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Bioinformatics. 2013 Nov 15;29(22):2844-51. doi: 10.1093/bioinformatics/btt508. Epub 2013 Sep 17.

Assessing the validity and reproducibility of genome-scale predictions.

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Center for Computational Molecular Biology and the Division of Applied Mathematics, Brown University, Providence, RI 02912, USA, St. Laurent Institute, 317 New Boston St, Woburn, MA 01801, USA and Department of Molecular Biology, Cell Biology and Biochemistry, Brown University, Providence, RI 02912, USA.



Validation and reproducibility of results is a central and pressing issue in genomics. Several recent embarrassing incidents involving the irreproducibility of high-profile studies have illustrated the importance of this issue and the need for rigorous methods for the assessment of reproducibility.


Here, we describe an existing statistical model that is very well suited to this problem. We explain its utility for assessing the reproducibility of validation experiments, and apply it to a genome-scale study of adenosine deaminase acting on RNA (ADAR)-mediated RNA editing in Drosophila. We also introduce a statistical method for planning validation experiments that will obtain the tightest reproducibility confidence limits, which, for a fixed total number of experiments, returns the optimal number of replicates for the study.


Downloadable software and a web service for both the analysis of data from a reproducibility study and for the optimal design of these studies is provided at .

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