bioRxivCancer BiologyPreprint
Ouro-seq: Improved Recovery of Full-Length circRNAs from Samples with Limited RNA Content
Birgit M. M. WeveriD, Yara van den Burgt, Florent C. MouliereiD, Dirk Michiel PegteliD, Maaike C.G. BleekeriD, Renske D.M. SteenbergeniD, Norbert MoldovániD
Abstract
Circular RNAs (circRNAs) are an emerging class of RNAs with biomarker potential, but their detection in liquid biopsies is challenging due to low abundance. We developed Ouro-seq, a novel long-read sequencing protocol optimized for full-length circRNA recovery. Applied to urine, cervico-vaginal self-samples from cervical cancer patients, and plasma from lung cancer patients and controls, Ouro-seq recovered 2-5 times more and substantially longer circRNA molecules than conventional methods. Plasma contained predominantly exonic circRNAs, while urine and cervico-vaginal samples were dominated by previously undercharacterized intergenic circRNAs. We also identified extensive alternative circularization and splicing events. Functional analysis revealed distinct specialization patterns: exonic circRNAs showed enhanced miRNA sponging potential, while circRNAs from unplaced genomic scaffolds demonstrated greater peptide-coding capacity. This study establishes Ouro-seq as a valuable tool for comprehensive circRNA characterization in low-yield clinical samples and advances circRNA biology understanding with potential biomarker discovery and disease monitoring applications. MotivationWhile circular RNAs (circRNAs) constitute a minor fraction of total RNA, they may play critical roles in cancer development. CircRNA concentrations are typically too low for detection by Oxford Nanopore Long-Read Sequencing (LRS), particularly in samples with limited RNA content, such as liquid biopsies. Consequently, LRS-based circRNA analysis from liquid biopsies remains unexplored. To overcome these technical limitations, we developed an optimized circRNA enrichment method utilizing short-amplicon suppression, enabling circRNA profiling from urine, plasma, and cervico-vaginal samples.
- organism: human
- assay: long-read sequencing
- research focus: cell type annotation
- approach: deep learning
- Citations
- 0
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