Evaluating Bioinformatic Pipeline Performance for Forensic Microbiome Analysis^*,†,‡

Microbial communities have potential evidential utility for forensic applications. However, bioinformatic analysis of high-throughput sequencing data varies widely among laboratories. These differences can potentially affect microbial community composition and downstream analyses. To illustrate the importance of standardizing methodology, we compared analyses of postmortem microbiome samples using several bioinformatic pipelines, varying minimum library size or minimum number of sequences per sample, and sample size. Using the same input sequence data, we found that three open-source bioinformatic pipelines, MG-RAST, mothur, and QIIME2, had significant differences in relative abundance, alpha-diversity, and beta-diversity, despite the same input data. Increasing minimum library size and sample size increased the number of low-abundant and infrequent taxa detected. Our results show that bioinformatic pipeline and parameter choice affect results in important ways. Given the growing potential application of forensic microbiology to the criminal justice system, continued research on standardizing computational methodology will be important for downstream applications.