CapTrap-seq: a platform-agnostic and quantitative approach for high-fidelity full-length RNA sequencing

Sílvia Carbonell-Sala, Tamara Perteghella, Julien Lagarde, Hiromi Nishiyori, Emilio Palumbo, Carme Arnan, Hazuki Takahashi, Piero Carninci, Barbara Uszczynska-Ratajczak () and Roderic Guigó ()
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Sílvia Carbonell-Sala: the Barcelona Institute of Science and Technology
Tamara Perteghella: the Barcelona Institute of Science and Technology
Julien Lagarde: the Barcelona Institute of Science and Technology
Hiromi Nishiyori: RIKEN Center for Integrative Medical Sciences (IMS)
Emilio Palumbo: the Barcelona Institute of Science and Technology
Carme Arnan: the Barcelona Institute of Science and Technology
Hazuki Takahashi: RIKEN Center for Integrative Medical Sciences (IMS)
Piero Carninci: RIKEN Center for Integrative Medical Sciences (IMS)
Barbara Uszczynska-Ratajczak: the Barcelona Institute of Science and Technology
Roderic Guigó: the Barcelona Institute of Science and Technology

Nature Communications, 2024, vol. 15, issue 1, 1-13

Abstract: Abstract Long-read RNA sequencing is essential to produce accurate and exhaustive annotation of eukaryotic genomes. Despite advancements in throughput and accuracy, achieving reliable end-to-end identification of RNA transcripts remains a challenge for long-read sequencing methods. To address this limitation, we develop CapTrap-seq, a cDNA library preparation method, which combines the Cap-trapping strategy with oligo(dT) priming to detect 5’ capped, full-length transcripts. In our study, we evaluate the performance of CapTrap-seq alongside other widely used RNA-seq library preparation protocols in human and mouse tissues, employing both ONT and PacBio sequencing technologies. To explore the quantitative capabilities of CapTrap-seq and its accuracy in reconstructing full-length RNA molecules, we implement a capping strategy for synthetic RNA spike-in sequences that mimics the natural 5’cap formation. Our benchmarks, incorporating the Long-read RNA-seq Genome Annotation Assessment Project (LRGASP) data, demonstrate that CapTrap-seq is a competitive, platform-agnostic RNA library preparation method for generating full-length transcript sequences.

Date: 2024
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DOI: 10.1038/s41467-024-49523-3

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