Author: Natalia Carreras

Joint with Alejandro Cáceres (ISGlobal), we have developed a method that can be applied to common GWAS for calling the inversion genotypes, which accounts for population stratification when an appropriate reference population is not known. This method is extremely useful when performing inversion association studies in a GWAS context were population stratification can be present. To install `invClust`, start R and enter: ``` source("http://www.creal.cat/media/upload/arxius/jr/CREAL_install/install.R") creal.install("invClust") ``` If you experiment some problem during this process, the source code of the package can be download from [here](http://www.creal.cat/media/upload/arxius/jr/inversions/invClust_1.0.tar.gz). The methods are described in the manuscript: > *Cáceres and González, J. R. (2015) Following the footprints of polymorphic inversions on SNP data: from detection to association tests. NAR doi:10.1093.*

This is a joint work with Roderic Guigó's group - Bioinformatics and Genomics program, Center for Genomic Regulatio (CRG). This R package is designed to compare transcript relative expression of different conditions obtained from RNA-seq experiments. Our approach is based on a distance-based non-parametric multivariate ANOVA method. The Linux version of the package is currently under development. To install a beta version of rasp, start R and enter: ``` library(devtools) install_github("isglobal-brge/rasp") ``` The performance of our approach has been compared with two other existing R packages (`DEXseq` and `EBseq`) using data from The Cancer Genome Atlas (TCGA). Exon abundances from RNA-seq data were obtained for several individuals diagnosed with Liver hepatocellular carcinoma [LIHC] and Bladder Urothelial Carcinoma [BLCA]. We have created two experimental data packages (`ExonCountDataLIHC` and `ExonCountDataBLCA`, respectively). So…

This package contains a set of tools to analyze and visualize methylation and gene expression data. This is a work of Carlos Ruiz (ISGlobal). The package can be installed through Bioconductor: ``` source("https://bioconductor.org/biocLite.R") biocLite("MEAL") ```

This package is created for combining several different sources of information (including different omic data-sets) into a single convenient structure. A `MultiDataSet` object can be manipulated (e.g., subsetted, copied, ...) conveniently, and can be the input or output from some Bioconductor packages designed to integrate multi-omic data. This is a joint work with Carlos Ruiz-Arenas (ISGlobal) and Carles Hernandez-Ferrer (ISGlobal). The package can be bound at **Bioconductor** and **GitHub**: * **Bioconductor**: [`MultiDataSet`](http://bioconductor.org/packages/release/bioc/html/MultiDataSet.html) * **GitHub**: [`MultiDataSet`](https://github.com/isglobal-brge/MultiDataSet) We published an article about its implementation (January 17th, 2017): > Carles Hernandez-Ferrer, ... Juan R. González; MultiDataSet: an R package for encapsulating multiple data sets with application to omic data integration. BMC Bioinformatics; https://doi.org/10.1186/s12859-016-1455-1