publication . Other literature type . Preprint . Article . 2016

iCOBRA: open, reproducible, standardized and live method benchmarking

Charlotte Soneson; Mark D Robinson;
Open Access
  • Published: 30 Mar 2016
  • Publisher: Springer Science and Business Media LLC
  • Country: Switzerland
Abstract
<jats:p>We present iCOBRA, a flexible general-purpose web-based application and accompanying R package to evaluate, compare and visualize the performance of methods for estimation or classification when ground truth is available. iCOBRA is interactive, can be run locally or remotely and generates customizable, publication-ready graphics. To facilitate open, reproducible and standardized method comparisons, expanding as new innovations are made, we encourage the community to provide benchmark results in a standard format.</jats:p>
Subjects
free text keywords: Institute of Molecular Life Sciences, 570 Life sciences; biology

Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139-40 (2010).

Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-Seq data with DESeq2. (2014). doi:10.1101/002832 Smyth, G. K. Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments. Stat. Appl. Genet. Mol. Biol. 3, Article 3 (2004).

Anders, S., Reyes, A. & Huber, W. Detecting differential usage of exons from RNAseq data. Genome Res. 22, 2008-17 (2012).

Paulson, J. N., Stine, O. C., Bravo, H. C. & Pop, M. Differential abundance analysis for microbial marker-gene surveys. Nat. Methods 10, 1200-2 (2013).

RStudio Inc. Easy web applications in R. (2013). at <http://www.rstudio.com/shiny/> R Core Team. R: A language and environment for statistical computing. (2015). at <http://www.r-project.org/> Huber, W. et al. Orchestrating high-throughput genomic analysis with Bioconductor.

Nat. Publ. Gr. 12, 115-121 (2015).

Sing, T., Sander, O., Beerenwinkel, N. & Lengauer, T. ROCR: Visualizing classifier performance in R. Bioinformatics 21, 3940-3941 (2005). [OpenAIRE]

Soneson, C. compcodeR - an R package for benchmarking differential expression methods for RNA-seq data. Bioinformatics 1-2 (2014). [OpenAIRE]

doi:10.1093/bioinformatics/btu324 Lindgreen, S., Adair, K. L. & Gardner, P. An evaluation of the accuracy and speed of metagenome analysis tools. bioRxiv (2015). at <http://biorxiv.org/content/early/2015/10/28/017830.abstract>

Abstract
<jats:p>We present iCOBRA, a flexible general-purpose web-based application and accompanying R package to evaluate, compare and visualize the performance of methods for estimation or classification when ground truth is available. iCOBRA is interactive, can be run locally or remotely and generates customizable, publication-ready graphics. To facilitate open, reproducible and standardized method comparisons, expanding as new innovations are made, we encourage the community to provide benchmark results in a standard format.</jats:p>
Subjects
free text keywords: Institute of Molecular Life Sciences, 570 Life sciences; biology

Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139-40 (2010).

Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-Seq data with DESeq2. (2014). doi:10.1101/002832 Smyth, G. K. Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments. Stat. Appl. Genet. Mol. Biol. 3, Article 3 (2004).

Anders, S., Reyes, A. & Huber, W. Detecting differential usage of exons from RNAseq data. Genome Res. 22, 2008-17 (2012).

Paulson, J. N., Stine, O. C., Bravo, H. C. & Pop, M. Differential abundance analysis for microbial marker-gene surveys. Nat. Methods 10, 1200-2 (2013).

RStudio Inc. Easy web applications in R. (2013). at <http://www.rstudio.com/shiny/> R Core Team. R: A language and environment for statistical computing. (2015). at <http://www.r-project.org/> Huber, W. et al. Orchestrating high-throughput genomic analysis with Bioconductor.

Nat. Publ. Gr. 12, 115-121 (2015).

Sing, T., Sander, O., Beerenwinkel, N. & Lengauer, T. ROCR: Visualizing classifier performance in R. Bioinformatics 21, 3940-3941 (2005). [OpenAIRE]

Soneson, C. compcodeR - an R package for benchmarking differential expression methods for RNA-seq data. Bioinformatics 1-2 (2014). [OpenAIRE]

doi:10.1093/bioinformatics/btu324 Lindgreen, S., Adair, K. L. & Gardner, P. An evaluation of the accuracy and speed of metagenome analysis tools. bioRxiv (2015). at <http://biorxiv.org/content/early/2015/10/28/017830.abstract>

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