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Biomedical subjects

Alberto Pascual-Montano

Publications and source records attributed to Alberto Pascual-Montano.

12 recordsLinked to original sources
PubMed2007

GENECODIS: a web-based tool for finding significant concurrent annotations in gene lists.

We present GENECODIS, a web-based tool that integrates different sources of information to search for annotations that frequently co-occur in a set of genes and rank them by statistical significance. The analysis of concurrent annotations provides significant information for the biologic interpretation of high-throughput experiments and may outperform the results of standard methods for the functional analysis of gene lists. GENECODIS is publicly available at http://genecodis.dacya.ucm.es/.

Computational Biology↗
PubMed2006

bioNMF: a versatile tool for non-negative matrix factorization in biology.

BACKGROUND: In the Bioinformatics field, a great deal of interest has been given to Non-negative matrix factorization technique (NMF), due to its capability of providing new insights and relevant information about the complex latent relationships in experimental data sets. This method, and some of its variants, has been successfully applied to gene expression, sequence analysis, functional characterization of genes and text mining. Even if the interest on this technique by the bioinformatics community has been increased during the last few years, there are not many available simple standalone tools to specifically perform these types of data analysis in an integrated environment. RESULTS: In this work we propose a versatile and user-friendly tool that implements the NMF methodology in different analysis contexts to support some of the most important reported applications of this new methodology. This includes clustering and biclustering gene expression data, protein sequence analysis, text mining of biomedical literature and sample classification using gene expression. The tool, which is named bioNMF, also contains a user-friendly graphical interface to explore results in an interactive manner and facilitate in this way the exploratory data analysis process. CONCLUSION: bioNMF is a standalone versatile application which does not require any special installation or libraries. It can be used for most of the multiple applications proposed in the bioinformatics field or to support new research using this method. This tool is publicly available at http://www.dacya.ucm.es/apascual/bioNMF.

Algorithms↗
PubMed2006

A literature-based similarity metric for biological processes.

BACKGROUND: Recent analyses in systems biology pursue the discovery of functional modules within the cell. Recognition of such modules requires the integrative analysis of genome-wide experimental data together with available functional schemes. In this line, methods to bridge the gap between the abstract definitions of cellular processes in current schemes and the interlinked nature of biological networks are required. RESULTS: This work explores the use of the scientific literature to establish potential relationships among cellular processes. To this end we have used a document based similarity method to compute pair-wise similarities of the biological processes described in the Gene Ontology (GO). The method has been applied to the biological processes annotated for the Saccharomyces cerevisiae genome. We compared our results with similarities obtained with two ontology-based metrics, as well as with gene product annotation relationships. We show that the literature-based metric conserves most direct ontological relationships, while reveals biologically sounded similarities that are not obtained using ontology-based metrics and/or genome annotation. CONCLUSION: The scientific literature is a valuable source of information from which to compute similarities among biological processes. The associations discovered by literature analysis are a valuable complement to those encoded in existing functional schemes, and those that arise by genome annotation. These similarities can be used to conveniently map the interlinked structure of cellular processes in a particular organism.

Databases, Bibliographic↗
PubMed2006

Biclustering of gene expression data by Non-smooth Non-negative Matrix Factorization.

BACKGROUND: The extended use of microarray technologies has enabled the generation and accumulation of gene expression datasets that contain expression levels of thousands of genes across tens or hundreds of different experimental conditions. One of the major challenges in the analysis of such datasets is to discover local structures composed by sets of genes that show coherent expression patterns across subsets of experimental conditions. These patterns may provide clues about the main biological processes associated to different physiological states. RESULTS: In this work we present a methodology able to cluster genes and conditions highly related in sub-portions of the data. Our approach is based on a new data mining technique, Non-smooth Non-Negative Matrix Factorization (nsNMF), able to identify localized patterns in large datasets. We assessed the potential of this methodology analyzing several synthetic datasets as well as two large and heterogeneous sets of gene expression profiles. In all cases the method was able to identify localized features related to sets of genes that show consistent expression patterns across subsets of experimental conditions. The uncovered structures showed a clear biological meaning in terms of relationships among functional annotations of genes and the phenotypes or physiological states of the associated conditions. CONCLUSION: The proposed approach can be a useful tool to analyze large and heterogeneous gene expression datasets. The method is able to identify complex relationships among genes and conditions that are difficult to identify by standard clustering algorithms.

Algorithms↗
PubMed2006

Integrated analysis of gene expression by Association Rules Discovery.

BACKGROUND: Microarray technology is generating huge amounts of data about the expression level of thousands of genes, or even whole genomes, across different experimental conditions. To extract biological knowledge, and to fully understand such datasets, it is essential to include external biological information about genes and gene products to the analysis of expression data. However, most of the current approaches to analyze microarray datasets are mainly focused on the analysis of experimental data, and external biological information is incorporated as a posterior process. RESULTS: In this study we present a method for the integrative analysis of microarray data based on the Association Rules Discovery data mining technique. The approach integrates gene annotations and expression data to discover intrinsic associations among both data sources based on co-occurrence patterns. We applied the proposed methodology to the analysis of gene expression datasets in which genes were annotated with metabolic pathways, transcriptional regulators and Gene Ontology categories. Automatically extracted associations revealed significant relationships among these gene attributes and expression patterns, where many of them are clearly supported by recently reported work. CONCLUSION: The integration of external biological information and gene expression data can provide insights about the biological processes associated to gene expression programs. In this paper we show that the proposed methodology is able to integrate multiple gene annotations and expression data in the same analytic framework and extract meaningful associations among heterogeneous sources of data. An implementation of the method is included in the Engene software package.

Algorithms↗
PubMed2006

Discovering semantic features in the literature: a foundation for building functional associations.

BACKGROUND: Experimental techniques such as DNA microarray, serial analysis of gene expression (SAGE) and mass spectrometry proteomics, among others, are generating large amounts of data related to genes and proteins at different levels. As in any other experimental approach, it is necessary to analyze these data in the context of previously known information about the biological entities under study. The literature is a particularly valuable source of information for experiment validation and interpretation. Therefore, the development of automated text mining tools to assist in such interpretation is one of the main challenges in current bioinformatics research. RESULTS: We present a method to create literature profiles for large sets of genes or proteins based on common semantic features extracted from a corpus of relevant documents. These profiles can be used to establish pair-wise similarities among genes, utilized in gene/protein classification or can be even combined with experimental measurements. Semantic features can be used by researchers to facilitate the understanding of the commonalities indicated by experimental results. Our approach is based on non-negative matrix factorization (NMF), a machine-learning algorithm for data analysis, capable of identifying local patterns that characterize a subset of the data. The literature is thus used to establish putative relationships among subsets of genes or proteins and to provide coherent justification for this clustering into subsets. We demonstrate the utility of the method by applying it to two independent and vastly different sets of genes. CONCLUSION: The presented method can create literature profiles from documents relevant to sets of genes. The representation of genes as additive linear combinations of semantic features allows for the exploration of functional associations as well as for clustering, suggesting a valuable methodology for the validation and interpretation of high-throughput experimental data.

Abstracting and Indexing↗
PubMed2006

Nonsmooth nonnegative matrix factorization (nsNMF).

We propose a novel nonnegative matrix factorization model that aims at finding localized, part-based, representations of nonnegative multivariate data items. Unlike the classical nonnegative matrix factorization (NMF) technique, this new model, denoted "nonsmooth nonnegative matrix factorization" (nsNMF), corresponds to the optimization of an unambiguous cost function designed to explicitly represent sparseness, in the form of nonsmoothness, which is controlled by a single parameter. In general, this method produces a set of basis and encoding vectors that are not only capable of representing the original data, but they also extract highly localized patterns, which generally lend themselves to improved interpretability. The properties of this new method are illustrated with several data sets. Comparisons to previously published methods show that the new nsNMF method has some advantages in keeping faithfulness to the data in the achieving a high degree of sparseness for both the estimated basis and the encoding vectors and in better interpretability of the factors.

Algorithms↗
PubMed2006

Host response to the attenuated poxvirus vector NYVAC: upregulation of apoptotic genes and NF-kappaB-responsive genes in infected HeLa cells.

NYVAC has been engineered as a safe, attenuated vaccinia virus (VV) vector for use in vaccination against a broad spectrum of pathogens and tumors. Due to the interest in NYVAC-based vectors as vaccines and current phase I/II clinical trials with this vector, there is a need to analyze the human host response to NYVAC infection. Using high-density cDNA microarrays, we found 368 differentially regulated genes after NYVAC infection of HeLa cells. Clustering of the regulated genes identified six discrete gene clusters with altered expression patterns. Clusters 1 to 3 represented 47.5% of the regulated genes, with three patterns of gene activation kinetics, whereas clusters 4 to 6 showed distinct repression kinetics. Quantitative real-time reverse transcription-PCR analysis of selected genes validated the array data. Upregulated transcripts correlated with genes implicated in immune responses, including those encoding interleukin-1 receptor 2 (IL-1R2), IL-6, ISG-15, CD-80, and TNFSF7. NYVAC upregulated several intermediates of apoptotic cascades, including caspase-9, correlating with its ability to induce apoptosis. NYVAC infection also stimulated the expression of NF-kappaB1 and NF-kappaB2 as well as that of NF-kappaB target genes. Expression of the VV host range K1L gene during NYVAC infection prevented NF-kappaB activation, but not the induction of apoptosis. This study is the first overall analysis of the transcriptional response of human cells to NYVAC infection and provides a framework for future functional studies to evaluate this vector and its derivatives as human vaccines.

Activating Transcription Factors↗
PubMed2004

Microarray analysis reveals characteristic changes of host cell gene expression in response to attenuated modified vaccinia virus Ankara infection of human HeLa cells.

The potential use of the modified vaccinia virus Ankara (MVA) strain as a live recombinant vector to deliver antigens and elicit protective immune responses against infectious diseases demands a comprehensive understanding of the effect of MVA infection on human host gene expression. We used microarrays containing more than 15,000 human cDNAs to identify gene expression changes in human HeLa cell cultures at 2, 6, and 16 h postinfection. Clustering of the 410 differentially regulated genes identified 11 discrete gene clusters with altered expression patterns after MVA infection. Clusters 1 and 2 (accounting for 16.59% [68 of 410] of the genes) contained 68 transcripts showing a robust induction pattern that was maintained during the course of infection. Changes in cellular gene transcription detected by microarrays after MVA infection were confirmed for selected genes by Northern blot analysis and by real-time reverse transcription-PCR. Upregulated transcripts in clusters 1 and 2 included 20 genes implicated in immune responses, including interleukin 1A (IL-1A), IL-6, IL-7, IL-8, and IL-15 genes. MVA infection also stimulated the expression of NF-kappaB and components of the NF-kappaB signal transduction pathway, including p50 and TRAF-interacting protein. A marked increase in the expression of histone family members was also induced during MVA infection. Expression of the Wiskott-Aldrich syndrome family members WAS, WASF1, and the small GTP-binding protein RAC-1, which are involved in actin cytoskeleton reorganization, was enhanced after MVA infection. This study demonstrates that MVA infection triggered the induction of groups of genes, some of which may be involved in host resistance and immune modulation during virus infection.

Blotting, Northern↗
PubMed2003

Cellular gene expression survey of vaccinia virus infection of human HeLa cells.

Vaccinia virus (VV) is a cytocidal virus that causes major changes in host cell machinery shortly after infecting cells. To define the consequences of virus infection on host gene expression, we used microarrays of approximately 15,000 human cDNAs to examine expression levels of mRNAs isolated at 2, 6, and 16 h postinfection from cultures of infected HeLa cells. The majority of profiling changes during VV infection corresponded to downregulation of genes at 16 h postinfection. Differentially expressed genes were clustered into seven groups to identify common regulatory pathways, with most of them (90%) belonging to clusters 6 and 7, which represent genes whose expression was repressed after infection. Cluster 1, however, contained 37 transcripts (2.81%) showing a robust pattern of induction that was maintained during the course of infection. Genes in cluster 1 included those for Wiskott-Aldrich syndrome protein (WASP) family member WASF1, thymosine, adenosine A2a receptor, glutamate decarboxylase 2, CD-80 antigen, KIAA0888 protein, selenophosphate synthetase, pericentrin, and attractin as well as several expressed sequence tags. We analyzed in more detail the fate of WASP protein in VV-infected cells, because a related family member, N-WASP, is involved in viral motility. WASP protein accumulated in the course of infection; its increase required viral DNA replication and de novo protein synthesis, and it localized in cytoplasmic structures distinct from uninfected cells. This study is the first quantitative analysis of host gene expression following VV infection of cultured human cells, demonstrating global changes in the expression profile, and identifies upregulated genes with potential roles in the virus replication cycle.

Blotting, Western↗
PubMed2002

Modeling shape and topology of low-resolution density maps of biological macromolecules.

In the present work we develop an efficient way of representing the geometry and topology of volumetric datasets of biological structures from medium to low resolution, aiming at storing and querying them in a database framework. We make use of a new vector quantization algorithm to select the points within the macromolecule that best approximate the probability density function of the original volume data. Connectivity among points is obtained with the use of the alpha shapes theory. This novel data representation has a number of interesting characteristics, such as 1) it allows us to automatically segment and quantify a number of important structural features from low-resolution maps, such as cavities and channels, opening the possibility of querying large collections of maps on the basis of these quantitative structural features; 2) it provides a compact representation in terms of size; 3) it contains a subset of three-dimensional points that optimally quantify the densities of medium resolution data; and 4) a general model of the geometry and topology of the macromolecule (as opposite to a spatially unrelated bunch of voxels) is easily obtained by the use of the alpha shapes theory.

Algorithms↗