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Additional file 8: Figure S7. Prevalence of species in different categories. Heatmap showing the prevalence (fraction of samples where a taxon is detected) of different taxa across categories. Too many taxa are present to individually name taxa.

Additional file 6: Figure S5. Rarefaction Analysis. The number of unique species detected in multiple samples of the same type. A) Sample sets appear to reach a maximum suggesting that the majority of species have been fully categorized in each category and ISO level. B) number of species detected when individual samples are rarefied to a maximum read count

Additional file of A comprehensive metagenomics framework to characterize organisms relevant for planetary protection

Additional file 8: Figure S7. Prevalence of species in different categories. Heatmap showing the prevalence (fraction of samples where a taxon is detected) of different taxa across categories. Too many taxa are present to individually name taxa.

Additional file 1: Table S1. Sample Sets and Categories for JPL Planetary Protection Testing

We present a global atlas of 4,728 metagenomic samples from mass-transit systems in 60 cities over 3 years, representing the first systematic, worldwide catalog of the urban microbial ecosystem. This atlas provides an annotated, geospatial profile of microbial strains, functional characteristics, antimicrobial resistance (AMR) markers, and genetic elements, including 10,928 viruses, 1,302 bacteria, 2 archaea, and 838,532 CRISPR arrays not found in reference databases. We identified 4,246 known species of urban microorganisms and a consistent set of 31 species found in 97% of samples that were distinct from human commensal organisms. Profiles of AMR genes varied widely in type and density across cities. Cities showed distinct microbial taxonomic signatures that were driven by climate and geographic differences. These results constitute a high-resolution global metagenomic atlas that enables discovery of organisms and genes, highlights potential public health and forensic applications, and provides a culture-independent view of AMR burden in cities.

Additional file 4: Figure S3. Comparison of QC Metrics in cases to controls. We detected 24 species across all control samples after initial filtering. Environmental metagenomic samples are qualitatively similar to samples of laboratory contamination so filtering based on controls is non-trivial. We compared the number of unique marker k-mers found for each taxa in each case sample to the maximum number found in one control (A) and, analogously, the total number of reads (B). We took all taxonomic assignments with a read ratio greater than 2 and a k-mer ratio greater than 5 (C). If a taxonomic assignment met both criteria in at least two samples, it was permitted (red taxa on bottom right) otherwise the taxa was filtered from all assignments (blue taxa on bottom right).

Additional file of A comprehensive metagenomics framework to characterize organisms relevant for planetary protection

Additional file 3: Figure S2. Distribution of QC Metrics. The distributions of various QC metrics for controls and ISO levels. Distributions of QC metrics for non-control samples are largely unaffected by category which suggests good overall quality. A) Distribution of read count. B) Coverage of marker k-mer set for each taxa detected in samples. C) Number of unique marker k-mers for each taxa detected in samples. D) The ratio of unique marker k-mers to read count for each taxa detected in sample. A low ratio (about 1) could indicate a false positive.

Additional file 5: Figure S4. Relationship of QC Values to Taxonomic Assignment. A. Total number of reads per sample, note log scale on y-axis. B. Top-Left) Diversity of marker k-mers compared to diversity of identified taxa measured by Shannon’s entropy, Pearson’s correlation coefficients rho=0.625. Top-Right) Fraction of singleton k-mers compared to diversity of identified taxa, rho=0.499. Bottom-Right) Read count compared to diversity of identified taxa, rho=0.781. Bottom-Left) Read count compared to total number of detected species (rarefied to 1,000 reads), rho=0.886.

Additional file 5: Figure S4. Relationship of QC Values to Taxonomic Assignment. A. Total number of reads per sample, note log scale on y-axis. B. Top-Left) Diversity of marker k-mers compared to diversity of identified taxa measured by Shannon’s entropy, Pearson’s correlation coefficients rho=0.625. Top-Right) Fraction of singleton k-mers compared to diversity of identified taxa, rho=0.499. Bottom-Right) Read count compared to diversity of identified taxa, rho=0.781. Bottom-Left) Read count compared to total number of detected species (rarefied to 1,000 reads), rho=0.886.

Additional file 3: Figure S2. Distribution of QC Metrics. The distributions of various QC metrics for controls and ISO levels. Distributions of QC metrics for non-control samples are largely unaffected by category which suggests good overall quality. A) Distribution of read count. B) Coverage of marker k-mer set for each taxa detected in samples. C) Number of unique marker k-mers for each taxa detected in samples. D) The ratio of unique marker k-mers to read count for each taxa detected in sample. A low ratio (about 1) could indicate a false positive.