Title | Systematic assessment of long-read RNA-seq methods for transcript identification and quantification. |
Publication Type | Journal Article |
Year of Publication | 2024 |
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, Martinez JMGonzalez, Hunt T, Lagarde J, Liang CE, Li H, Meade MJerryd, Amador DAMoraga, Prjibelski AD, Birol I, Bostan H, Brooks AM, Çelik MHasan, Chen Y, Du MRM, Felton C, Göke J, Hafezqorani S, Herwig R, Kawaji H, Lee J, Li J-L, Lienhard M, Mikheenko A, Mulligan D, Nip KMing, Pertea M, Ritchie ME, Sim AD, Tang AD, Wan YKei, 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 MLaird, Suner M-M, 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 KFai, Sheynkman GM, Mortazavi A, Conesa A, Brooks AN |
Journal | Nat Methods |
Volume | 21 |
Issue | 7 |
Pagination | 1349-1363 |
Date Published | 2024 Jul |
ISSN | 1548-7105 |
Keywords | Animals, Gene Expression Profiling, Humans, Mice, Molecular Sequence Annotation, RNA-Seq, Sequence Analysis, RNA, Transcriptome |
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. |
DOI | 10.1038/s41592-024-02298-3 |
Alternate Journal | Nat Methods |
PubMed ID | 38849569 |
PubMed Central ID | 310877 |
Grant List | P30 ES030283 / ES / NIEHS NIH HHS / United States R21 HG011280 / HG / NHGRI NIH HHS / United States R35GM138122 / / U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (NIGMS) / UM1 HG009443 / HG / NHGRI NIH HHS / United States U41HG007234 / / U.S. Department of Health & Human Services | NIH | National Human Genome Research Institute (NHGRI) / 73023672 / / Saint Petersburg State University (St. Petersburg State University) / WT108749/Z/15/Z / / Wellcome Trust (Wellcome) / F31HG010999 / / U.S. Department of Health & Human Services | NIH | National Human Genome Research Institute (NHGRI) / U41 HG007234 / HG / NHGRI NIH HHS / United States R01HG008759 / / U.S. Department of Health & Human Services | NIH | National Human Genome Research Institute (NHGRI) / R01HG011469 / / U.S. Department of Health & Human Services | NIH | National Human Genome Research Institute (NHGRI) / R01GM136886 / / U.S. Department of Health & Human Services | NIH | National Human Genome Research Institute (NHGRI) / R35GM14264 / / U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (NIGMS) / |