Journal Article

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

Çelik, Muhammed Hasan, Kondratova, Liudmyla, Ren, Xingjie, Rouchka, Eric, Capella-Gutierrez, Salvador, Chen, Ying, Li, Jian-Liang, Mikheenko, Alla, Wong, Brandon Y., Carninci, Piero, Lagarde, Julien, Pertea, Mihaela, Adams, Matthew S., Hernández-Ferrer, Carles, Liang, Cindy E., Herwig, Ralf, Denslow, Nancy D., Vollmers, Christopher, Carbonell-Sala, Sílvia, Sim, Andre D., Frankish, Adam, Göke, Jonathan, Suner, Marie-Marthe, Brooks, Ashley M., Panayotova, Nedka G., Du, Mei R. M., Youngworth, Ingrid A., Hunter, Margaret E., Hunt, Toby, Shen, Yin, Wang, Changqing, Sheynkman, Gloria M., Brooks, Angela N., Liu, Tianyuan, Wan, Yuk Kei, Martinez-Martin, Alessandra, Sapena, Enrique, Repchevsky, Dmitry, Moraga Amador, David A., Barnes, If, Birol, Inanc, Fernandez-Gonzalez, Jose M., Wold, Barbara J., Paniagua, Alejandro, Li, Haoran, Meade, Marcus Jerryd, Mortazavi, Ali, Behera, Amit K., Saint-John, Brandon, Balderrama-Gutierrez, Gabriela, Götz, Stefan, De María, Maite, Cousineau, Alyssa, Berry, Andrew E., Smith, Melissa Laird, Nip, Ka Ming, Conesa, Ana, Wang, Dingjie, Yang, Chen, Dhillon, Namrita, Bostan, Hamed, Fer

2024

Overview

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. This Registered Report presents the results of the Long-read RNA-Seq Genome Annotation Assessment Project, which is a community effort for benchmarking long-read methods for transcriptome analyses, including transcript isoform detection, quantification and de novo transcript detection.

• Publisher: Nature Publishing Group US,Nature Publishing Group
• Subject: 631/114/2184 / 631/1647/2217/2018 / 631/1647/514/1949 / 631/1647/794 / Animals / Annotations / Bioinformatics / Biological Microscopy / Biological Techniques / Biomedical and Life Sciences / Biomedical Engineering/Biotechnology / Consortia / DNA sequencing / Gene Expression Profiling - methods / Gene sequencing / Genomes / Genomic analysis / Humans / Life Sciences / Mice / Molecular Sequence Annotation - methods / Nucleotide sequence / Proteomics / Registered Report / Ribonucleic acid / RNA / RNA-Seq - methods / Sequence Analysis, RNA - methods / Transcriptome / Transcriptomes