@article {1066, title = {A novel locus for a hereditary recurrent neuropathy on chromosome 21q21.}, journal = {Neuromuscular disorders : NMD}, volume = {24}, number = {8}, year = {2014}, month = {2014 May 9}, pages = {660-5}, abstract = {Hereditary recurrent neuropathies are uncommon. Disorders with a known molecular basis falling within this group include hereditary neuropathy with liability to pressure palsies (HNPP) due to the deletion of the PMP22 gene or to mutations in this same gene, and hereditary neuralgic amyotrophy (HNA) caused by mutations in the SEPT9 gene. We report a three-generation family presenting a hereditary recurrent neuropathy without pathological changes in either PMP22 or SEPT9 genes. We performed a genome-wide mapping, which yielded a locus of 12.4Mb on chromosome 21q21. The constructed haplotype fully segregated with the disease and we found significant evidence of linkage. After mutational screening of genes located within this locus, encoding for proteins and microRNAs, as well as analysis of large deletions/insertions, we identified 71 benign polymorphisms. Our findings suggest a novel genetic locus for a recurrent hereditary neuropathy of which the molecular defect remains elusive. Our results further underscore the clinical and genetic heterogeneity of this group of neuropathies.}, issn = {1873-2364}, doi = {10.1016/j.nmd.2014.04.004}, url = {http://www.sciencedirect.com/science/article/pii/S0960896614001060$\#$}, author = {Calpena, E and Mart{\'\i}nez-Rubio, D and Arpa, J and Garc{\'\i}a-Pe{\~n}as, J J and Montaner, D. and Dopazo, J. and Palau, F and Espin{\'o}s, C} } @inbook {680, title = {New Trends in the Analysis of Functional Genomic Data}, booktitle = {Progress in Industrial Mathematics at ECMI 2006}, volume = {12}, year = {2007}, pages = {576-580}, publisher = {Springer}, organization = {Springer}, address = {Berlin}, issn = {978-3-540-71991-5}, doi = {10.1007/978-3-540-71992-2_94}, url = {http://www.springerlink.com/content/m62p07r8111004vr/}, author = {Montaner, D. and Fatima Al-Shahrour and Dopazo, J.} } @article {16845056, title = {Next station in microarray data analysis: GEPAS}, journal = {Nucleic Acids Res}, volume = {34}, year = {2006}, note = {

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{\textquoteright}t England Nucleic acids research Nucleic Acids Res. 2006 Jul 1;34(Web Server issue):W486-91.

}, pages = {W486-91}, abstract = {

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.

}, keywords = {gepas, microarray data analysis}, url = {http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve\&db=PubMed\&dopt=Citation\&list_uids=16845056}, author = {Montaner, D. and Tarraga, J. and Huerta-Cepas, J. and Burguet, J. and Vaquerizas, J. M. and L. Conde and Minguez, P. and Vera, J. and Mukherjee, S. and Valls, J. and Pujana, M. A. and Alloza, E. and Herrero, J. and Fatima Al-Shahrour and Dopazo, J.} } @article {15609347, title = {A novel candidate region linked to development of both pheochromocytoma and head/neck paraganglioma}, journal = {Genes Chromosomes Cancer}, volume = {42}, number = {3}, year = {2005}, note = {Cascon, Alberto Ruiz-Llorente, Sergio Rodriguez-Perales, Sandra Honrado, Emiliano Martinez-Ramirez, Angel Leton, Rocio Montero-Conde, Cristina Benitez, Javier Dopazo, Joaquin Cigudosa, Juan C Robledo, Mercedes Research Support, Non-U.S. Gov{\textquoteright}t United States Genes, chromosomes \& cancer Genes Chromosomes Cancer. 2005 Mar;42(3):260-8.}, pages = {260-8}, abstract = {Although the histologic distinction between pheochromocytomas and head and neck paragangliomas is clear, little is known about the genetic differences between them. To date, various sets of genes have been found to be involved in inherited susceptibility to developing both tumor types, but the genes involved in sporadic pathogenesis are still unknown. To define new candidate regions, we performed CGH analysis on 29 pheochromocytomas and on 24 paragangliomas mainly of head and neck origin (20 of 24), which allowed us to differentiate between the two tumor types. Loss of 3q was significantly more frequent in pheochromocytomas, and loss of 1q appeared only in paragangliomas. We also found gain of 11q13 to be a significantly frequent alteration in malignant cases of both types. In addition, recurrent loss of 8p22-23 was found in 62\% of pheochromocytomas (including all malignant cases) versus in 33\% of paragangliomas, suggesting that this region contains candidate genes involved in the pathogenesis of this abnormality. Using FISH analysis on tissue microarrays, we confirmed genomic deletion of this region in 55\% of pheochromocytomas compared to 12\% of paragangliomas. Loss of 8p22-23 appears to be an important event in the sporadic development of these tumors, and additional molecular studies are necessary to identify candidate genes in this chromosomal region.}, keywords = {80 and over Child Chromosomes, Adolescent Adrenal Gland Neoplasms/*genetics Adult Aged Aged, Biological/*genetics, Human, Pair 1/genetics Chromosomes, Pair 11/genetics Chromosomes, Pair 3/genetics Chromosomes, Pair 8/genetics Female Gene Deletion Head and Neck Neoplasms/*genetics Humans Male Middle Aged Nucleic Acid Hybridization Paraganglioma/*genetics Pheochromocytoma/*genetics Tumor Markers}, url = {http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve\&db=PubMed\&dopt=Citation\&list_uids=15609347}, author = {Cascon, A. and Ruiz-Llorente, S. and Rodriguez-Perales, S. and Honrado, E. and Martinez-Ramirez, A. and Leton, R. and Montero-Conde, C. and Benitez, J. and Dopazo, J. and Cigudosa, J. C. and M. Robledo} } @article {15215434, title = {New challenges in gene expression data analysis and the extended GEPAS}, journal = {Nucleic Acids Res}, volume = {32}, year = {2004}, note = {

Herrero, Javier Vaquerizas, Juan M Al-Shahrour, Fatima Conde, Lucia Mateos, Alvaro Diaz-Uriarte, Javier Santoyo Ramon Dopazo, Joaquin England Nucleic acids research Nucleic Acids Res. 2004 Jul 1;32(Web Server issue):W485-91.

}, pages = {W485-91}, abstract = {

Since the first papers published in the late nineties, including, for the first time, a comprehensive analysis of microarray data, the number of questions that have been addressed through this technique have both increased and diversified. Initially, interest focussed on genes coexpressing across sets of experimental conditions, implying, essentially, the use of clustering techniques. Recently, however, interest has focussed more on finding genes differentially expressed among distinct classes of experiments, or correlated to diverse clinical outcomes, as well as in building predictors. In addition to this, the availability of accurate genomic data and the recent implementation of CGH arrays has made mapping expression and genomic data on the chromosomes possible. There is also a clear demand for methods that allow the automatic transfer of biological information to the results of microarray experiments. Different initiatives, such as the Gene Ontology (GO) consortium, pathways databases, protein functional motifs, etc., provide curated annotations for genes. Whereas many resources on the web focus mainly on clustering methods, GEPAS has evolved to cope with the aforementioned new challenges that have recently arisen in the field of microarray data analysis. The web-based pipeline for microarray gene expression data, GEPAS, is available at http://gepas.bioinfo.cnio.es.

}, keywords = {gepas, microarray data analysis}, url = {http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve\&db=PubMed\&dopt=Citation\&list_uids=15215434}, author = {Herrero, J. and Vaquerizas, J. M. and Fatima Al-Shahrour and L. Conde and A. Mateos and Diaz-Uriarte, J. S. and Dopazo, J.} }