SNPassoc

Software
This package was built when I was working at Xavier Estivill’s lab at Center for Genomic Regulation and it is written in collaboration with Victor Moreno and his colleagues. The R package SNPassoc contains classes and methods to help the analysis of whole genome association studies. SNPassoc utilizes S4 classes and extends haplo.stats R package to facilitate haplotype analyses. The package is useful to carry out most common analysis when performing whole genome association studies. These analyses include descriptive statistics and exploratory analysis of missing values, calculation of Hardy-Weinberg equilibrium, analysis of association based on generalized linear models (either for quantitative or binary traits), and analysis of multiple SNPs (haplotype and epistasis analysis). Permutation and related tests (sum statistic and truncated product) are also implemented. The package is available from…
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rexposome

Software
rexposome is an R package for exposome characterization and exposome-outcome test association. It was developed by Carles Hernandez-Ferrer as part of its PhD project and used in the analysis performed in HELIX Project. It depends in a series of third party R packages to provide: A basic pipeline for missing-data imputation in exposome, include the imputation of values under limit of detection. A series of functions to describe and characterize the exposome, including PCA in exposures and samples space, correlation between exposures and clustering of samples through exposure levels. Two different approaches to test the association exposome-diseasom in terms of Exposome-Wide Association Studies (ExWAS and mExWAS). The package can be found at GitHub and at Bioconductor: GitHub: rexposome Bioconductor: rexposome We are writing an article about the exposome pipeline analysis,…
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CTDquerier

Software
The *Comparative Toxicogenomics Database* is a public resource for toxicogenomic information manually curated from the peer-reviewed scientific literature about chemicals, gene and health outcomes interactions. It includes more than 30.5 million toxicogenomic connections. The `CTDquerier` package allows to retrieve CTD data into R environment to be used in CRNA or Bioconductor downstream analysis. The package can be bound at **GitHub**: * **GitHub**: [`CTDquerier`](https://github.com/isglobal-brge/CTDquerier) We have submitted an article about `CTDquerier` (sent: July 4th, 201): > **Carles Hernandez-Ferrer, Juan R. Gonzalez; CTDquerier: a tool for enrichment analysis in genetic and toxicological studies using CTD; Bioinformatics*
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omicRexposome

Software
This R package is built to extend the capabilities of the R package rexposome and to allow to find exposome molecular signatures. This is archived by implementing a limma wrapper that works with ExposomeSet objects. This is a work of the PhD project of Carles Hernandez-Ferrer. The package can be found at GitHub and at Bioconductor: GitHub: omicRexposome Bioconductor: omicRexposome We are writing an article about the exposome pipeline analysis, which is joint with rexposome package: Carles Hernandez-Ferrer, ... Juan R. Gonzalez; Comprehensive analysis of the exposome, exposomehealth associations and omics intermediates; XXXXXX
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MAD (Mosaic Alteration Detector)

Software
This is a joint work with Benjamin Rodriguez-Santiago (qGenomics) and Luis Pérez-Jurado (UPF). MAD is a software tool to detect mosaic events from SNP arrays using BAF and LRR values. The algorithm is based on a segmentation procedure which uses the main features of GADA (and R package to detect CNVs). To install MAD, start R and enter: [generic] source("http://www.creal.cat/media/upload/arxius/jr/CREAL_install/install.R") creal.install("mad") [/generic] The methodological paper has been accepted in BMC Bioinformatics and can be found here. An example about how to use the software is described in the vignette. The algorithm has been used to discover mosaic alterations in a large collaborative study: > *Jacobs KB, Yeager M, Zhou W, ... Gonzalez JR, ... Rothman N, Pérez-Jurado LA, Chanock SJ. Detectable clonal mosaicism and its relationship to aging and cancer.…
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inveRsion

Software
`inveRsion` is an R package for the detection of genetic inversions using SNP-array data. This is a joint collaboration with Alejandro Caceres (ISGlobal) and Suzanne Sindi (Center for Computational Molecular Biology, Brown University). `inveRsion` is available at Bioconductor, and a manuscript is published at BMC Bioinformatics: > *Cáceres A, Sindi SS, Raphael BJ, Cáceres M, González JR. Identification of polymorphic inversions from genotypes. BMC Bioinformatics. 2012 Feb 9;13:28.* Our aim is to use SNP-array data of large cohorts, for which phenotype information has been collected, to assess the association of inversions with disease. We also intent to use the tool to assist in the mapping of human inversions; a project headed by Mario Caceres (Universitat Autonoma de Barcelona). * **Bioconductor**: [`inveRsion`](http://bioconductor.org/packages/release/bioc/html/inveRsion.html)
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tweeDEseq

Software
`tweeDEseq` is an R package for analyzing RNAseq count data. It implements Poisson-Tweedie family of distributions to model count data distribution. This family includes Poisson and Negative Bionomial as particular cases. The testPT test is used to detect genes that are deferentially expressed (DE). The methods are described in the manuscript published at BMC Bioinformatics: > *Esnaola M, Puig P, Gonzalez D, Castelo R, Gonzalez JR. A flexible count data model to fit the wide diversity of expression profiles arising from extensively replicated RNA-seq experiments. BMC Bioinformatics 2013, 14:254.* The manuscript illustrates the performance of our proposed method using a real RNA-seq data set comprising 69 Nigerian. We have created an experimental data pacakge (tweeDEseqCountData) that is available at Bioconductor. `tweeDEseq` is available from Bioconductor: * **Bioconductor**: [`tweeDEseq`](http://bioconductor.org/packages/release/bioc/html/tweeDEseq.html)
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invClust

Software
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.*
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rasp

Software
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…
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MEAL

Software
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") ```
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