548-7091. doi: 10.1038/s41592-024-02483-4. Fulltekst i vitenarkiv
• Torgersen, Eivind; Rand, Knut Dagestad; Grytten, Ivar & Sandve, Geir Kjetil Ferkingstad (2024). Nå blir det enklere å kode for forskere som jobber med biologiske data. [Journal]. Digi.no. Fulltekst i vitenarkiv
• Balaban, Gabriel; Grytten, Ivar; Rand, Knut Dagestad; Scheffer, Lonneke & Sandve, Geir Kjetil (2021). Ten simple rules for quick and dirty scientific programming. PLoS Computational Biology. ISSN 1553-734X. 17:e1008549(3), s. 1–15. doi: 10.1371/journal.pcbi.1008549. Fulltekst i vitenarkiv
• Fan, Qiong; Nørgaard, Rikke Christine; Grytten, Ivar; Ness, Cecilie Maria; Lucas, Christin & Vekterud, Kristin [Vis alle 15 forfattere av denne artikkelen] (2019). LXRα interacts with the glucose-sensing transcription factor ChREBPα to regulate its transcriptional activity. Fulltekst i vitenarkiv Vis sammendrag

  The cholesterol-sensing nuclear receptor Liver X Receptor (LXR) and the glucose-sensing transcription factor carbohydrate responsive element-binding protein (ChREBP) are central players in the regulation of glucose and lipid metabolism. LXR does this job in part by regulating the expression of ChREBP. We have previously shown that LXR also regulates ChREBP activity. To get a better understanding of mechanisms at play, we asked if LXR and ChREBP interact physically. Interestingly, LXRα binds to ChREBPα, but not the shorter isoform ChREBPβ. Co-immunoprecipitation (CoIP) of different LXR and ChRBEP domains shows that it is ChREBPα’s low glucose inhibitory domain (LID), which is lacking in ChREBPβ, that interacts with the ligand-binding domain (LBD) of LXRα. In line with this, we see a surprisingly high co-occupancy of LXR and ChREBP on regulatory regions in the mouse genome when re-analysing two independently published chromatin immunoprecipitation-sequencing (ChIP-seq) datasets. Moreover, Functional studies show that LXRα is able to co-activate together with ChREBPα, but not ChREBPβ, and increase ChREBP-specific target gene expression in vitro and in vivo. Unexpectedly however, ligand-engaged LXR exhibits a repressive effect on the expression of the same genes in primary mouse hepatocytes, in contrast to what we observe with target genes that are common to LXR and ChREBP. Performing CoIP and ChIP on selected target genes, we demonstrate mechanistically that the repressive effect most likely is due to a weakened ChREBPα:LXRα interaction and reduced binding of ChREBP to chromatin. Altogether, the novel transcriptional complex comprising ChREBPα and LXRα adds to the intricate integration of nutrient signals in glucose and lipid metabolism.

• Fan, Qiong; Nørgaard, Rikke Christine; Grytten, Ivar; Ulven, Stine Marie; Lucas, Christin & Bindesbøll, Christian [Vis alle 11 forfattere av denne artikkelen] (2018). LXRα interacts with the glucose-sensing transcription factor ChREBPα and increases its transcriptional activity. Fulltekst i vitenarkiv

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