论文标题
通过Burrows Wheeler Transform在云上索引序列的大数据方法
A Big Data Approach for Sequences Indexing on the Cloud via Burrows Wheeler Transform
论文作者
论文摘要
在精确医学的背景下，索引序列数据很重要，在精确医学的背景下，必须每天收集和分析大量的``omics''数据，以便对患者进行分类并确定最有效的疗法。在这里，我们提出了一种依靠大数据技术（即Apache Spark和Hadoop）来计算Burrows Wheeler变换的算法。我们的方法是第一个分发索引计算的方法，而不仅仅是输入数据集，从而使可用的云资源完全受益。
Indexing sequence data is important in the context of Precision Medicine, where large amounts of ``omics'' data have to be daily collected and analyzed in order to categorize patients and identify the most effective therapies. Here we propose an algorithm for the computation of Burrows Wheeler transform relying on Big Data technologies, i.e., Apache Spark and Hadoop. Our approach is the first that distributes the index computation and not only the input dataset, allowing to fully benefit of the available cloud resources.
