<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bonifaci, N.</style></author><author><style face="normal" font="default" size="100%">Berenguer, A.</style></author><author><style face="normal" font="default" size="100%">Diez, J.</style></author><author><style face="normal" font="default" size="100%">Reina, O.</style></author><author><style face="normal" font="default" size="100%">Medina, Ignacio</style></author><author><style face="normal" font="default" size="100%">Dopazo, J.</style></author><author><style face="normal" font="default" size="100%">Moreno, V.</style></author><author><style face="normal" font="default" size="100%">Pujana, M. A.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Biological processes, properties and molecular wiring diagrams of candidate low-penetrance breast cancer susceptibility genes</style></title><secondary-title><style face="normal" font="default" size="100%">BMC Med Genomics</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">gene set</style></keyword><keyword><style  face="normal" font="default" size="100%">GWAS</style></keyword><keyword><style  face="normal" font="default" size="100%">SNP</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Citation&amp;list_uids=19094230</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">1</style></volume><pages><style face="normal" font="default" size="100%">62</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;ABSTRACT: BACKGROUND: Recent advances in whole-genome association studies (WGASs) for human cancer risk are beginning to provide the part lists of low-penetrance susceptibility genes. However, statistical analysis in these studies is complicated by the vast number of genetic variants examined and the weak effects observed, as a result of which constraints must be incorporated into the study design and analytical approach. In this scenario, biological attributes beyond the adjusted statistics generally receive little attention and, more importantly, the fundamental biological characteristics of low-penetrance susceptibility genes have yet to be determined. METHODS: We applied an integrative approach for identifying candidate low-penetrance breast cancer susceptibility genes, their characteristics and molecular networks through the analysis of diverse sources of biological evidence. RESULTS: First, examination of the distribution of Gene Ontology terms in ordered WGAS results identified asymmetrical distribution of Cell Communication and Cell Death processes linked to risk. Second, analysis of 11 different types of molecular or functional relationships in genomic and proteomic data sets defined the &amp;quot;omic&amp;quot; properties of candidate genes: i/ differential expression in tumors relative to normal tissue; ii/ somatic genomic copy number changes correlating with gene expression levels; iii/ differentially expressed across age at diagnosis; and iv/ expression changes after BRCA1 perturbation. Finally, network modeling of the effects of variants on germline gene expression showed higher connectivity than expected by chance between novel candidates and with known susceptibility genes, which supports functional relationships and provides mechanistic hypotheses of risk. CONCLUSION: This study proposes that cell communication and cell death are major biological processes perturbed in risk of breast cancer conferred by low-penetrance variants, and defines the common omic properties, molecular interactions and possible functional effects of candidate genes and proteins.&lt;/p&gt;</style></abstract><notes><style face="normal" font="default" size="100%">&lt;p&gt;Bonifaci, Nuria Berenguer, Antoni Diez, Javier Reina, Oscar Medina, Ignacio Dopazo, Joaquin Moreno, Victor Pujana, Miguel Angel England BMC medical genomics BMC Med Genomics. 2008 Dec 18;1:62.&lt;/p&gt;</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Valls, J.</style></author><author><style face="normal" font="default" size="100%">Grau, M.</style></author><author><style face="normal" font="default" size="100%">Sole, X.</style></author><author><style face="normal" font="default" size="100%">Hernandez, P.</style></author><author><style face="normal" font="default" size="100%">Montaner, D.</style></author><author><style face="normal" font="default" size="100%">Dopazo, J.</style></author><author><style face="normal" font="default" size="100%">Peinado, M. A.</style></author><author><style face="normal" font="default" size="100%">Capella, G.</style></author><author><style face="normal" font="default" size="100%">Moreno, V.</style></author><author><style face="normal" font="default" size="100%">Pujana, M. A.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">CLEAR-test: combining inference for differential expression and variability in microarray data analysis</style></title><secondary-title><style face="normal" font="default" size="100%">J Biomed Inform</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">*Algorithms Artificial Intelligence *Data Interpretation</style></keyword><keyword><style  face="normal" font="default" size="100%">Statistical Gene Expression Profiling/*methods Gene Expression Regulation/*physiology Oligonucleotide Array Sequence Analysis/*methods Proteome/*metabolism Signal Transduction/*physiology</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Citation&amp;list_uids=17597009</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">1</style></number><volume><style face="normal" font="default" size="100%">41</style></volume><pages><style face="normal" font="default" size="100%">33-45</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;A common goal of microarray experiments is to detect genes that are differentially expressed under distinct experimental conditions. Several statistical tests have been proposed to determine whether the observed changes in gene expression are significant. The t-test assigns a score to each gene on the basis of changes in its expression relative to its estimated variability, in such a way that genes with a higher score (in absolute values) are more likely to be significant. Most variants of the t-test use the complete set of genes to influence the variance estimate for each single gene. However, no inference is made in terms of the variability itself. Here, we highlight the problem of low observed variances in the t-test, when genes with relatively small changes are declared differentially expressed. Alternatively, the z-test could be used although, unlike the t-test, it can declare differentially expressed genes with high observed variances. To overcome this, we propose to combine the z-test, which focuses on large changes, with a chi(2) test to evaluate variability. We call this procedure CLEAR-test and we provide a combined p-value that offers a compromise between both aspects. Analysis of three publicly available microarray datasets reveals the greater performance of the CLEAR-test relative to the t-test and alternative methods. Finally, empirical and simulated data analyses demonstrate the greater reproducibility and statistical power of the CLEAR-test and z-test with respect to current alternative methods. In addition, the CLEAR-test improves the z-test by capturing reproducible genes with high variability.&lt;/p&gt;</style></abstract><notes><style face="normal" font="default" size="100%">&lt;p&gt;Valls, Joan Grau, Monica Sole, Xavier Hernandez, Pilar Montaner, David Dopazo, Joaquin Peinado, Miguel A Capella, Gabriel Moreno, Victor Pujana, Miguel Angel Comparative Study Research Support, Non-U.S. Gov’t United States Journal of biomedical informatics J Biomed Inform. 2008 Feb;41(1):33-45. Epub 2007 May 17.&lt;/p&gt;</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Hernandez, P.</style></author><author><style face="normal" font="default" size="100%">Huerta-Cepas, J.</style></author><author><style face="normal" font="default" size="100%">Montaner, D.</style></author><author><style face="normal" font="default" size="100%">Fatima Al-Shahrour</style></author><author><style face="normal" font="default" size="100%">Valls, J.</style></author><author><style face="normal" font="default" size="100%">Gomez, L.</style></author><author><style face="normal" font="default" size="100%">Capella, G.</style></author><author><style face="normal" font="default" size="100%">Dopazo, J.</style></author><author><style face="normal" font="default" size="100%">Pujana, M. A.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Evidence for systems-level molecular mechanisms of tumorigenesis</style></title><secondary-title><style face="normal" font="default" size="100%">BMC Genomics</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">*Cell Transformation</style></keyword><keyword><style  face="normal" font="default" size="100%">Biological Models</style></keyword><keyword><style  face="normal" font="default" size="100%">Genetic Models</style></keyword><keyword><style  face="normal" font="default" size="100%">Messenger/metabolism Signal Transduction Systems Biology</style></keyword><keyword><style  face="normal" font="default" size="100%">Neoplastic *Gene Expression Profiling *Gene Expression Regulation</style></keyword><keyword><style  face="normal" font="default" size="100%">Neoplastic Humans Male Models</style></keyword><keyword><style  face="normal" font="default" size="100%">Statistical Neoplasm Proteins/*physiology Neoplasms/etiology/*genetics Prostatic Neoplasms/genetics Protein Interaction Mapping RNA</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Citation&amp;list_uids=17584915</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">8</style></volume><pages><style face="normal" font="default" size="100%">185</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">BACKGROUND: Cancer arises from the consecutive acquisition of genetic alterations. Increasing evidence suggests that as a consequence of these alterations, molecular interactions are reprogrammed in the context of highly connected and regulated cellular networks. Coordinated reprogramming would allow the cell to acquire the capabilities for malignant growth. RESULTS: Here, we determine the coordinated function of cancer gene products (i.e., proteins encoded by differentially expressed genes in tumors relative to healthy tissue counterparts, hereafter referred to as &quot;CGPs&quot;) defined as their topological properties and organization in the interactome network. We show that CGPs are central to information exchange and propagation and that they are specifically organized to promote tumorigenesis. Centrality is identified by both local (degree) and global (betweenness and closeness) measures, and systematically appears in down-regulated CGPs. Up-regulated CGPs do not consistently exhibit centrality, but both types of cancer products determine the overall integrity of the network structure. In addition to centrality, down-regulated CGPs show topological association that correlates with common biological processes and pathways involved in tumorigenesis. CONCLUSION: Given the current limited coverage of the human interactome, this study proposes that tumorigenesis takes place in a specific and organized way at the molecular systems-level and suggests a model that comprises the precise down-regulation of groups of topologically-associated proteins involved in particular functions, orchestrated with the up-regulation of specific proteins.</style></abstract><notes><style face="normal" font="default" size="100%">Hernandez, Pilar Huerta-Cepas, Jaime Montaner, David Al-Shahrour, Fatima Valls, Joan Gomez, Laia Capella, Gabriel Dopazo, Joaquin Pujana, Miguel Angel Research Support, Non-U.S. Gov’t England BMC genomics BMC Genomics. 2007 Jun 20;8:185.</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Montaner, D.</style></author><author><style face="normal" font="default" size="100%">Tarraga, J.</style></author><author><style face="normal" font="default" size="100%">Huerta-Cepas, J.</style></author><author><style face="normal" font="default" size="100%">Burguet, J.</style></author><author><style face="normal" font="default" size="100%">Vaquerizas, J. M.</style></author><author><style face="normal" font="default" size="100%">L. Conde</style></author><author><style face="normal" font="default" size="100%">Minguez, P.</style></author><author><style face="normal" font="default" size="100%">Vera, J.</style></author><author><style face="normal" font="default" size="100%">Mukherjee, S.</style></author><author><style face="normal" font="default" size="100%">Valls, J.</style></author><author><style face="normal" font="default" size="100%">Pujana, M. A.</style></author><author><style face="normal" font="default" size="100%">Alloza, E.</style></author><author><style face="normal" font="default" size="100%">Herrero, J.</style></author><author><style face="normal" font="default" size="100%">Fatima Al-Shahrour</style></author><author><style face="normal" font="default" size="100%">Dopazo, J.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Next station in microarray data analysis: GEPAS</style></title><secondary-title><style face="normal" font="default" size="100%">Nucleic Acids Res</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">gepas</style></keyword><keyword><style  face="normal" font="default" size="100%">microarray data analysis</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2006</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Citation&amp;list_uids=16845056</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">34</style></volume><pages><style face="normal" font="default" size="100%">W486-91</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The Gene Expression Profile Analysis Suite (GEPAS) has been running for more than four years. During this time it has evolved to keep pace with the new interests and trends in the still changing world of microarray data analysis. GEPAS has been designed to provide an intuitive although powerful web-based interface that offers diverse analysis options from the early step of preprocessing (normalization of Affymetrix and two-colour microarray experiments and other preprocessing options), to the final step of the functional annotation of the experiment (using Gene Ontology, pathways, PubMed abstracts etc.), and include different possibilities for clustering, gene selection, class prediction and array-comparative genomic hybridization management. GEPAS is extensively used by researchers of many countries and its records indicate an average usage rate of 400 experiments per day. The web-based pipeline for microarray gene expression data, GEPAS, is available at http://www.gepas.org.&lt;/p&gt;</style></abstract><notes><style face="normal" font="default" size="100%">&lt;p&gt;Montaner, David Tarraga, Joaquin Huerta-Cepas, Jaime Burguet, Jordi Vaquerizas, Juan M Conde, Lucia Minguez, Pablo Vera, Javier Mukherjee, Sach Valls, Joan Pujana, Miguel A G Alloza, Eva Herrero, Javier Al-Shahrour, Fatima Dopazo, Joaquin Research Support, Non-U.S. Gov’t England Nucleic acids research Nucleic Acids Res. 2006 Jul 1;34(Web Server issue):W486-91.&lt;/p&gt;</style></notes></record></records></xml>