Core C: Integrated Cancer and Aging Bioinformatics

项目总结-核心c

PROJECT SUMMARY – CORE C Understanding aging in the liver and the role of aging in liver cancer development will require the collection, QC, analysis, and integration of a large collection of high-throughput datasets. The Integrative Aging and Cancer Bioinformatics Core, Core C, will achieve this goal by offering three key services for PPG members. The first key service will establish the infrastructure needed for day-to-day activities. This will include the hiring and training of two dedicated PhD-level bioinformatics analysts that will work closely with PPG members on all aspects of their projects from data curation and analysis to custom tool and pipeline implementation, data presentation, and scientific writing. In addition to key personnel, the core will also obtain the hardware needed to store data and bioinformatic pipelines required to answer specific questions. The final aspect of this service is to work with Core A to communicate and make available data and analyses to PPG members. The second key service will be to perform state-of-the-art focused analyses on PPG member data which will include performing data quality control, generation of cleaned quantified datasets, identifying features that are changing, such as genes or pathways central to liver aging and tumor development, and compiling the analyses into visual presentations that can be disseminated and shared with PPG members. An important part of this second key service will be to educate and train PPG members on the interpretation and limitations of the analyses and to provide access to shared servers and educational resources and trainings that would enable PPG members and trainees to perform some of their own analyses and become more intimately acquainted with their data. The final crucial service will be the integration of these datasets into a holistic view of aging and tumor development in the liver. This will include integrating gene expression, protein expression, and metabolite abundance measures using network-based and statistical approaches with the goal of identifying molecular mechanisms changing with age. Also, single-cell assays will be combined to reveal not only changes in gene expression, but also spatial organization, T-cell receptor diversity, and chromatin accessibility of individual cells in livers. Finally, machine learning approaches will be used to combine datasets into predictive models of tumor formation and possible effects of preventative interventions on tumor formation.