CSVS, a crowdsourcing database of the Spanish population genetic variability. - Peña-Chilet, Maria; Roldán, Gema; Perez-Florido, Javier; Ortuno, Francisco M; Carmona, Rosario; Aquino, Virginia; López-López, Daniel; Loucera, Carlos; Fernandez-Rueda, Jose L; Gallego, Asunción; Garcia-Garcia, Francisco; González-Neira, Anna; et al. (Nucleic Acids Res, 2021)
The knowledge of the genetic variability of the local population is of utmost importance in personalized medicine and has been revealed as a critical factor for the discovery of new disease variants. Here, we present the Collaborative Spanish Variability Server (CSVS), which currently contains more than 2000 genomes and exomes of unrelated Spanish individuals. This database has been generated in a collaborative crowdsourcing effort collecting sequencing data produced by local genomic projects and for other purposes. Sequences have been grouped by ICD10 upper categories. A web interface allows querying the database removing one or more ICD10 categories. In this way, aggregated counts of allele frequencies of the pseudo-control Spanish population can be obtained for diseases belonging to the category removed. Interestingly, in addition to pseudo-control studies, some population studies can be made, as, for example, prevalence of pharmacogenomic variants, etc. In addition, this genomic data has been used to define the first Spanish Genome Reference Panel (SGRP1.0) for imputation. This is the first local repository of variability entirely produced by a crowdsourcing effort and constitutes an example for future initiatives to characterize local variability worldwide. CSVS is also part of the GA4GH Beacon network. CSVS can be accessed at: http://csvs.babelomics.org/.
Keywords: Alleles; Chromosome Mapping; Crowdsourcing; Databases, Genetic; Exome; Gene Frequency; Genetic Variation; Genetics, Population; Genome, Human; Genomics; Humans; Internet; Precision Medicine; Software; Spain

Discovering the hidden sub-network component in a ranked list of genes or proteins derived from genomic experiments. - García-Alonso, Luz; Alonso, Roberto; Vidal, Enrique; Amadoz, Alicia; De Maria, Alejandro; Minguez, Pablo; Medina, Ignacio; Dopazo, Joaquin (Nucleic Acids Res, 2012)
Genomic experiments (e.g. differential gene expression, single-nucleotide polymorphism association) typically produce ranked list of genes. We present a simple but powerful approach which uses protein-protein interaction data to detect sub-networks within such ranked lists of genes or proteins. We performed an exhaustive study of network parameters that allowed us concluding that the average number of components and the average number of nodes per component are the parameters that best discriminate between real and random networks. A novel aspect that increases the efficiency of this strategy in finding sub-networks is that, in addition to direct connections, also connections mediated by intermediate nodes are considered to build up the sub-networks. The possibility of using of such intermediate nodes makes this approach more robust to noise. It also overcomes some limitations intrinsic to experimental designs based on differential expression, in which some nodes are invariant across conditions. The proposed approach can also be used for candidate disease-gene prioritization. Here, we demonstrate the usefulness of the approach by means of several case examples that include a differential expression analysis in Fanconi Anemia, a genome-wide association study of bipolar disorder and a genome-scale study of essentiality in cancer genes. An efficient and easy-to-use web interface (available at http://www.babelomics.org) based on HTML5 technologies is also provided to run the algorithm and represent the network.
Keywords: Bipolar Disorder; Fanconi Anemia; Gene Regulatory Networks; Genes, Neoplasm; Genome-Wide Association Study; Genomics; Humans; Protein Interaction Mapping

Gene set internal coherence in the context of functional profiling. - Montaner, David; Minguez, Pablo; Al-Shahrour, Fátima; Dopazo, Joaquin (BMC Genomics, 2009)
BACKGROUND: Functional profiling methods have been extensively used in the context of high-throughput experiments and, in particular, in microarray data analysis. Such methods use available biological information to define different types of functional gene modules (e.g. gene ontology -GO-, KEGG pathways, etc.) whose representation in a pre-defined list of genes is further studied. In the most popular type of microarray experimental designs (e.g. up- or down-regulated genes, clusters of co-expressing genes, etc.) or in other genomic experiments (e.g. Chip-on-chip, epigenomics, etc.) these lists are composed by genes with a high degree of co-expression. Therefore, an implicit assumption in the application of functional profiling methods within this context is that the genes corresponding to the modules tested are effectively defining sets of co-expressing genes. Nevertheless not all the functional modules are biologically coherent entities in terms of co-expression, which will eventually hinder its detection with conventional methods of functional enrichment. RESULTS: Using a large collection of microarray data we have carried out a detailed survey of internal correlation in GO terms and KEGG pathways, providing a coherence index to be used for measuring functional module co-regulation. An unexpected low level of internal correlation was found among the modules studied. Only around 30% of the modules defined by GO terms and 57% of the modules defined by KEGG pathways display an internal correlation higher than the expected by chance.This information on the internal correlation of the genes within the functional modules can be used in the context of a logistic regression model in a simple way to improve their detection in gene expression experiments. CONCLUSION: For the first time, an exhaustive study on the internal co-expression of the most popular functional categories has been carried out. Interestingly, the real level of coexpression within many of them is lower than expected (or even inexistent), which will preclude its detection by means of most conventional functional profiling methods. If the gene-to-function correlation information is used in functional profiling methods, the results obtained improve the ones obtained by conventional enrichment methods.
Keywords: Algorithms; Breast Neoplasms; Carcinoma, Intraductal, Noninfiltrating; Computational Biology; Databases, Nucleic Acid; Female; Gene Expression Profiling; Genomics; Humans; Oligonucleotide Array Sequence Analysis; Papillomavirus Infections; Reproducibility of Results

SNOW, a web-based tool for the statistical analysis of protein-protein interaction networks. - Minguez, Pablo; Götz, Stefan; Montaner, David; Al-Shahrour, Fátima; Dopazo, Joaquin (Nucleic Acids Res, 2009)
Understanding the structure and the dynamics of the complex intercellular network of interactions that contributes to the structure and function of a living cell is one of the main challenges of today's biology. SNOW inputs a collection of protein (or gene) identifiers and, by using the interactome as scaffold, draws the connections among them, calculates several relevant network parameters and, as a novelty among the rest of tools of its class, it estimates their statistical significance. The parameters calculated for each node are: connectivity, betweenness and clustering coefficient. It also calculates the number of components, number of bicomponents and articulation points. An interactive network viewer is also available to explore the resulting network. SNOW is available at http://snow.bioinfo.cipf.es.
Keywords: Computer Graphics; Data Interpretation, Statistical; Databases, Protein; Humans; Internet; Protein Interaction Mapping; Software

FatiGO +: a functional profiling tool for genomic data. Integration of functional annotation, regulatory motifs and interaction data with microarray experiments. - Al-Shahrour, Fátima; Minguez, Pablo; Tárraga, Joaquín; Medina, Ignacio; Alloza, Eva; Montaner, David; Dopazo, Joaquin (Nucleic Acids Res, 2007)
The ultimate goal of any genome-scale experiment is to provide a functional interpretation of the data, relating the available information with the hypotheses that originated the experiment. Thus, functional profiling methods have become essential in diverse scenarios such as microarray experiments, proteomics, etc. We present the FatiGO+, a web-based tool for the functional profiling of genome-scale experiments, specially oriented to the interpretation of microarray experiments. In addition to different functional annotations (gene ontology, KEGG pathways, Interpro motifs, Swissprot keywords and text-mining based bioentities related to diseases and chemical compounds) FatiGO+ includes, as a novelty, regulatory and structural information. The regulatory information used includes predictions of targets for distinct regulatory elements (obtained from the Transfac and CisRed databases). Additionally FatiGO+ uses predictions of target motifs of miRNA to infer which of these can be activated or deactivated in the sample of genes studied. Finally, properties of gene products related to their relative location and connections in the interactome have also been used. Also, enrichment of any of these functional terms can be directly analysed on chromosomal coordinates. FatiGO+ can be found at: http://www.fatigoplus.org and within the Babelomics environment http://www.babelomics.org.
Keywords: Amino Acid Motifs; Animals; Binding Sites; Computational Biology; Gene Expression Profiling; Genes; Genomics; Humans; Internet; Oligonucleotide Array Sequence Analysis; Programming Languages; Software; Systems Integration; Transcription Factors