Novel methods for large-scale genomic interval comparison

摘要 表观基因组数据正在推动遗传变异和基因调控的生物医学分析的发现。外基因 由实验方案如ATAC-seq或ChIP-seq产生的数据通常被总结为 由染色体加上起始和结束坐标定义的基因组间隔。数据库现在提供数百个 成千上万的这样的区域集，每个区域集可能包含数十万个单独的区域。这 数据为了解基因调控和疾病提供了巨大的希望，因为许多健康结果都是 受调控DNA中遗传变异或表观遗传扰动的影响。 已经开发了许多不同的工具和方法来评估这样的基因组间隔集。这些ap- 方法用于广泛的生物医学研究，例如注释与遗传相关的遗传变异。 疾病特征。为了支持基于区域的分析，我们和其他人开发了新的数据结构， 比较区域集合的相似性和计算间隔集合之间的重叠的算法， 在数百万个地区进行比较。但是随着基因组间隔集数据源在规模和范围上的增长， 需要更快的算法和新颖的方法来比较这种重要的数据类型。 随着可用数据量的增加，计算精确的重叠变得越来越困难。此外，委员会认为， 最快的算法只分析纯间隔，而不是信号值，信号值可用于比较间隔集 更准确地说。现有的方法在定义规范区间集以 进一步简化分析。 在这里，我们以几种方式解决这些限制：首先，我们开发新颖的，更具可扩展性的算法， 近似计算和定义区间集宇宙的想法，以巩固分析。二是 开发一种创新的方法来分析区域集，而不仅仅是计算重叠， 依靠尖端的机器学习方法来更准确地学习和测量相似性。我们提出 一种新的集合论方法，建立在自然语言处理技术的基础上，以比较区间。 总之，我们提出了一个第一遍过滤器，可以合理地计算包含数十亿到数万亿的数据集 的基因组间隔，其次是更准确的分析，以确定区域集之间更微妙的关系。 这些进步将提高现有生物医学研究方法的效率和准确性， 为探索庞大且不断增长的基因组间隔数据库的新方法打开了大门。

ABSTRACT Epigenome data are driving discovery in biomedical analysis of genetic variation and gene regulation. Epigenome data produced by experimental protocols such as ATAC-seq or ChIP-seq are often summarized into sets of genomic intervals defined by a chromosome plus start and end coordinates. Databases now provide hundreds of thousands of such region sets, each containing potentially hundreds of thousands of individual regions. This data holds tremendous promise to understand gene regulation and disease because many health outcomes are affected by genetic variation or epigenetic perturbation in regulatory DNA. Many different tools and methods have been developed to assess such sets of genomic intervals. These ap- proaches are used for a broad array of biomedical research, such as annotating genetic variation associated with disease traits. Supporting region-based analyses, we and others have developed novel data structures and algorithms to compare similarity of region sets and to compute overlaps between interval sets, enabling interval comparisons on millions of regions. But as the genomic interval set data sources grow in size and scope, we require both faster algorithms and novel methods to compare this important data type. As the amount of available data increases, it is becoming intractable to compute exact overlaps. Furthermore, the fastest algorithms only analyze pure intervals, not signal values, which could be used to compare interval sets more accurately. Existing approaches have made little progress in the area of defining canonical interval sets to simplify analysis even further. Here, we address these limitations in several ways: First, we develop novel, more scalable algorithms using approximate computations and define the idea of interval set universes to consolidate analysis. Second, we develop an innovative approach to analyzing region sets that goes beyond simply counting overlaps, instead relying on cutting-edge machine learning methods to learn and measure similarity more accurately. We propose a novel set theoretic approach building on techniques from natural language processing to compare intervals. Together, we propose a first-pass filter that can be reasonably computed on data sets containing billions to trillions of genomic intervals, followed by a more accurate analysis to identify more subtle relationships among region sets. These advances will improve both the efficiency and accuracy of existing biomedical research approaches, and open the door to new ways of exploring the vast and growing corpus of genome interval data.