Optimizing Read Mapping to Reference Genomes to Determine Composition and Species Prevalence in Microbial Communities

The Human Microbiome Project (HMP) aims to characterize the microbial communities of 18 body sites from healthy individuals. To accomplish this, the HMP generated two types of shotgun data: reference shotgun sequences isolated from different anatomical sites on the human body and shotgun metagenomic sequences from the microbial communities of each site. The alignment strategy for characterizing these metagenomic communities using available reference sequence is important to the success of HMP data analysis. Six next-generation aligners were used to align a community of known composition against a database comprising reference organisms known to be present in that community. All aligners report nearly complete genome coverage (>97%) for strains with over 6X depth of coverage, however they differ in speed, memory requirement and ease of use issues such as database size limitations and supported mapping strategies. The selected aligner was tested across a range of parameters to maximize sensitivity while maintaining a low false positive rate. We found that constraining alignment length had more impact on sensitivity than does constraining similarity in all cases tested. However, when reference species were replaced with phylogenetic neighbors, similarity begins to play a larger role in detection. We also show that choosing the top hit randomly when multiple, equally strong mappings are available increases overall sensitivity at the expense of taxonomic resolution. The results of this study identified a strategy that was used to map over 3 tera-bases of microbial sequence against a database of more than 5,000 reference genomes in just over a month.