Desarrollo de algoritmos bioinformáticos para estudios de genómica funcional: aplicaciones en cáncer

[ES]La presente Tesis Doctoral se enmarca en las áreas de conocimiento de la Bioinformática y Biología Computacional y también de la Genómica Funcional y Genómica del Cáncer. El objetivo fundamental de la Genómica Funcional es entender cómo funciona el genoma en su conjunto mediante el análisis de la actividad de todos sus genes y de los múltiples factores que regulan o influyen la expresión de los mismos, así como otras entidades biomoleculares relacionadas. La recolección sistemática de información y datos procedentes de tecnologías genómicas experimentales globales a gran escala proporciona un punto de partida para desvelar la actividad del genoma y el comportamiento de los sistemas vivos asociado a su genoma. En este marco temático, el trabajo de esta Tesis Doctoral ha sido el desarrollo y aplicación de varios algoritmos bioinformáticos para el análisis de datos sobre muestras humanas de pacientes con cáncer procedentes de diversas plataformas genómicas de alta densidad, así como su integración e interpretación para descubrir los genes y procesos biológicos alterados en dichas patologías. En concreto se han analizado datos de los tipos mayoritarios de leucemias agudas y crónicas (ALL, AML, CLL, CML), de cáncer colorectal (CRC) metastásico y de tumores cerebrales primarios de tipo glioblastoma multiforme (GBM). Los resultados concretos obtenidos, enunciados modo breve, son: (1) desarrollo de un clasificador multiclase para diferenciar subtipos patológicos basado en perfiles globales de expresión (¿geNetClassifier¿); (2) desarrollo de un método para análisis cuantitativo de alteraciones genómicas del número de copias de DNA (CNA) y detección de puntos de ruptura en el genoma, aplicado a muestras de cáncer; (3) desarrollo de un método para análisis integrado de alteraciones genómicas en número de copias (CN) y alteraciones transcriptómicas de la expresión génica (GE); (4) desarrollo de un algoritmo y una aplicación web para análisis biológico funcional basado en asociación recíproca múltiple de genes y términos biológicos derivados de diferentes espacios de anotación[EN]The present thesis is part of the knowledge areas of Bioinformatics and Computational Biology and Functional Genomics and Cancer Genomics . The fundamental objective of the Functional Genomics is to understand how the genome works as a whole by analyzing the activity of all genes and the multiple factors that regulate or influence the expression of these and other biomolecular related entities. The systematic collection of information and data from global experimental large-scale genomic technologies provides a starting point to unravel genome activity and behavior of living systems associated genome. The work of this thesis has been the development and implementation of several bioinformatics algorithms for analyzing data on human samples of cancer patients from different genomic platforms high density as well as their integration and interpretation to discover altered genes and biological processes in these diseases . Specifically used data of the major types of acute and chronic leukemia (ALL , AML, CLL , CML ) , metastatic colorectal cancer ( CRC) and primary brain tumors glioblastoma multiforme (GBM ) type . Concrete results , statements briefly, they are: (1 ) development of a classifier multiclass to differentiate pathologic subtypes based on global expression profiles ( geNetClassifier ) , (2 ) development of a method for quantitative analysis of genomic alterations in the number DNA copy (CNA ) and detection of breakpoints within the genome , applied to cancer samples , (3) development of a method for analysis of genomic alterations in integrated copy number (CN) and transcriptomic alterations of gene expression (GE ) , (4 ) development of an algorithm and a Web application to biological functional analysis based on mutual association of multiple genes and biologically derived annotation of different spaces

Desarrollo de algoritmos bioinformáticos para estudios de genómica funcional: aplicaciones en cáncer

Tesis Doctoral presentada por Dña. Celia Fontanillo Fontanillo alumna del programa de Doctorado del CiC-IBMCC de la Universidad de Salamanca.[ES]: La presente Tesis Doctoral se enmarca en las áreas de conocimiento de la Bioinformática y Biología Computacional y también de la Genómica Funcional y Genómica del Cáncer. El objetivo fundamental de la Genómica Funcional es entender cómo funciona el genoma en su conjunto mediante el análisis de la actividad de todos sus genes y de los múltiples factores que regulan o influyen la expresión de los mismos, así como otras entidades biomoleculares relacionadas. La recolección sistemática de información y datos procedentes de tecnologías genómicas experimentales globales a gran escala proporciona un punto de partida para desvelar la actividad del genoma y el comportamiento de los sistemas vivos asociado a su genoma. En este marco temático, el trabajo de esta Tesis Doctoral ha sido el desarrollo y aplicación de varios algoritmos bioinformáticos para el análisis de datos sobre muestras humanas de pacientes con cáncer procedentes de diversas plataformas genómicas de alta densidad, así como su integración e interpretación para descubrir los genes y procesos biológicos alterados en dichas patologías.[EN]. The present thesis is part of the knowledge areas of Bioinformatics and Computational Biology and Functional Genomics and Cancer Genomics. The fundamental objective of the Functional Genomics is to understand how the genome works as a whole by analyzing the activity of all genes and the multiple factors that regulate or influence the expression of these and other biomolecular related entities. The systematic collection of information and data from global experimental large-scale genomic technologies provides a starting point to unravel genome activity and behavior of living systems associated genome. The work of this thesis has been the development and implementation of several bioinformatics algorithms for analyzing data on human samples of cancer patients from different genomic platforms high density as well as their integration and interpretation to discover altered genes and biological processes in these diseases.Peer Reviewe

Protein-protein interaction networks: unraveling the wiring of molecular machines within the cell

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License.Mapping and understanding of the protein interaction networks with their key modules and hubs can provide deeper insights into the molecular machinery underlying complex phenotypes. In this article, we present the basic characteristics and definitions of protein networks, starting with a distinction of the different types of associations between proteins. We focus the review on protein-protein interactions (PPIs), a subset of associations defined as physical contacts between proteins that occur by selective molecular docking in a particular biological context. We present such definition as opposed to other types of protein associations derived from regulatory, genetic, structural or functional relations. To determine PPIs, a variety of binary and co-complex methods exist; however, not all the technologies provide the same information and data quality. A way of increasing confidence in a given protein interaction is to integrate orthogonal experimental evidences. The use of several complementary methods testing each single interaction assesses the accuracy of PPI data and tries to minimize the occurrence of false interactions. Following this approach there have been important efforts to unify primary databases of experimentally proven PPIs into integrated databases. These meta-databases provide a measure of the confidence of interactions based on the number of experimental proofs that report them. As a conclusion, we can state that integrated information allows the building of more reliable interaction networks. Identification of communities, cliques, modules and hubs by analysing the topological parameters and graph properties of the protein networks allows the discovery of central/critical nodes, which are candidates to regulate cellular flux and dynamics.This work was supported by the Consejo Superior de Investigaciones Cientificas (CSIC) [project iLINK0398]; the Spanish Government (ISCiii) [project PS09/00843]; and the European Commission [project FP7-HEALTH-2007-223411].Peer Reviewe

Functional Gene Networks: R/Bioc package to generate and analyse gene networks derived from functional enrichment and clustering

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License.Functional Gene Networks (FGNet) is an R/Bioconductor package that generates gene networks derived from the results of functional enrichment analysis (FEA) and annotation clustering. The sets of genes enriched with specific biological terms (obtained from a FEA platform) are transformed into a network by establishing links between genes based on common functional annotations and common clusters. The network provides a new view of FEA results revealing gene modules with similar functions and genes that are related to multiple functions. In addition to building the functional network, FGNet analyses the similarity between the groups of genes and provides a distance heatmap and a bipartite network of functionally overlapping genes. The application includes an interface to directly perform FEA queries using different external tools: DAVID, GeneTerm Linker, TopGO or GAGE; and a graphical interface to facilitate the use.This work was supported by the “Accion Estrategica en Salud” (AES) of the “Instituto de Salud Carlos III” (ISCiii) from the Spanish Government (projects granted to J.D.L.R.: PS09/00843 and PI12/00624); and by the “Consejeria de Educación” of the “Junta Castilla y León” (JCyL) and the European Social Fund (ESF) with grants given to S.A. and C.D.Peer Reviewe

A proteome-scale map of the human interactome network

PMCID: PMC4266588.-- et al.Just as reference genome sequences revolutionized human genetics, reference maps of interactome networks will be critical to fully understand genotype-phenotype relationships. Here, we describe a systematic map of ∼14,000 high-quality human binary protein-protein interactions. At equal quality, this map is ∼30% larger than what is available from small-scale studies published in the literature in the last few decades. While currently available information is highly biased and only covers a relatively small portion of the proteome, our systematic map appears strikingly more homogeneous, revealing a >broader> human interactome network than currently appreciated. The map also uncovers significant interconnectivity between known and candidate cancer gene products, providing unbiased evidence for an expanded functional cancer landscape, while demonstrating how high-quality interactome models will help >connect the dots> of the genomic revolution.This work was supported primarily by NHGRI grant R01/U01HG001715 awarded to M.V., D.E.H., F.P.R., and J.T. and in part by the following grants and agencies: NHGRI P50HG004233 to M.V., F.P.R., and A.-L.B.; NHLBI U01HL098166 subaward to M.V.; NHLBI U01HL108630 subaward to A.-L.B.; NCI U54CA112962 subaward to M.V.; NCI R33CA132073 to M.V.; NIH RC4HG006066 to M.V., D.E.H., and T.H.; NICHD ARRA R01HD065288, R21MH104766, and R01MH105524 to L.M.I.; NIMH R01MH091350 to L.M.I. and T.H.; NSF CCF-1219007 and NSERC RGPIN-2014-03892 to Y.X.; Canada Excellence Research Chair, Krembil Foundation, Ontario Research Fund–Research Excellence Award, Avon Foundation, grant CSI07A09 from Junta de Castilla y Leon (Valladolid, Spain), grant PI12/00624 from Ministerio de Economia y Competitividad (AES 2012, ISCiii, Madrid, Spain), and grant i-Link0398 from Consejo Superior de Investigaciones Científicas (CSIC, Madrid, Spain) to J.D.L.R.; Spanish Ministerio de Ciencia e Innovación (BIO2010-22073) and the European Commission through the FP7 project SyStemAge grant agreement n: 306240 to P.A.; Group-ID Multidisciplinary Research Partnerships of Ghent University, grant FWO-V G.0864.10 from the Fund for Scientific Research-Flanders and ERC Advanced Grant N° 340941 to J.T.; EMBO long-term fellowship to A.K.; Institute Sponsored Research funds from the Dana-Farber Cancer Institute Strategic Initiative to M.V. I.L. is a postdoctoral fellow with the FWO-V. M.V. is a “Chercheur Qualifié Honoraire” from the Fonds de la Recherche Scientifique (FRS-FNRS, Wallonia-Brussels Federation, Belgium). Since performing the work described, C. Fontanillo has become an employee of Celgene Research SL, part of the Celgene Corporation.Peer Reviewe

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

Combined analysis of genome-wide expression and copy number profiles to identify key altered genomic regions in cancer

This is an open access article distributed under the terms of the Creative Commons Attribution License.-- Proceedings of the International Conference of the Brazilian Association for Bioinformatics and Computational Biology (X-meeting 2011).[Background]: Analysis of DNA copy number alterations and gene expression changes in human samples have been used to find potential target genes in complex diseases. Recent studies have combined these two types of data using different strategies, but focusing on finding gene-based relationships. However, it has been proposed that these data can be used to identify key genomic regions, which may enclose causal genes under the assumption that disease-associated gene expression changes are caused by genomic alterations. [Results]: Following this proposal, we undertake a new integrative analysis of genome-wide expression and copy number datasets. The analysis is based on the combined location of both types of signals along the genome. Our approach takes into account the genomic location in the copy number (CN) analysis and also in the gene expression (GE) analysis. To achieve this we apply a segmentation algorithm to both types of data using paired samples. Then, we perform a correlation analysis and a frequency analysis of the gene loci in the segmented CN regions and the segmented GE regions; selecting in both cases the statistically significant loci. In this way, we find CN alterations that show strong correspondence with GE changes. We applied our method to a human dataset of 64 Glioblastoma Multiforme samples finding key loci and hotspots that correspond to major alterations previously described for this type of tumors. [Conclusions]: Identification of key altered genomic loci constitutes a first step to find the genes that drive the alteration in a malignant state. These driver genes can be found in regions that show high correlation in copy number alterations and expression changesThis work has been supported by funds provided by the Local Government Junta de Castilla y León (JCyL, ref. project: CSI07A09), by the Spanish Government (ISCiii, ref. project PS09/00843) and by the European Commission (Research Grant ref. FP7-HEALTH-2007-223411). SA thanks the JCyL and the European Social Fund (ESF-EU) for a research grant.Peer Reviewe

Functional Analysis beyond Enrichment: Non-Redundant Reciprocal Linkage of Genes and Biological Terms

Functional analysis of large sets of genes and proteins is becoming more and more necessary with the increase of experimental biomolecular data at omic-scale. Enrichment analysis is by far the most popular available methodology to derive functional implications of sets of cooperating genes. The problem with these techniques relies in the redundancy of resulting information, that in most cases generate lots of trivial results with high risk to mask the reality of key biological events. We present and describe a computational method, called GeneTerm Linker, that filters and links enriched output data identifying sets of associated genes and terms, producing metagroups of coherent biological significance. The method uses fuzzy reciprocal linkage between genes and terms to unravel their functional convergence and associations. The algorithm is tested with a small set of well known interacting proteins from yeast and with a large collection of reference sets from three heterogeneous resources: multiprotein complexes (CORUM), cellular pathways (SGD) and human diseases (OMIM). Statistical Precision, Recall and balanced F-score are calculated showing robust results, even when different levels of random noise are included in the test sets. Although we could not find an equivalent method, we present a comparative analysis with a widely used method that combines enrichment and functional annotation clustering. A web application to use the method here proposed is provided at http://gtlinker.cnb.csic.es

Unique genetic profile of sporadic colorectal cancer liver metastasis versus primary tumors by SNP-Arrays

Trabajo presentado como póster al XXVI Congreso Nacional de Genética Humana y a la XIX Reunión Anual de la SEGCD celebradas en Murcia del 30 de marzo al 1 de abril del 2011.-- et al.Peer reviewe