Publications

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Journal Article
Götz S, García-Gómez JMiguel, Terol J, et al. High-throughput functional annotation and data mining with the Blast2GO suite. Nucleic Acids Res. 2008;36(10):3420-35. doi:10.1093/nar/gkn176.
Santoyo J, Vaquerizas JM, Dopazo J. Highly specific and accurate selection of siRNAs for high-throughput functional assays. Bioinformatics. 2005;21:1376-82. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=15591357.
Medina I, Tárraga J, Martínez H, et al. Highly sensitive and ultrafast read mapping for RNA-seq analysis. DNA Res. 2016;23(2):93-100. doi:10.1093/dnares/dsv039.
Ortuno FM, Loucera C, Casimiro-Soriguer CS, et al. Highly accurate whole-genome imputation of SARS-CoV-2 from partial or low-quality sequences. Gigascience. 2021;10(12). doi:10.1093/gigascience/giab078.
Hidalgo MR, Cubuk C, Amadoz A, Salavert F, Carbonell-Caballero J, Dopazo J. High throughput estimation of functional cell activities reveals disease mechanisms and predicts relevant clinical outcomes. Oncotarget. 2017;8(3):5160-5178. doi:10.18632/oncotarget.14107.
Herrero J, Valencia A, Dopazo J. A hierarchical unsupervised growing neural network for clustering gene expression patterns. Bioinformatics. 2001;17:126-36. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11238068.
Lopez J, Coll J, Haimel M, et al. HGVA: the Human Genome Variation Archive. Nucleic Acids Res. 2017;45(W1):W189-W194. doi:10.1093/nar/gkx445.
Hoffmann R, Dopazo J, Cigudosa JC, Valencia A. HCAD, closing the gap between breakpoints and genes. Nucleic Acids Res. 2005;33:D511-3. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=15608250.
Dopazo J, Erten C. Graph-theoretical comparison of normal and tumor networks in identifying BRCA genes. BMC Systems Biology. 2017;11(1). doi:10.1186/s12918-017-0495-0.
Dopazo J, Erten C. Graph-theoretical comparison of normal and tumor networks in identifying BRCA genes. BMC Syst Biol. 2017;11(1):110. doi:10.1186/s12918-017-0495-0.
Sánchez-Tena S, Lizarraga D, Miranda A, et al. Grape antioxidant dietary fiber inhibits intestinal polyposis in ApcMin/+ mice: relation to cell cycle and immune response. Carcinogenesis. 2013;34(8):1881-8. doi:10.1093/carcin/bgt140.
Sánchez-Tena S, Lizarraga D, Miranda A, et al. Grape antioxidant dietary fiber (GADF) inhibits intestinal polyposis in ApcMin/+ mice: relation to cell cycle and immune response. Carcinogenesis. 2013. doi:10.1093/carcin/bgt140.
Gil-Ibañez P, Garcia-Garcia F, Dopazo J, Bernal J, Morte B. Global Transcriptome Analysis of Primary Cerebrocortical Cells: Identification of Genes Regulated by Triiodothyronine in Specific Cell Types. Cereb Cortex. 2017;27(1):706-717. doi:10.1093/cercor/bhv273.
Roca-Ayats N, Balcells S, Garcia-Giralt N, et al. GGPS1 Mutation and Atypical Femoral Fractures with Bisphosphonates. N Engl J Med. 2017;376(18):1794-1795. doi:10.1056/NEJMc1612804.PDF icon Roca-Ayats-2017NEJM - GGPS1 Mutation and Atypical Femoral Fractures with Bisphosphonates.pdf (214.03 KB)
Vaquerizas JM, Conde L, Yankilevich P, et al. GEPAS, an experiment-oriented pipeline for the analysis of microarray gene expression data. Nucleic Acids Res. 2005;33:W616-20. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=15980548.
Tárraga J, Medina I, Carbonell J, et al. GEPAS, a web-based tool for microarray data analysis and interpretation. Nucleic Acids Res. 2008;36(Web Server issue):W308-14. doi:10.1093/nar/gkn303.
Tarraga J, Medina I, Carbonell J, et al. GEPAS, a web-based tool for microarray data analysis and interpretation. Nucleic Acids Res. 2008;36:W308-14. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=18508806.
Herrero J, Al-Shahrour F, Diaz-Uriarte R, et al. GEPAS: A web-based resource for microarray gene expression data analysis. Nucleic Acids Res. 2003;31:3461-7. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=12824345.
Fernández-Palacios P, Galán-Sánchez F, Casimiro-Soriguer CS, et al. Genotypic characterization and antimicrobial susceptibility of human isolates in Southern Spain. Microbiol Spectr. 2024;12(10):e0102824. doi:10.1128/spectrum.01028-24.
Wu GAlbert, Terol J, Ibañez V, et al. Genomics of the origin and evolution of Citrus. Nature. 2018;554(7692):311-316. doi:10.1038/nature25447.
Dopazo J. Genomics and transcriptomics in drug discovery. Drug discovery today. 2014;19:126-32. doi:10.1016/j.drudis.2013.06.003.
Puig-Butille JAnton, Gimenez-Xavier P, Visconti A, et al. Genomic expression differences between cutaneous cells from red hair color individuals and black hair color individuals based on bioinformatic analysis. Oncotarget. 2017;8(7):11589-11599. doi:10.18632/oncotarget.14140.
Arbiza L, Patricio M, Dopazo H, Posada D. Genome-wide heterogeneity of nucleotide substitution model fit. Genome biology and evolution. 2011;3:896-908.
Villalba-Benito L, López-López D, Torroglosa A, et al. Genome-wide analysis of DNA methylation in Hirschsprung enteric precursor cells: unraveling the epigenetic landscape of enteric nervous system developmentAbstractBackgroundResultsConclusionsGraphic abstract. Clinical Epigenetics. 2021;13(1). doi:10.1186/s13148-021-01040-6.
Rian K, Hidalgo MR, Cubuk C, et al. Genome-scale mechanistic modeling of signaling pathways made easy: A bioconductor/cytoscape/web server framework for the analysis of omic data. Computational and Structural Biotechnology Journal. 2021;19:2968 - 2978. doi:10.1016/j.csbj.2021.05.022.