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

Pardo-Palacios, FJ; Wang, D; Reese, F; Diekhans, M; Carbonell-Sala, S; Williams, B; Loveland, JE; De María, M; Adams, MS; Balderrama-Gutierrez, G; Behera, AK; Gonzalez Martinez, JM; Hunt, T; Lagarde, J; Liang, CE; Li, H; Meade, MJ; Moraga Amador, DA; Prjibelski, AD; Birol, I; Bostan, H; Brooks, AM; Çelik, MH; Chen, Y; Du, MRM; Felton, C; Göke, J; Hafezqorani, S; Herwig, R; Kawaji, H; Lee, J; Li, JL; Lienhard, M; Mikheenko, A; Mulligan, D; Nip, KM; Pertea, M; Ritchie, ME; Sim, AD; Tang, AD; Wan, YK; Wang, C; Wong, BY; Yang, C; Barnes, I; Berry, AE; Capella-Gutierrez, S; Cousineau, A; Dhillon, N; Fernandez-Gonzalez, JM; Ferrández-Peral, L; Garcia-Reyero, N; Götz, S; Hernández-Ferrer, C; Kondratova, L; Liu, T; Martinez-Martin, A; Menor, C; Mestre-Tomás, J; Mudge, JM; Panayotova, NG; Paniagua, A; Repchevsky, D; Ren, X; Rouchka, E; Saint-John, B; Sapena, E; Sheynkman, L; Smith, ML; Suner, MM; Takahashi, H; Youngworth, IA; Carninci, P; Denslow, ND; Guigó, R; Hunter, ME; Maehr, R; Shen, Y; Tilgner, HU; Wold, BJ; Vollmers, C; Frankish, A; Au, KF; Sheynkman, GM; Mortazavi, A; Conesa, A; Brooks, AN

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

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Authors

Pardo-Palacios, FJ; Wang, D; Reese, F; Diekhans, M; Carbonell-Sala, S; Williams, B; Loveland, JE; De María, M; Adams, MS; Balderrama-Gutierrez, G...

Date

2024-07-01

Source Title

Nature Methods

Publisher

NATURE PORTFOLIO

Melbourne Authors

Ritchie, Matthew

Affiliation

Medical Biology (W.E.H.I.)

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Document Type

Journal Article

Citation

Pardo-Palacios, F. J., Wang, D., Reese, F., Diekhans, M., Carbonell-Sala, S., Williams, B., Loveland, J. E., De María, M., Adams, M. S., Balderrama-Gutierrez, G., Behera, A. K., Gonzalez Martinez, J. M., Hunt, T., Lagarde, J., Liang, C. E., Li, H., Meade, M. J., Moraga Amador, D. A., Prjibelski, A. D. ,... Brooks, A. N. (2024). Systematic assessment of long-read RNA-seq methods for transcript identification and quantification. Nature Methods, 21 (7), pp.1349-1363. https://doi.org/10.1038/s41592-024-02298-3.

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Open Access

URI

https://hdl.handle.net/11343/352696

DOI

10.1038/s41592-024-02298-3

Open Access URL

published version

Open Access at PMC

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11543605

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.