@article {384, title = {Genomic expression differences between cutaneous cells from red hair color individuals and black hair color individuals based on bioinformatic analysis.}, journal = {Oncotarget}, volume = {8}, year = {2017}, month = {2017 Feb 14}, pages = {11589-11599}, abstract = {

The MC1R gene plays a crucial role in pigmentation synthesis. Loss-of-function MC1R variants, which impair protein function, are associated with red hair color (RHC) phenotype and increased skin cancer risk. Cultured cutaneous cells bearing loss-of-function MC1R variants show a distinct gene expression profile compared to wild-type MC1R cultured cutaneous cells. We analysed the gene signature associated with RHC co-cultured melanocytes and keratinocytes by Protein-Protein interaction (PPI) network analysis to identify genes related with non-functional MC1R variants. From two detected networks, we selected 23 nodes as hub genes based on topological parameters. Differential expression of hub genes was then evaluated in healthy skin biopsies from RHC and black hair color (BHC) individuals. We also compared gene expression in melanoma tumors from individuals with RHC versus BHC. Gene expression in normal skin from RHC cutaneous cells showed dysregulation in 8 out of 23 hub genes (CLN3, ATG10, WIPI2, SNX2, GABARAPL2, YWHA, PCNA and GBAS). Hub genes did not differ between melanoma tumors in RHC versus BHC individuals. The study suggests that healthy skin cells from RHC individuals present a constitutive genomic deregulation associated with the red hair phenotype and identify novel genes involved in melanocyte biology.

}, keywords = {Adult, Coculture Techniques, Computational Biology, gene expression, Genetic Predisposition to Disease, Genomics, Hair Color, Humans, Keratinocytes, Melanocytes, Middle Aged, Phenotype, Receptor, Melanocortin, Type 1}, issn = {1949-2553}, doi = {10.18632/oncotarget.14140}, url = {http://www.impactjournals.com/oncotarget/index.php?journal=oncotarget\&page=article\&op=view\&path\%5B\%5D=14140\&path\%5B\%5D=45094}, author = {Puig-Butille, Joan Anton and Gimenez-Xavier, Pol and Visconti, Alessia and Nsengimana, J{\'e}r{\'e}mie and Garcia-Garcia, Francisco and Tell-Marti, Gemma and Escamez, Maria Jos{\'e} and Newton-Bishop, Julia and Bataille, Veronique and Del Rio, Marcela and Dopazo, Joaquin and Falchi, Mario and Puig, Susana} } @article {439, title = {Integrated gene set analysis for microRNA studies.}, journal = {Bioinformatics}, volume = {32}, year = {2016}, month = {2016 Sep 15}, pages = {2809-16}, abstract = {

MOTIVATION: Functional interpretation of miRNA expression data is currently done in a three step procedure: select differentially expressed miRNAs, find their target genes, and carry out gene set overrepresentation analysis Nevertheless, major limitations of this approach have already been described at the gene level, while some newer arise in the miRNA scenario.Here, we propose an enhanced methodology that builds on the well-established gene set analysis paradigm. Evidence for differential expression at the miRNA level is transferred to a gene differential inhibition score which is easily interpretable in terms of gene sets or pathways. Such transferred indexes account for the additive effect of several miRNAs targeting the same gene, and also incorporate cancellation effects between cases and controls. Together, these two desirable characteristics allow for more accurate modeling of regulatory processes.

RESULTS: We analyze high-throughput sequencing data from 20 different cancer types and provide exhaustive reports of gene and Gene Ontology-term deregulation by miRNA action.

AVAILABILITY AND IMPLEMENTATION: The proposed methodology was implemented in the Bioconductor library mdgsa http://bioconductor.org/packages/mdgsa For the purpose of reproducibility all of the scripts are available at https://github.com/dmontaner-papers/gsa4mirna

CONTACT: : david.montaner@gmail.com

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

}, keywords = {Computational Biology, Gene Expression Profiling, Gene ontology, Gene Regulatory Networks, High-Throughput Nucleotide Sequencing, Humans, MicroRNAs, Neoplasms, Reproducibility of Results}, issn = {1367-4811}, doi = {10.1093/bioinformatics/btw334}, author = {Garcia-Garcia, Francisco and Panadero, Joaquin and Dopazo, Joaquin and Montaner, David} } @article {590, title = {Direct functional assessment of the composite phenotype through multivariate projection strategies.}, journal = {Genomics}, volume = {92}, year = {2008}, month = {2008 Dec}, pages = {373-83}, abstract = {

We present a novel approach for the analysis of transcriptomics data that integrates functional annotation of gene sets with expression values in a multivariate fashion, and directly assesses the relation of functional features to a multivariate space of response phenotypical variables. Multivariate projection methods are used to obtain new correlated variables for a set of genes that share a given function. These new functional variables are then related to the response variables of interest. The analysis of the principal directions of the multivariate regression allows for the identification of gene function features correlated with the phenotype. Two different transcriptomics studies are used to illustrate the statistical and interpretative aspects of the methodology. We demonstrate the superiority of the proposed method over equivalent approaches.

}, keywords = {Breast Neoplasms, Computational Biology, Databases, Genetic, Female, Gene Expression Profiling, Humans, Mathematical Computing, Multivariate Analysis, Phenotype}, issn = {1089-8646}, doi = {10.1016/j.ygeno.2008.05.015}, author = {Conesa, Ana and Bro, Rasmus and Garcia-Garcia, Francisco and Prats, Jos{\'e} Manuel and G{\"o}tz, Stefan and Kjeldahl, Karin and Montaner, David and Dopazo, Joaquin} }