Ieva Rauluseviciute

• Ferenc, Katalin Terezia; Martini, Lorenzo; Rauluseviciute, Ieva; Sandve, Geir Kjetil Ferkingstad & Mathelier, Anthony (2026). inMOTIFin: a lightweight end-to-end simulation software for regulatory sequences. Bioinformatics. ISSN 1367-4803. doi: 10.1093/bioinformatics/btag026. Fulltekst i vitenarkiv Vis sammendrag

  The accurate development, assessment, interpretation, and benchmarking of bioinformatics frameworks for analyzing transcriptional regulatory grammars rely on controlled simulations to validate the underlying methods. However, existing simulators often lack end-to-end flexibility or ease of integration, which limits their practical use. We present inMOTIFin, a lightweight, modular, and user-friendly Python-based software that addresses these gaps by providing versatile and efficient simulation and modification of DNA regulatory sequences. inMOTIFin enables users to simulate or modify regulatory sequences efficiently for the customizable generation of motifs and insertion of motif instances with precise control over their positions, co-occurrences, and spacing, as well as direct modification of real sequences, facilitating a comprehensive evaluation of motif-based methods and interpretation tools. We demonstrate inMOTIFin applications for the assessment of de novo motif discovery, the analysis of transcription factor cooperativity, and the support of explainability analyses for deep learning models. inMOTIFin ensures robust and reproducible analyses for studying transcriptional regulatory grammars.

• 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.

• Ferenc, Katalin Terezia; Rauluseviciute, Ieva; Hovan, Ladislav; Kumar, Vipin; Kuijjer, Marieke & Mathelier, Anthony (2024). Improving bioinformatics software quality through teamwork. Bioinformatics. ISSN 1367-4803. 40(11). doi: 10.1093/bioinformatics/btae632. Fulltekst i vitenarkiv Vis sammendrag

  Since high-throughput techniques became a staple in biological science laboratories, computational algorithms, and scientific software have boomed. However, the development of bioinformatics software usually lacks software development quality standards. The resulting software code is hard to test, reuse, and maintain. We believe that the root of inefficiency in implementing the best software development practices in academic settings is the individualistic approach, which has traditionally been the norm for recognizing scientific achievements and, by extension, for developing specialized software. Software development is a collective effort in most software-heavy endeavors. Indeed, the literature suggests teamwork directly impacts code quality through knowledge sharing, collective software development, and established coding standards. In our computational biology research groups, we sustainably involve all group members in learning, sharing, and discussing software development while maintaining the personal ownership of research projects and related software products. We found that group members involved in this endeavor improved their coding skills, became more efficient bioinformaticians, and obtained detailed knowledge about their peers’ work, triggering new collaborative projects. We strongly advocate for improving software development culture within bioinformatics through collective effort in computational biology groups or institutes with three or more bioinformaticians.

• Rauluseviciute, Ieva; Launay, Timothée; Barzaghi, Guido; Nikumbh, Sarvesh; Lenhard, Boris & Krebs, Arnaud Regis [Vis alle 8 forfattere av denne artikkelen] (2024). Identification of transcription factor co-binding patterns with non-negative matrix factorization. Nucleic Acids Research (NAR). ISSN 0305-1048. 52(18), s. e85–e85. doi: 10.1093/nar/gkae743. Fulltekst i vitenarkiv Vis sammendrag

  Transcription factor (TF) binding to DNA is critical to transcription regulation. Although the binding properties of numerous individual TFs are well-documented, a more detailed comprehension of how TFs interact cooperatively with DNA is required. We present COBIND, a novel method based on non-negative matrix factorization (NMF) to identify TF co-binding patterns automatically. COBIND applies NMF to one-hot encoded regions flanking known TF binding sites (TFBSs) to pinpoint enriched DNA patterns at fixed distances. We applied COBIND to 5699 TFBS datasets from UniBind for 401 TFs in seven species. The method uncovered already established co-binding patterns and new co-binding configurations not yet reported in the literature and inferred through motif similarity and protein-protein interaction knowledge. Our extensive analyses across species revealed that 67% of the TFs shared a co-binding motif with other TFs from the same structural family. The co-binding patterns captured by COBIND are likely functionally relevant as they harbor higher evolutionarily conservation than isolated TFBSs. Open chromatin data from matching human cell lines further supported the co-binding predictions. Finally, we used single-molecule footprinting data from mouse embryonic stem cells to confirm that the COBIND-predicted co-binding events associated with some TFs likely occurred on the same DNA molecules.

• 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.

• Rauluseviciute, Ieva; Launay, Timothée; Barzaghi, Guido; Nikumbh, Sarvesh; Lenhard, Boris & Krebs, Arnaud Regis [Vis alle 8 forfattere av denne artikkelen] (2023). Identification of transcription factor co-binding patterns with non-negative matrix factorization. BioRxiv. ISSN 0362-4331. doi: 10.1101/2023.04.28.538684. Fulltekst i vitenarkiv
• Mondragon, Jaime Abraham Castro; Puig, Rafael Riudavets; Rauluseviciute, Ieva; Lemma, Roza Berhanu; Turchi, Laura & Blanc-Mathieu, Romain [Vis alle 23 forfattere av denne artikkelen] (2021). JASPAR 2022: the 9th release of the open-acess database of transcription factor binding profiles. Nucleic Acids Research (NAR). ISSN 0305-1048. doi: 10.1093/nar/gkab1113. Fulltekst i vitenarkiv Vis sammendrag

  Abstract JASPAR (http://jaspar.genereg.net/) is an open-access database containing manually curated, non-redundant transcription factor (TF) binding profiles for TFs across six taxonomic groups. In this 9th release, we expanded the CORE collection with 341 new profiles (148 for plants, 101 for vertebrates, 85 for urochordates, and 7 for insects), which corresponds to a 19% expansion over the previous release. We added 298 new profiles to the Unvalidated collection when no orthogonal evidence was found in the literature. All the profiles were clustered to provide familial binding profiles for each taxonomic group. Moreover, we revised the structural classification of DNA binding domains to consider plant-specific TFs. This release introduces word clouds to represent the scientific knowledge associated with each TF. We updated the genome tracks of TFBSs predicted with JASPAR profiles in eight organisms; the human and mouse TFBS predictions can be visualized as native tracks in the UCSC Genome Browser. Finally, we provide a new tool to perform JASPAR TFBS enrichment analysis in user-provided genomic regions. All the data is accessible through the JASPAR website, its associated RESTful API, the R/Bioconductor data package, and a new Python package, pyJASPAR, that facilitates serverless access to the data.

• Rauluseviciute, Ieva; Drabløs, Finn & Rye, Morten Beck (2020). DNA hypermethylation associated with upregulated gene expression in prostate cancer demonstrates the diversity of epigenetic regulation. BMC Medical Genomics. 13(1), s. 6–6. doi: 10.1186/s12920-020-0657-6. Fulltekst i vitenarkiv Vis sammendrag

  Background Prostate cancer (PCa) has the highest incidence rates of cancers in men in western countries. Unlike several other types of cancer, PCa has few genetic drivers, which has led researchers to look for additional epigenetic and transcriptomic contributors to PCa development and progression. Especially datasets on DNA methylation, the most commonly studied epigenetic marker, have recently been measured and analysed in several PCa patient cohorts. DNA methylation is most commonly associated with downregulation of gene expression. However, positive associations of DNA methylation to gene expression have also been reported, suggesting a more diverse mechanism of epigenetic regulation. Such additional complexity could have important implications for understanding prostate cancer development but has not been studied at a genome-wide scale. Results In this study, we have compared three sets of genome-wide single-site DNA methylation data from 870 PCa and normal tissue samples with multi-cohort gene expression data from 1117 samples, including 532 samples where DNA methylation and gene expression have been measured on the exact same samples. Genes were classified according to their corresponding methylation and expression profiles. A large group of hypermethylated genes was robustly associated with increased gene expression (UPUP group) in all three methylation datasets. These genes demonstrated distinct patterns of correlation between DNA methylation and gene expression compared to the genes showing the canonical negative association between methylation and expression (UPDOWN group). This indicates a more diversified role of DNA methylation in regulating gene expression than previously appreciated. Moreover, UPUP and UPDOWN genes were associated with different compartments — UPUP genes were related to the structures in nucleus, while UPDOWN genes were linked to extracellular features. Conclusion We identified a robust association between hypermethylation and upregulation of gene expression when comparing samples from prostate cancer and normal tissue. These results challenge the classical view where DNA methylation is always associated with suppression of gene expression, which underlines the importance of considering corresponding expression data when assessing the downstream regulatory effect of DNA methylation.

• Reuluseviciute, Ieva; Drabløs, Finn & Rye, Morten Beck (2019). DNA methylation data by sequencing: experimental approaches and recommendations for tools and pipelines for data analysis. Clinical Epigenetics. 11:193, s. 1–13. doi: 10.1186/s13148-019-0795-x. Fulltekst i vitenarkiv Vis sammendrag

  Sequencing technologies have changed not only our approaches to classical genetics, but also the field of epigenetics. Specific methods allow scientists to identify novel genome-wide epigenetic patterns of DNA methylation down to single-nucleotide resolution. DNA methylation is the most researched epigenetic mark involved in various processes in the human cell, including gene regulation and development of diseases, such as cancer. Increasing numbers of DNA methylation sequencing datasets from human genome are produced using various platforms—from methylated DNA precipitation to the whole genome bisulfite sequencing. Many of those datasets are fully accessible for repeated analyses. Sequencing experiments have become routine in laboratories around the world, while analysis of outcoming data is still a challenge among the majority of scientists, since in many cases it requires advanced computational skills. Even though various tools are being created and published, guidelines for their selection are often not clear, especially to non-bioinformaticians with limited experience in computational analyses. Separate tools are often used for individual steps in the analysis, and these can be challenging to manage and integrate. However, in some instances, tools are combined into pipelines that are capable to complete all the essential steps to achieve the result. In the case of DNA methylation sequencing analysis, the goal of such pipeline is to map sequencing reads, calculate methylation levels, and distinguish differentially methylated positions and/or regions. The objective of this review is to describe basic principles and steps in the analysis of DNA methylation sequencing data that in particular have been used for mammalian genomes, and more importantly to present and discuss the most pronounced computational pipelines that can be used to analyze such data. We aim to provide a good starting point for scientists with limited experience in computational analyses of DNA methylation and hydroxymethylation data, and recommend a few tools that are powerful, but still easy enough to use for their own data analysis.

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• Fatima, Nazeefa; Lemma, Roza Berhanu; Rauluseviciute, Ieva; Belova, Tatiana; Kumar, Vipin & Hsieh, Ping-Han [Vis alle 12 forfattere av denne artikkelen] (2022). Developing Bioinformatics Societies in Norway and Nordic. Fulltekst i vitenarkiv Vis sammendrag

  With the increasing amount of biological data, the need for computational skills has become prominent. Interdisciplinary fields such as computational biology require interdisciplinary collaborations; in order to create an environment for sharing skills and experiences, opportunities for funding and training, career path advice, and research ideas. Creating such environments also help to establish an inclusive platform where students and early-career researchers in computational biology can socialise and help each other in an informal setting. Currently, this is achieved locally at department level at a university or industrial company. The social interactions between students and established scientists are, therefore, consequently limited to the geographical locations of their institutions. The Regional Study Group Norway (RSG-Norway), part of the International Society of Computational Biology (ISCB), aims to create a country-wide platform, to bring together aspiring and established bioinformaticians and to develop an active presence in the Norwegian bioinformatics community. Regional level communities such as RSG-Norway are particularly helpful for students to broaden training and career opportunities. It can also help foreign researchers and students, in settling as new arrivals, through building contacts with those who are familiar with studying and working in Norway. Finally, the study group can be a channel for students and trainees to initiate and/or realise their own ideas regarding social and professional communication with fellow bioinformaticians. RSG-Norway works together with Nordic Computational Biology (NCB), which serves as a resource and knowledge-exchange hub to bring together people from industries and academia as well as local communities such as Regional Student Groups across the Nordics. As part of the NCB, Nordic RSGs are able to build cross-border collaborations and organise open events that reach a wider audience strengthening the community's knowledge-base. The presentation for this abstract will include information on past events and initiatives by RSG-Norway and NCB, their future activities and plans, and how to get involved as a member and an advisor to help develop the computational biology community in Norway and the Nordics.

• Rauluseviciute, Ieva; Castro-Mondragon, Jaime A. & Mathelier, Anthony (2022). Identification of transcription factor co-binding patterns with non-negative matrix factorization. Fulltekst i vitenarkiv
• Rauluseviciute, Ieva; Castro-Mondragon, Jaime A. & Mathelier, Anthony (2022). Identification of transcription factor co-binding patterns with non-negative matrix factorization. Fulltekst i vitenarkiv
• Rauluseviciute, Ieva; Castro-Mondragon, Jaime A. & Mathelier, Anthony (2022). Identification of transcription factor co-binding partners with non-negative matrix factorization. Fulltekst i vitenarkiv

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