Systems Biology, Bioinformatics, & Data Integration

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Genome-wide research strategies provide unprecedented opportunities for insight but also major bioinformatic challenges due to the size and complexity of the data. The multidisciplinary research in this TBRU utilizes cutting-edge research methods that utilize a broad spectrum of ‘omics platforms, including proteomics, genomics (RNA-seq), genome-wide association studies (GWAS) of the host and pathogen, cellular experimental screens with host and pathogen data, and targeted model organism experiments. Integration of these datasets and research strategies requires innovative approaches to mechanistically examine how Mtb and host genetic variants modulate TB pathogenesis. Core B uses pathway-driven and cutting-edge bioinformatics approaches to integrate the genetic results from Core A with multiple large-scale and diverse datasets from each project (proteomics, Path-Seq, RNAseq) to dynamically identify and prioritize pathways and protein networks for functional testing. While each of these experiments are analyzed individually within each project, the results have potential for greater insight beyond each dataset. Core B represents a key source of synergy as data will flow between all the Projects and Cores and will generate models leading to targeted experiments with an iterative analytic and hypothesis testing process. This Core will bring together expertise across the Projects for the different ‘omic platforms as well as bioinformatic strategies for data integration. Aim 1 provides integrated analyses of the diverse datasets from Core A and each Project. Aim 2 utilizes network propagation, a systems biology method applied to diverse disease areas, which uses networks to identify convergent pathways highlighted by distinct omics-level datasets. This method is useful when the individual gene overlap between studies is poor, while genes from distinct studies do possess pathway/functional overlap with one another. Here we apply it to study phenotypic variation in human TB and use it as an independent method to extract insights and new disease gene targets from the diverse and complex datasets of this consortium. The overall goal is to generate models from data integration that prioritize research directions across the Projects and Core A and create testable mechanistic hypotheses that are iteratively assessed between Core B and all TBRU components.