Meeting Abstract
P1.152 Friday, Jan. 4 MMAP: A Pipeline for Metagenomic Analysis of Complex Microbial Populations MCKENNEY, E.*; WU, S.; YODER, A.D.; RODRIGO, A.; Duke University; Duke University; Duke University; Duke University eam50@duke.edu
Comparative metagenomics is here to stay. Investigators have increasing needs for characterizing microbial communities in unexplored environments and biological systems, and next generation sequencing technologies provide a nearly instantaneous means for describing these communities at the DNA sequence level. While several pipelines exist for the integrated analysis of phylogenetic datasets, to date no program is available to streamline a similar approach to whole genome shotgun sequences (WGS). To meet this challenge, we introduce Microbial Metagenomic Analysis Pipeline (MMAP), a bioinformatic program that integrates software packages to synthesize WGS reads (MetaSim-optional), align reads into contigs (Genovo), identify open reading frames (Glimmer), perform a blast search for gene ontology (GO) terms, and finally compare the resulting profiles between metagenomic libraries (MINE). MMAP inputs WGS to retrieve GO terms, instead of identifying individual genes, to paint a comprehensive picture of community functions. This approach is valuable for the study of microbial populations, which may display considerable convergent evolution despite high biodiversity. In the gastrointestinal tract (GIT), for example, functional redundancy results from a combination of evolutionary pressures unique to that environment and the microbes’ ability to transfer genes laterally amongst themselves. Yet, despite the core metagenome exhibited across GIT microbial consortia, genetic profiles can and do change with differences in population composition and host factors. We built MMAP to tease apart these intertwining patterns and to elucidate the relationship between taxonomy and functionality within the GIT microbial community. Our open-source program is written in Python and can be downloaded at https://github.com/YoderLab/MMAP.
