Literature DB >> 30016409

Reproducible and replicable comparisons using SummarizedBenchmark.

Patrick K Kimes1,2, Alejandro Reyes1,2.   

Abstract

Summary: Benchmark studies are widely used to compare and evaluate tools developed for answering various biological questions. Despite the popularity of these comparisons, the implementation is often ad hoc, with little consistency across studies. To address this problem, we developed SummarizedBenchmark, an R package and framework for organizing and structuring benchmark comparisons. SummarizedBenchmark defines a general grammar for benchmarking and allows for easier setup and execution of benchmark comparisons, while improving the reproducibility and replicability of such comparisons. We demonstrate the wide applicability of our framework using four examples from different applications. Availability and implementation: SummarizedBenchmark is an R package available through Bioconductor (http://bioconductor.org/packages/SummarizedBenchmark). Supplementary information: Supplementary data are available at Bioinformatics online.

Mesh:

Year:  2019        PMID: 30016409      PMCID: PMC6298041          DOI: 10.1093/bioinformatics/bty627

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  9 in total

Review 1.  Orchestrating high-throughput genomic analysis with Bioconductor.

Authors:  Wolfgang Huber; Vincent J Carey; Robert Gentleman; Simon Anders; Marc Carlson; Benilton S Carvalho; Hector Corrada Bravo; Sean Davis; Laurent Gatto; Thomas Girke; Raphael Gottardo; Florian Hahne; Kasper D Hansen; Rafael A Irizarry; Michael Lawrence; Michael I Love; James MacDonald; Valerie Obenchain; Andrzej K Oleś; Hervé Pagès; Alejandro Reyes; Paul Shannon; Gordon K Smyth; Dan Tenenbaum; Levi Waldron; Martin Morgan
Journal:  Nat Methods       Date:  2015-02       Impact factor: 28.547

2.  iCOBRA: open, reproducible, standardized and live method benchmarking.

Authors:  Charlotte Soneson; Mark D Robinson
Journal:  Nat Methods       Date:  2016-04       Impact factor: 28.547

3.  Bias, robustness and scalability in single-cell differential expression analysis.

Authors:  Charlotte Soneson; Mark D Robinson
Journal:  Nat Methods       Date:  2018-02-26       Impact factor: 28.547

4.  Evaluation of algorithm performance in ChIP-seq peak detection.

Authors:  Elizabeth G Wilbanks; Marc T Facciotti
Journal:  PLoS One       Date:  2010-07-08       Impact factor: 3.240

5.  How many biological replicates are needed in an RNA-seq experiment and which differential expression tool should you use?

Authors:  Nicholas J Schurch; Pietá Schofield; Marek Gierliński; Christian Cole; Alexander Sherstnev; Vijender Singh; Nicola Wrobel; Karim Gharbi; Gordon G Simpson; Tom Owen-Hughes; Mark Blaxter; Geoffrey J Barton
Journal:  RNA       Date:  2016-03-28       Impact factor: 4.942

6.  A benchmark for RNA-seq quantification pipelines.

Authors:  Mingxiang Teng; Michael I Love; Carrie A Davis; Sarah Djebali; Alexander Dobin; Brenton R Graveley; Sheng Li; Christopher E Mason; Sara Olson; Dmitri Pervouchine; Cricket A Sloan; Xintao Wei; Lijun Zhan; Rafael A Irizarry
Journal:  Genome Biol       Date:  2016-04-23       Impact factor: 13.583

7.  UpSetR: an R package for the visualization of intersecting sets and their properties.

Authors:  Jake R Conway; Alexander Lex; Nils Gehlenborg
Journal:  Bioinformatics       Date:  2017-09-15       Impact factor: 6.937

8.  Assessment of transcript reconstruction methods for RNA-seq.

Authors:  Josep F Abril; Pär G Engström; Felix Kokocinski; Tamara Steijger; Tim J Hubbard; Roderic Guigó; Jennifer Harrow; Paul Bertone
Journal:  Nat Methods       Date:  2013-11-03       Impact factor: 28.547

9.  Systematic comparison of variant calling pipelines using gold standard personal exome variants.

Authors:  Sohyun Hwang; Eiru Kim; Insuk Lee; Edward M Marcotte
Journal:  Sci Rep       Date:  2015-12-07       Impact factor: 4.379

  9 in total
  4 in total

Review 1.  Essential guidelines for computational method benchmarking.

Authors:  Lukas M Weber; Wouter Saelens; Robrecht Cannoodt; Charlotte Soneson; Alexander Hapfelmeier; Paul P Gardner; Anne-Laure Boulesteix; Yvan Saeys; Mark D Robinson
Journal:  Genome Biol       Date:  2019-06-20       Impact factor: 13.583

2.  A practical guide to methods controlling false discoveries in computational biology.

Authors:  Keegan Korthauer; Patrick K Kimes; Claire Duvallet; Alejandro Reyes; Ayshwarya Subramanian; Mingxiang Teng; Chinmay Shukla; Eric J Alm; Stephanie C Hicks
Journal:  Genome Biol       Date:  2019-06-04       Impact factor: 13.583

3.  CellBench: R/Bioconductor software for comparing single-cell RNA-seq analysis methods.

Authors:  Shian Su; Luyi Tian; Xueyi Dong; Peter F Hickey; Saskia Freytag; Matthew E Ritchie
Journal:  Bioinformatics       Date:  2020-04-01       Impact factor: 6.937

Review 4.  Orchestrating single-cell analysis with Bioconductor.

Authors:  Robert A Amezquita; Aaron T L Lun; Etienne Becht; Vince J Carey; Lindsay N Carpp; Ludwig Geistlinger; Federico Marini; Kevin Rue-Albrecht; Davide Risso; Charlotte Soneson; Levi Waldron; Hervé Pagès; Mike L Smith; Wolfgang Huber; Martin Morgan; Raphael Gottardo; Stephanie C Hicks
Journal:  Nat Methods       Date:  2019-12-02       Impact factor: 28.547

  4 in total

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