Systematic assessment of long-read RNA-seq methods for transcript identification and quantification

Pardo-Palacios, Francisco J., Wang, Dingjie, Reese, Fairlie, Diekhans, Mark, Carbonell-Sala, Sílvia, Williams, Brian, Loveland, Jane E., De María, Maite, Adams, Matthew S., Balderrama-Gutierrez, Gabriela, Behera, Amit K., Gonzalez Martinez, Jose M., Hunt, Toby, Lagarde, Julien, Liang, Cindy E., Li, Haoran, Meade, Marcus Jerryd, Moraga Amador, David A., Prjibelski, Andrey D., Birol, Inanc, Bostan, Hamed, Brooks, Ashley M., Çelik, Muhammed Hasan, Chen, Ying, Du, Mei R. M., Felton, Colette, Göke, Jonathan, Hafezqorani, Saber, Herwig, Ralf, Kawaji, Hideya, Lee, Joseph, Li, Jian-Liang, Lienhard, Matthias, Mikheenko, Alla, Mulligan, Dennis, Nip, Ka Ming, Pertea, Mihaela, Ritchie, Matthew E., Sim, Andre D., Tang, Alison D., Wan, Yuk Kei, Wang, Changqing, Wong, Brandon Y., Yang, Chen, Barnes, If, Berry, Andrew E., Capella-Gutierrez, Salvador, Cousineau, Alyssa, Dhillon, Namrita, Fernandez-Gonzalez, Jose M., Ferrández-Peral, Luis, Garcia-Reyero, Natàlia, Götz, Stefan, Hernández-Ferrer, Carles, Kondratova, Liudmyla, Liu, Tianyuan ORCID: https://orcid.org/0000-0002-8561-6239, Martinez-Martin, Alessandra, Menor, Carlos, Mestre-Tomás, Jorge, Mudge, Jonathan M., Panayotova, Nedka G., Paniagua, Alejandro, Repchevsky, Dmitry, Ren, Xingjie, Rouchka, Eric, Saint-John, Brandon, Sapena, Enrique, Sheynkman, Leon, Smith, Melissa Laird, Suner, Marie-Marthe, Takahashi, Hazuki, Youngworth, Ingrid A., Carninci, Piero, Denslow, Nancy D., Guigó, Roderic, Hunter, Margaret E., Maehr, Rene, Shen, Yin, Tilgner, Hagen U., Wold, Barbara J., Vollmers, Christopher, Frankish, Adam, Au, Kin Fai, Sheynkman, Gloria M., Mortazavi, Ali, Conesa, Ana and Brooks, Angela N. 2024. Systematic assessment of long-read RNA-seq methods for transcript identification and quantification. Nature Methods 21 (7) , pp. 1349-1363. 10.1038/s41592-024-02298-3

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Abstract

The Long-read RNA-Seq Genome Annotation Assessment Project Consortium was formed to evaluate the effectiveness of long-read approaches for transcriptome analysis. Using different protocols and sequencing platforms, the consortium generated over 427 million long-read sequences from complementary DNA and direct RNA datasets, encompassing human, mouse and manatee species. Developers utilized these data to address challenges in transcript isoform detection, quantification and de novo transcript detection. The study revealed that libraries with longer, more accurate sequences produce more accurate transcripts than those with increased read depth, whereas greater read depth improved quantification accuracy. In well-annotated genomes, tools based on reference sequences demonstrated the best performance. Incorporating additional orthogonal data and replicate samples is advised when aiming to detect rare and novel transcripts or using reference-free approaches. This collaborative study offers a benchmark for current practices and provides direction for future method development in transcriptome analysis.

Item Type:	Article
Date Type:	Published Online
Status:	Published
Schools:	Engineering
Additional Information:	License information from Publisher: LICENSE 1: URL: http://creativecommons.org/licenses/by/4.0/, Type: open-access
Publisher:	Nature Research
ISSN:	1548-7091
Date of First Compliant Deposit:	15 July 2024
Date of Acceptance:	3 May 2024
Last Modified:	15 Jul 2024 10:31
URI:	https://orca.cardiff.ac.uk/id/eprint/170594

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