@article {701, title = {CSVS, a crowdsourcing database of the Spanish population genetic variability.}, journal = {Nucleic Acids Res}, volume = {49}, year = {2021}, month = {2021 01 08}, pages = {D1130-D1137}, abstract = {

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}, issn = {1362-4962}, doi = {10.1093/nar/gkaa794}, author = {Pe{\~n}a-Chilet, Maria and Rold{\'a}n, Gema and Perez-Florido, Javier and Ortuno, Francisco M and Carmona, Rosario and Aquino, Virginia and L{\'o}pez-L{\'o}pez, Daniel and Loucera, Carlos and Fernandez-Rueda, Jose L and Gallego, Asunci{\'o}n and Garcia-Garcia, Francisco and Gonz{\'a}lez-Neira, Anna and Pita, Guillermo and N{\'u}{\~n}ez-Torres, Roc{\'\i}o and Santoyo-L{\'o}pez, Javier and Ayuso, Carmen and Minguez, Pablo and Avila-Fernandez, Almudena and Corton, Marta and Moreno-Pelayo, Miguel {\'A}ngel and Morin, Mat{\'\i}as and Gallego-Martinez, Alvaro and Lopez-Escamez, Jose A and Borrego, Salud and Anti{\v n}olo, Guillermo and Amigo, Jorge and Salgado-Garrido, Josefa and Pasalodos-Sanchez, Sara and Morte, Beatriz and Carracedo, {\'A}ngel and Alonso, {\'A}ngel and Dopazo, Joaquin} } @article {432, title = {ATGC transcriptomics: a web-based application to integrate, explore and analyze de novo transcriptomic data.}, journal = {BMC Bioinformatics}, volume = {18}, year = {2017}, month = {2017 Feb 22}, pages = {121}, abstract = {

BACKGROUND: In the last years, applications based on massively parallelized RNA sequencing (RNA-seq) have become valuable approaches for studying non-model species, e.g., without a fully sequenced genome. RNA-seq is a useful tool for detecting novel transcripts and genetic variations and for evaluating differential gene expression by digital measurements. The large and complex datasets resulting from functional genomic experiments represent a challenge in data processing, management, and analysis. This problem is especially significant for small research groups working with non-model species.

RESULTS: We developed a web-based application, called ATGC transcriptomics, with a flexible and adaptable interface that allows users to work with new generation sequencing (NGS) transcriptomic analysis results using an ontology-driven database. This new application simplifies data exploration, visualization, and integration for a better comprehension of the results.

CONCLUSIONS: ATGC transcriptomics provides access to non-expert computer users and small research groups to a scalable storage option and simple data integration, including database administration and management. The software is freely available under the terms of GNU public license at http://atgcinta.sourceforge.net .

}, keywords = {Animals, Databases, Genetic, Gene Expression Profiling, High-Throughput Nucleotide Sequencing, Internet, Sequence Analysis, RNA, Transcriptome, User-Computer Interface}, issn = {1471-2105}, doi = {10.1186/s12859-017-1494-2}, author = {Gonzalez, Sergio and Clavijo, Bernardo and Rivarola, M{\'a}ximo and Moreno, Patricio and Fernandez, Paula and Dopazo, Joaquin and Paniego, Norma} } @article {517, title = {Inferring the regulatory network behind a gene expression experiment.}, journal = {Nucleic Acids Res}, volume = {40}, year = {2012}, month = {2012 Jul}, pages = {W168-72}, abstract = {

Transcription factors (TFs) and miRNAs are the most important dynamic regulators in the control of gene expression in multicellular organisms. These regulatory elements play crucial roles in development, cell cycling and cell signaling, and they have also been associated with many diseases. The Regulatory Network Analysis Tool (RENATO) web server makes the exploration of regulatory networks easy, enabling a better understanding of functional modularity and network integrity under specific perturbations. RENATO is suitable for the analysis of the result of expression profiling experiments. The program analyses lists of genes and search for the regulators compatible with its activation or deactivation. Tests of single enrichment or gene set enrichment allow the selection of the subset of TFs or miRNAs significantly involved in the regulation of the query genes. RENATO also offers an interactive advanced graphical interface that allows exploring the regulatory network found.RENATO is available at: http://renato.bioinfo.cipf.es/.

}, keywords = {Binding Sites, Databases, Genetic, Fanconi Anemia, Gene Regulatory Networks, Internet, MicroRNAs, Software, Transcription Factors, Transcriptome}, issn = {1362-4962}, doi = {10.1093/nar/gks573}, author = {Bleda, Marta and Medina, Ignacio and Alonso, Roberto and De Maria, Alejandro and Salavert, Francisco and Dopazo, Joaquin} } @article {541, title = {myKaryoView: a light-weight client for visualization of genomic data.}, journal = {PLoS One}, volume = {6}, year = {2011}, month = {2011}, pages = {e26345}, abstract = {

The Distributed Annotation System (DAS) is a protocol for easy sharing and integration of biological annotations. In order to visualize feature annotations in a genomic context a client is required. Here we present myKaryoView, a simple light-weight DAS tool for visualization of genomic annotation. myKaryoView has been specifically configured to help analyse data derived from personal genomics, although it can also be used as a generic genome browser visualization. Several well-known data sources are provided to facilitate comparison of known genes and normal variation regions. The navigation experience is enhanced by simultaneous rendering of different levels of detail across chromosomes. A simple interface is provided to allow searches for any SNP, gene or chromosomal region. User-defined DAS data sources may also be added when querying the system. We demonstrate myKaryoView capabilities for adding user-defined sources with a set of genetic profiles of family-related individuals downloaded directly from 23andMe. myKaryoView is a web tool for visualization of genomic data specifically designed for direct-to-consumer genomic data that uses publicly available data distributed throughout the Internet. It does not require data to be held locally and it is capable of rendering any feature as long as it conforms to DAS specifications. Configuration and addition of sources to myKaryoView can be done through the interface. Here we show a proof of principle of myKaryoView{\textquoteright}s ability to display personal genomics data with 23andMe genome data sources. The tool is available at: http://mykaryoview.com.

}, keywords = {Computer Graphics, Databases, Genetic, Genomics, Internet, Molecular Sequence Annotation, User-Computer Interface}, issn = {1932-6203}, doi = {10.1371/journal.pone.0026345}, author = {Jimenez, Rafael C and Salazar, Gustavo A and Gel, Bernat and Dopazo, Joaquin and Mulder, Nicola and Corpas, Manuel} } @article {537, title = {Natural selection on functional modules, a genome-wide analysis.}, journal = {PLoS Comput Biol}, volume = {7}, year = {2011}, month = {2011 Mar}, pages = {e1001093}, abstract = {

Classically, the functional consequences of natural selection over genomes have been analyzed as the compound effects of individual genes. The current paradigm for large-scale analysis of adaptation is based on the observed significant deviations of rates of individual genes from neutral evolutionary expectation. This approach, which assumed independence among genes, has not been able to identify biological functions significantly enriched in positively selected genes in individual species. Alternatively, pooling related species has enhanced the search for signatures of selection. However, grouping signatures does not allow testing for adaptive differences between species. Here we introduce the Gene-Set Selection Analysis (GSSA), a new genome-wide approach to test for evidences of natural selection on functional modules. GSSA is able to detect lineage specific evolutionary rate changes in a notable number of functional modules. For example, in nine mammal and Drosophilae genomes GSSA identifies hundreds of functional modules with significant associations to high and low rates of evolution. Many of the detected functional modules with high evolutionary rates have been previously identified as biological functions under positive selection. Notably, GSSA identifies conserved functional modules with many positively selected genes, which questions whether they are exclusively selected for fitting genomes to environmental changes. Our results agree with previous studies suggesting that adaptation requires positive selection, but not every mutation under positive selection contributes to the adaptive dynamical process of the evolution of species.

}, keywords = {Animals, Databases, Genetic, Drosophila, Genome, Insect, Genome-Wide Association Study, Genomics, Mammals, Phylogeny, Selection, Genetic, Sequence Analysis, DNA}, issn = {1553-7358}, doi = {10.1371/journal.pcbi.1001093}, author = {Serra, Fran{\c c}ois and Arbiza, Leonardo and Dopazo, Joaquin and Dopazo, Hern{\'a}n} } @article {548, title = {ETE: a python Environment for Tree Exploration.}, journal = {BMC Bioinformatics}, volume = {11}, year = {2010}, month = {2010 Jan 13}, pages = {24}, abstract = {

BACKGROUND: Many bioinformatics analyses, ranging from gene clustering to phylogenetics, produce hierarchical trees as their main result. These are used to represent the relationships among different biological entities, thus facilitating their analysis and interpretation. A number of standalone programs are available that focus on tree visualization or that perform specific analyses on them. However, such applications are rarely suitable for large-scale surveys, in which a higher level of automation is required. Currently, many genome-wide analyses rely on tree-like data representation and hence there is a growing need for scalable tools to handle tree structures at large scale.

RESULTS: Here we present the Environment for Tree Exploration (ETE), a python programming toolkit that assists in the automated manipulation, analysis and visualization of hierarchical trees. ETE libraries provide a broad set of tree handling options as well as specific methods to analyze phylogenetic and clustering trees. Among other features, ETE allows for the independent analysis of tree partitions, has support for the extended newick format, provides an integrated node annotation system and permits to link trees to external data such as multiple sequence alignments or numerical arrays. In addition, ETE implements a number of built-in analytical tools, including phylogeny-based orthology prediction and cluster validation techniques. Finally, ETE{\textquoteright}s programmable tree drawing engine can be used to automate the graphical rendering of trees with customized node-specific visualizations.

CONCLUSIONS: ETE provides a complete set of methods to manipulate tree data structures that extends current functionality in other bioinformatic toolkits of a more general purpose. ETE is free software and can be downloaded from http://ete.cgenomics.org.

}, keywords = {Computational Biology, Databases, Genetic, Phylogeny, Software}, issn = {1471-2105}, doi = {10.1186/1471-2105-11-24}, author = {Huerta-Cepas, Jaime and Dopazo, Joaquin and Gabald{\'o}n, Toni} } @article {574, title = {Multidimensional gene set analysis of genomic data.}, journal = {PLoS One}, volume = {5}, year = {2010}, month = {2010 Apr 27}, pages = {e10348}, abstract = {

Understanding the functional implications of changes in gene expression, mutations, etc., is the aim of most genomic experiments. To achieve this, several functional profiling methods have been proposed. Such methods study the behaviour of different gene modules (e.g. gene ontology terms) in response to one particular variable (e.g. differential gene expression). In spite to the wealth of information provided by functional profiling methods, a common limitation to all of them is their inherent unidimensional nature. In order to overcome this restriction we present a multidimensional logistic model that allows studying the relationship of gene modules with different genome-scale measurements (e.g. differential expression, genotyping association, methylation, copy number alterations, heterozygosity, etc.) simultaneously. Moreover, the relationship of such functional modules with the interactions among the variables can also be studied, which produces novel results impossible to be derived from the conventional unidimensional functional profiling methods. We report sound results of gene sets associations that remained undetected by the conventional one-dimensional gene set analysis in several examples. Our findings demonstrate the potential of the proposed approach for the discovery of new cell functionalities with complex dependences on more than one variable.

}, keywords = {Databases, Genetic, Gene Expression Profiling, Gene Regulatory Networks, Genome, Human, Genomics, Humans, Models, Statistical}, issn = {1932-6203}, doi = {10.1371/journal.pone.0010348}, author = {Montaner, David and Dopazo, Joaquin} } @article {590, title = {Direct functional assessment of the composite phenotype through multivariate projection strategies.}, journal = {Genomics}, volume = {92}, year = {2008}, month = {2008 Dec}, pages = {373-83}, abstract = {

We present a novel approach for the analysis of transcriptomics data that integrates functional annotation of gene sets with expression values in a multivariate fashion, and directly assesses the relation of functional features to a multivariate space of response phenotypical variables. Multivariate projection methods are used to obtain new correlated variables for a set of genes that share a given function. These new functional variables are then related to the response variables of interest. The analysis of the principal directions of the multivariate regression allows for the identification of gene function features correlated with the phenotype. Two different transcriptomics studies are used to illustrate the statistical and interpretative aspects of the methodology. We demonstrate the superiority of the proposed method over equivalent approaches.

}, keywords = {Breast Neoplasms, Computational Biology, Databases, Genetic, Female, Gene Expression Profiling, Humans, Mathematical Computing, Multivariate Analysis, Phenotype}, issn = {1089-8646}, doi = {10.1016/j.ygeno.2008.05.015}, author = {Conesa, Ana and Bro, Rasmus and Garcia-Garcia, Francisco and Prats, Jos{\'e} Manuel and G{\"o}tz, Stefan and Kjeldahl, Karin and Montaner, David and Dopazo, Joaquin} } @article {594, title = {High-throughput functional annotation and data mining with the Blast2GO suite.}, journal = {Nucleic Acids Res}, volume = {36}, year = {2008}, month = {2008 Jun}, pages = {3420-35}, abstract = {

Functional genomics technologies have been widely adopted in the biological research of both model and non-model species. An efficient functional annotation of DNA or protein sequences is a major requirement for the successful application of these approaches as functional information on gene products is often the key to the interpretation of experimental results. Therefore, there is an increasing need for bioinformatics resources which are able to cope with large amount of sequence data, produce valuable annotation results and are easily accessible to laboratories where functional genomics projects are being undertaken. We present the Blast2GO suite as an integrated and biologist-oriented solution for the high-throughput and automatic functional annotation of DNA or protein sequences based on the Gene Ontology vocabulary. The most outstanding Blast2GO features are: (i) the combination of various annotation strategies and tools controlling type and intensity of annotation, (ii) the numerous graphical features such as the interactive GO-graph visualization for gene-set function profiling or descriptive charts, (iii) the general sequence management features and (iv) high-throughput capabilities. We used the Blast2GO framework to carry out a detailed analysis of annotation behaviour through homology transfer and its impact in functional genomics research. Our aim is to offer biologists useful information to take into account when addressing the task of functionally characterizing their sequence data.

}, keywords = {Animals, Computational Biology, Computer Graphics, Databases, Genetic, Expressed Sequence Tags, Genes, Genomics, Sequence Analysis, DNA, Sequence Analysis, Protein, Software, Vocabulary, Controlled}, issn = {1362-4962}, doi = {10.1093/nar/gkn176}, author = {G{\"o}tz, Stefan and Garc{\'\i}a-G{\'o}mez, Juan Miguel and Terol, Javier and Williams, Tim D and Nagaraj, Shivashankar H and Nueda, Maria Jos{\'e} and Robles, Montserrat and Talon, Manuel and Dopazo, Joaquin and Conesa, Ana} } @article {596, title = {Joint annotation of coding and non-coding single nucleotide polymorphisms and mutations in the SNPeffect and PupaSuite databases.}, journal = {Nucleic Acids Res}, volume = {36}, year = {2008}, month = {2008 Jan}, pages = {D825-9}, abstract = {

Single nucleotide polymorphisms (SNPs) are, together with copy number variation, the primary source of variation in the human genome. SNPs are associated with altered response to drug treatment, susceptibility to disease and other phenotypic variation. Furthermore, during genetic screens for disease-associated mutations in groups of patients and control individuals, the distinction between disease causing mutation and polymorphism is often unclear. Annotation of the functional and structural implications of single nucleotide changes thus provides valuable information to interpret and guide experiments. The SNPeffect and PupaSuite databases are now synchronized to deliver annotations for both non-coding and coding SNP, as well as annotations for the SwissProt set of human disease mutations. In addition, SNPeffect now contains predictions of Tango2: an improved aggregation detector, and Waltz: a novel predictor of amyloid-forming sequences, as well as improved predictors for regions that are recognized by the Hsp70 family of chaperones. The new PupaSuite version incorporates predictions for SNPs in silencers and miRNAs including their targets, as well as additional methods for predicting SNPs in TFBSs and splice sites. Also predictions for mouse and rat genomes have been added. In addition, a PupaSuite web service has been developed to enable data access, programmatically. The combined database holds annotations for 4,965,073 regulatory as well as 133,505 coding human SNPs and 14,935 disease mutations, and phenotypic descriptions of 43,797 human proteins and is accessible via http://snpeffect.vib.be and http://pupasuite.bioinfo.cipf.es/.

}, keywords = {Amino Acid Substitution, Animals, Databases, Genetic, Genetic Diseases, Inborn, HSP70 Heat-Shock Proteins, Humans, Internet, Mice, MicroRNAs, mutation, Polymorphism, Single Nucleotide, Proteins, Rats, RNA Splice Sites, Transcription Factors}, issn = {1362-4962}, doi = {10.1093/nar/gkm979}, author = {Reumers, Joke and Conde, Lucia and Medina, Ignacio and Maurer-Stroh, Sebastian and Van Durme, Joost and Dopazo, Joaquin and Rousseau, Frederic and Schymkowitz, Joost} } @article {599, title = {SNP and haplotype mapping for genetic analysis in the rat.}, journal = {Nat Genet}, volume = {40}, year = {2008}, month = {2008 May}, pages = {560-6}, abstract = {

The laboratory rat is one of the most extensively studied model organisms. Inbred laboratory rat strains originated from limited Rattus norvegicus founder populations, and the inherited genetic variation provides an excellent resource for the correlation of genotype to phenotype. Here, we report a survey of genetic variation based on almost 3 million newly identified SNPs. We obtained accurate and complete genotypes for a subset of 20,238 SNPs across 167 distinct inbred rat strains, two rat recombinant inbred panels and an F2 intercross. Using 81\% of these SNPs, we constructed high-density genetic maps, creating a large dataset of fully characterized SNPs for disease gene mapping. Our data characterize the population structure and illustrate the degree of linkage disequilibrium. We provide a detailed SNP map and demonstrate its utility for mapping of quantitative trait loci. This community resource is openly available and augments the genetic tools for this workhorse of physiological studies.

}, keywords = {Animals, Chromosome Mapping, Databases, Genetic, Genome, Haplotypes, Linkage Disequilibrium, Phylogeny, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Rats, Rats, Inbred Strains, Recombination, Genetic}, issn = {1546-1718}, doi = {10.1038/ng.124}, author = {Saar, Kathrin and Beck, Alfred and Bihoreau, Marie-Th{\'e}r{\`e}se and Birney, Ewan and Brocklebank, Denise and Chen, Yuan and Cuppen, Edwin and Demonchy, Stephanie and Dopazo, Joaquin and Flicek, Paul and Foglio, Mario and Fujiyama, Asao and Gut, Ivo G and Gauguier, Dominique and Guig{\'o}, Roderic and Guryev, Victor and Heinig, Matthias and Hummel, Oliver and Jahn, Niels and Klages, Sven and Kren, Vladimir and Kube, Michael and Kuhl, Heiner and Kuramoto, Takashi and Kuroki, Yoko and Lechner, Doris and Lee, Young-Ae and Lopez-Bigas, Nuria and Lathrop, G Mark and Mashimo, Tomoji and Medina, Ignacio and Mott, Richard and Patone, Giannino and Perrier-Cornet, Jeanne-Antide and Platzer, Matthias and Pravenec, Michal and Reinhardt, Richard and Sakaki, Yoshiyuki and Schilhabel, Markus and Schulz, Herbert and Serikawa, Tadao and Shikhagaie, Medya and Tatsumoto, Shouji and Taudien, Stefan and Toyoda, Atsushi and Voigt, Birger and Zelenika, Diana and Zimdahl, Heike and Hubner, Norbert} }