Summary: Chainy is a cross-platform web tool providing systematic pipelines and steady criteria to process real-time PCR data, including the calculation of efficiencies from raw data by kinetic methods, evaluation of the suitability of multiple references, standardized normalization using one or more references, and group-wise relative quantification statistical testing. We illustrate the utility of Chainy for differential expression and chromatin immunoprecipitation enrichment (ChIP-QPCR) analysis. Availability and Implementation: Chainy is open source and freely available at http://maplab.cat/chainy. Contact: [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online.

Bioinformatics (2017) doi: 10.1093/bioinformatics/btw839 The authors note the R/Bioconductor libraries sourced at Chainy were described at the supplementary material, section ‘Availability and runtime’ owing to the application note length constraints. To acknowledge the qpcR, NormqPCR and RDML developers the main manuscript should have contained the following section (last paragraph, before Acknowledgements): Availability and runtime Chainy is written in R/shiny and freely accessible at http://maplab.cat/chainy without registration. As the calculations are performed at the server-side, the application can be accessed from commodity computers or mobile devices, as far as they have a recent Web browser installed. Chainy can be run locally by downloading its source code from http://bitbucket.com/imallona/chainy (under the GNU Public License terms); an R script to check and install dependencies as well to launch the application is included. Chainy imports the qpcR (Ritz and Spiess, 2008), NormqPCR (Perkins et al., 2012) and RDML (Blagodatskikh et al., 2016) R packages. The user manual is available at the main page and covers results interpretation. A zip-compressed verbose report conveying the outputs and the user parameters can be downloaded at the final step. The final report includes barplots for both the scaled and the differential quantifications (for the sake of simplicity, the dynamic interface only renders tables to summarize these results).

MOTIVATION: Statistically assessing the relation between a set of genomic regions and other genomic features is a common challenging task in genomic and epigenomic analyses. Randomization based approaches implicitly take into account the complexity of the genome without the need of assuming an underlying statistical model. SUMMARY: regioneR is an R package that implements a permutation test framework specifically designed to work with genomic regions. In addition to the predefined randomization and evaluation strategies, regioneR is fully customizable allowing the use of custom strategies to adapt it to specific questions. Finally, it also implements a novel function to evaluate the local specificity of the detected association. AVAILABILITY AND IMPLEMENTATION: regioneR is an R package released under Artistic-2.0 License. The source code and documents are freely available through Bioconductor (http://www.bioconductor.org/packages/regioneR). CONTACT: [email protected].