MOESM1 of Performance evaluation of a new custom, multi-component DNA isolation method optimized for use in shotgun metagenomic sequencing-based aerosol microbiome research

Additional file 1: Figure S1. Rarefaction curves with α-diversity measures: “Observed”, “Shannon”, and “Simpson” for subway air samples (N = 6) that were split and processed with the MetaSUB (N = 3) and Jiang (N = 3) or MetaSUB (N = 3) and Zymobiomics (N = 3) methods. Figure S2. Rarefaction curves with α-diversity measures: “Observed”, “Shannon”, and “Simpson” for the intermediate pellet (N = 6) and supernatant (N = 6) fractions from subway air samples (N = 6) processed separately with the MetaSUB method. Figure S3. Proportion of total DNA and 16S rRNA gene copy yield found in the supernatant fractions, referencing the total yield in the combined pellet and supernatant fractions, from subway air samples (N = 24) where the intermediate pellet and supernatant fractions were processed separately with the MetaSUB method. Figure S4. The 20 fungal species that were among the top 100 species from the random forest classification analysis of subway air samples (N = 3) that were split and processed with the MetaSUB (N = 3) and Jiang (N = 3) methods, where Z-score distributions were compared with linear models

MOESM2 of The subway microbiome: seasonal dynamics and direct comparison of air and surface bacterial communities

Additional file 1: Table S1. Type of environment, latitude and longitude for all sampled stations. Table S2. Overview of all samples included in the analyses. Table S3. PCR program for 16S rRNA gene amplicon sequencing. Table S4. The best-fit models of qPCR 16S rRNA gene copies for air samples and surface samples. Table S5. Top 20 phyla, families, and genera and species in surface samples collected on kiosks, benches, and railings. Table S6. Random forest classification models of samples collected from different surface types. Figure S1. The significant predictors of qPCR 16S rRNA gene copy yields in air samples. Figure S2. The significant predictors of qPCR 16S rRNA gene copy yields in surface samples. Figure S3. Quality profile of filtered reads. Figure S4. Rarefaction curves with observed diversity and Shannon’s Diversity Index. Figure S5. A) Relative abundances of the top 15 phyla across the three surface types and seasons. B) Heatmap of most abundant families. Figure S6. Top 20 most important genera in random forest classification analysis of samples collected in different seasons. Figure S7. Top 20 most important genera in random forest classification analysis of air and surface samples. Figure S8. Interaction effect between temperature (°C) and air/surface in the linear model of Shannon’s diversity index. Figure S9. PCoA plot of Bray Curtis dissimilarity distances with the only significant predictor (surface type) from the PERMANOVA model that included only surface-specific predictors

Additional file 1: of The Mycobacterium tuberculosis transcriptional landscape under genotoxic stress

List of differentially expressed (DE) genes encoding mRNA under each genotoxic stress. (XLSX 96 kb

Additional file 8: of The Mycobacterium tuberculosis transcriptional landscape under genotoxic stress

List of oligos used for riboprobe construction. (DOCX 14 kb