二进制区间搜索（BITS）：一种可扩展的区间相交计数算法
Binary Interval Search (BITS): A Scalable Algorithm for Counting Interval Intersections
摘要：
动机：不同基因组数据集的比较是理解基因组生物学的基础。研究人员必须探索许多基因组间隔的大型数据集（例如，基因、序列比对），将他们的实验结果置于更广泛的背景下，并做出新的发现。基因组数据集之间的关系通常通过识别相交的区间来测量：也就是说，它们重叠并因此共享共同的基因组区间。鉴于DNA测序技术的不断进步，测量许多组基因组特征之间统计学显著关系的有效方法对于未来的发现至关重要。结果：我们介绍了二进制区间搜索（BITS）算法，一种新颖的和可扩展的方法，区间集相交。我们证明，BITS优于现有的方法在计数区间交叉点。此外，我们表明，BITS本质上是适合于并行计算架构，如图形处理单元（GPU），通过说明其效用的高效蒙特卡罗模拟测量组之间的关系的基因组间隔的意义。
英文摘要：
Motivation: The comparison of diverse genomic datasets is fundamental to understanding genome biology. Researchers must explore many large datasets of genome intervals (e.g., genes, sequence alignments) to place their experimental results in a broader context and to make new discoveries. Relationships between genomic datasets are typically measured by identifying intervals that intersect: that is, they overlap and thus share a common genome interval. Given the continued advances in DNA sequencing technologies, efficient methods for measuring statistically significant relationships between many sets of genomic features is crucial for future discovery. Results: We introduce the Binary Interval Search (BITS) algorithm, a novel and scalable approach to interval set intersection. We demonstrate that BITS outperforms existing methods at counting interval intersections. Moreover, we show that BITS is intrinsically suited to parallel computing architectures such as Graphics Processing Units (GPUs) by illustrating its utility for efficient Monte-Carlo simulations measuring the significance of relationships between sets of genomic intervals.
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