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2015
Ewing AD, Houlahan KE, Hu Y, et al. Combining tumor genome simulation with crowdsourcing to benchmark somatic single-nucleotide-variant detection. participants ICGC-TCGADREAMSoma, Xi L, Dewal N, et al., eds. Nature methods. 2015. doi:10.1038/nmeth.3407.
Porta-Pardo E, García-Alonso L, Hrabe T, Dopazo J, Godzik A. A Pan-Cancer Catalogue of Cancer Driver Protein Interaction Interfaces. PLoS Comput Biol. 2015;11(10):e1004518. doi:10.1371/journal.pcbi.1004518.
Eduati F, Mangravite LM, Wang T, et al. Prediction of human population responses to toxic compounds by a collaborative competition. Nature biotechnology. 2015. doi:10.1038/nbt.3299.
Minguez P, Letunic I, Parca L, et al. PTMcode v2: a resource for functional associations of post-translational modifications within and between proteins. Nucleic Acids Res. 2015;43(Database issue):D494-502. doi:10.1093/nar/gku1081.
2016
Salavert F, Hidago MR, Amadoz A, et al. Actionable pathways: interactive discovery of therapeutic targets using signaling pathway models. Nucleic acids research. 2016. doi:10.1093/nar/gkw369.
Lagarde J, Uszczynska-Ratajczak B, Santoyo-López J, et al. Extension of human lncRNA transcripts by RACE coupled with long-read high-throughput sequencing (RACE-Seq). Nature Communications. 2016;7(1). doi:10.1038/ncomms12339.
Lagarde J, Uszczynska-Ratajczak B, Santoyo-López J, et al. Extension of human lncRNA transcripts by RACE coupled with long-read high-throughput sequencing (RACE-Seq). Nature Communications. 2016;7(1). doi:10.1038/ncomms12339.
Lagarde J, Uszczynska-Ratajczak B, Santoyo-López J, et al. Extension of human lncRNA transcripts by RACE coupled with long-read high-throughput sequencing (RACE-Seq). Nature communications. 2016;7:12339. doi:10.1038/ncomms12339.
Lagarde J, Uszczynska-Ratajczak B, Santoyo-López J, et al. Extension of human lncRNA transcripts by RACE coupled with long-read high-throughput sequencing (RACE-Seq). Nature communications. 2016;7:12339. doi:10.1038/ncomms12339.
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.
Sanchez-Mut JV, Heyn H, Vidal E, et al. Human DNA methylomes of neurodegenerative diseases show common epigenomic patterns. Transl Psychiatry. 2016;6:e718. doi:10.1038/tp.2015.214.
Moschen S, Luoni SBengoa, Di Rienzo JA, et al. Integrating transcriptomic and metabolomic analysis to understand natural leaf senescence in sunflower. Plant Biotechnol J. 2016;14(2):719-34. doi:10.1111/pbi.12422.
Moschen S, Luoni SBengoa, Di Rienzo JA, et al. Integrating transcriptomic and metabolomic analysis to understand natural leaf senescence in sunflower. Plant Biotechnol J. 2016;14(2):719-34. doi:10.1111/pbi.12422.
Moschen S, Luoni SBengoa, Di Rienzo JA, et al. Integrating transcriptomic and metabolomic analysis to understand natural leaf senescence in sunflower. Plant Biotechnol J. 2016;14(2):719-34. doi:10.1111/pbi.12422.
Moschen S, Luoni SBengoa, Di Rienzo JA, et al. Integrating transcriptomic and metabolomic analysis to understand natural leaf senescence in sunflower. Plant Biotechnol J. 2016;14(2):719-34. doi:10.1111/pbi.12422.
Hillung J, Garcia-Garcia F, Dopazo J, Cuevas JM, Elena SF. The transcriptomics of an experimentally evolved plant-virus interaction. Sci Rep. 2016;6:24901. doi:10.1038/srep24901.
Lucariello M, Vidal E, Vidal S, et al. Whole exome sequencing of Rett syndrome-like patients reveals the mutational diversity of the clinical phenotype. Hum Genet. 2016;135(12):1343-1354. doi:10.1007/s00439-016-1721-3.
2017
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.
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.
Moschen S, Di Rienzo JA, Higgins J, et al. Integration of transcriptomic and metabolic data reveals hub transcription factors involved in drought stress response in sunflower (Helianthus annuus L.). Plant Mol Biol. 2017;94(4-5):549-564. doi:10.1007/s11103-017-0625-5.
Moschen S, Di Rienzo JA, Higgins J, et al. Integration of transcriptomic and metabolic data reveals hub transcription factors involved in drought stress response in sunflower (Helianthus annuus L.). Plant Mol Biol. 2017;94(4-5):549-564. doi:10.1007/s11103-017-0625-5.
Moschen S, Di Rienzo JA, Higgins J, et al. Integration of transcriptomic and metabolic data reveals hub transcription factors involved in drought stress response in sunflower (Helianthus annuus L.). Plant Mol Biol. 2017;94(4-5):549-564. doi:10.1007/s11103-017-0625-5.
Moschen S, Di Rienzo JA, Higgins J, et al. Integration of transcriptomic and metabolic data reveals hub transcription factors involved in drought stress response in sunflower (Helianthus annuus L.). Plant Mol Biol. 2017;94(4-5):549-564. doi:10.1007/s11103-017-0625-5.
Carbonell-Caballero J, Amadoz A, Alonso R, et al. Reference genome assessment from a population scale perspective: an accurate profile of variability and noise. Bioinformatics. 2017;33(22):3511-3517. doi:10.1093/bioinformatics/btx482.
Gui H, Schriemer D, Cheng WW, et al. Whole exome sequencing coupled with unbiased functional analysis reveals new Hirschsprung disease genes. Genome biology. 2017;18:48. doi:10.1186/s13059-017-1174-6.