PROJECT SUMMARY With the advance of genomic technologies including microarray and next generation sequencing (NGS) in recent years, a vast amount of genomic data has been generated. The quality control, statistical analysis and integration/interpretation of multi-dimensional large-scale data are facing new challenges. Bioinformatics, a interdisciplinary field that involves computer science, statistics and biology, is playing pivotal roles in genomic data analysis and integration in biomedical research. The major goals of Bioinformatics Core are 1) to promote the understanding the genomics and molecular mechanism of HIV-associated kidney diseases in human or mouse models and 2) to facilitate the interaction and data sharing among internal and external collaborators. To achieve these goals, we propose the following specific aims: 1) we will provide bioinformatic data analysis on high throughput deep-sequencing/microarray experiments to PPG projects ad statistical analysis to pathology and clinical core. We will be responsible for experimental design, data quality control, statistic and system biology analysis/interpretation of sequence data, transcriptomic and epigenomic profiles using next generation sequencing or microarray technologies; We will be fully engaged in each PPG project/research core that needs bioinformatics support and closely work with researchers and PI on any aspect related to bioinformatics/statistics. 2) We will implement a web-based data portal for data sharing among both internal and external investigators. We will develop a MIAME-compliant centralized database for storage of genomic and phenotypic/ pathological/clinical data from PPG project/core and a user-friendly interface for data query and online visualization; we will also integrate the data portal with LINCS-BD2K system for systematic data analysis to identify potential drug targets. 3) We will develop novel pipelines to facilitate analysis and integration of diverse genomic data from HIV virus and the host and meta-analysis of multiple datasets generated from PPG projects and relevant public datasets. We will identify meta signatures across multiple datasets and meta co-regulated network for better understanding of gene regulation associated with HIV- associated kidney diseases.