Conceptual modeling for genomics: Building an integrated repository of open data

Many repositories of open data for genomics, collected by world-wide consortia, are important enablers of biological research; moreover, all experimental datasets leading to publications in genomics must be deposited to public repositories and made available to the research community. These datasets are typically used by biologists for validating or enriching their experiments; their content is documented by metadata. However, emphasis on data sharing is not matched by accuracy in data documentation; metadata are not standardized across the sources and often unstructured and incomplete. In this paper, we propose a conceptual model of genomic metadata, whose purpose is to query the underlying data sources for locating relevant experimental datasets. First, we analyze the most typical metadata attributes of genomic sources and define their semantic properties. Then, we use a top-down method for building a global-as-view integrated schema, by abstracting the most important conceptual properties of genomic sources. Finally, we describe the validation of the conceptual model by mapping it to three well-known data sources: TCGA, ENCODE, and Gene Expression Omnibus

BEAT: Bioinformatics Exon Array Tool to store, analyze and visualize Affymetrix GeneChip Human Exon Array data from disease experiments

<p>Abstract</p> <p>Background</p> <p>It is known from recent studies that more than 90% of human multi-exon genes are subject to Alternative Splicing (AS), a key molecular mechanism in which multiple transcripts may be generated from a single gene. It is widely recognized that a breakdown in AS mechanisms plays an important role in cellular differentiation and pathologies. Polymerase Chain Reactions, microarrays and sequencing technologies have been applied to the study of transcript diversity arising from alternative expression. Last generation Affymetrix GeneChip Human Exon 1.0 ST Arrays offer a more detailed view of the gene expression profile providing information on the AS patterns. The exon array technology, with more than five million data points, can detect approximately one million exons, and it allows performing analyses at both gene and exon level. In this paper we describe BEAT, an integrated user-friendly bioinformatics framework to store, analyze and visualize exon arrays datasets. It combines a data warehouse approach with some rigorous statistical methods for assessing the AS of genes involved in diseases. Meta statistics are proposed as a novel approach to explore the analysis results. BEAT is available at <url>http://beat.ba.itb.cnr.it</url>.</p> <p>Results</p> <p>BEAT is a web tool which allows uploading and analyzing exon array datasets using standard statistical methods and an easy-to-use graphical web front-end. BEAT has been tested on a dataset with 173 samples and tuned using new datasets of exon array experiments from 28 colorectal cancer and 26 renal cell cancer samples produced at the Medical Genetics Unit of IRCCS Casa Sollievo della Sofferenza.</p> <p>To highlight all possible AS events, alternative names, accession Ids, Gene Ontology terms and biochemical pathways annotations are integrated with exon and gene level expression plots. The user can customize the results choosing custom thresholds for the statistical parameters and exploiting the available clinical data of the samples for a multivariate AS analysis.</p> <p>Conclusions</p> <p>Despite exon array chips being widely used for transcriptomics studies, there is a lack of analysis tools offering advanced statistical features and requiring no programming knowledge. BEAT provides a user-friendly platform for a comprehensive study of AS events in human diseases, displaying the analysis results with easily interpretable and interactive tables and graphics.</p

pubmed2ensembl: A Resource for Mining the Biological Literature on Genes

The last two decades have witnessed a dramatic acceleration in the production of genomic sequence information and publication of biomedical articles. Despite the fact that genome sequence data and publications are two of the most heavily relied-upon sources of information for many biologists, very little effort has been made to systematically integrate data from genomic sequences directly with the biological literature. For a limited number of model organisms dedicated teams manually curate publications about genes; however for species with no such dedicated staff many thousands of articles are never mapped to genes or genomic regions.To overcome the lack of integration between genomic data and biological literature, we have developed pubmed2ensembl (http://www.pubmed2ensembl.org), an extension to the BioMart system that links over 2,000,000 articles in PubMed to nearly 150,000 genes in Ensembl from 50 species. We use several sources of curated (e.g., Entrez Gene) and automatically generated (e.g., gene names extracted through text-mining on MEDLINE records) sources of gene-publication links, allowing users to filter and combine different data sources to suit their individual needs for information extraction and biological discovery. In addition to extending the Ensembl BioMart database to include published information on genes, we also implemented a scripting language for automated BioMart construction and a novel BioMart interface that allows text-based queries to be performed against PubMed and PubMed Central documents in conjunction with constraints on genomic features. Finally, we illustrate the potential of pubmed2ensembl through typical use cases that involve integrated queries across the biomedical literature and genomic data.By allowing biologists to find the relevant literature on specific genomic regions or sets of functionally related genes more easily, pubmed2ensembl offers a much-needed genome informatics inspired solution to accessing the ever-increasing biomedical literature

Modelo de una bodega de datos para el soporte a la investigación bioinformática

La Bioinformática1 es el uso de herramientas computacionales que permiten analizar, depurar y agilizar el manejo de grandes cantidades de datos de la biología en términos fisicoquímicos y permitir comprender y organizar la información asociada. La bioinformática parte de datos encontrados experimentalmente, los cuales son almacenados y sobre estos se aplican técnicas de consulta, de análisis y de extracción de conocimiento

Modelo de una bodega de datos para el soporte a la investigación bioinformática

La Bioinformática1 es el uso de herramientas computacionales que permiten analizar, depurar y agilizar el manejo de grandes cantidades de datos de la biología en términos fisicoquímicos y permitir comprender y organizar la información asociada. La bioinformática parte de datos encontrados experimentalmente, los cuales son almacenados y sobre estos se aplican técnicas de consulta, de análisis y de extracción de conocimiento

BioGPS: an extensible and customizable portal for querying and organizing gene annotation resources

BioGPS is a community based customisable gene annotation portal bringing together gene annotation resources on to a single platform

Tecnologías bioinformáticas para el análisis de secuencias de ADN

La información contenida en secuencias de ADN,por su contenido voluminoso requiere de técnicas inteligentes para el modelamiento de los datos y de métodos computacionales avanzados para el procesamiento de estos. Se busca optimizar el tiempo en el que se ejecutan cálculos e inferencias, y mejorar la confiabilidad de los análisis que se realizan a partir de los resultados obtenidos, los cuales pueden servir de base para el desarrollo de investigaciones científicas. El grupo de investigación GIA del programa Ingeniería de Sistemas y Computación de la Universidad Tecnológica de Pereira, se encuentra trabajando en la determinación de tecnologías informáticas que permitan hacer avances significativos en los desarrollos científicos en el campo de la biología. Este artículo explora que técnicas computacionales son pertinentes en el desarrollo de aplicaciones bioinformáticas.