Ivar Grytten

• 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
• Salvatore, Stefania; Rand, Knut Dagestad; Grytten, Ivar; Ferkingstad, Egil; Domanska, Diana; Holden, Lars; Gheorghe, Marius; Mathelier, Anthony; Glad, Ingrid Kristine & Sandve, Geir Kjetil (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 Vis sammendrag

  The generation and systematic collection of genome-wide data is ever-increasing. This vast amount of data has enabled researchers to study relations between a variety of genomic and epigenomic features, including genetic variation, gene regulation and phenotypic traits. Such relations are typically investigated by comparatively assessing genomic co-occurrence. Technically, this corresponds to assessing the similarity of pairs of genome-wide binary vectors. A variety of similarity measures have been proposed for this problem in other fields like ecology. However, while several of these measures have been employed for assessing genomic co-occurrence, their appropriateness for the genomic setting has never been investigated. We show that the choice of similarity measure may strongly influence results and propose two alternative modelling assumptions that can be used to guide this choice. On both simulated and real genomic data, the Jaccard index is strongly altered by dataset size and should be used with caution. The Forbes coefficient (fold change) and tetrachoric correlation are less influenced by dataset size, but one should be aware of increased variance for small datasets. All results on simulated and real data can be inspected and reproduced at https://hyperbrowser.uio.no/sim-measure.

• 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.  ISSN 1471-2105.  18:263, s 1- 8 . doi: 10.1186/s12859-017-1678-9 Fulltekst i vitenarkiv.
• Simovski, Boris; Vodak, Daniel; Gundersen, Sveinung; Domanska, Diana Ewa; Azab, Abdulrahman; Holden, Lars; Holden, Marit; Grytten, Ivar; Rand, Knut Dagestad; Drabløs, Finn; Johansen, Morten; Mora Ortiz, Antonio Carlos; Lund-Andersen, Christin; Fromm, Bastian; Eskeland, Ragnhild; Gabrielsen, Odd Stokke; Ferkingstad, Egil; Nakken, Sigve; Bengtsen, Mads; Nederbragt, Alexander Johan; Thorarensen, Hildur Sif; Akse, Johannes Andreas; Glad, Ingrid Kristine; Hovig, Johannes Eivind & Sandve, Geir Kjetil (2017). GSuite HyperBrowser: integrative analysis of dataset collections across the genome and epigenome. GigaScience.  ISSN 2047-217X.  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

• 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

Se alle arbeider i Cristin

• Fan, Qiong; Nørgaard, Rikke Christine; Grytten, Ivar; Ness, Cecilie Maria; Lucas, Christin; Vekterud, Kristin; Södling, Helen; Matthews, Jason; Lemma, Roza Berhanu; Gabrielsen, Odd Stokke; Bindesbøll, Christian; Ulven, Stine Marie; Nebb, Hilde Irene; Grønning-Wang, Line Mariann & Sæther, Thomas (2019). LXRα interacts with the glucose-sensing transcription factor ChREBPα to regulate its transcriptional activity.. 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; Lemma, Roza Berhanu; Gabrielsen, Odd Stokke; Grønning-Wang, Line Mariann; Nebb, Hilde Irene & Sæther, Thomas (2018). LXRα interacts with the glucose-sensing transcription factor ChREBPα and increases its transcriptional activity.

Se alle arbeider i Cristin