Background Microbes and their viruses are hidden engines driving Earth’s ecosystems from the oceans and soils to humans and bioreactors. Though gene marker approaches can now be complemented by genome-resolved studies of inter-(macrodiversity) and intra-(microdiversity) population variation, analytical tools to do so remain scattered or under-developed. Results Here, we introduce MetaPop, an open-source bioinformatic pipeline that provides a single interface to analyze and visualize microbial and viral community metagenomes at both the macro- and microdiversity levels. Macrodiversity estimates include population abundances and α- and β-diversity. Microdiversity calculations include identification of single nucleotide polymorphisms, novel codon-constrained linkage of SNPs, nucleotide diversity (π and θ), and selective pressures (pN/pS and Tajima’s D) within and fixation indices (FST) between populations. MetaPop will also identify genes with distinct codon usage. Following rigorous validation, we applied MetaPop to the gut viromes of autistic children that underwent fecal microbiota transfers and their neurotypical peers. The macrodiversity results confirmed our prior findings for viral populations (microbial shotgun metagenomes were not available) that diversity did not significantly differ between autistic and neurotypical children. However, by also quantifying microdiversity, MetaPop revealed lower average viral nucleotide diversity (π) in autistic children. Analysis of the percentage of genomes detected under positive selection was also lower among autistic children, suggesting that higher viral π in neurotypical children may be beneficial because it allows populations to better “bet hedge” in changing environments. Further, comparisons of microdiversity pre- and post-FMT in autistic children revealed that the delivery FMT method (oral versus rectal) may influence viral activity and engraftment of microdiverse viral populations, with children who received their FMT rectally having higher microdiversity post-FMT. Overall, these results show that analyses at the macro level alone can miss important biological differences. Conclusions These findings suggest that standardized population and genetic variation analyses will be invaluable for maximizing biological inference, and MetaPop provides a convenient tool package to explore the dual impact of macro- and microdiversity across microbial communities.

Estimation of whole-genome relatedness and taxonomic identification are two important bioinformatics tasks in describing environmental or clinical microbiomes. The genome-aggregate Average Nucleotide Identity (ANI) is routinely used to derive the relatedness of closely related (species level) microbial and viral genomes, but it is not appropriate for more divergent genomes. Average Amino Acid Identity (AAI) can be used in the latter cases, but no current AAI implementation can efficiently compare thousands of genomes. Here we present FastAAI, a tool that estimates whole-genome pairwise relatedness using shared tetramers of universal proteins in a matter of microseconds, providing a speedup of up to 5 orders of magnitude when compared with current methods of calculating AAI or alternative whole-genome metrics. Further, FastAAI resolves distantly related genomes related at the phylum level with comparable accuracy to the phylogeny of ribosomal rRNA genes, substantially improving on a known limitation of current AAI implementations. Therefore, Fast AAI uniquely expands the toolbox for microbiome analysis and allows it to scale to millions of genomes.

Mapping of short metagenomic (or metatranscriptomic) read data to reference isolate or single-cell genomes or metagenome-assembled genomes (MAGs) to assess microbial population relative abundance and/or structure represents an essential task of many studies across environmental and clinical settings. The filtering for the quality of the read match and assessment of read mapping results are frequently performed without visual aids or with the assistance of visualizations produced through ad-hoc, in-house approaches. Here, we introduce RecruitPlotEasy, a fully automated, user-friendly pipeline for these purposes that integrates statistical approaches to quantify intra-population sequence and gene-content diversity and identify co-occurring relative populations in the sample. Hence, RecruitPlotEasy should also greatly facilitate population genetics studies.RecruitPlotEasy is implemented in Python and R languages and is freely available open source software under the Artistic License 2.0 from https://github.com/KGerhardt/RecruitPlotEasy.