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Systematic assessment of long-read RNA-seq methods for transcript identification and quantification
The Long-read RNA-Seq Genome Annotation Assessment Project (LRGASP) Consortium was formed to evaluate the effectiveness of long-read approaches for transcriptome analysis. The consortium generated over 427 million long-read sequences from cDNA and direct RNA datasets, encompassing human, mouse, and...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Cold Spring Harbor Laboratory
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10402094/ https://www.ncbi.nlm.nih.gov/pubmed/37546854 http://dx.doi.org/10.1101/2023.07.25.550582 |
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author | Pardo-Palacios, Francisco J. Wang, Dingjie Reese, Fairlie Diekhans, Mark Carbonell-Sala, Sílvia Williams, Brian Loveland, Jane E. De María, Maite Adams, Matthew S. Balderrama-Gutierrez, Gabriela Behera, Amit K. Gonzalez, Jose M. Hunt, Toby Lagarde, Julien Liang, Cindy E. Li, Haoran Jerryd Meade, Marcus Moraga Amador, David A. Prjibelski, Andrey D. Birol, Inanc Bostan, Hamed Brooks, Ashley M. Hasan Çelik, Muhammed Chen, Ying Du, Mei R,M. Felton, Colette Göke, Jonathan Hafezqorani, Saber Herwig, Ralf Kawaji, Hideya Lee, Joseph Liang Li, Jian Lienhard, Matthias Mikheenko, Alla Mulligan, Dennis Ming Nip, Ka Pertea, Mihaela Ritchie, Matthew E. Sim, Andre D. Tang, Alison D. Kei Wan, Yuk Wang, Changqing Wong, Brandon Y. Yang, Chen Barnes, If Berry, Andrew Capella, Salvador Dhillon, Namrita Fernandez-Gonzalez, Jose M. Ferrández-Peral, Luis Garcia-Reyero, Natàlia Goetz, Stefan Hernández-Ferrer, Carles Kondratova, Liudmyla Liu, Tianyuan Martinez-Martin, Alessandra Menor, Carlos Mestre-Tomás, Jorge Mudge, Jonathan M. Panayotova, Nedka G. Paniagua, Alejandro Repchevsky, Dmitry Rouchka, Eric Saint-John, Brandon Sapena, Enrique Sheynkman, Leon Laird Smith, Melissa Suner, Marie-Marthe Takahashi, Hazuki Youngworth, Ingrid Ashley. Carninci, Piero Denslow, Nancy D. Guigó, Roderic Hunter, Margaret E. Tilgner, Hagen U. Wold, Barbara J. Vollmers, Christopher Frankish, Adam Fai Au, Kin Sheynkman, Gloria M. Mortazavi, Ali Conesa, Ana Brooks, Angela N. |
author_facet | Pardo-Palacios, Francisco J. Wang, Dingjie Reese, Fairlie Diekhans, Mark Carbonell-Sala, Sílvia Williams, Brian Loveland, Jane E. De María, Maite Adams, Matthew S. Balderrama-Gutierrez, Gabriela Behera, Amit K. Gonzalez, Jose M. Hunt, Toby Lagarde, Julien Liang, Cindy E. Li, Haoran Jerryd Meade, Marcus Moraga Amador, David A. Prjibelski, Andrey D. Birol, Inanc Bostan, Hamed Brooks, Ashley M. Hasan Çelik, Muhammed Chen, Ying Du, Mei R,M. Felton, Colette Göke, Jonathan Hafezqorani, Saber Herwig, Ralf Kawaji, Hideya Lee, Joseph Liang Li, Jian Lienhard, Matthias Mikheenko, Alla Mulligan, Dennis Ming Nip, Ka Pertea, Mihaela Ritchie, Matthew E. Sim, Andre D. Tang, Alison D. Kei Wan, Yuk Wang, Changqing Wong, Brandon Y. Yang, Chen Barnes, If Berry, Andrew Capella, Salvador Dhillon, Namrita Fernandez-Gonzalez, Jose M. Ferrández-Peral, Luis Garcia-Reyero, Natàlia Goetz, Stefan Hernández-Ferrer, Carles Kondratova, Liudmyla Liu, Tianyuan Martinez-Martin, Alessandra Menor, Carlos Mestre-Tomás, Jorge Mudge, Jonathan M. Panayotova, Nedka G. Paniagua, Alejandro Repchevsky, Dmitry Rouchka, Eric Saint-John, Brandon Sapena, Enrique Sheynkman, Leon Laird Smith, Melissa Suner, Marie-Marthe Takahashi, Hazuki Youngworth, Ingrid Ashley. Carninci, Piero Denslow, Nancy D. Guigó, Roderic Hunter, Margaret E. Tilgner, Hagen U. Wold, Barbara J. Vollmers, Christopher Frankish, Adam Fai Au, Kin Sheynkman, Gloria M. Mortazavi, Ali Conesa, Ana Brooks, Angela N. |
author_sort | Pardo-Palacios, Francisco J. |
collection | PubMed |
description | The Long-read RNA-Seq Genome Annotation Assessment Project (LRGASP) Consortium was formed to evaluate the effectiveness of long-read approaches for transcriptome analysis. The consortium generated over 427 million long-read sequences from cDNA and direct RNA datasets, encompassing human, mouse, and manatee species, using different protocols and sequencing platforms. These data were utilized by developers to address challenges in transcript isoform detection and quantification, as well as de novo transcript isoform identification. 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. When aiming to detect rare and novel transcripts or when using reference-free approaches, incorporating additional orthogonal data and replicate samples are advised. This collaborative study offers a benchmark for current practices and provides direction for future method development in transcriptome analysis. |
format | Online Article Text |
id | pubmed-10402094 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Cold Spring Harbor Laboratory |
record_format | MEDLINE/PubMed |
spelling | pubmed-104020942023-08-05 Systematic assessment of long-read RNA-seq methods for transcript identification and quantification Pardo-Palacios, Francisco J. Wang, Dingjie Reese, Fairlie Diekhans, Mark Carbonell-Sala, Sílvia Williams, Brian Loveland, Jane E. De María, Maite Adams, Matthew S. Balderrama-Gutierrez, Gabriela Behera, Amit K. Gonzalez, Jose M. Hunt, Toby Lagarde, Julien Liang, Cindy E. Li, Haoran Jerryd Meade, Marcus Moraga Amador, David A. Prjibelski, Andrey D. Birol, Inanc Bostan, Hamed Brooks, Ashley M. Hasan Çelik, Muhammed Chen, Ying Du, Mei R,M. Felton, Colette Göke, Jonathan Hafezqorani, Saber Herwig, Ralf Kawaji, Hideya Lee, Joseph Liang Li, Jian Lienhard, Matthias Mikheenko, Alla Mulligan, Dennis Ming Nip, Ka Pertea, Mihaela Ritchie, Matthew E. Sim, Andre D. Tang, Alison D. Kei Wan, Yuk Wang, Changqing Wong, Brandon Y. Yang, Chen Barnes, If Berry, Andrew Capella, Salvador Dhillon, Namrita Fernandez-Gonzalez, Jose M. Ferrández-Peral, Luis Garcia-Reyero, Natàlia Goetz, Stefan Hernández-Ferrer, Carles Kondratova, Liudmyla Liu, Tianyuan Martinez-Martin, Alessandra Menor, Carlos Mestre-Tomás, Jorge Mudge, Jonathan M. Panayotova, Nedka G. Paniagua, Alejandro Repchevsky, Dmitry Rouchka, Eric Saint-John, Brandon Sapena, Enrique Sheynkman, Leon Laird Smith, Melissa Suner, Marie-Marthe Takahashi, Hazuki Youngworth, Ingrid Ashley. Carninci, Piero Denslow, Nancy D. Guigó, Roderic Hunter, Margaret E. Tilgner, Hagen U. Wold, Barbara J. Vollmers, Christopher Frankish, Adam Fai Au, Kin Sheynkman, Gloria M. Mortazavi, Ali Conesa, Ana Brooks, Angela N. bioRxiv Article The Long-read RNA-Seq Genome Annotation Assessment Project (LRGASP) Consortium was formed to evaluate the effectiveness of long-read approaches for transcriptome analysis. The consortium generated over 427 million long-read sequences from cDNA and direct RNA datasets, encompassing human, mouse, and manatee species, using different protocols and sequencing platforms. These data were utilized by developers to address challenges in transcript isoform detection and quantification, as well as de novo transcript isoform identification. 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. When aiming to detect rare and novel transcripts or when using reference-free approaches, incorporating additional orthogonal data and replicate samples are advised. This collaborative study offers a benchmark for current practices and provides direction for future method development in transcriptome analysis. Cold Spring Harbor Laboratory 2023-07-27 /pmc/articles/PMC10402094/ /pubmed/37546854 http://dx.doi.org/10.1101/2023.07.25.550582 Text en https://creativecommons.org/licenses/by/4.0/This work is licensed under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/) , which allows reusers to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator. The license allows for commercial use. |
spellingShingle | Article Pardo-Palacios, Francisco J. Wang, Dingjie Reese, Fairlie Diekhans, Mark Carbonell-Sala, Sílvia Williams, Brian Loveland, Jane E. De María, Maite Adams, Matthew S. Balderrama-Gutierrez, Gabriela Behera, Amit K. Gonzalez, Jose M. Hunt, Toby Lagarde, Julien Liang, Cindy E. Li, Haoran Jerryd Meade, Marcus Moraga Amador, David A. Prjibelski, Andrey D. Birol, Inanc Bostan, Hamed Brooks, Ashley M. Hasan Çelik, Muhammed Chen, Ying Du, Mei R,M. Felton, Colette Göke, Jonathan Hafezqorani, Saber Herwig, Ralf Kawaji, Hideya Lee, Joseph Liang Li, Jian Lienhard, Matthias Mikheenko, Alla Mulligan, Dennis Ming Nip, Ka Pertea, Mihaela Ritchie, Matthew E. Sim, Andre D. Tang, Alison D. Kei Wan, Yuk Wang, Changqing Wong, Brandon Y. Yang, Chen Barnes, If Berry, Andrew Capella, Salvador Dhillon, Namrita Fernandez-Gonzalez, Jose M. Ferrández-Peral, Luis Garcia-Reyero, Natàlia Goetz, Stefan Hernández-Ferrer, Carles Kondratova, Liudmyla Liu, Tianyuan Martinez-Martin, Alessandra Menor, Carlos Mestre-Tomás, Jorge Mudge, Jonathan M. Panayotova, Nedka G. Paniagua, Alejandro Repchevsky, Dmitry Rouchka, Eric Saint-John, Brandon Sapena, Enrique Sheynkman, Leon Laird Smith, Melissa Suner, Marie-Marthe Takahashi, Hazuki Youngworth, Ingrid Ashley. Carninci, Piero Denslow, Nancy D. Guigó, Roderic Hunter, Margaret E. Tilgner, Hagen U. Wold, Barbara J. Vollmers, Christopher Frankish, Adam Fai Au, Kin Sheynkman, Gloria M. Mortazavi, Ali Conesa, Ana Brooks, Angela N. Systematic assessment of long-read RNA-seq methods for transcript identification and quantification |
title | Systematic assessment of long-read RNA-seq methods for transcript identification and quantification |
title_full | Systematic assessment of long-read RNA-seq methods for transcript identification and quantification |
title_fullStr | Systematic assessment of long-read RNA-seq methods for transcript identification and quantification |
title_full_unstemmed | Systematic assessment of long-read RNA-seq methods for transcript identification and quantification |
title_short | Systematic assessment of long-read RNA-seq methods for transcript identification and quantification |
title_sort | systematic assessment of long-read rna-seq methods for transcript identification and quantification |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10402094/ https://www.ncbi.nlm.nih.gov/pubmed/37546854 http://dx.doi.org/10.1101/2023.07.25.550582 |
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