Data from: Aligner optimization increases accuracy and decreases compute times in multi-species sequence data

As sequencing technologies have evolved, the tools to analyze these sequences have made similar advances. However, for multi-species samples, we observed important and adverse differences in alignment specificity and computation time for bwa- mem (Burrows–Wheeler aligner-maximum exact matches) relative to bwa-aln. Therefore, we sought to optimize bwa-mem for alignment of data from multi-species samples in order to reduce alignment time and increase the specificity of alignments. In the multi-species cases examined, there was one majority member (i.e. Plasmodium falciparum or Brugia malayi) and one minority member (i.e. human or the Wolbachia endosymbiont wBm) of the sequence data. Increasing bwa-mem seed length from the default value reduced the number of read pairs from the majority sequence member that incorrectly aligned to the reference genome of the minority sequence member. Combining both source genomes into a single reference genome increased the specificity of mapping, while also reducing the central processing unit (CPU) time. In Plasmodium, at a seed length of 18 nt, 24.1 % of reads mapped to the human genome using 1.7±0.1 CPU hours, while 83.6 % of reads mapped to the Plasmodium genome using 0.2±0.0 CPU hours (total: 107.7 % reads mapping; in 1.9±0.1 CPU hours). In contrast, 97.1 % of the reads mapped to a combined Plasmodium–human reference in only 0.7±0.0 CPU hours. Overall, the results suggest that combining all references into a single reference database and using a 23 nt seed length reduces the computational time, while maximizing specificity. Similar results were found for simulated sequence reads from a mock metagenomic data set. We found similar improvements to computation time in a publicly available human-only data set.随着测序技术的迭代演进，用于分析测序序列的生物信息学工具也实现了同步发展。然而，针对多物种样本，我们观察到相较于bwa-aln，bwa-mem（Burrows–Wheeler aligner-maximum exact matches，即Burrows-Wheeler比对器-最大精确匹配算法）在比对特异性与计算耗时方面存在显著且不利的差异。为此，我们旨在针对多物种样本的测序数据比对任务优化bwa-mem算法，以期缩短比对时长并提升比对特异性。本研究涉及的多物种样本序列数据包含两类组分：一类为占比占优的物种（即恶性疟原虫*Plasmodium falciparum*或马来丝虫*Brugia malayi*），另一类为占比偏低的物种（即人类或沃尔巴克氏体内共生体wBm）。将bwa-mem的比对种子长度从默认值调高，可有效减少多数物种的测序读对错误比对至少数物种参考基因组的数量。将两类源基因组整合为单一参考基因组，不仅能够提升序列比对的特异性，还可缩短中央处理器（Central Processing Unit，CPU）的计算耗时。以恶性疟原虫相关实验为例，当种子长度设置为18 nt时，24.1%的读段比对至人类参考基因组，耗时1.7±0.1 CPU小时；同时有83.6%的读段比对至恶性疟原虫参考基因组，耗时0.2±0.0 CPU小时（总比对读段占比为107.7%，总耗时1.9±0.1 CPU小时）。与之形成对比的是，将恶性疟原虫与人类基因组整合为单一参考基因组后，仅需0.7±0.0 CPU小时，即可实现97.1%的读段比对至该整合参考序列。综合所有实验结果来看，将所有参考序列整合为单一参考数据库，并采用23 nt的种子长度，可在缩短计算时长的同时最大化比对特异性。针对模拟宏基因组数据集的测序读段，我们也得到了一致的实验结果。此外，在公开的人类单物种数据集上，我们同样观测到了计算时长的显著优化。