Assessing the expression of Long INterspersed Elements (LINEs) via long-read sequencing in diverse
Karleena Rybacki, BS, 1,2, Mingyi Xia, MS, 1,2, Mian Umair Ahsan, MS,2, Jinchuan Xing, Ph.D.,3,4, Kai Wang, Ph.D.,1,2 1 Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA 2 Raymond G. Perelman Center for Cellular and Molecular Therapeutics, The Children's Hospital of Philadelphia, Philadelphia, PA, USA 3 Department of Genetics, Rutgers, The State University of New Jersey, Piscataway, NJ, USA 4 Human Genetics Institute of New Jersey, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
Poster # 11
Transposable Elements, such as Long INterspersed Elements (LINEs), are DNA sequences that can replicate within genomes. LINEs replicate using an RNA intermediate followed by reverse transcription and are typically a few kilobases in length. LINE activity creates genomic structural variants in human populations and leads to somatic alterations in cancer genomes. Long-read RNA sequencing technologies, including Oxford Nanopore and PacBio, can sequence relatively long transcripts directly without breaking up the transcripts into fragments, thus providing the opportunity to examine full-length expressed LINEs. This study focuses on the development of a new bioinformatics pipeline for the identification and quantification of active, full-length LINEs in diverse human tissues and cell lines. The pipeline utilized RepeatMasker to identify LINE-1 (L1) expression in human tissues, based on several criteria including divergence and length, to focus on young L1 elements with intact retrotransposition machinery. Comparisons of cancerous and normal cell lines, as well as healthy human tissue samples, revealed higher expression levels of younger, active LINEs in cancer, at intact and fully transcribed L1 loci. Active and inactive L1 elements were analyzed to illustrate the presence and impact of highly mutated and less mutated LINEs across various tissue and cell line types. By employing advanced bioinformatics methodologies on full-length transcriptome data, this study demonstrates the landscape of L1 elements and their contributions in normal and oncogenic cellular processes.