Comparative Analyses and Genomic Sequence-Only Prediction of DNA Breakpoints via Gradient Boosting Machines and Deep Learning

基因组插入/删除改变，通过DNA链断裂的形成而发生，是仅次于点突变的第二大DNA修饰。然而，与点突变不同，DNA链断点的长期和短期序列上下文依赖性，以及基因组中断裂倾向的详细区域差异，尚未通过尖端的计算手段得到广泛的研究。这种情况之所以普遍存在，是因为这种DNA改变的可用数据相对较少，再加上DNA链断裂的复杂多病因性质，将进一步使已经稀疏的数据分层。然而，理解DNA断裂的重要性导致了许多计算工作，概述了CNV断点与非b DNA构象、频繁的SCNA断点与癌症基因、诱导DNA断裂与染色质包装和基本位点的潜在关联。基于CNV数据开发的离散值二元隐马尔可夫模型对人类基因组中约400个断点的预测分辨率可达~300bp，这表明基于序列的DNA断点预测至少对CNV具有更高分辨率的前景。过去十年见证了用于DNA断点全基因组实验测绘的特定测序方法的出现，并且随着最近在生物大数据上的杰出应用，尖端机器学习技术开始变得容易获得。因此，现在是时候将最先进的机器学习技术应用于从经历不同生理、自发和病理过程的组织中大量扩展和分层良好的DNA断点数据，以揭示其精细的短期和长期序列依赖性，以及它们在导致DNA链断裂的不同过程中的共性和差异。在整个项目中，学生将获得并实施计算生物学、高级数据分析、机器学习和统计学方面的技能（“定量技能”），同时使用基因组学数据集，并整合从热力学和核酸化学考虑的许多自下而上的发展（“跨学科技能”）。

Genomic insertion/deletion alterations, which occur through the formation of DNA strand breaks, are the second most significant DNA modifications after point mutations. However, unlike point mutations, the long- and short-range sequence-context dependence of DNA strand breakpoints, as well as the detailed regional variation of breakage propensities in the genome, have not been extensively interrogated through cutting edge computational means. This has prevalently been the case because of the relative sparseness of available data on such DNA alterations, coupled with complex multi-etiologic nature of DNA strand breaks that would further stratify already sparse data. Nevertheless, the importance of understanding DNA breakages led to a number of computational works outlining potential associations of CNV breakpoints vs. non-B DNA conformations, frequent SCNA breakpoints vs. cancer genes, induced DNA breaks vs. chromatin packing and abasic sites. Discrete-valued bivariate hidden Markov model developed on CNV data could reach a predictive resolution of ~300bp for about 400 breakpoints in the human genome, showing the promise for higher resolution sequence- based prediction of DNA breakpoints for, at least, CNVs. The past decade has witnessed the advent of specific sequencing methodologies for experimental genome-wide mapping of DNA breakpoints, and, cutting edge machine learning techniques started to become readily available with recent outstanding applications on biological big data. It is therefore the right time to apply the state-of-the-art machine learning techniques on the substantially expanded and well stratified DNA breakpoint data from tissues undergoing different physiological, spontaneous and pathological processes, in order to reveal their fine short- and long-range sequence dependences, with their commonalities and differences across different processes leading to DNA strand breaks. Throughout the project, the student will gain and implement skills in computational biology, advanced data analysis, machine learning and statistics ("Quantitative skills"), while working with genomics datasets and integrating many bottom-to-top developments from thermodynamic and chemical considerations of nucleic acids ("Interdisciplinary skills").