[Show abstract][Hide abstract] ABSTRACT: Genome browsers have gained importance as more genomes and related genomic information become available. However, the increase of information brought about by new generation sequencing technologies is, at the same time, causing a subtle but continuous decrease in the efficiency of conventional genome browsers. Here, we present Genome Maps, a genome browser that implements an innovative model of data transfer and management. The program uses highly efficient technologies from the new HTML5 standard, such as scalable vector graphics, that optimize workloads at both server and client sides and ensure future scalability. Thus, data management and representation are entirely carried out by the browser, without the need of any Java Applet, Flash or other plug-in technology installation. Relevant biological data on genes, transcripts, exons, regulatory features, single-nucleotide polymorphisms, karyotype and so forth, are imported from web services and are available as tracks. In addition, several DAS servers are already included in Genome Maps. As a novelty, this web-based genome browser allows the local upload of huge genomic data files (e.g. VCF or BAM) that can be dynamically visualized in real time at the client side, thus facilitating the management of medical data affected by privacy restrictions. Finally, Genome Maps can easily be integrated in any web application by including only a few lines of code. Genome Maps is an open source collaborative initiative available in the GitHub repository (https://github.com/compbio-bigdata-viz/genome-maps). Genome Maps is available at: http://www.genomemaps.org.
Nucleic Acids Research 06/2013; 41(Web Server issue). DOI:10.1093/nar/gkt530 · 9.11 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: 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.
Nucleic Acids Research 07/2012; 40(20). DOI:10.1093/nar/gks699 · 9.11 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: During the past years, the advances in high-throughput technologies have produced an unprecedented growth in the number and size of repositories and databases storing relevant biological data. Today, there is more biological information than ever but, unfortunately, the current status of many of these repositories is far from being optimal. Some of the most common problems are that the information is spread out in many small databases; frequently there are different standards among repositories and some databases are no longer supported or they contain too specific and unconnected information. In addition, data size is increasingly becoming an obstacle when accessing or storing biological data. All these issues make very difficult to extract and integrate information from different sources, to analyze experiments or to access and query this information in a programmatic way. CellBase provides a solution to the growing necessity of integration by easing the access to biological data. CellBase implements a set of RESTful web services that query a centralized database containing the most relevant biological data sources. The database is hosted in our servers and is regularly updated. CellBase documentation can be found at http://docs.bioinfo.cipf.es/projects/cellbase.
Nucleic Acids Research 06/2012; 40(Web Server issue):W609-14. DOI:10.1093/nar/gks575 · 9.11 Impact Factor
[Show abstract][Hide abstract] 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/.
Nucleic Acids Research 06/2012; 40(Web Server issue):W168-72. DOI:10.1093/nar/gks573 · 9.11 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: The massive use of Next-Generation Sequencing (NGS) technologies is uncovering an unexpected amount of variability. The functional characterization of such variability, particularly in the most common form of variation found, the Single Nucleotide Variants (SNVs), has become a priority that needs to be addressed in a systematic way. VARIANT (VARIant ANalyis Tool) reports information on the variants found that include consequence type and annotations taken from different databases and repositories (SNPs and variants from dbSNP and 1000 genomes, and disease-related variants from the Genome-Wide Association Study (GWAS) catalog, Online Mendelian Inheritance in Man (OMIM), Catalog of Somatic Mutations in Cancer (COSMIC) mutations, etc). VARIANT also produces a rich variety of annotations that include information on the regulatory (transcription factor or miRNA-binding sites, etc.) or structural roles, or on the selective pressures on the sites affected by the variation. This information allows extending the conventional reports beyond the coding regions and expands the knowledge on the contribution of non-coding or synonymous variants to the phenotype studied. Contrarily to other tools, VARIANT uses a remote database and operates through efficient RESTful Web Services that optimize search and transaction operations. In this way, local problems of installation, update or disk size limitations are overcome without the need of sacrifice speed (thousands of variants are processed per minute). VARIANT is available at: http://variant.bioinfo.cipf.es.
Nucleic Acids Research 06/2012; 40(Web Server issue):W54-8. DOI:10.1093/nar/gks572 · 9.11 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Phylemon 2.0 is a new release of the suite of web tools for molecular evolution, phylogenetics, phylogenomics and hypotheses testing. It has been designed as a response to the increasing demand of molecular sequence analyses for experts and non-expert users. Phylemon 2.0 has several unique features that differentiates it from other similar web resources: (i) it offers an integrated environment that enables evolutionary analyses, format conversion, file storage and edition of results; (ii) it suggests further analyses, thereby guiding the users through the web server; and (iii) it allows users to design and save phylogenetic pipelines to be used over multiple genes (phylogenomics). Altogether, Phylemon 2.0 integrates a suite of 30 tools covering sequence alignment reconstruction and trimming; tree reconstruction, visualization and manipulation; and evolutionary hypotheses testing.
Nucleic Acids Research 06/2011; 39(Web Server issue):W470-4. DOI:10.1093/nar/gkr408 · 9.11 Impact Factor