@article {1077, title = {A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequencing Quality Control Consortium.}, journal = {Nature biotechnology}, volume = {32}, year = {2014}, month = {2014 Aug 24}, pages = {903{\textendash}914}, abstract = {We present primary results from the Sequencing Quality Control (SEQC) project, coordinated by the US Food and Drug Administration. Examining Illumina HiSeq, Life Technologies SOLiD and Roche 454 platforms at multiple laboratory sites using reference RNA samples with built-in controls, we assess RNA sequencing (RNA-seq) performance for junction discovery and differential expression profiling and compare it to microarray and quantitative PCR (qPCR) data using complementary metrics. At all sequencing depths, we discover unannotated exon-exon junctions, with >80\% validated by qPCR. We find that measurements of relative expression are accurate and reproducible across sites and platforms if specific filters are used. In contrast, RNA-seq and microarrays do not provide accurate absolute measurements, and gene-specific biases are observed for all examined platforms, including qPCR. Measurement performance depends on the platform and data analysis pipeline, and variation is large for transcript-level profiling. The complete SEQC data sets, comprising >100 billion reads (10Tb), provide unique resources for evaluating RNA-seq analyses for clinical and regulatory settings.}, keywords = {NGS, RNA-seq, SEQC}, issn = {1546-1696}, doi = {10.1038/nbt.2957}, url = {http://www.nature.com/nbt/journal/vaop/ncurrent/full/nbt.2957.html}, author = {Su, Z. and Labaj, P.P. and .... and Dopazo, J. and .... and Mason, C.E. and Shi, L} } @article {17597009, title = {CLEAR-test: combining inference for differential expression and variability in microarray data analysis}, journal = {J Biomed Inform}, volume = {41}, number = {1}, year = {2008}, note = {

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{\textquoteright}t United States Journal of biomedical informatics J Biomed Inform. 2008 Feb;41(1):33-45. Epub 2007 May 17.

}, pages = {33-45}, abstract = {

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.

}, keywords = {*Algorithms Artificial Intelligence *Data Interpretation, Statistical Gene Expression Profiling/*methods Gene Expression Regulation/*physiology Oligonucleotide Array Sequence Analysis/*methods Proteome/*metabolism Signal Transduction/*physiology}, url = {http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve\&db=PubMed\&dopt=Citation\&list_uids=17597009}, author = {Valls, J. and Grau, M. and Sole, X. and Hernandez, P. and Montaner, D. and Dopazo, J. and Peinado, M. A. and Capella, G. and Moreno, V. and Pujana, M. A.} } @article {18697870, title = {Controlled ovarian stimulation induces a functional genomic delay of the endometrium with potential clinical implications}, journal = {J Clin Endocrinol Metab}, volume = {93}, number = {11}, year = {2008}, note = {

Horcajadas, Jose A Minguez, Pablo Dopazo, Joaquin Esteban, Francisco J Dominguez, Francisco Giudice, Linda C Pellicer, Antonio Simon, Carlos Research Support, Non-U.S. Gov{\textquoteright}t United States The Journal of clinical endocrinology and metabolism J Clin Endocrinol Metab. 2008 Nov;93(11):4500-10. Epub 2008 Aug 12.

}, pages = {4500-10}, abstract = {

CONTEXT: Controlled ovarian stimulation induces morphological, biochemical, and functional genomic modifications of the human endometrium during the window of implantation. OBJECTIVE: Our objective was to compare the gene expression profile of the human endometrium in natural vs. controlled ovarian stimulation cycles throughout the early-mid secretory transition using microarray technology. METHOD: Microarray data from 49 endometrial biopsies obtained from LH+1 to LH+9 (n=25) in natural cycles and from human chorionic gonadotropin (hCG) +1 to hCG+9 in controlled ovarian stimulation cycles (n=24) were analyzed using different methods, such as clustering, profiling of biological processes, and selection of differentially expressed genes, as implemented in Gene Expression Pattern Analysis Suite and Babelomics programs. RESULTS: Endometria from natural cycles followed different genomic patterns compared with controlled ovarian stimulation cycles in the transition from the pre-receptive (days LH/hCG+1 until LH/hCG+5) to the receptive phase (day LH+7/hCG+7). Specifically, we have demonstrated the existence of a 2-d delay in the activation/repression of two clusters composed by 218 and 133 genes, respectively, on day hCG+7 vs. LH+7. Many of these delayed genes belong to the class window of implantation genes affecting basic biological processes in the receptive endometrium. CONCLUSIONS: These results demonstrate that gene expression profiling of the endometrium is different between natural and controlled ovarian stimulation cycles in the receptive phase. Identification of these differentially regulated genes can be used to understand the different developmental profiles of receptive endometrium during controlled ovarian stimulation and to search for the best controlled ovarian stimulation treatment in terms of minimal endometrial impact.

}, keywords = {Algorithms Chorionic Gonadotropin/genetics Endometrium/cytology/pathology/*physiology/physiopathology Female Gene Expression Regulation Genome, Human Glutathione Peroxidase/genetics Humans Insulin-Like Growth Factor Binding Proteins/genetics Luteal Phase/physiology Luteinizing Hormone/genetics Menstrual Cycle Oligonucleotide Array Sequence Analysis Ovulation Induction/*methods RNA/genetics/isola}, url = {http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve\&db=PubMed\&dopt=Citation\&list_uids=18697870}, author = {Horcajadas, J. A. and Minguez, P. and Dopazo, J. and Esteban, F. J. and Dominguez, F. and Giudice, L. C. and Pellicer, A. and Simon, C.} } @inbook {477, title = {Clustering - Class discovery in the post-genomic era}, booktitle = {Fundamentals of data mining in genomics and proteomics}, year = {2007}, publisher = {Springer-Verlag, W. Dubitzky, M. Granzow and D.P. Berrar}, organization = {Springer-Verlag, W. Dubitzky, M. Granzow and D.P. Berrar}, address = {New York, USA}, author = {Dopazo, J.} } @article {12695324, title = {Comparing bacterial genomes through conservation profiles}, journal = {Genome Res}, volume = {13}, number = {5}, year = {2003}, note = {Martin, Maria J Herrero, Javier Mateos, Alvaro Dopazo, Joaquin Comparative Study United States Genome research Genome Res. 2003 May;13(5):991-8. Epub 2003 Apr 14.}, pages = {991-8}, abstract = {We constructed two-dimensional representations of profiles of gene conservation across different genomes using the genome of Escherichia coli as a model. These profiles permit both the visualization at the genome level of different traits in the organism studied and, at the same time, reveal features related to the genomes analyzed (such as defective genomes or genomes that lack a particular system). Conserved genes are not uniformly distributed along the E. coli genome but tend to cluster together. The study of gene distribution patterns across genomes is important for the understanding of how sets of genes seem to be dependent on each other, probably having some functional link. This provides additional evidence that can be used for the elucidation of the function of unannotated genes. Clustering these patterns produces families of genes which can be arranged in a hierarchy of closeness. In this way, functions can be defined at different levels of generality depending on the level of the hierarchy that is studied. The combined study of conservation and phenotypic traits opens up the possibility of defining phenotype/genotype associations, and ultimately inferring the gene or genes responsible for a particular trait.}, keywords = {Bacterial Genotype Models, Bacterial/genetics Cluster Analysis Conserved Sequence/*genetics DNA, Bacterial/genetics Escherichia coli/classification/*genetics Evolution, Bacterial/genetics Gene Order/genetics Genes, Bacterial/genetics/physiology *Genome, Chromosome Mapping/methods Chromosomes, Genetic Phenotype Phylogeny Sequence Homology, Molecular Gene Expression Profiling/methods Gene Expression Regulation, Nucleic Acid Species Specificity Terminology as Topic}, url = {http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve\&db=PubMed\&dopt=Citation\&list_uids=12695324}, author = {Martin, M. J. and Herrero, J. and A. Mateos and Dopazo, J.} } @article {12645919, title = {Combining hierarchical clustering and self-organizing maps for exploratory analysis of gene expression patterns}, journal = {J Proteome Res}, volume = {1}, number = {5}, year = {2002}, note = {Herrero, Javier Dopazo, Joaquin Research Support, Non-U.S. Gov{\textquoteright}t United States Journal of proteome research J Proteome Res. 2002 Sep-Oct;1(5):467-70.}, pages = {467-70}, abstract = {Self-organizing maps (SOM) constitute an alternative to classical clustering methods because of its linear run times and superior performance to deal with noisy data. Nevertheless, the clustering obtained with SOM is dependent on the relative sizes of the clusters. Here, we show how the combination of SOM with hierarchical clustering methods constitutes an excellent tool for exploratory analysis of massive data like DNA microarray expression patterns.}, keywords = {Cluster Analysis Computational Biology/methods *Gene Expression Genes, Fungal/genetics *Genome Oligonucleotide Array Sequence Analysis/*methods Statistics as Topic/*methods Time Factors}, url = {http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve\&db=PubMed\&dopt=Citation\&list_uids=12645919}, author = {Herrero, J. and Dopazo, J.} }