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Extensible benchmarking of methods that identify and quantify polyadenylation sites from RNA-seq data

The tremendous rate with which data is generated and analysis methods emerge makes it increasingly difficult to keep track of their domain of applicability, assumptions, and limitations and consequently, of the efficacy and precision with which they solve specific tasks. Therefore, there is an incre...

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Autores principales: Bryce-Smith, Sam, Burri, Dominik, Gazzara, Matthew R., Herrmann, Christina J., Danecka, Weronika, Fitzsimmons, Christina M., Wan, Yuk Kei, Zhuang, Farica, Fansler, Mervin M., Fernández, José M., Ferret, Meritxell, Gonzalez-Uriarte, Asier, Haynes, Samuel, Herdman, Chelsea, Kanitz, Alexander, Katsantoni, Maria, Marini, Federico, McDonnel, Euan, Nicolet, Ben, Poon, Chi-Lam, Rot, Gregor, Schärfen, Leonard, Wu, Pin-Jou, Yoon, Yoseop, Barash, Yoseph, Zavolan, Mihaela
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cold Spring Harbor Laboratory 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10327023/
https://www.ncbi.nlm.nih.gov/pubmed/37425672
http://dx.doi.org/10.1101/2023.06.23.546284
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author Bryce-Smith, Sam
Burri, Dominik
Gazzara, Matthew R.
Herrmann, Christina J.
Danecka, Weronika
Fitzsimmons, Christina M.
Wan, Yuk Kei
Zhuang, Farica
Fansler, Mervin M.
Fernández, José M.
Ferret, Meritxell
Gonzalez-Uriarte, Asier
Haynes, Samuel
Herdman, Chelsea
Kanitz, Alexander
Katsantoni, Maria
Marini, Federico
McDonnel, Euan
Nicolet, Ben
Poon, Chi-Lam
Rot, Gregor
Schärfen, Leonard
Wu, Pin-Jou
Yoon, Yoseop
Barash, Yoseph
Zavolan, Mihaela
author_facet Bryce-Smith, Sam
Burri, Dominik
Gazzara, Matthew R.
Herrmann, Christina J.
Danecka, Weronika
Fitzsimmons, Christina M.
Wan, Yuk Kei
Zhuang, Farica
Fansler, Mervin M.
Fernández, José M.
Ferret, Meritxell
Gonzalez-Uriarte, Asier
Haynes, Samuel
Herdman, Chelsea
Kanitz, Alexander
Katsantoni, Maria
Marini, Federico
McDonnel, Euan
Nicolet, Ben
Poon, Chi-Lam
Rot, Gregor
Schärfen, Leonard
Wu, Pin-Jou
Yoon, Yoseop
Barash, Yoseph
Zavolan, Mihaela
author_sort Bryce-Smith, Sam
collection PubMed
description The tremendous rate with which data is generated and analysis methods emerge makes it increasingly difficult to keep track of their domain of applicability, assumptions, and limitations and consequently, of the efficacy and precision with which they solve specific tasks. Therefore, there is an increasing need for benchmarks, and for the provision of infrastructure for continuous method evaluation. APAeval is an international community effort, organized by the RNA Society in 2021, to benchmark tools for the identification and quantification of the usage of alternative polyadenylation (APA) sites from short-read, bulk RNA-sequencing (RNA-seq) data. Here, we reviewed 17 tools and benchmarked eight on their ability to perform APA identification and quantification, using a comprehensive set of RNA-seq experiments comprising real, synthetic, and matched 3′-end sequencing data. To support continuous benchmarking, we have incorporated the results into the OpenEBench online platform, which allows for seamless extension of the set of methods, metrics, and challenges. We envisage that our analyses will assist researchers in selecting the appropriate tools for their studies. Furthermore, the containers and reproducible workflows generated in the course of this project can be seamlessly deployed and extended in the future to evaluate new methods or datasets.
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spelling pubmed-103270232023-07-08 Extensible benchmarking of methods that identify and quantify polyadenylation sites from RNA-seq data Bryce-Smith, Sam Burri, Dominik Gazzara, Matthew R. Herrmann, Christina J. Danecka, Weronika Fitzsimmons, Christina M. Wan, Yuk Kei Zhuang, Farica Fansler, Mervin M. Fernández, José M. Ferret, Meritxell Gonzalez-Uriarte, Asier Haynes, Samuel Herdman, Chelsea Kanitz, Alexander Katsantoni, Maria Marini, Federico McDonnel, Euan Nicolet, Ben Poon, Chi-Lam Rot, Gregor Schärfen, Leonard Wu, Pin-Jou Yoon, Yoseop Barash, Yoseph Zavolan, Mihaela bioRxiv Article The tremendous rate with which data is generated and analysis methods emerge makes it increasingly difficult to keep track of their domain of applicability, assumptions, and limitations and consequently, of the efficacy and precision with which they solve specific tasks. Therefore, there is an increasing need for benchmarks, and for the provision of infrastructure for continuous method evaluation. APAeval is an international community effort, organized by the RNA Society in 2021, to benchmark tools for the identification and quantification of the usage of alternative polyadenylation (APA) sites from short-read, bulk RNA-sequencing (RNA-seq) data. Here, we reviewed 17 tools and benchmarked eight on their ability to perform APA identification and quantification, using a comprehensive set of RNA-seq experiments comprising real, synthetic, and matched 3′-end sequencing data. To support continuous benchmarking, we have incorporated the results into the OpenEBench online platform, which allows for seamless extension of the set of methods, metrics, and challenges. We envisage that our analyses will assist researchers in selecting the appropriate tools for their studies. Furthermore, the containers and reproducible workflows generated in the course of this project can be seamlessly deployed and extended in the future to evaluate new methods or datasets. Cold Spring Harbor Laboratory 2023-06-26 /pmc/articles/PMC10327023/ /pubmed/37425672 http://dx.doi.org/10.1101/2023.06.23.546284 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
Bryce-Smith, Sam
Burri, Dominik
Gazzara, Matthew R.
Herrmann, Christina J.
Danecka, Weronika
Fitzsimmons, Christina M.
Wan, Yuk Kei
Zhuang, Farica
Fansler, Mervin M.
Fernández, José M.
Ferret, Meritxell
Gonzalez-Uriarte, Asier
Haynes, Samuel
Herdman, Chelsea
Kanitz, Alexander
Katsantoni, Maria
Marini, Federico
McDonnel, Euan
Nicolet, Ben
Poon, Chi-Lam
Rot, Gregor
Schärfen, Leonard
Wu, Pin-Jou
Yoon, Yoseop
Barash, Yoseph
Zavolan, Mihaela
Extensible benchmarking of methods that identify and quantify polyadenylation sites from RNA-seq data
title Extensible benchmarking of methods that identify and quantify polyadenylation sites from RNA-seq data
title_full Extensible benchmarking of methods that identify and quantify polyadenylation sites from RNA-seq data
title_fullStr Extensible benchmarking of methods that identify and quantify polyadenylation sites from RNA-seq data
title_full_unstemmed Extensible benchmarking of methods that identify and quantify polyadenylation sites from RNA-seq data
title_short Extensible benchmarking of methods that identify and quantify polyadenylation sites from RNA-seq data
title_sort extensible benchmarking of methods that identify and quantify polyadenylation sites from rna-seq data
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10327023/
https://www.ncbi.nlm.nih.gov/pubmed/37425672
http://dx.doi.org/10.1101/2023.06.23.546284
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