Morten Johansen

• Rauluseviciute, Ieva; Riudavets-Puig, Rafael; Blanc-Mathieu, Romain; Castro Mondragon, Jaime Abraham; Ferenc, Katalin Terezia & Kumar, Vipin [Show all 20 contributors for this article] (2023). JASPAR 2024: 20th anniversary of the open-access database of transcription factor binding profiles. Nucleic Acids Research (NAR). ISSN 0305-1048. 52(D1), p. D174–D182. doi: 10.1093/nar/gkad1059. Full text in Research Archive Show summary

  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.

• Fromm, Bastian; Høye, Eirik; Domanska, Diana; Zhong, Xiangfu; Aparicio-Puerta, Ernesto & Ovchinnikov, Vladimir [Show all 18 contributors for this article] (2022). MirGeneDB 2.1: toward a complete sampling of all major animal phyla. Nucleic Acids Research (NAR). ISSN 0305-1048. 50(D1), p. D204–D210. doi: 10.1093/nar/gkab1101. Full text in Research Archive
• Simovski, Boris; Vodak, Daniel; Gundersen, Sveinung; Domanska, Diana Ewa; Azab, Abdulrahman & Holden, Lars [Show all 25 contributors for this article] (2017). GSuite HyperBrowser: integrative analysis of dataset collections across the genome and epigenome. GigaScience. ISSN 2047-217X. 6(7), p. 1–12. doi: 10.1093/gigascience/gix032. Full text in Research Archive Show summary

  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

• Fromm, Bastian; Billipp, Tyler; Peck, Liam E.; Johansen, Morten; Tarver, James E. & King, Benjamin L. [Show all 11 contributors for this article] (2015). A Uniform System for the Annotation of Vertebrate microRNA Genes and the Evolution of the Human microRNAome. Annual Review of Genetics. ISSN 0066-4197. 49, p. 213–242. doi: 10.1146/annurev-genet-120213-092023.
• Ekstrøm, Per Olaf; Nakken, Sigve; Johansen, Morten & Hovig, Johannes Eivind (2015). Automated amplicon design suitable for analysis of DNA variants by melting techniques. BMC Research Notes. ISSN 1756-0500. 8(1). doi: 10.1186/s13104-015-1624-8.
• Sandve, Geir Kjetil; Gundersen, Sveinung; Johansen, Morten; Glad, Ingrid Kristine; Gunathasan, Krishanthi & Holden, Lars [Show all 21 contributors for this article] (2013). The Genomic HyperBrowser: an analysis web server for genome-scale data. Nucleic Acids Research (NAR). ISSN 0305-1048. 41(W1), p. W133–W141. doi: 10.1093/nar/gkt342. Full text in Research Archive Show summary

  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.

• Nakken, Sigve; Johansen, Morten; Fillebeen, Julien; Berge, Ole Petter; Kirkerød, Harald & Jenssen, Tor-Kristian [Show all 7 contributors for this article] (2012). CellLineMiner: a knowledge portal for human cell lines. Bioinformation. ISSN 0973-8894. 8(22), p. 1119–1122. doi: 10.6026/97320630081119.
• Sandve, Geir Kjetil; Gundersen, Sveinung; Rydbeck, Halfdan; Glad, Ingrid Kristine; Holden, Lars & Holden, Marit [Show all 15 contributors for this article] (2011). The differential disease regulome. BMC Genomics. ISSN 1471-2164. 12. doi: 10.1186/1471-2164-12-353. Show summary

  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 [Show all 14 contributors for this article] (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. Full text in Research Archive Show summary

  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.

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• Fromm, Bastian; Domanska, Diana; Høye, Eirik; Ovchinnikov, Vladimir; Kang, Wenjing & Aparicio-Puerta, Ernesto [Show all 13 contributors for this article] (2020). Erratum: MirGeneDB 2.0: The metazoan microRNA complement (Nucleic Acids Research (2019) DOI: 10.1093/nar/gkz885). Nucleic Acids Research (NAR). ISSN 0305-1048. 48(1). doi: 10.1093/nar/gkz1016.
• Simovski, Boris; Drabløs, Finn Sverre; Gundersen, Sveinung; Johansen, Morten; Domanska, Diana Ewa & Azab, Abdulrahman [Show all 8 contributors for this article] (2016). The Genomic HyperBrowser.

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