GATExplorer: Genomic and Transcriptomic Explorer; mapping expression probes to gene loci, transcripts, exons and ncRNAs

Background: Genome-wide expression studies have developed exponentially in recent years as a result of extensive use of microarray technology. However, expression signals are typically calculated using the assignment of "probesets" to genes, without addressing the problem of "gene" definition or proper consideration of the location of the measuring probes in the context of the currently known genomes and transcriptomes. Moreover, as our knowledge of metazoan genomes improves, the number of both protein-coding and noncoding genes, as well as their associated isoforms, continues to increase. Consequently, there is a need for new databases that combine genomic and transcriptomic information and provide updated mapping of expression probes to current genomic annotations.Results: GATExplorer (Genomic and Transcriptomic Explorer) is a database and web platform that integrates a gene loci browser with nucleotide level mappings of oligo probes from expression microarrays. It allows interactive exploration of gene loci, transcripts and exons of human, mouse and rat genomes, and shows the specific location of all mappable Affymetrix microarray probes and their respective expression levels in a broad set of biological samples. The web site allows visualization of probes in their genomic context together with any associated protein-coding or noncoding transcripts. In the case of all-exon arrays, this provides a means by which the expression of the individual exons within a gene can be compared, thereby facilitating the identification and analysis of alternatively spliced exons. The application integrates data from four major source databases: Ensembl, RNAdb, Affymetrix and GeneAtlas; and it provides the users with a series of files and packages (R CDFs) to analyze particular query expression datasets. The maps cover both the widely used Affymetrix GeneChip microarrays based on 3' expression (e.g. human HG U133 series) and the all-exon expression microarrays (Gene 1.0 and Exon 1.0).Conclusions: GATExplorer is an integrated database that combines genomic/transcriptomic visualization with nucleotide-level probe mapping. By considering expression at the nucleotide level rather than the gene level, it shows that the arrays detect expression signals from entities that most researchers do not contemplate or discriminate. This approach provides the means to undertake a higher resolution analysis of microarray data and potentially extract considerably more detailed and biologically accurate information from existing and future microarray experiments

The Percepción Smart Campus system

Ponènica presentada a IberSPEECH 2014, VIII Jornadas en Tecnología del Habla and IV Iberian SLTech Workshop, celebrat a Las Palmas de Gran Canaria els dies 19-21 de novembre de 2014This paper presents the capabilities of the Smart Campus system developed during the Percepcion project. The Smart Campus system is able to locate the user of the application in a limited environment, including indoor location. The system is able to show routes and data (using virtual reality) on the different elements of the environment. Speech queries could be used to locate places and get routes and information on that places

TET2

TET2 is involved in a variety of hematopoietic malignancies, mainly in myeloid malignancies. Most mutations of TET2 have been identified in myeloid disorders, but some have also recently been described in mature lymphoid neoplasms. In contrast to the large amount of data about mutations of TET2, some data are available for gene expression. Moreover, the role of TET2 in chronic lymphocytic leukemia (CLL) is unknown. This study analyzes both TET2 expression and mutations in 48 CLL patients. TET2 expression was analyzed by exon arrays and quantitative real-time polymerase chain reaction (qRT-PCR). Next-generation sequencing (NGS) technology was applied to investigate the presence of TET2 variations. Overexpression of TET2 was observed in B-cell lymphocytes from CLL patients compared with healthy donors (P = 0.004). In addition, in CLL patients, an overexpression of TET2 was also observed in the clonal B cells compared with the nontumoral cells (P = 0.002). However, no novel mutations were observed. Therefore, overexpression of TET2 in CLL seems to be unrelated to the presence of genomic TET2 variations

ColoLipidGene: Signature of lipid metabolism-related genes to predict prognosis in stage-II colon cancer patients

Lipid metabolism plays an essential role in carcinogenesis due to the requirements of tumoral cells to sustain increased structural, energetic and biosynthetic precursor demands for cell proliferation. We investigated the association between expression of lipid metabolism-related genes and clinical outcome in intermediate-stage colon cancer patients with the aim of identifying a metabolic profile associated with greater malignancy and increased risk of relapse. Expression profile of 70 lipid metabolismrelated genes was determined in 77 patients with stage II colon cancer. Cox regression analyses using c-index methodology was applied to identify a metabolic-related signature associated to prognosis. The metabolic signature was further confirmed in two independent validation sets of 120 patients and additionally, in a group of 264 patients from a public database. The combined analysis of these 4 genes, ABCA1, ACSL1, AGPAT1 and SCD, constitutes a metabolic-signature (ColoLipidGene) able to accurately stratify stage II colon cancer patients with 5-fold higher risk of relapse with strong statistical power in the four independent groups of patients. The identification of a group of 4 genes that predict survival in intermediate-stage colon cancer patients allows delineation of a high-risk group that may benefit from adjuvant therapy, and avoids the toxic and unnecessary chemotherapy in patients classified as low-risk groupThis work was supported by Ministerio de Ciencia e Innovación del Gobierno de España (Plan Nacional I + D + i AGL2013–48943-C2–2-R and IPT-2011–1248-060000), Comunidad de Madrid (P2013/ABI-2728. ALIBIRDCM) and European Union Structural Funds. CIBEREHD is funded by the Instituto de Salud Carlos III. This is a collaborative study between the Molecular Oncology Unit of The Institute of Advanced Studies of Madrid IMDEA Food and the Grupo Español Multidisciplinar en Cáncer Digestivo (GEMCAD

Cystatin D locates in the nucleus at sites of active transcription and modulates gene and protein expression

Cystatin D is an inhibitor of lysosomal and secreted cysteine proteases. Strikingly, cystatin D has been found to inhibit proliferation, migration, and invasion of colon carcinoma cells indicating tumor suppressor activity that is unrelated to protease inhibition. Here, we demonstrate that a proportion of cystatin D locates within the cell nucleus at specific transcriptionally active chromatin sites. Consistently, transcriptomic analysis show that cystatin D alters gene expression, including that of genes encoding transcription factors such as RUNX1, RUNX2, and MEF2C in HCT116 cells. In concordance with transcriptomic data, quantitative proteomic analysis identified 292 proteins differentially expressed in cystatin D-expressing cells involved in cell adhesion, cytoskeleton, and RNA synthesis and processing. Furthermore, using cytokine arrays we found that cystatin D reduces the secretion of several protumor cytokines such as fibroblast growth factor-4, CX3CL1/fractalkine, neurotrophin 4 oncostatin-M, pulmonary and activation-regulated chemokine/CCL18, and transforming growth factor B3. These results support an unanticipated role of cystatin D in the cell nucleus, controlling the transcription of specific genes involved in crucial cellular functions, which may mediate its protective action in colon cancer

Human Gene Coexpression Landscape: Confident Network Derived from Tissue Transcriptomic Profiles

This is an open-access article distributed under the terms of the Creative Commons Attribution License.[Background]: Analysis of gene expression data using genome-wide microarrays is a technique often used in genomic studies to find coexpression patterns and locate groups of co-transcribed genes. However, most studies done at global >omic> scale are not focused on human samples and when they correspond to human very often include heterogeneous datasets, mixing normal with disease-altered samples. Moreover, the technical noise present in genome-wide expression microarrays is another well reported problem that many times is not addressed with robust statistical methods, and the estimation of errors in the data is not provided. [Methodology/Principal Findings]: Human genome-wide expression data from a controlled set of normal-healthy tissues is used to build a confident human gene coexpression network avoiding both pathological and technical noise. To achieve this we describe a new method that combines several statistical and computational strategies: robust normalization and expression signal calculation; correlation coefficients obtained by parametric and non-parametric methods; random cross-validations; and estimation of the statistical accuracy and coverage of the data. All these methods provide a series of coexpression datasets where the level of error is measured and can be tuned. To define the errors, the rates of true positives are calculated by assignment to biological pathways. The results provide a confident human gene coexpression network that includes 3327 gene-nodes and 15841 coexpression-links and a comparative analysis shows good improvement over previously published datasets. Further functional analysis of a subset core network, validated by two independent methods, shows coherent biological modules that share common transcription factors. The network reveals a map of coexpression clusters organized in well defined functional constellations. Two major regions in this network correspond to genes involved in nuclear and mitochondrial metabolism and investigations on their functional assignment indicate that more than 60% are house-keeping and essential genes. The network displays new non-described gene associations and it allows the placement in a functional context of some unknown non-assigned genes based on their interactions with known gene families. [Conclusions/Significance]: The identification of stable and reliable human gene to gene coexpression networks is essential to unravel the interactions and functional correlations between human genes at an omic scale. This work contributes to this aim, and we are making available for the scientific community the validated human gene coexpression networks obtained, to allow further analyses on the network or on some specific gene associations. The data are available free online at http://bioinfow.dep.usal.es/coexpression/. © 2008 Prieto et al.Funding and grant support was provided by the Ministery of Health, Spanish Government (ISCiii-FIS, MSyC; Project reference PI061153) and by the Ministery of Education, Castilla-Leon Local Government (JCyL; Project reference CSI03A06).Peer Reviewe

Bioinformática aplicada a estudios del transcriptoma humano: análisis de expresión de genes, isoformas génicas y ncRNAs en muestras sanas y en cáncer

[EN] This work is based on in-depth analysis of microarray expression data produced by the company Affymetrix, and improvements that expand biological knowledge. 1a. - Improved method of microarray data analysis by replacing the original annotation provided by Affymetrix annotation for alternative, updated and focused on biological entities, that is linked to genes, transcripts and exons defined in the genomic databases more current. 1b. - Integration of data generated in the previous objective interactive web platform with a browser for exploring genomic single mode and display both the structure of the gene loci as probes to map all Affymetrix microarrays and certain gene expression data. 2. - Development and implementation of a differential expression analysis to identify marker genes in several data sets for cancer samples and robust recognition of microRNAs (miRNAs) in multiple myeloma data. 3. - Design and development of new algorithm allows robust identification of alternative splicing in genes from microarray data obtained exons (Exon 1.0 Affymetrix). Applying the algorithm to a set of samples of cancer. 4. - Development of a global transcriptomic study coexpression of human genes based on microarray data obtained for several series of healthy tissue samples. Identification of sets of genes co-expressed, as well as recognition of tissue specific genes (tissue-specific genes, TSG) and general maintenance genes (house-keeping genes, HKG). Evolutionary study analyzing the two types of genes in different species conservation[ES] Este trabajo tiene como base profundizar en el análisis de datos de microarrays de expresión producidos por la empresa Affymetrix, y la introducción de mejoras que permitan ampliar el conocimiento biológico. 1a.- Mejora del método de análisis de datos de microarrays sustituyendo la anotación original proporcionada por Affymetrix por una anotación alternativa, actualizada y centrada en las entidades biológicas; que tome como referencia los genes, transcritos y exones definidos en las bases de datos genómicas más actuales. 1b.- Integración de los datos generados en el objetivo anterior en una plataforma web interactiva con un navegador genómico que permite explorar y visualizar de modo simple tanto la estructura de los loci génicos, como el mapeo de sondas de todos los microarrays de Affymetrix y ciertos datos de expresión de genes. 2.- Desarrollo y aplicación de un análisis de expresión diferencial para identificar genes marcadores en varios conjuntos de datos de muestras de cáncer y para reconocimiento robusto de microRNAs (miRNAs) en datos de mieloma múltiple. 3.- Diseño y desarrollo de nuevo algoritmo que permite la identificación robusta de splicing alternativo en los genes a partir de datos obtenidos con microarrays de exones (Exon 1.0 Affymetrix). Aplicación de dicho algoritmo a un conjunto de muestras de cáncer. 4.- Desarrollo de un estudio transcriptómico global de coexpresión de genes humanos basado en datos de microarrays obtenidos para varias series de muestras de tejidos sanos. Identificación de conjuntos de genes que coexpresan, así como reconocimiento de genes específicos de tejido (tissue-specific genes, TSg) y genes generales de mantenimiento (house-keeping genes, HKg). Estudio evolutivo de ambos tipos de genes analizando su conservación en distintas especie