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

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
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
Source :
Nature methods [Nat Methods] 2024 Jul; Vol. 21 (7), pp. 1349-1363. Date of Electronic Publication: 2024 Jun 07.
Publication Year :
2024

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.<br /> (© 2024. The Author(s).)

Details

Language :
English
ISSN :
1548-7105
Volume :
21
Issue :
7
Database :
MEDLINE
Journal :
Nature methods
Publication Type :
Academic Journal
Accession number :
38849569
Full Text :
https://doi.org/10.1038/s41592-024-02298-3