Bayesian Evolution-Aware Methods for tumor single cell sequences

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Project Summary/Abstract Tumor research has entered an exciting phase with the advent of single cell sequencing techniques, which have the potential to revolutionize the profiling of genome variation at the highest cellular resolutions. However, the state-of-the-art single-cell sequencing technologies produce data with too many uncertainties and errors, which impedes tumor research and discourages the use of such advanced techniques. We will develop innovative evolution-aware methods that will significantly improve the accuracy of single-cell sequences, accelerating the advances on the understanding of tumor heterogeneity and evolution. Specifically, our first aim will be to develop a Bayesian Evolutionary-Aware Method (BEAM) to impute missing data and correct errors in single cell sequences. Our second aim will be to advance BEAM so that it can efficiently use variation profiles from bulk-sequencing cell population profiling data to further enhance the accuracy of single-cell sequences. To aid researchers, we will produce a freely-available software for high-throughput analysis of tumor genomic data. Ultimately, the proposed software and research developments will lead to advances in cancer evolution, bioinformatics, and biomedicine. New software and its source code will be made available free of charge for research, education, and training.