rexposome project

R package | R

rexposome project is a set of R packages developed at Bioinformatics Research Group in Epidemiology (BRGE) from Barcelona Global Health Institute (ISGlobal). The project aims to provide a framework to incorporate exposome data in R/Bioconductor pipelines, with the goal of describing and analyzin exposome data. Currently, the Bioconductor packages included in rexposome project are rexposome and omicRexposome. The first package, rexposome, offers an interface to load exposome data into Bioconductor-like objects and functions to describe and characterize the exposome. It also includes functions to perform Exposome-Wide Association Studies. The second package, omicRexposome, contains functions to perform exposome-omic association studies and multi-omic integration of exposome and omic data-sets.


web application | R, shiny

WEScover is a R/Shiny web application to highlight global breadth of coverage in gene level and inter-individual variation for genes along with corresponding genetic tests listed in the National Institutes of Health Genetic Testing Registry (GTR). WEScover helps to check whether genes of interest could be sufficiently covered in terms of breadth and depth by whole exome sequencing (WES). With the goal to minimize the change of false negative, for each transcript, breadth of coverage data was calculated at 10x, 20x, and 30x read depth from the 1000 Genomes Project (1KGP).


R package | R

Reduction in the cost of genomic assays has generated large amounts of biomedical-related data. As a result, current studies perform multiple experiments in the same subjects. MultiDataSet is a new R class based on Bioconductor standards and designed to encapsulate multiple data sets. MultiDataSet deals with the usual difficulties of managing multiple and non-complete data sets while offering a simple and general way of subsetting features and selecting samples. We illustrate the use of MultiDataSet in three common situations: 1) performing integration analysis with third party packages; 2) creating new methods and functions for omic data integration; 3) encapsulating new unimplemented data from any biological experiment.

Chemical Explorer

web application | R, shiny

The AutismExposome Chemical Explorer is an interactive web to explorer the different chemical measures obtained from a set of 93 samples including 28 Control, 22 Cases, 27 Father (cases related) and 16 Mother (cases related). This platform allows to explore the different levels of measured chemicals from both targeted (GC-MS) and un-targeted (LC-HRMS) metabolome data.


R package | R

The CTDquerier R/Bioconductor package allows users to query CTD by gene, by chemical and by disease using single or multiple terms. The terms are validated against the CTD vocabulary used to download the multiple results from cTD as TSV files. The TSV files are read as DataFrames and once encapsulated in an S4 object of class CTDdata. Three methods are provided for CTDdata objects: (i) get_terms retrieves the terms that are validated in CTD vocabulary files; (ii) get_table fetches data from CTD as an object of class DataFrame; and (iii) enrich performs a Fisher’s exact test between two CTDdata objects.


R package | R, python, third party binaries | unmaintained

The well-known Genome-Wide Association Studies (GWAS) had led to many scientific discoveries using SNP data. Even so, they were not able to explain the full heritability of complex diseases. Other structural variants like copy number variants or DNA inversions, either germ-line or in mosaicism events, are being studies. affy2sv is an R package to pre-process Affymetrix CytoScan HD/750k array (also for Genome-Wide SNP 5.0/6.0 and Axiom) in structural variant studies.

Crossing Clusters

web application | C/C++, python, php | unmaintained

Web application aiming to analyze microarray data by: 1) applying a series of clustering methods to classify samples, 2) applying PCOP characterization to each pair of microarray genes, and 3) integrate both results to determine which genes are capable to classify the clusters of samples.