Login / Signup

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

Sam Bryce-SmithDominik BurriMatthew R GazzaraChristina J HerrmannWeronika DaneckaChristina M FitzsimmonsYuk Kei WanFarica ZhuangMervin M FanslerJosé M FernándezMeritxell FerretAsier Gonzalez-UriarteSamuel HaynesChelsea HerdmanAlexander KanitzMaria KatsantoniFederico MariniEuan McDonnelBenoit P NicoletChi-Lam PoonGregor RotLeonard SchärfenPin-Jou WuYoseop YoonYoseph BarashMihaela Zavolan
Published in: RNA (New York, N.Y.) (2023)
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 datasets.
Keyphrases
  • rna seq
  • single cell
  • electronic health record
  • high throughput
  • big data
  • mental health
  • machine learning
  • data analysis