Due to the exponential growth of genomic data, constructing dedicated data structures has become the principal bottleneck in common bioinformatics applications. In particular, the Burrows-Wheeler Transform (BWT) is the basis of some of the most popular self-indexes for genomic data, due to its known favourable behaviour on repetitive data. Some tools that exploit the intrinsic repetitiveness of biological data have risen in popularity, due to their speed and low space consumption. We introduce a new algorithm for computing the BWT, which takes advantage of the redundancy of the data through a compressed version of matching statistics, the $\textit{CMS}$ of [Lipt\'ak et al., WABI 2022]. We show that it suffices to sort a small subset of suffixes, lowering both computation time and space. Our result is due to a new insight which links the so-called insert-heads of [Lipt\'ak et al., WABI 2022] to the well-known run boundaries of the BWT. We give two implementations of our algorithm, called $\texttt{CMS}$-$\texttt{BWT}$, both competitive in our experimental validation on highly repetitive real-life datasets. In most cases, they outperform other tools w.r.t. running time, trading off a higher memory footprint, which, however, is still considerably smaller than the total size of the input data.

翻译：随着基因组数据的指数级增长，构建专用数据结构已成为常见生物信息学应用中的主要瓶颈。其中，Burrows-Wheeler变换（BWT）因其在处理重复数据时表现出的优良特性，成为目前最流行的基因组数据自索引方法之一。部分利用生物数据固有重复性的工具，凭借其速度和低空间消耗而日益普及。我们提出了一种新的BWT计算算法，该算法通过[Lipták等人，WABI 2022]提出的压缩版匹配统计量（$\textit{CMS}$）来利用数据的冗余性。研究表明，仅需对少量后缀进行排序即可降低计算时间和空间消耗。这一成果源于一项新见解，该见解将[Lipták等人，WABI 2022]中所谓的插入头与BWT众所周知的运行边界联系起来。我们给出了该算法的两个实现版本，均命名为$\texttt{CMS}$-$\texttt{BWT}$，并在高重复性真实数据集上的实验验证中展现了竞争力。在大多数情况下，这些实现以牺牲更高的内存占用为代价在运行时间上优于其他工具，但内存占用仍显著小于输入数据的总规模。