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

作者：Francisco J. Pardo-Palacios, Dingjie Wang, Fairlie Reese, Mark Diekhans, Sílvia Carbonell Sala, Brian A. Williams, Jane Loveland, Maite De María, Matthew S. Adams, Gabriela Balderrama-Gutierrez, Amit K. Behera, José M. González, Toby Hunt, Julien Lagarde, Cindy Liang, Haoran Li, Marcus J. Meade, David A. Moraga Amador, Andrey D. Prjibelski, İnanç Birol, Hamed Bostan, Ashley M. Brooks, Ashley M. Brooks, Muhammed Hasan Çelik, Ying Chen, Mei R. M. Du, Colette Felton, Jonathan Göke, Saber Hafezqorani, Ralf Herwig, Hideya Kawaji, Jian‐Liang Li, Matthias Lienhard, Matthias Lienhard, Alla Mikheenko, Dennis Mulligan, Ka Ming Nip, Mihaela Pertea, Matthew E. Ritchie, Andre Sim, Alison D. Tang, Changqing Wang, Changqing Wang, Brandon Wong, Chen Yang, If Barnes, Andrew Berry, Salvador Capella-Gutiérrez, Alyssa Cousineau, Namrita Dhillon, José M. Fernández, Luis Ferrández-Peral, Natàlia Garcia-Reyero, Stefan Götz, Carles Hernandéz-Ferrer, Liudmyla Kondratova, Tianyuan Liu, Alessandra Martinez-Martin, Carlos Menor, Jorge Mestre‐Tomás, Jonathan M. Mudge, Nedka G. Panayotova, Alejandro Paniagua, Dmitry Repchevsky, Xingjie Ren, Eric C. Rouchka, Brandon Saint-John, Enrique Sapena, Leon Sheynkman, Melissa Smith, Marie‐Marthe Suner, Hazuki Takahashi, Ingrid Youngworth, Piero Carninci, Nancy D. Denslow, Roderic Guigó, Margaret E. Hunter, René Maehr, Yin Shen, Hagen Tilgner, B Wold, Christopher Vollmers, Adam Frankish, Kin Fai Au, Gloria Sheynkman, A Mortazavi, Ana Conesa, Angela N. Brooks, Angela N. Brooks · 发表于：Nature Methods · 年份：2024 · DOI：10.1038/s41592-024-02298-3 · 被引用次数：226 · 研究领域：Genomics and Phylogenetic Studies、Molecular Biology Techniques and Applications、Cancer-related molecular mechanisms research

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.