%A Le Clerc,Sigrid %A Limou,Sophie %A Zagury,Jean-François %D 2019 %J Frontiers in Genetics %C %F %G English %K HIV - human immunodeficiency virus,AIDS - acquired immunodeficiency syndrome,GWAS - genome-wide association study,Genomics,big data,Omics and Big Data Analysis,trasncriptomics,siRNA - small interfering RNA,methylome analysis %Q %R 10.3389/fgene.2019.00799 %W %L %M %P %7 %8 2019-September-13 %9 Review %+ Sophie Limou,Centre de Recherche en Transplantation et Immunologie UMR1064, INSERM, Université de Nantes,France,sophie.limou@univ-nantes.fr %+ Sophie Limou,Institut de Transplantation en Urologie et Néphrologie (ITUN), CHU de Nantes,France,sophie.limou@univ-nantes.fr %+ Sophie Limou,Computer Sciences and Mathematics Department, Ecole Centrale de Nantes,France,sophie.limou@univ-nantes.fr %+ Jean-François Zagury,Laboratoire GBCM, EA7528, Conservatoire National des Arts et Métiers, HESAM Université,France,sophie.limou@univ-nantes.fr %# %! OMICS STUDIES IN AIDS %* %< %T Large-Scale “OMICS” Studies to Explore the Physiopatholgy of HIV-1 Infection %U https://www.frontiersin.org/articles/10.3389/fgene.2019.00799 %V 10 %0 JOURNAL ARTICLE %@ 1664-8021 %X In this review, we present the main large-scale experimental studies that have been performed in the HIV/AIDS field. These “omics” studies are based on several technologies including genotyping, RNA interference, and transcriptome or epigenome analysis. Due to the direct connection with disease evolution, there has been a large focus on genotyping cohorts of well-characterized patients through genome-wide association studies (GWASs), but there have also been several invitro studies such as small interfering RNA (siRNA) interference or transcriptome analyses of HIV-1–infected cells. After describing the major results obtained with these omics technologies—including some with a high relevance for HIV-1 treatment—we discuss the next steps that the community needs to embrace in order to derive new actionable therapeutic or diagnostic targets. Only integrative approaches that combine all big data results and consider their complex interactions will allow us to capture the global picture of HIV molecular pathogenesis. This novel challenge will require large collaborative efforts and represents a huge open field for innovative bioinformatics approaches.