Exploring Regulatory Roles of Transposable Elements in EMT and MET through Data-Driven Analysis: Insights from regulaTER


Eskier D., Yetkin S., Arslan N., Karakülah G., Alotaibi H.

JOURNAL OF MOLECULAR BIOLOGY, cilt.437, sa.2, 2025 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 437 Sayı: 2
  • Basım Tarihi: 2025
  • Doi Numarası: 10.1016/j.jmb.2024.168887
  • Dergi Adı: JOURNAL OF MOLECULAR BIOLOGY
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Applied Science & Technology Source, Artic & Antarctic Regions, BIOSIS, CAB Abstracts, Chemical Abstracts Core, Veterinary Science Database
  • Dokuz Eylül Üniversitesi Adresli: Evet

Özet

Gene expression is regulated at the transcriptional and translational levels and a plethora of epigenetic mechanisms. Regulation of gene expression by transposable elements is well documented. However, a comprehensive analysis of their regulatory roles is challenging due to the lack of dedicated approaches to define their contribution. Here, we present regulaTER, a new R library dedicated to deciphering the regulatory potential of transposable elements in a given phenotype. regulaTER utilizes a variety of genomics data of any origin and combines gene expression level information to predict the regulatory roles of transposable elements. We further validated its capabilities using data generated from an epithelialmesenchymal and mesenchymal-epithelial transition cellular model. regulaTER stands out as an essential asset for uncovering the impact of transposable elements on the regulation of gene expression, with high flexibility to perform a range of transposable element-focused analyses. Our results also provided insights on the contribution of the MIR and B element subfamilies in regulating EMT and MET through the FoxA transcription factor family. regulaTER is publicly available and can be downloaded from https://github. (c) 2024 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.