Aligner optimization increases accuracy and decreases compute times in multi-species sequence data. Issue 9 (8th July 2017) - Robinson, Kelly M.; Hawkins, Aziah S.; Santana-Cruz, Ivette; Adkins, Ricky S.; Shetty, Amol C.; Nagaraj, Sushma; Sadzewicz, Lisa; Tallon, Luke J.; Rasko, David A.; Fraser, Claire M.; Mahurkar, Anup; Silva, Joana C.; et al.
10.1099/mgen.0.000122.001 Abstract : 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 forbwa- mem (Burrows–Wheeler aligner-maximum exact matches) relative tobwa -aln . Therefore, we sought to optimizebwa -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 w Bm) of the sequence data. Increasingbwa -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.