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 Maria, 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; Celik, MH; Chen, Y; Du, MRM; Felton, C; Goke, 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; Ferrandez-Peral, L; Garcia-Reyero, N; Gotz, S; Hernandez-Ferrer, C; Kondratova, L; Liu, T; Martinez-Martin, A; Menor, C; Mestre-Tomas, 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; Guigo, 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

Author(s): Pardo-Palacios, FJ; Wang, D; Reese, F; Diekhans, M; Carbonell-Sala, S; Williams, B; Loveland, JE; De Maria, 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; Celik, MH; Chen, Y; Du, MRM; Felton, C; Goke, 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; Ferrandez-Peral, L; Garcia-Reyero, N; Gotz, S; Hernandez-Ferrer, C; Kondratova, L; Liu, T; Martinez-Martin, A; Menor, C; Mestre-Tomas, 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; Guigo, 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;
Details: Publication Year 2024-07,Volume 21,Issue #7,Page 1349-1363
Journal Title: Nature Methods
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.
Publisher: Springer Nature
Keywords: Humans; Animals; Mice; *RNA-Seq/methods; *Gene Expression Profiling/methods; Transcriptome; Sequence Analysis, RNA/methods; Molecular Sequence Annotation/methods
Research Division(s): Epigenetics And Development
PubMed ID: 38849569
Publisher's Version: https://doi.org/10.1038/s41592-024-02298-3
Open Access at Publisher's Site: https://doi.org/10.1038/s41592-024-02298-3
Terms of Use/Rights Notice: Refer to copyright notice on published article.

Creation Date: 2025-01-20 02:14:08

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