Romana Tabita Pop

• Pop, Romana Tabita; Hsieh, Ping-Han; Belova, Tatiana; Mathelier, Anthony & Kuijjer, Marieke (2025). Gene regulatory network integration with multi-omics data enhances survival predictions in cancer. Briefings in Bioinformatics. ISSN 1467-5463. 26(4). doi: 10.1093/bib/bbaf315. Full text in Research Archive Show summary

  The emergence of high-throughput omics technologies has resulted in their wide application to cancer studies, greatly increasing our understanding of the disruptions occurring at different molecular levels. To fully harness these data, integrative approaches have emerged as essential tools, enabling the combination of multiple omics modalities to uncover disease mechanisms. However, many such approaches overlook gene regulatory mechanisms, which play a central role in the development and progression of cancer. Patient-specific gene regulatory networks (GRNs), representing interactions between regulators (such as transcription factors) and their target genes in each individual tumour, offer a powerful framework to bridge this gap and investigate the regulatory landscape of cancer. In this study, we introduce a novel approach for integrating patient-specific GRNs with multi-omic data and assess whether their inclusion in joint dimensionality reduction models improves survival prediction across multiple cancer types. By applying our method on ten cancer datasets from The Cancer Genome Atlas, we demonstrate that incorporating GRNs enhances associations with patient survival in several cancer types. Focusing on liver cancer, with validation in independent data, our methodology identifies potential mechanisms of gene regulatory dysregulation associated with cancer progression. These were linked to dysregulated fatty acid metabolism, and identified JUND as a potential novel transcriptional regulator driving these processes. Our findings highlight the value of network-based multi-omics integration for uncovering clinically relevant regulatory mechanisms and improving our understanding of cancer biology at the patient-specific level.

• Pop, Romana Tabita; Pisante, Alessandra; Nagy, Dorka; Martin, Patrick C. N.; Mikheeva, Liudmila A. & Hayat, Ateequllah [Show all 8 contributors for this article] (2023). Identification of mammalian transcription factors that bind to inaccessible chromatin. Nucleic Acids Research (NAR). ISSN 0305-1048. 51(16), p. 8480–8495. doi: 10.1093/nar/gkad614. Full text in Research Archive Show summary

  Abstract Transcription factors (TFs) are proteins that affect gene expression by binding to regulatory regions of DNA in a sequence specific manner. The binding of TFs to DNA is controlled by many factors, including the DNA sequence, concentration of TF, chromatin accessibility and co-factors. Here, we systematically investigated the binding mechanism of hundreds of TFs by analysing ChIP-seq data with our explainable statistical model, ChIPanalyser. This tool uses as inputs the DNA sequence binding motif; the capacity to distinguish between strong and weak binding sites; the concentration of TF; and chromatin accessibility. We found that approximately one third of TFs are predicted to bind the genome in a DNA accessibility independent fashion, which includes TFs that can open the chromatin, their co-factors and TFs with similar motifs. Our model predicted this to be the case when the TF binds to its strongest binding regions in the genome, and only a small number of TFs have the capacity to bind dense chromatin at their weakest binding regions, such as CTCF, USF2 and CEBPB. Our study demonstrated that the binding of hundreds of human and mouse TFs is predicted by ChIPanalyser with high accuracy and showed that many TFs can bind dense chromatin.

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• Pop, Romana Tabita (2023). Integrating gene regulatory networks and multi-omics data using joint dimensionality reduction. Full text in Research Archive
• Pop, Romana Tabita (2023). Integrating gene regulatory networks and multi-omics data using joint dimensionality reduction. Full text in Research Archive
• Pop, Romana-Tabita (2022). Integrating gene regulatory networks with multi-omics data with MOFA+. Full text in Research Archive
• Pop, Romana-Tabita (2022). ELAND: Desparsifying somatic mutations based on network communities. Full text in Research Archive
• Guebila, Marouen Ben; Weighill, Deborah; Lopes-Ramos, Camila M.; Burkholz, Rebekka; Pop, Romana-Tabita & Palepu, Kalyan [Show all 14 contributors for this article] (2022). An online notebook resource for reproducible inference, analysis and publication of gene regulatory networks. Nature Methods. ISSN 1548-7091. 19, p. 511–513. doi: 10.1038/s41592-022-01479-2. Full text in Research Archive
• Fatima, Nazeefa; Lemma, Roza Berhanu; Rauluseviciute, Ieva; Belova, Tatiana; Kumar, Vipin & Hsieh, Ping-Han [Show all 12 contributors for this article] (2022). Developing Bioinformatics Societies in Norway and Nordic. Full text in Research Archive Show summary

  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.

• Pop, Romana Tabita (2026). Omics data integration with gene regulatory networks in health and disease. Universitetet i Oslo. ISSN 9788234807817. Full text in Research Archive Show summary

  This thesis lies at the intersection of computational biology, network science, and cancer systems biology. It investigates whether gene regulatory networks (GRNs) can improve multi-omics data integration, enhance prediction of cancer patient survival, and be analyzed more effectively using scalable network methods. It also examines how accessibility and reproducibility of GRN analyses can be strengthened. To address these questions, sample-specific GRNs were integrated with ten cancer multi-omics datasets from TCGA using the MOFA+ framework, with validation in an independent liver cancer cohort. A new algorithm, HEDGEHOG, was developed to detect hierarchical and overlapping communities in large bipartite biological networks and applied to 34 tissue-specific GRNs from GTEx. In parallel, an online platform, NetBooks, was created to provide reproducible workflows, code, and tutorials. Results show that incorporating GRNs into multi-omics models improves associations with patient survival in several cancer types and highlights biologically meaningful pathways, including fatty-acid metabolism in liver cancer. HEDGEHOG identifies hundreds of stable network communities per tissue, many corresponding to known biological processes, demonstrating its value for interpreting large regulatory systems. The reproducibility resources further support transparent and reusable analyses. Overall, the thesis demonstrates that integrating regulatory network information with multi-omics data yields more informative models of disease biology and that scalable network methods combined with open tools can substantially advance systems-level cancer research.

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