Extensible benchmarking of methods that identify and quantify polyadenylation sites from RNA-seq data
- Sam Bryce-Smith1,24,
- Dominik Burri2,3,24,
- Matthew R. Gazzara4,24,
- Christina J. Herrmann2,3,24,
- Weronika Danecka5,25,
- Christina M. Fitzsimmons6,25,
- Yuk Kei Wan7,8,25,
- Farica Zhuang9,25,
- Mervin M. Fansler10,11,
- José M. Fernández12,13,
- Meritxell Ferret12,13,
- Asier Gonzalez-Uriarte12,13,
- Samuel Haynes5,
- Chelsea Herdman14,
- Alexander Kanitz2,3,
- Maria Katsantoni2,3,
- Federico Marini15,
- Euan McDonnel16,
- Ben Nicolet17,18,
- Chi-Lam Poon19,
- Gregor Rot3,20,
- Leonard Schärfen21,
- Pin-Jou Wu22,
- Yoseop Yoon23,
- Yoseph Barash4,9 and
- Mihaela Zavolan2,3
- 1Department of Neuromuscular Diseases, UCL Queen Square Motor Neuron Disease Centre, UCL Queen Square Institute of Neurology, UCL, London WC1N 3BG, United Kingdom
- 2Biozentrum, University of Basel, 4056 Basel, Switzerland
- 3Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
- 4Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
- 5Institute for Cell Biology, School of Biological Sciences, The University of Edinburgh, Edinburgh EH9 3FF, United Kingdom
- 6Laboratory of Cell Biology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland 20892, USA
- 7Genome Institute of Singapore, Buona Vista, Singapore 138672
- 8Yong Loo Lin School of Medicine, National University of Singapore, Kent Ridge, Singapore 119228
- 9Department of Computer and Information Science, School of Engineering, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
- 10Tri-Institutional Program in Computational Biology and Medicine, Weill Cornell Graduate Studies, New York, New York 10065, USA
- 11Cancer Biology and Genetics, Sloan-Kettering Institute, MSKCC, New York, New York 10065, USA
- 12Life Sciences Department, Barcelona Supercomputing Center, 08034 Barcelona, Spain
- 13Spanish National Bioinformatics Institute (INB/ELIXIR-ES), 28029 Madrid, Spain
- 14Department of Neurobiology, University of Utah, Salt Lake City, Utah 84132, USA
- 15Institute of Medical Biostatistics, Epidemiology and Informatics (IMBEI), University Medical Center of the Johannes Gutenberg-University Mainz, 55118 Mainz, Germany
- 16Leeds Institute for Data Analytics, School of Molecular and Cellular Biology, University of Leeds, Leeds LS2 9NL, United Kingdom
- 17Department of Hematopoiesis, Sanquin Research, Landsteiner Laboratory, Amsterdam UMC, University of Amsterdam, 1066 CX Amsterdam, The Netherlands
- 18Oncode Institute, 3521 AL Utrecht, The Netherlands
- 19Graduate School of Medical Sciences, Weill Cornell Medicine, New York, New York 10065, USA
- 20Institute of Molecular Life Sciences, University of Zurich, 8057 Zurich, Switzerland
- 21Department of Molecular Biophysics & Biochemistry, Yale University, New Haven, Connecticut 06520, USA
- 22Center for Plant Molecular Biology (ZMBP), University of Tübingen, 72076 Tübingen, Germany
- 23Department of Microbiology and Molecular Genetics, School of Medicine, University of California Irvine, Irvine, California 92617, USA
- Corresponding authors: yosephb{at}upenn.edu, mihaela.zavolan{at}unibas.ch
Abstract
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, 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 continuous 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, while the containers and reproducible workflows could easily be deployed and extended to evaluate new methods or data sets.
Keywords
This article, published in RNA, is available under a Creative Commons License (Attribution-NonCommercial 4.0 International), as described at http://creativecommons.org/licenses/by-nc/4.0/.










