Dataset Information

RTCpredictor: identification of read-through chimeric RNAs from RNA sequencing data.

ABSTRACT: Read-through chimeric RNAs are being recognized as a means to expand the functional transcriptome and contribute to cancer tumorigenesis when mis-regulated. However, current software tools often fail to predict them. We have developed RTCpredictor, utilizing a fast ripgrep tool to search for all possible exon-exon combinations of parental gene pairs. We also added exonic variants allowing searches containing common SNPs. To our knowledge, it is the first read-through chimeric RNA specific prediction method that also provides breakpoint coordinates. Compared with 10 other popular tools, RTCpredictor achieved high sensitivity on a simulated and three real datasets. In addition, RTCpredictor has less memory requirements and faster execution time, making it ideal for applying on large datasets.

SUBMITTER: Singh S

PROVIDER: S-EPMC11128028 | biostudies-literature | 2024 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

RTCpredictor: identification of read-through chimeric RNAs from RNA sequencing data.

Singh Sandeep S Shi Xinrui X Haddox Samuel S Elfman Justin J Ahmad Syed Basil SB Lynch Sarah S Manley Tommy T Piczak Claire C Phung Christopher C Sun Yunan Y Sharma Aadi A Li Hui H

Briefings in bioinformatics 20240501 4

Read-through chimeric RNAs are being recognized as a means to expand the functional transcriptome and contribute to cancer tumorigenesis when mis-regulated. However, current software tools often fail to predict them. We have developed RTCpredictor, utilizing a fast ripgrep tool to search for all possible exon-exon combinations of parental gene pairs. We also added exonic variants allowing searches containing common SNPs. To our knowledge, it is the first read-through chimeric RNA specific predic ...[more]

PMID: 38796690

Similar Datasets

Project description:BackgroundTransposable elements (TEs), which constitute nearly half of the human genome, have long been regarded as genomic "dark matter". However, their reactivation in tumor cells, resulting in the production of TE-chimeric transcripts (TCTs), has emerged as a potential driver of cancer progression. The complexity and full extent of these transcripts remain elusive, largely due to the limitations of short-read next-generation sequencing technologies. These methods have struggled to comprehensively capture the diversity and structure of TCTs, particularly those involving short interspersed nuclear elements (SINEs) or closely co-transcribed TEs.MethodsLeveraging full-length cDNA sequencing technology based on nanopore sequencing platform, we developed a customized pipeline for identifying and quantifying TCTs in 19 lung adenocarcinoma (LUAD) cell lines. The short-read RNA-seq dataset from a LUAD corhort (~ 200 tumor samples) was employed to validate the identified TCTs and explore their association with tumor progression. To assess the functional roles of a specific TCTs, cell migration and cell proliferation assays were performed.ResultsWe uncovered 208 unique TCT candidates in the LUAD cell lines. Our approach allowed for the identification of cryptic promoters and terminators within non-transposing TEs. Notably, we identified a chimeric transcript involving MIR_HKDC1, which appears to play a significant role in the progression of LUAD. Furthermore, the expression of these TCTs were associated with poor clinical outcomes in a cohort of LUAD patients, suggesting their potential as novel biomarkers for both LUAD progression and prognosis.ConclusionsOur study underscores the application of long-read sequencing to unravel the complex landscape of TCTs in LUAD. We provide a comprehensive characterization of TCTs in LUAD, exploring their potential regulatory roles in cancer progression. These findings contribute to a deeper understanding of the genomic intricacies underlying cancer, and offer new directions for the development of targeted therapies and personalized treatment strategies for LUAD. This research highlights the potential of TCTs as both biomarkers and therapeutic targets in the oncogenesis, offering new insights into the interplay between transposable elements and gene regulation in cancer.

Dataset Information

RTCpredictor: identification of read-through chimeric RNAs from RNA sequencing data.

Publications

RTCpredictor: identification of read-through chimeric RNAs from RNA sequencing data.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets