Title: Discovering Neoantigens
Abstract:
Cancer immunotherapy is revolutionizing cancer treatment. A key step to enable personalized cancer immunotherapy is to discover neoantigens on the surface of the cancer cells of each patient, sensitively and efficiently. We show how to combine the most advanced mass spectrometry technology and deep learning to discover neoantigens.
Title: Integrated analysis of cancer data and precision medicine
Abstract:
Large biological datasets are currently available, and their analysis has applications to basic science and medicine. While inquiry of each dataset separately often provides insights, integrative analysis may reveal more holistic, systems-level findings. We demonstrate the power of integrated analysis in cancer on two levels: (1) in analysis of one omic in many cancer types together, and (2) in analysis of multiple omics for the same cancer. In both levels we develop novel methods and observe a clear advantage to integration. We also describe a novel method for identifying and ranking driver genes in an individual's tumor and demonstrate its advantage over prior art.
Title: Statistical and Computational Approaches for the Identification of Novel Viruses and Virus-host Interactions
Abstract:
Viruses play important roles in controlling bacterial population size, altering host metabolism, and have broader impacts on the functions of microbial communities, such as human gut, soil, and ocean microbiomes. However, the investigations of viruses and their functions were vastly underdeveloped. Metagenomic studies provide enormous resources for the identifications of novel viruses and their hosts. We recently developed a k-mer based method, VirFinder, for the identification of novel virus contigs in metagenomic samples [1]. Applications to a liver cirrhosis metagenomic data suggest that viruses play important roles in the development of the disease. We also developed an alignment-free statistic, VirHost-Matcher, for the identification of bacterial hosts of viruses [2]. I will present other computational tools for metagenomics including local similarity analysis (LSA) for inferring microbial associations and COCACOLA for contig binning that were recently developed from my lab.
1. J Ren, NA Ahlgren, et al. (2017) Microbiome 5(1):69
2. NA Ahlgren, J Ren, et al. (2017) Nucleic Acids Research 45(1):39-53