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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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Autores principales: 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.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cold Spring Harbor Laboratory 2023
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.
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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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