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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...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , |
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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/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. |
format | Online Article Text |
id | pubmed-10327023 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Cold Spring Harbor Laboratory |
record_format | MEDLINE/PubMed |
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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