Ieva Rauluseviciute

• Rauluseviciute, Ieva; Launay, Timothée; Barzaghi, Guido; Nikumbh, Sarvesh; Lenhard, Boris & Krebs, Arnaud Regis [Show all 8 contributors for this article] (2023). Identification of transcription factor co-binding patterns with non-negative matrix factorization. bioRxiv. ISSN 2692-8205. doi: 10.1101/2023.04.28.538684.
• 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.

• Castro Mondragon, Jaime Abraham; Riudavets Puig, Rafael; Rauluseviciute, Ieva; Lemma, Roza Berhanu; Turchi, Laura & Blanc-Mathieu, Romain [Show all 23 contributors for this article] (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. Full text in Research Archive
• 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. ISSN 1755-8794. 13(1). doi: 10.1186/s12920-020-0657-6. Full text in Research Archive
• 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. ISSN 1868-7075. 11:193, p. 1–13. doi: 10.1186/s13148-019-0795-x. Full text in Research Archive

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• Kumar, Vipin; Fatima, Nazeefa; Hsieh, Ping-Han; Belova, Tatiana; Ferenc, Katalin Terezia & Rauluseviciute, Ieva [Show all 9 contributors for this article] (2022). Introducing ISCB's (International Society for Computational Biology) regional student group in Norway's (RSG-Norway's) activities to the PhD and postdoc forum at the Institute of Cancer research, Radium hospital, OUS .
• 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. 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.

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

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