A Big Data Approach for Sequences Indexing on the Cloud via Burrows Wheeler Transform

Burrows Wheeler Transform on a Large Scale: Algorithms Implemented in Apache Spark

With the rapid growth of Next Generation Sequencing (NGS) technologies, large amounts of "omics" data are daily collected and need to be processed. Indexing and compressing large sequences datasets are some of the most important tasks in this context. Here we propose algorithms for the computation of Burrows Wheeler transform relying on Big Data technologies, i.e., Apache Spark and Hadoop. Our algorithms are the first ones that distribute the index computation and not only the input dataset, allowing to fully benefit of the available cloud resources.

用EBWT实现加权序列比较的简化

Parallelization of Weighted Sequence Comparison by using EBWT

扩展Burrows Wheeler变换（EBWT）有助于找到两个序列之间的距离。现有算法的实现对于小尺寸序列需要相当多的时间。在本文中，我们给出了一个并行实现该算法使用NVIDIA计算统一设备架构（CUDA）。平均而言，我们在性能上获得了2倍的改进。