web-based interactive framework to assist in the prioritization of disease candidate genes in whole exome sequencing studies.

Whole-exome sequencing (WES) has become a fundamental tool for the discovery of disease-related genes of familial diseases and the identification of somatic driver variants in cancer. However, finding the causal mutation among the enormous background of individual variability in a small number of samples is still a big challenge.

Here we describe a web-based tool, BiERapp, which efficiently helps in the identification of causative variants in family and sporadic genetic diseases. The program reads lists of predicted variants (nucleotide substitutions and indels) in affected individuals or tumor samples and controls. In family studies, different modes of inheritance can easily be defined to filter out variants that do not segregate with the disease along the family. Moreover, BiERapp integrates additional information such as allelic frequencies in the general population and the most popular damaging scores to further narrow down the number of putative variants in successive filtering steps.

BiERapp provides an interactive and user-friendly interface that implements the filtering strategy used in the context of a large-scale genomic project carried out by the Spanish Network for Research, in Rare Diseases (CIBERER) and the Medical Genome Project. in which more than 800 exomes have been analyzed.


A web tool for the design and management of panels of genes for targeted enrichment and massive sequencing for clinical applications. Disease targeted sequencing is gaining importance as a powerful and cost-effective application of high throughput sequencing technologies to the diagnosis. However, the lack of proper tools to process the data hinders its extensive adoption. Here we present TEAM, an intuitive and easy-to-use web tool that fills the gap between the predicted mutations and the final diagnostic in targeted enrichment sequencing analysis. The tool searches for known diagnostic mutations, corresponding to a disease panel, among the predicted patient’s variants. Diagnostic variants for the disease are taken from four databases of disease-related variants (HGMD-public, HUMSAVAR , ClinVar and COSMIC.) If no primary diagnostic variant is found, then a list of secondary findings that can help to establish a diagnostic is produced. TEAM also provides with an interface for the definition of and customization of panels, by means of which, genes and mutations can be added or discarded to adjust panel definitions.


Babelomics is an integrative platform for the analysis of transcriptomics, proteomics and genomic data with advanced functional profiling. This new version of Babelomics integrates primary (normalization, calls, etc.) and secondary (signatures, predictors, associations, TDTs, clustering, etc.) analysis tools within an environment that allows relating genomic data and/or interpreting them by means of different functional enrichment or gene set methods. Such interpretation is made not only using functional definitions (GO, KEGG, Biocarta, etc.) but also regulatory information (from Transfac, Jaspar, etc.) and other levels of regulation such as miRNA-mediated interference, protein-protein interactions, text-mining module definitions and the possibility of producing de novo annotations through the Blast2GO system.

MIBP (Multiple Interactome Based prioritization)

All Gene candidates within a hereditary disease study are prioritized in terms of presence/weight over the entire set of independent candidates provided by each family or subject group. Multiple interactomes are used to compute gene scores and their association with a specific disease, which allows to reduce the search of the related disease genes to a small set of candidates. MIBP tool can be also used to prioritize independent gene lists which come from heterogenic selection procedures as SNP/Indel genotyping, differential expression or GWAS studies.

Genome Maps

Genome browsers are extremely useful to represent genomic data, such as SNPs, gene expression, methylation, etc., on the genomic context. Different genome browsers on the web are available, being the most popular the Ensembl and the UCSC. However, with the continuous increase in the available genomic data and metadata along with the limitations derived from extensive data traffic imposed by client/server architecture, such browsers become inevitably slower. Genome Maps is based on the new HTML5 standards, including SVG and Javascript and runs 100% in the in the modern web browsers (in a philosophy similar to Google). This makes unnecessary the installation of any Flash plug-in, Java Applet or any other technology and results in a fast and dynamic response to user requests. Genome Maps allows real-time navigation along chromosomes and karyotypes, representing different types of data over many types of genomic information. There are numerous pre-configured tracks such as genes, transcripts, SNPs, mutations, miRNA targets, conserved regions, TFBS, etc. There are also several DAS sources available, but new ones can easily be added to the system.


VARIANT (VARIant ANalysis Tool) can report the functional properties of any variant in all the human, mouse or rat genes (and soon new model organisms will be added) and the corresponding neighborhoods. Also other non-coding extra-genic regions, such as miRNAs are included in the analysis. VARIANT not only reports the obvious functional effects in the coding regions but also analyzes noncoding SNVs situated both within the gene and in the neighborhood that could affect different regulatory motifs, splicing signals, and other structural elements. These include: Jaspar regulatory motifs, miRNA targets, splice sites, exonic splicing silencers, calculations of selective pressures on the particular polymorphic positions, etc. Software analysis pipelines used in the analysis of NGS data are highly modular, heterogeneous, and rapidly evolving. VARIANT can easily be incorporated into a NGS resequencing pipeline either as a CLI or invoked a webservice. It inputs data directly from the most widely used programs for SNV detection.


RENATO (REgulatory Network Analysis TOol) is a network-based analysis web tool for the interpretation and visualization of transcriptional and post-transcriptional regulatory information, designed to identify common regulatory elements in a list of genes. RENATO maps these genes to the regulatory network, extracts the corresponding regulatory connections and evaluate each regulator for significant over-representation in the list. Ranked gene lists can also be analysed with RENATO.

Phylemon 2.0

Phylemon 2.0 is the second release of the suite of web-tools for molecular evolution, phylogenetics, phylogenomics and hypotheses testing. It is conceived as a response to the increasing demand of molecular sequence analyses for experts and non-experts users. Phylemon 2.0 has several features that differentiates from similar web resources: it offers an integrated environment enabling evolutionary analyses, format conversions, files storage and edition of results, it suggests the next possible analysis guiding the user through the web server, and users can define and save phy


PhenUMA es una aplicación web par la integración y visualización de redes biomédicas. Estas redes se basan en relaciones fenotípicas y funcionales y son generadas a partir de diferentes tipos de entradas proporcionadas por el usuario, que pueden ser genes, enfermedades OMIM, enfermedades raras (Orphanet) o fenotipos. Además, se proporciona un entorno que permite la integración de interacciones conocidas y nuevas relaciones que se establecen usando diferentes bases de datos.


El objetivo de este trabajo fue construir una herramienta útil para investigadores básicos y clínicos que trabajen en peroxosisomas y en enfermedades peroxisomales. En este trabajo la U741 del BIER contribuyó con la integración de datos metabólicos al proyecto dirigido por la Dra. Aurora Pujol (IDIBELL-CIBERER). En Peroxisome DB se puede encontrar: El metaboloma del peroxisoma incluyendo genes y proteínas, sus interacciones moleculares y las rutas metabólicas que conforman. Genética comparada: predicción de ortólogos y alineamientos filogenéticos. Información sobre enfermedades peroxisomales clasificadas por criterios clínicos. Predictores de localización y función peroxisomal.