Bayesian Evolution-Aware Methods for tumor single cell sequences

项目总结/摘要 随着单细胞测序技术的出现，肿瘤研究进入了一个令人兴奋的阶段， 有可能在最高细胞分辨率下彻底改变基因组变异的概况。然而，在这方面， 现有技术的单细胞测序技术产生的数据具有太多的不确定性和误差， 这阻碍了肿瘤研究并阻碍了这种先进技术的使用。我们将开发 创新的进化感知方法将显著提高单细胞序列的准确性， 加速对肿瘤异质性和进化的认识。具体来说，我们的第一个 目的是开发贝叶斯进化感知方法（BEAM）来估算缺失数据并纠正 单细胞序列中的错误。我们的第二个目标是推进BEAM，使其能够有效地利用变化 从批量测序细胞群体分析数据中提取特征，以进一步提高单细胞分析的准确性 序列的为了帮助研究人员，我们将制作一个免费提供的软件，用于高通量分析， 肿瘤基因组数据。最终，拟议的软件和研究开发将导致以下方面的进步： 癌症进化、生物信息学和生物医学。将提供新软件及其源代码 免费用于研究、教育和培训。

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.