# Transfer of environmental microbes to the skin and respiratory tract after urban green space exposure # Selway CA, et al. # Analysis of microbiota data # BCL files from Illumina Miseq v2 were converted to fastq sequences using bcl2fastq v1.8.4 # Running all analysis through QIIME2 v2019.7 # Decontam was used via R (v3.6.1) to detected lab contaminants # Figures were created in R (v3.6.1) ############################# CALLING QIIME2 ############################# source activate qiime2-2019.7 ################### IMPORTING OF SEQUENCES INTO QIIME2 ################### qiime tools import \ --type EMPSingleEndSequences \ --input-path hgs161222_emp-single-end-sequences \ --output-path hgs161222_emp-single-end-sequences.qza qiime tools import \ --type EMPSingleEndSequences \ --input-path hgs170307_emp-single-end-sequences \ --output-path hgs170307_emp-single-end-sequences.qza qiime tools import \ --type EMPSingleEndSequences \ --input-path hgs170410_emp-single-end-sequences \ --output-path hgs170410_emp-single-end-sequences.qza qiime tools import \ --type EMPSingleEndSequences \ --input-path hgs170424_emp-single-end-sequences \ --output-path hgs170424_emp-single-end-sequences.qza qiime tools import \ --type EMPSingleEndSequences \ --input-path hgs170705_emp-single-end-sequences \ --output-path hgs170705_emp-single-end-sequences.qza qiime tools import \ --type EMPSingleEndSequences \ --input-path hgs170706_emp-single-end-sequences \ --output-path hgs170706_emp-single-end-sequences.qza ##################### DEMULTIPLEXING SEQUENCING RUNS ##################### qiime demux emp-single \ --i-seqs hgs161222_emp-single-end-sequences.qza \ --m-barcodes-file 161221_IMS_CSeJMiLWe_16S_PlayfordRep1HumanGreenSpaces_mappingfile.txt \ --m-barcodes-column BarcodeSequence \ --o-per-sample-sequences hgs161222_demux.qza qiime tools export \ --input-path hgs161222_demux.qza \ --output-path hgs161222_demux qiime demux emp-single \ --i-seqs hgs170307_emp-single-end-sequences.qza \ --m-barcodes-file 170307_IMS_CSeESk_16S_PlayfordRep2and3HumanGreenSpaces_mappingfile.txt \ --m-barcodes-column BarcodeSequence \ --p-rev-comp-mapping-barcodes \ --o-per-sample-sequences hgs170307_demux.qza qiime tools export \ --input-path hgs170307_demux.qza \ --output-path hgs170307_demux qiime demux emp-single \ --i-seqs hgs170410_emp-single-end-sequences.qza \ --m-barcodes-file 170410_IMS_CSe_16S_UKHumanGreenSpaces_mappingfile.txt \ --m-barcodes-column BarcodeSequence \ --p-rev-comp-mapping-barcodes \ --o-per-sample-sequences hgs170410_demux.qza qiime tools export \ --input-path hgs170410_demux.qza \ --output-path hgs170410_demux qiime demux emp-single \ --i-seqs hgs170424_emp-single-end-sequences.qza \ --m-barcodes-file 170424_IMS_ESk_16S_IndiaHGS_TimeStorage_mappingfile.txt \ --m-barcodes-column BarcodeSequence \ --p-rev-comp-mapping-barcodes \ --o-per-sample-sequences hgs170424_demux.qza qiime tools export \ --input-path hgs170424_demux.qza \ --output-path hgs170424_demux qiime demux emp-single \ --i-seqs hgs170705_emp-single-end-sequences.qza \ --m-barcodes-file 170705_IMS_JYoJMi_16S_WTGreenSpaces1_mappingfile.txt \ --m-barcodes-column BarcodeSequence \ --p-rev-comp-mapping-barcodes \ --o-per-sample-sequences hgs170705_demux.qza qiime tools export \ --input-path hgs170705_demux.qza \ --output-path hgs170705_demux qiime demux emp-single \ --i-seqs hgs170706_emp-single-end-sequences.qza \ --m-barcodes-file 170706_IMS_JYoJMi_16S_WTGreenSpaces2_mappingfile.txt \ --m-barcodes-column BarcodeSequence \ --p-rev-comp-mapping-barcodes \ --o-per-sample-sequences hgs170706_demux.qza qiime tools export \ --input-path hgs170706_demux.qza \ --output-path hgs170706_demux ####################### SUMMARISING DEMUX RESULTS ######################## qiime demux summarize \ --i-data hgs161222_demux.qza \ --o-visualization hgs161222_demux.qzv qiime demux summarize \ --i-data hgs170307_demux.qza \ --o-visualization hgs170307_demux.qzv qiime demux summarize \ --i-data hgs170410_demux.qza \ --o-visualization hgs170410_demux.qzv qiime demux summarize \ --i-data hgs170424_demux.qza \ --o-visualization hgs170424_demux.qzv qiime demux summarize \ --i-data hgs170705_demux.qza \ --o-visualization hgs170705_demux.qzv qiime demux summarize \ --i-data hgs170706_demux.qza \ --o-visualization hgs170706_demux.qzv ########################### QUALITY FILTERING ############################ qiime quality-filter q-score \ --i-demux hgs161222_demux.qza \ --o-filtered-sequences hgs161222_demux-filtered.qza \ --o-filter-stats hgs161222_demux-filter-stats.qza qiime quality-filter q-score \ --i-demux hgs170307_demux.qza \ --o-filtered-sequences hgs170307_demux-filtered.qza \ --o-filter-stats hgs170307_demux-filter-stats.qza qiime quality-filter q-score \ --i-demux hgs170410_demux.qza \ --o-filtered-sequences hgs170410_demux-filtered.qza \ --o-filter-stats hgs170410_demux-filter-stats.qza qiime quality-filter q-score \ --i-demux hgs170424_demux.qza \ --o-filtered-sequences hgs170424_demux-filtered.qza \ --o-filter-stats hgs170424_demux-filter-stats.qza qiime quality-filter q-score \ --i-demux hgs170705_demux.qza \ --o-filtered-sequences hgs170705_demux-filtered.qza \ --o-filter-stats hgs170705_demux-filter-stats.qza qiime quality-filter q-score \ --i-demux hgs170706_demux.qza \ --o-filtered-sequences hgs170706_demux-filtered.qza \ --o-filter-stats hgs170706_demux-filter-stats.qza ################### ASSIGNING SEQUENCES THROUGH DEBLUR ################### # After investigating the quality of the sequencing runs, we chose a length of 150bp qiime deblur denoise-16S \ --i-demultiplexed-seqs hgs161222_demux-filtered.qza \ --p-trim-length 150 \ --o-representative-sequences hgs161222_rep-seqs-deblur.qza \ --o-table hgs161222_table-deblur.qza \ --p-sample-stats \ --o-stats hgs161222_deblur-stats.qza qiime deblur denoise-16S \ --i-demultiplexed-seqs hgs170307_demux-filtered.qza \ --p-trim-length 150 \ --o-representative-sequences hgs170307_rep-seqs-deblur.qza \ --o-table hgs170307_table-deblur.qza \ --p-sample-stats \ --o-stats hgs170307_deblur-stats.qza qiime deblur denoise-16S \ --i-demultiplexed-seqs hgs170410_demux-filtered.qza \ --p-trim-length 150 \ --o-representative-sequences hgs170410_rep-seqs-deblur.qza \ --o-table hgs170410_table-deblur.qza \ --p-sample-stats \ --o-stats hgs170410_deblur-stats.qza qiime deblur denoise-16S \ --i-demultiplexed-seqs hgs170424_demux-filtered.qza \ --p-trim-length 150 \ --o-representative-sequences hgs170424_rep-seqs-deblur.qza \ --o-table hgs170424_table-deblur.qza \ --p-sample-stats \ --o-stats hgs170424_deblur-stats.qza qiime deblur denoise-16S \ --i-demultiplexed-seqs hgs170705_demux-filtered.qza \ --p-trim-length 150 \ --o-representative-sequences hgs170705_rep-seqs-deblur.qza \ --o-table hgs170705_table-deblur.qza \ --p-sample-stats \ --o-stats hgs170705_deblur-stats.qza qiime deblur denoise-16S \ --i-demultiplexed-seqs hgs170706_demux-filtered.qza \ --p-trim-length 150 \ --o-representative-sequences hgs170706_rep-seqs-deblur.qza \ --o-table hgs170706_table-deblur.qza \ --p-sample-stats \ --o-stats hgs170706_deblur-stats.qza ####################### SUMMARISING DEBLUR RESULTS ####################### qiime metadata tabulate \ --m-input-file hgs161222_demux-filter-stats.qza \ --o-visualization hgs161222_demux-filter-stats.qzv qiime deblur visualize-stats \ --i-deblur-stats hgs161222_deblur-stats.qza \ --o-visualization hgs161222_deblur-stats.qzv qiime metadata tabulate \ --m-input-file hgs170307_demux-filter-stats.qza \ --o-visualization hgs170307_demux-filter-stats.qzv qiime deblur visualize-stats \ --i-deblur-stats hgs170307_deblur-stats.qza \ --o-visualization hgs170307_deblur-stats.qzv qiime metadata tabulate \ --m-input-file hgs170410_demux-filter-stats.qza \ --o-visualization hgs170410_demux-filter-stats.qzv qiime deblur visualize-stats \ --i-deblur-stats hgs170410_deblur-stats.qza \ --o-visualization hgs170410_deblur-stats.qzv qiime metadata tabulate \ --m-input-file hgs170424_demux-filter-stats.qza \ --o-visualization hgs170424_demux-filter-stats.qzv qiime deblur visualize-stats \ --i-deblur-stats hgs170424_deblur-stats.qza \ --o-visualization hgs170424_deblur-stats.qzv qiime metadata tabulate \ --m-input-file hgs170705_demux-filter-stats.qza \ --o-visualization hgs170705_demux-filter-stats.qzv qiime deblur visualize-stats \ --i-deblur-stats hgs170705_deblur-stats.qza \ --o-visualization hgs170705_deblur-stats.qzv qiime metadata tabulate \ --m-input-file hgs170706_demux-filter-stats.qza \ --o-visualization hgs170706_demux-filter-stats.qzv qiime deblur visualize-stats \ --i-deblur-stats hgs170706_deblur-stats.qza \ --o-visualization hgs170706_deblur-stats.qzv ###### MERGING FEATURE TABLES AND SEQUENCES FROM ALL SEQUENCING RUNS ##### # table qiime feature-table merge \ --i-tables hgs161222_table-deblur.qza \ --i-tables hgs170307_table-deblur.qza \ --i-tables hgs170410_table-deblur.qza \ --i-tables hgs170424_table-deblur.qza \ --i-tables hgs170705_table-deblur.qza \ --i-tables hgs170706_table-deblur.qza \ --o-merged-table human_green_space_merged_table.qza # representative sequences qiime feature-table merge-seqs \ --i-data hgs161222_rep-seqs-deblur.qza \ --i-data hgs170307_rep-seqs-deblur.qza \ --i-data hgs170410_rep-seqs-deblur.qza \ --i-data hgs170424_rep-seqs-deblur.qza \ --i-data hgs170705_rep-seqs-deblur.qza \ --i-data hgs170706_rep-seqs-deblur.qza \ --o-merged-data human_green_space_merged_seqs.qza ################### REMOVE SAMPLES FROM OTHER PROJECTS ################### qiime feature-table filter-samples \ --i-table human_green_space_merged_table.qza \ --m-metadata-file Playford_UK_India_mapping_file.txt \ --p-where "Project='HumanGreenSpaces'" \ --o-filtered-table human_green_space_merged_table.qza ######### SUMMARISING FEATURE TABLE AND REPRESENTATIVE SEQUENCES ######### qiime feature-table summarize \ --i-table human_green_space_merged_table.qza \ --o-visualization human_green_space_merged_table.qzv \ --m-sample-metadata-file Playford_UK_India_mapping_file.txt qiime feature-table tabulate-seqs \ --i-data human_green_space_merged_seqs.qza \ --o-visualization human_green_space_merged_seqs.qzv ####################### CREATING PHYLOGENETIC TREE ######################## # We want to create a insertion tree (SEPP tree) qiime fragment-insertion sepp \ --i-representative-sequences human_green_space_merged_seqs.qza \ --o-tree insertion-tree.qza \ --o-placements insertion-placements.qza qiime fragment-insertion filter-features \ --i-table human_green_space_merged_table.qza \ --i-tree insertion-tree.qza \ --o-filtered-table filteredfeatures_human_green_space_merged_table.qza \ --o-removed-table removed_table.qza qiime feature-table summarize \ --i-table removed_table.qza \ --o-visualization removed_table.qzv \ --m-sample-metadata-file Playford_UK_India_mapping_file.txt qiime feature-table summarize \ --i-table filtered_human_green_space_merged_table.qza \ --o-visualization filteredfeatures_human_green_space_merged_table.qzv \ --m-sample-metadata-file Playford_UK_India_mapping_file.txt ########################### FILTERING FEATURES ########################### # removing features observed less than 11 times over the dataset qiime feature-table filter-features \ --i-table filteredfeatures_human_green_space_merged_table.qza \ --p-min-frequency 11 \ --o-filtered-table filtered_human_green_space_merged_table_removed10seqs.qza qiime feature-table summarize \ --i-table filtered_human_green_space_merged_table_removed10seqs.qza \ --o-visualization filtered_human_green_space_merged_table_removed10seqs.qzv \ --m-sample-metadata-file Playford_UK_India_mapping_file.txt # removing features only observed in 1 sample qiime feature-table filter-features \ --i-table filtered_human_green_space_merged_table_removed10seqs.qza \ --p-min-samples 2 \ --o-filtered-table filtered_human_green_space_merged_table_removed10seqs_removefeatures1sample.qza qiime feature-table summarize \ --i-table filtered_human_green_space_merged_table_removed10seqs_removefeatures1sample.qza \ --o-visualization filtered_human_green_space_merged_table_removed10seqs_removefeatures1sample.qzv \ --m-sample-metadata-file Playford_UK_India_mapping_file.txt # filter unused sequences from representative seqs file qiime feature-table filter-seqs \ --i-data human_green_space_merged_seqs.qza \ --i-table filtered_human_green_space_merged_table_removed10seqs_removefeatures1sample.qza \ --o-filtered-data filtered_human_green_space_merged_seqs.qza qiime feature-table tabulate-seqs \ --i-data filtered_human_green_space_merged_seqs.qza \ --o-visualization filtered_human_green_space_merged_seqs.qzv ######################### CLASSIFYING SEQUENCES ########################## ## note: this was performed on a high performance computer and imported back to the local desktop qiime feature-classifier classify-sklearn \ --i-classifier silva-132-99-515-806-nb-classifier.qza \ --i-reads filtered_human_green_space_merged_seqs.qza \ --p-n-jobs -2 \ --p-reads-per-batch 100000 \ --o-classification silva_human_green_space_taxonomy.qza qiime metadata tabulate \ --m-input-file silva_human_green_space_taxonomy.qza \ --o-visualization silva_human_green_space_taxonomy.qzv ########################### DECONTAM PREPARATION ######################### # for decontam, we want to compare biological samples to EBC1 and PCRnegs ## therefore we are removing EBC2s (as they show cross-contamination, rather than contaminants observed from the lab) qiime feature-table filter-samples \ --i-table filtered_human_green_space_merged_table_removed10seqs_removefeatures1sample.qza \ --m-metadata-file Playford_UK_India_mapping_file.txt \ --p-where "KeepWithControls='Y'" \ --o-filtered-table filtered_EBC1PCRnegsAndSamples_human_green_space_merged_table_controls.qza qiime feature-table summarize \ --i-table filtered_EBC1PCRnegsAndSamples_human_green_space_merged_table_controls.qza \ --o-visualization filtered_EBC1PCRnegsAndSamples_human_green_space_merged_table_controls.qzv \ --m-sample-metadata-file Playford_UK_India_mapping_file.txt ################################# DECONTAM ############################### ### Now moving into R to use the Decontam package # Installing packages library(tidyverse) library(readr) library(phyloseq) library(ggplot2) library(decontam) library(scales) library(qiime2R) # Importing QIIME2 files for Decontam metadata<-read_tsv("Playford_UK_India_mapping_file.txt") ASVs <- read_qza("filtered_EBC1PCRnegsAndSamples_human_green_space_merged_table_controls.qza") silva_taxonomy <- read_qza("silva_human_green_space_taxonomy.qza") insertion_tree <- read_qza("insertion-tree.qza") # Separating the headers for the SILVA taxonomy file taxtable<-silva_taxonomy$data %>% as.tibble() %>% separate(Taxon, sep=";", c("Kingdom","Phylum","Class","Order","Family","Genus","Species")) # Creating a phyloseq object physeq<-phyloseq(otu_table(ASVs$data, taxa_are_rows = T), phy_tree(insertion_tree$data), tax_table(as.data.frame(taxtable) %>% select(-Confidence) %>% column_to_rownames("Feature.ID") %>% as.matrix()), sample_data(metadata %>% as.data.frame() %>% column_to_rownames("#SampleID"))) # Creating a dataframe of the phyloseq object HGS_df <- as.data.frame(sample_data(physeq)) # Now to look at the library size HGS_df$LibrarySize<-sample_sums(physeq) # We want to visualise the library size to make sure most of the EBCs have a lower seq count HGS_df <- HGS_df[order(HGS_df$LibrarySize),] HGS_df$Index <- seq(nrow(HGS_df)) ggplot(data=HGS_df, aes(x=Index, y=LibrarySize, color=EBC1_PCRneg)) +geom_point() # Now to identify the prevalence of contaminants # After investigating the prevalence plot, we identified a threshold of 0.57 to be best for this dataset sample_data(physeq)$is.neg <- sample_data(physeq)$EBC1_PCRneg == "Y" HGS_contamdf.prev <- isContaminant(physeq, method="prevalence", neg="is.neg", threshold=0.57) ggplot(data = HGS_contamdf.prev, aes(x=p)) + geom_histogram(binwidth = 0.01) + labs(x = 'decontam Score', y='Number of species')# Now to look at the frequencies table(HGS_contamdf.prev$contaminant) #FALSE TRUE #24083 65 # We want to create a plot to look at the presence/absence of features in samples and controls ## This includes making p-a objects of the controls and samples and a new dataframe of prevalence for all samples physeq.pa <- transform_sample_counts(physeq, function(abund) 1*(abund>0)) physeq.pa.controls <- prune_samples(sample_data(physeq.pa)$EBC1_PCRneg == "Y", physeq.pa) physeq.pa.samples <- prune_samples(sample_data(physeq.pa)$EBC1_PCRneg == "N", physeq.pa) HGS_df05.pa <- data.frame(pa.pos=taxa_sums(physeq.pa.samples), pa.neg=taxa_sums(physeq.pa.controls), contaminant=HGS_contamdf.prev05$contaminant) ggplot(data=HGS_df.pa, aes(x=pa.neg, y=pa.pos, color=contaminant)) + geom_point() + xlab("Prevalence (Negative Controls)") + ylab("Prevalence (Samples)") # Now we want to remove the contaminant ASVs listed below ### Contaminant information is reported in Table S1 in the manuscript e4ae1bae7de57f294934b880478985d0 9bea9b5f71445a91e5c3891771d6e082 726bcd6bfc2556e03208fce21f81ed24 f65114ea452e68521c4d87e46119613b 8e4004d7f58f942af2f609f415ae4070 0374d69a06fe46ad16ded615ae85090d 7a40009876a3734096b76c7492c5a607 7cc8273fa6f3c9f55f426c3765741834 e3a4a62adc4e83c274978806164b474a e62c251a875bb2771e929bccef959266 505e1db623887f714a204ca9f6c7e33e a00b7191133090783f3d0c5e6af0a331 f56cf41f73b7af74bc65102bbcb8dbf5 21307575d44fc90099a75188cc39f149 c93cf1f3f099e8ee6c6eab047a3ffffd 64487332c86a1167bd17614bf16f0b04 a4ecfb2e708863a17978a0942358ffc3 c251dd75189230758c44e830e6594146 4b747139d897ddf6c2bdbc36adcec0f6 f09e1068f1f04baa69134c7ccebf610e d2fd07e11d510d7a7862ce6d99daa51f c8f0e4307d045dd8b29d6e5c1adcb89b a148bd855fdf35b23b20751aeb334ab8 18f569bccc8333aa6850652db7922234 d4d20c8a4b0b24df88825a93233ab689 c22da6c79af27ac1f4eabbaa551ef052 d8a590d953f4c92d537fff74753a8708 4917ab997227a3f876bdd6d1a4e1623f 209f2320d0caddd6446a42c7154ee39e 66ccb3b333214ae50d6fb7f8b092c1c3 1536bfe63a374c76fa0a4de8b927f947 6ebf4f21e7698424e1e3683df98e46fa db6e4e3d6bea23bd5c619e4dd4ef5b87 1929d4bea6ee530f8c203c84312844d2 2277e8a50d90b15a159ca53ca9eb18db a64d359f4d1cc00d369259b4cda78f04 b1c63c327a79b8d43a1c70f533bfa770 dc814a0c23d7d651f824916c4683a448 5733cb74bf58681c1f1498a8634a8d89 2e266fede273e6fb32c3d9f34ed5c955 0e1275da09e6a474f0c9721251029877 2018c5a1e9a729f8c8e13162c2c6b845 7ee1511330d0be7522c12272e11ab2cc da60ae1dc37f6bb9523eab308019e775 aff4b4461490c4b12098cb3a868497b9 0540bf08e9a1eb8691a9b1be2afe577a 272fd5bb7d45eea7228a18bcb514bcc8 8c4a10b2a19004ba2649bd4b04120296 5379a6255a53c7e663b32cd64e56a247 28eced060b28f389bda27d35da1d44ef 9caa712e4effb2b58ffcf5af378217b5 0cd84c82fad243186502ec732c96effd 0418f4e384e517133f6a2af7e89fa355 16c95e79be11b41723d16ee3eb14acf8 c870a3c711d733d9058cd2274a101381 92182ae1b946790e243abbebd1e58b49 e88029bd082d9573011a20488e0f4a5b b9fc9c4cadda727c0d5c9aa7e7d3ecc9 5d912098060d9c8fc5476688ed938be2 9fcf240f2815580b51579ccc998c1608 16a283d6a5cd2c328067ce230b8f316b f7fbf249a413bd3510629efdfb5bbc9c 614c1fdeb84fa86567a6cd31afb6ec1b 0d4b343802645f544e320bde8567ec75 0e49ba0e2db47582094f97da110e0aa0 # As there are issues with biom format and the latest version of R, we will remove these manually ## To do this we will create a .txt file with the column names "FeatureID" and "Frequency" ## Insert the contaminant ASVs under the "FeatureID" header ## Now we save that and move back into QIIME2 to remove these contaminants ########################## REMOVAL OF CONTAMINANTS ####################### # We have to remove the contaminants from the .txt file created qiime feature-table filter-features \ --i-table filtered_EBC1PCRnegsAndSamples_human_green_space_merged_table_controls.qza \ --p-exclude-ids \ --m-metadata-file ContaminantsToRemove.txt \ --o-filtered-table decontamFiltered_EBC1PCRnegsAndSamples_human_green_space_merged_table_controls.qza qiime feature-table summarize \ --i-table decontamFiltered_EBC1PCRnegsAndSamples_human_green_space_merged_table_controls.qza \ --o-visualization decontamFiltered_EBC1PCRnegsAndSamples_human_green_space_merged_table_controls.qzv \ --m-sample-metadata-file Playford_UK_India_mapping_file.txt ############################ REMOVAL OF CONTROLS ######################### # We have to remove the contaminants from the .txt file created qiime feature-table filter-samples \ --i-table decontamFiltered_EBC1PCRnegsAndSamples_human_green_space_merged_table_controls.qza \ --m-metadata-file Playford_UK_India_mapping_file.txt \ --p-where "KeepWithoutControls='Y'" \ --o-filtered-table final_human_green_space_merged_table_BiologicalSamples_beforeLowReadRemoval.qza qiime feature-table summarize \ --i-table final_human_green_space_merged_table_BiologicalSamples_beforeLowReadRemoval.qza \ --o-visualization final_human_green_space_merged_table_BiologicalSamples_beforeLowReadRemoval.qzv \ --m-sample-metadata-file Playford_UK_India_mapping_file.txt ############## REMOVING SINGELTONS AND SAMPLES WITH LOW READS ############ # Removing singletons that have been created from removing contaminants before samples with low reads qiime feature-table filter-features \ --i-table final_human_green_space_merged_table_BiologicalSamples_beforeLowReadRemoval.qza \ --p-min-frequency 2 \ --o-filtered-table final_human_green_space_merged_table_BiologicalSamples_beforeLowReadRemoval_SingletonsRemoved.qza # Now to remove samples with less than 500 sequences qiime feature-table filter-samples \ --i-table final_human_green_space_merged_table_BiologicalSamples_beforeLowReadRemoval_SingletonsRemoved.qza \ --p-min-frequency 500 \ --o-filtered-table final_human_green_space_merged_table.qza qiime feature-table summarize \ --i-table final_human_green_space_merged_table.qza \ --o-visualization final_human_green_space_merged_table.qzv \ --m-sample-metadata-file Playford_UK_India_mapping_file.txt ### Information for the number of sequences per sample + the information regarding samples that were removed are reported in Table S2 in the manuscript # Now that we have our final table, we can perform diversity analyses, compositional analyses, and statistical analyses ####################### SEQUENCING INFO - TABLE S3 ####################### # pre-exposure skin qiime feature-table filter-samples \ --i-table final_human_green_space_merged_table.qza \ --m-metadata-file Playford_UK_India_mapping_file.txt \ --p-where "Sample_Type IN ('1_Morning_Skin_Swab')" \ --o-filtered-table final_human_green_space_merged_table_preExposureSkin.qza qiime feature-table summarize \ --i-table final_human_green_space_merged_table_preExposureSkin.qza \ --o-visualization final_human_green_space_merged_table_preExposureSkin.qzv \ --m-sample-metadata-file Playford_UK_India_mapping_file.txt # post-exposure skin qiime feature-table filter-samples \ --i-table final_human_green_space_merged_table.qza \ --m-metadata-file Playford_UK_India_mapping_file.txt \ --p-where "Sample_Type IN ('2_Skin_Swab')" \ --o-filtered-table final_human_green_space_merged_table_postExposureSkin.qza qiime feature-table summarize \ --i-table final_human_green_space_merged_table_postExposureSkin.qza \ --o-visualization final_human_green_space_merged_table_postExposureSkin.qzv \ --m-sample-metadata-file Playford_UK_India_mapping_file.txt # pre-exposure nasal qiime feature-table filter-samples \ --i-table final_human_green_space_merged_table.qza \ --m-metadata-file Playford_UK_India_mapping_file.txt \ --p-where "Sample_Type IN ('3_Morning_Nasal_Swab')" \ --o-filtered-table final_human_green_space_merged_table_preExposureNasal.qza qiime feature-table summarize \ --i-table final_human_green_space_merged_table_preExposureNasal.qza \ --o-visualization final_human_green_space_merged_table_preExposureNasal.qzv \ --m-sample-metadata-file Playford_UK_India_mapping_file.txt # post-exposure nasal qiime feature-table filter-samples \ --i-table final_human_green_space_merged_table.qza \ --m-metadata-file Playford_UK_India_mapping_file.txt \ --p-where "Sample_Type IN ('4_Nasal_Swab')" \ --o-filtered-table final_human_green_space_merged_table_postExposureNasal.qza qiime feature-table summarize \ --i-table final_human_green_space_merged_table_postExposureNasal.qza \ --o-visualization final_human_green_space_merged_table_postExposureNasal.qzv \ --m-sample-metadata-file Playford_UK_India_mapping_file.txt # air filter qiime feature-table filter-samples \ --i-table final_Adelaide_human_green_space_merged_table.qza \ --m-metadata-file Playford_UK_India_mapping_file.txt \ --p-where "Sample_Type='5_Air_Filter'" \ --o-filtered-table final_Adelaide_human_green_space_merged_table_air.qza qiime feature-table summarize \ --i-table final_Adelaide_human_green_space_merged_table_air.qza \ --o-visualization final_Adelaide_human_green_space_merged_air.qzv \ --m-sample-metadata-file Playford_UK_India_mapping_file.txt # leaf qiime feature-table filter-samples \ --i-table final_Adelaide_human_green_space_merged_table.qza \ --m-metadata-file Playford_UK_India_mapping_file.txt \ --p-where "Sample_Type='6_Leaf'" \ --o-filtered-table final_Adelaide_human_green_space_merged_table_leaf.qza qiime feature-table summarize \ --i-table final_Adelaide_human_green_space_merged_table_leaf.qza \ --o-visualization final_Adelaide_human_green_space_merged_table_leaf.qzv \ --m-sample-metadata-file Playford_UK_India_mapping_file.txt # soil qiime feature-table filter-samples \ --i-table final_Adelaide_human_green_space_merged_table.qza \ --m-metadata-file Playford_UK_India_mapping_file.txt \ --p-where "Sample_Type='7_Soil'" \ --o-filtered-table final_Adelaide_human_green_space_merged_table_soil.qza qiime feature-table summarize \ --i-table final_Adelaide_human_green_space_merged_table_soil.qza \ --o-visualization final_Adelaide_human_green_space_merged_table_soil.qzv \ --m-sample-metadata-file Playford_UK_India_mapping_file.txt # field control qiime feature-table filter-samples \ --i-table final_human_green_space_merged_table.qza \ --m-metadata-file Playford_UK_India_mapping_file.txt \ --p-where "Sample_Type IN ('8_Field control')" \ --o-filtered-table final_human_green_space_merged_table_FieldControl.qza qiime feature-table summarize \ --i-table final_human_green_space_merged_table_FieldControl.qza \ --o-visualization final_human_green_space_merged_table_FieldControl.qzv \ --m-sample-metadata-file Playford_UK_India_mapping_file.txt ########################## ALPHA AND BETA DIVERSITY ###################### # first, we want to run the core metrics command qiime diversity core-metrics-phylogenetic \ --i-phylogeny insertion-tree.qza \ --i-table final_human_green_space_merged_table.qza \ --p-sampling-depth 1000 \ --m-metadata-file Playford_UK_India_mapping_file.txt \ --output-dir core-metrics-results-insertion-decontamFilteredSeqsSings-human-green-space-1000rare # alpha (observed otus and faith's phylogenetic diversity) and beta diversity (PERMANOVA) statistics # alpha diversity will be Figure 1 in the main text # beta diversity will be Figure 2 in the main text qiime diversity alpha-group-significance \ --i-alpha-diversity core-metrics-results-insertion-decontamFilteredSeqsSings-human-green-space-1000rare/observed_otus_vector.qza \ --m-metadata-file Playford_UK_India_mapping_file.txt \ --o-visualization core-metrics-results-insertion-decontamFilteredSeqsSings-human-green-space-1000rare/observed-otus-significance.qzv qiime diversity alpha-group-significance \ --i-alpha-diversity core-metrics-results-insertion-decontamFilteredSeqsSings-human-green-space-1000rare/faith_pd_vector.qza \ --m-metadata-file Playford_UK_India_mapping_file.txt \ --o-visualization core-metrics-results-insertion-decontamFilteredSeqsSings-human-green-space-1000rare/faith-pd-group-significance.qzv qiime diversity beta-group-significance \ --i-distance-matrix core-metrics-results-insertion-decontamFilteredSeqsSings-human-green-space-1000rare/unweighted_unifrac_distance_matrix.qza \ --m-metadata-file Playford_UK_India_mapping_file.txt \ --m-metadata-column Sample_Type_Location \ --o-visualization core-metrics-results-insertion-decontamFilteredSeqsSings-human-green-space-1000rare/unweighted-unifrac-anosim-sampleTypeLocation-significance.qzv \ --p-pairwise ########################### TAXONOMIC BAR PLOT ########################### # taxonomic barplot will be Figure 3 in the main text qiime taxa barplot \ --i-table final_human_green_space_merged_table.qza \ --i-taxonomy silva_human_green_space_taxonomy.qza \ --m-metadata-file Playford_UK_India_mapping_file.txt \ --o-visualization final_human_green_space_taxa-plot.qzv # To get barplots into groups (to look at percentages), we have to merge samples from the same categories together ## We will do this for Sample_Type_Location and Sample_type_Location_Subject qiime feature-table group \ --i-table final_human_green_space_merged_table.qza \ --p-axis sample \ --m-metadata-file Playford_UK_India_mapping_file.txt \ --m-metadata-column Sample_Type_Location \ --p-mode sum \ --o-grouped-table grouped-table_STL_sum.qza qiime taxa barplot \ --i-table grouped-table_STL_sum.qza \ --i-taxonomy silva_human_green_space_taxonomy.qza \ --m-metadata-file GroupMetadata.txt \ --o-visualization grouped-table_STL_sum_taxaplot.qzv qiime feature-table group \ --i-table final_human_green_space_merged_table.qza \ --p-axis sample \ --m-metadata-file Playford_UK_India_mapping_file.txt \ --m-metadata-column Sample_Type_Location_Subject \ --p-mode sum \ --o-grouped-table grouped-table_STLS_sum.qza qiime taxa barplot \ --i-table grouped-table_STLS_sum.qza \ --i-taxonomy taxtable.qza \ --m-metadata-file GroupIndividualMetadata.txt \ --o-visualization grouped-table_STLS_sum_taxaplot.qzv qiime tools import \ --type FeatureData[Taxonomy] \ --input-path taxtable1.txt \ --input-format HeaderlessTSVTaxonomyFormat \ --output-path taxtable.qza ######################## PRESENCE/ABSENCE OF ASVs ######################## # TO BE DONE IN ADELAIDE SAMPLES ONLY qiime feature-table filter-samples \ --i-table final_human_green_space_merged_table.qza \ --m-metadata-file Playford_UK_India_mapping_file.txt \ --p-where "City='Adelaide'" \ --o-filtered-table final_Adelaide_human_green_space_merged_table.qza qiime feature-table summarize \ --i-table final_Adelaide_human_green_space_merged_table.qza \ --o-visualization final_Adelaide_human_green_space_merged_table.qzv \ --m-sample-metadata-file Playford_UK_India_mapping_file.txt # To look at presence/absence we will compare the number of shared ASVs between post-exposure samples and pre-exposure and environmental samples # First we will need to download .tsv files from all the sample group # In excel, we will then determine shared ASVs (i.e. found in post-exposure sample + another sample type) for each of the pairs # We can then work out the proportion of ASVs shared between groups # pre-exposure skin qiime feature-table filter-samples \ --i-table final_Adelaide_human_green_space_merged_table.qza \ --m-metadata-file Playford_UK_India_mapping_file.txt \ --p-where "Sample_Type='1_Morning_Skin_Swab'" \ --o-filtered-table final_Adelaide_human_green_space_merged_table_morning_skin.qza qiime feature-table summarize \ --i-table final_Adelaide_human_green_space_merged_table_morning_skin.qza \ --o-visualization final_Adelaide_human_green_space_merged_table_morning_skin.qzv \ --m-sample-metadata-file Playford_UK_India_mapping_file.txt # post-exposure skin qiime feature-table filter-samples \ --i-table final_Adelaide_human_green_space_merged_table.qza \ --m-metadata-file Playford_UK_India_mapping_file.txt \ --p-where "Sample_Type='2_Skin_Swab'" \ --o-filtered-table final_Adelaide_human_green_space_merged_table_postExposure_skin.qza qiime feature-table summarize \ --i-table final_Adelaide_human_green_space_merged_table_postExposure_skin.qza \ --o-visualization final_Adelaide_human_green_space_merged_table_postExposure_skin.qzv \ --m-sample-metadata-file Playford_UK_India_mapping_file.txt # pre-exposure nasal qiime feature-table filter-samples \ --i-table final_Adelaide_human_green_space_merged_table.qza \ --m-metadata-file Playford_UK_India_mapping_file.txt \ --p-where "Sample_Type='3_Morning_Nasal_Swab'" \ --o-filtered-table final_Adelaide_human_green_space_merged_table_morning_nasal.qza qiime feature-table summarize \ --i-table final_Adelaide_human_green_space_merged_table_morning_nasal.qza \ --o-visualization final_Adelaide_human_green_space_merged_table_morning_nasal.qzv \ --m-sample-metadata-file Playford_UK_India_mapping_file.txt # post-exposure nasal qiime feature-table filter-samples \ --i-table final_Adelaide_human_green_space_merged_table.qza \ --m-metadata-file Playford_UK_India_mapping_file.txt \ --p-where "Sample_Type='4_Nasal_Swab'" \ --o-filtered-table final_Adelaide_human_green_space_merged_table_postExposure_nasal.qza qiime feature-table summarize \ --i-table final_Adelaide_human_green_space_merged_table_postExposure_nasal.qza \ --o-visualization final_Adelaide_human_green_space_merged_table_postExposure_nasal.qzv \ --m-sample-metadata-file Playford_UK_India_mapping_file.txt # Environmental feature tables were created above. See: ## final_Adelaide_human_green_space_merged_air.qzv ## final_Adelaide_human_green_space_merged_leaf.qzv ## final_Adelaide_human_green_space_merged_soil.qzv ### Information for presence/absence observational analysis was collated and presented as Figure 4 in the manuscript ########################## CORE MICROBIOME ########################### # First we want to see what is shared between post-exposure samples and environmental and pre-exposure samples # To perform a core microbiome analysis, we must first create feature tables with the groups we want to compare # post-exposure skin vs pre-exposure skin qiime feature-table filter-samples \ --i-table final_Adelaide_human_green_space_merged_table.qza \ --m-metadata-file Playford_UK_India_mapping_file.txt \ --p-where "Sample_Type IN ('2_Skin_Swab', '1_Morning_Skin_Swab')" \ --o-filtered-table CoreMicrobiome/beforeRemoval/final_Adelaide_human_green_space_merged_table_skin_morning.qza qiime feature-table summarize \ --i-table CoreMicrobiome/beforeRemoval/final_Adelaide_human_green_space_merged_table_skin_morning.qza \ --o-visualization CoreMicrobiome/beforeRemoval/final_Adelaide_human_green_space_merged_table_skin_morning.qzv \ --m-sample-metadata-file Playford_UK_India_mapping_file.txt # post-exposure skin vs air qiime feature-table filter-samples \ --i-table final_Adelaide_human_green_space_merged_table.qza \ --m-metadata-file Playford_UK_India_mapping_file.txt \ --p-where "Sample_Type IN ('2_Skin_Swab', '5_Air_Filter')" \ --o-filtered-table CoreMicrobiome/beforeRemoval/final_Adelaide_human_green_space_merged_table_skin_air.qza qiime feature-table summarize \ --i-table CoreMicrobiome/beforeRemoval/final_Adelaide_human_green_space_merged_table_skin_air.qza \ --o-visualization CoreMicrobiome/beforeRemoval/final_Adelaide_human_green_space_merged_table_skin_air.qzv \ --m-sample-metadata-file Playford_UK_India_mapping_file.txt # post-exposure skin vs leaf qiime feature-table filter-samples \ --i-table final_Adelaide_human_green_space_merged_table.qza \ --m-metadata-file Playford_UK_India_mapping_file.txt \ --p-where "Sample_Type IN ('2_Skin_Swab', '6_Leaf')" \ --o-filtered-table CoreMicrobiome/beforeRemoval/final_Adelaide_human_green_space_merged_table_skin_leaf.qza qiime feature-table summarize \ --i-table CoreMicrobiome/beforeRemoval/final_Adelaide_human_green_space_merged_table_skin_leaf.qza \ --o-visualization CoreMicrobiome/beforeRemoval/final_Adelaide_human_green_space_merged_table_skin_leaf.qzv \ --m-sample-metadata-file Playford_UK_India_mapping_file.txt # post-exposure skin vs soil qiime feature-table filter-samples \ --i-table final_Adelaide_human_green_space_merged_table.qza \ --m-metadata-file Playford_UK_India_mapping_file.txt \ --p-where "Sample_Type IN ('2_Skin_Swab', '7_Soil')" \ --o-filtered-table CoreMicrobiome/beforeRemoval/final_Adelaide_human_green_space_merged_table_skin_soil.qza qiime feature-table summarize \ --i-table CoreMicrobiome/beforeRemoval/final_Adelaide_human_green_space_merged_table_skin_soil.qza \ --o-visualization CoreMicrobiome/beforeRemoval/final_Adelaide_human_green_space_merged_table_skin_soil.qzv \ --m-sample-metadata-file Playford_UK_India_mapping_file.txt # post-exposure nasal vs pre-exposure nasal qiime feature-table filter-samples \ --i-table final_Adelaide_human_green_space_merged_table.qza \ --m-metadata-file Playford_UK_India_mapping_file.txt \ --p-where "Sample_Type IN ('4_Nasal_Swab', '3_Morning_Nasal_Swab')" \ --o-filtered-table CoreMicrobiome/beforeRemoval/final_Adelaide_human_green_space_merged_table_nasal_morning.qza qiime feature-table summarize \ --i-table CoreMicrobiome/beforeRemoval/final_Adelaide_human_green_space_merged_table_nasal_morning.qza \ --o-visualization CoreMicrobiome/beforeRemoval/final_Adelaide_human_green_space_merged_table_nasal_morning.qzv \ --m-sample-metadata-file Playford_UK_India_mapping_file.txt # post-exposure nasal vs air qiime feature-table filter-samples \ --i-table final_Adelaide_human_green_space_merged_table.qza \ --m-metadata-file Playford_UK_India_mapping_file.txt \ --p-where "Sample_Type IN ('4_Nasal_Swab', '5_Air_Filter')" \ --o-filtered-table CoreMicrobiome/beforeRemoval/final_Adelaide_human_green_space_merged_table_nasal_air.qza qiime feature-table summarize \ --i-table CoreMicrobiome/beforeRemoval/final_Adelaide_human_green_space_merged_table_nasal_air.qza \ --o-visualization CoreMicrobiome/beforeRemoval/final_Adelaide_human_green_space_merged_table_nasal_air.qzv \ --m-sample-metadata-file Playford_UK_India_mapping_file.txt # post-exposure nasal vs leaf qiime feature-table filter-samples \ --i-table final_Adelaide_human_green_space_merged_table.qza \ --m-metadata-file Playford_UK_India_mapping_file.txt \ --p-where "Sample_Type IN ('4_Nasal_Swab', '6_Leaf')" \ --o-filtered-table CoreMicrobiome/beforeRemoval/final_Adelaide_human_green_space_merged_table_nasal_leaf.qza qiime feature-table summarize \ --i-table CoreMicrobiome/beforeRemoval/final_Adelaide_human_green_space_merged_table_nasal_leaf.qza \ --o-visualization CoreMicrobiome/beforeRemoval/final_Adelaide_human_green_space_merged_table_nasal_leaf.qzv \ --m-sample-metadata-file Playford_UK_India_mapping_file.txt # post-exposure nasal vs soil qiime feature-table filter-samples \ --i-table final_Adelaide_human_green_space_merged_table.qza \ --m-metadata-file Playford_UK_India_mapping_file.txt \ --p-where "Sample_Type IN ('4_Nasal_Swab', '7_Soil')" \ --o-filtered-table CoreMicrobiome/beforeRemoval/final_Adelaide_human_green_space_merged_table_nasal_soil.qza qiime feature-table summarize \ --i-table CoreMicrobiome/beforeRemoval/final_Adelaide_human_green_space_merged_table_nasal_soil.qza \ --o-visualization CoreMicrobiome/beforeRemoval/final_Adelaide_human_green_space_merged_table_nasal_soil.qzv \ --m-sample-metadata-file Playford_UK_India_mapping_file.txt # Now that we have all of our feature tables ready, we can now perform the core microbiome analysis # post-exposure skin vs pre-exposure skin qiime feature-table core-features \ --i-table CoreMicrobiome/beforeRemoval/final_Adelaide_human_green_space_merged_table_skin_morning.qza \ --p-min-fraction 0.5 \ --p-max-fraction 1.0 \ --o-visualization CoreMicrobiome/beforeRemoval/skin_morning_beforeRemoval_core_microbiome.qzv # post-exposure skin vs air qiime feature-table core-features \ --i-table CoreMicrobiome/beforeRemoval/final_Adelaide_human_green_space_merged_table_skin_air.qza \ --p-min-fraction 0.5 \ --p-max-fraction 1.0 \ --o-visualization CoreMicrobiome/beforeRemoval/skin_air_beforeRemoval_core_microbiome.qzv # post-exposure skin vs leaf qiime feature-table core-features \ --i-table CoreMicrobiome/beforeRemoval/final_Adelaide_human_green_space_merged_table_skin_leaf.qza \ --p-min-fraction 0.5 \ --p-max-fraction 1.0 \ --o-visualization CoreMicrobiome/beforeRemoval/skin_leaf_beforeRemoval_core_microbiome.qzv # post-exposure skin vs soil qiime feature-table core-features \ --i-table CoreMicrobiome/beforeRemoval/final_Adelaide_human_green_space_merged_table_skin_soil.qza \ --p-min-fraction 0.5 \ --p-max-fraction 1.0 \ --o-visualization CoreMicrobiome/beforeRemoval/skin_soil_beforeRemoval_core_microbiome.qzv # post-exposure nasal vs pre-exposure nasal qiime feature-table core-features \ --i-table CoreMicrobiome/beforeRemoval/final_Adelaide_human_green_space_merged_table_nasal_morning.qza \ --p-min-fraction 0.5 \ --p-max-fraction 1.0 \ --o-visualization CoreMicrobiome/beforeRemoval/nasal_morning_beforeRemoval_core_microbiome.qzv # post-exposure nasal vs air qiime feature-table core-features \ --i-table CoreMicrobiome/beforeRemoval/final_Adelaide_human_green_space_merged_table_nasal_air.qza \ --p-min-fraction 0.5 \ --p-max-fraction 1.0 \ --o-visualization CoreMicrobiome/beforeRemoval/nasal_air_beforeRemoval_core_microbiome.qzv # post-exposure nasal vs leaf qiime feature-table core-features \ --i-table CoreMicrobiome/beforeRemoval/final_Adelaide_human_green_space_merged_table_nasal_leaf.qza \ --p-min-fraction 0.5 \ --p-max-fraction 1.0 \ --o-visualization CoreMicrobiome/beforeRemoval/nasal_leaf_beforeRemoval_core_microbiome.qzv # post-exposure nasal vs soil qiime feature-table core-features \ --i-table CoreMicrobiome/beforeRemoval/final_Adelaide_human_green_space_merged_table_nasal_soil.qza \ --p-min-fraction 0.5 \ --p-max-fraction 1.0 \ --o-visualization CoreMicrobiome/beforeRemoval/nasal_soil_beforeRemoval_core_microbiome.qzv ### Information for an 80% core microbiome analysis was collated and presented as Table 1 in the manuscript ###################### REMOVING PRE-EXPOSURE ASVS ######################## # to look at what is introduced from the environment, I will remove features that are observed in the pre-exposure samples from the post-exposure samples # first, the feature tables from the pre-exposure samples need to be uploaded into view.qiime.org # then, I will download the tsv containing the feature information # I then import the tsv files into excel and relabel the column titles to make them compatible for filtering features # After this, I save the excel sheet as a txt files, so I can chose this file as the 'metadata-file' input # now to remove the pre-exposure (morning) ASVs from the post-exposure samples # skin qiime feature-table filter-features \ --i-table final_Adelaide_human_green_space_merged_table_postExposure_skin.qza \ --p-exclude-ids \ --m-metadata-file MorningSkin.txt \ --o-filtered-table final_Adelaide_human_green_space_merged_table_postExposure_skin_morningRemoved.qza qiime feature-table summarize \ --i-table final_Adelaide_human_green_space_merged_table_postExposure_skin_morningRemoved.qza \ --o-visualization final_Adelaide_human_green_space_merged_table_postExposure_skin_morningRemoved.qzv \ --m-sample-metadata-file Playford_UK_India_mapping_file.txt # nasal qiime feature-table filter-features \ --i-table final_Adelaide_human_green_space_merged_table_postExposure_nasal.qza \ --p-exclude-ids \ --m-metadata-file MorningNasal.txt \ --o-filtered-table final_Adelaide_human_green_space_merged_table_postExposure_nasal_morningRemoved.qza qiime feature-table summarize \ --i-table final_Adelaide_human_green_space_merged_table_postExposure_nasal_morningRemoved.qza \ --o-visualization final_Adelaide_human_green_space_merged_table_postExposure_nasal_morningRemoved.qzv \ --m-sample-metadata-file Playford_UK_India_mapping_file.txt ######################## PRESENCE/ABSENCE OF ASVs ######################## # we performed the shared ASVs (presence/absence) observational analysis as we did before with the post-exposure samples with pre-exposure ASVs removed. # we do this for the following comparisons: ### post-exposure skin vs air ### post-exposure skin vs leaf ### post-exposure skin vs soil ### post-exposure nasal vs air ### post-exposure nasal vs leaf ### post-exposure nasal vs soil ### Information for presence/absence observational analysis was collated and presented as Figure 4 in the manuscript ########################## CORE MICROBIOME ########################### # Before performing a core microbiome analysis again, we have to merge the new tables with the environmental samples # post-exposure skin vs air qiime feature-table merge \ --i-tables final_Adelaide_human_green_space_merged_table_postExposure_skin_morningRemoved.qza \ --i-tables final_Adelaide_human_green_space_merged_table_air.qza \ --o-merged-table CoreMicrobiome/afterRemoval/final_Adelaide_human_green_space_merged_table_skin_air.qza qiime feature-table summarize \ --i-table CoreMicrobiome/afterRemoval/final_Adelaide_human_green_space_merged_table_skin_air.qza \ --o-visualization CoreMicrobiome/afterRemoval/final_Adelaide_human_green_space_merged_table_skin_air.qzv \ --m-sample-metadata-file Playford_UK_India_mapping_file.txt # post-exposure skin vs leaf qiime feature-table merge \ --i-tables final_Adelaide_human_green_space_merged_table_postExposure_skin_morningRemoved.qza \ --i-tables final_Adelaide_human_green_space_merged_table_leaf.qza \ --o-merged-table CoreMicrobiome/afterRemoval/final_Adelaide_human_green_space_merged_table_skin_leaf.qza qiime feature-table summarize \ --i-table CoreMicrobiome/afterRemoval/final_Adelaide_human_green_space_merged_table_skin_leaf.qza \ --o-visualization CoreMicrobiome/afterRemoval/final_Adelaide_human_green_space_merged_table_skin_leaf.qzv \ --m-sample-metadata-file Playford_UK_India_mapping_file.txt # post-exposure skin vs soil qiime feature-table merge \ --i-tables final_Adelaide_human_green_space_merged_table_postExposure_skin_morningRemoved.qza \ --i-tables final_Adelaide_human_green_space_merged_table_soil.qza \ --o-merged-table CoreMicrobiome/afterRemoval/final_Adelaide_human_green_space_merged_table_skin_soil.qza qiime feature-table summarize \ --i-table CoreMicrobiome/afterRemoval/final_Adelaide_human_green_space_merged_table_skin_soil.qza \ --o-visualization CoreMicrobiome/afterRemoval/final_Adelaide_human_green_space_merged_table_skin_soil.qzv \ --m-sample-metadata-file Playford_UK_India_mapping_file.txt # post-exposure nasal vs air qiime feature-table merge \ --i-tables final_Adelaide_human_green_space_merged_table_postExposure_nasal_morningRemoved.qza \ --i-tables final_Adelaide_human_green_space_merged_table_air.qza \ --o-merged-table CoreMicrobiome/afterRemoval/final_Adelaide_human_green_space_merged_table_nasal_air.qza qiime feature-table summarize \ --i-table CoreMicrobiome/afterRemoval/final_Adelaide_human_green_space_merged_table_nasal_air.qza \ --o-visualization CoreMicrobiome/afterRemoval/final_Adelaide_human_green_space_merged_table_nasal_air.qzv \ --m-sample-metadata-file Playford_UK_India_mapping_file.txt # post-exposure nasal vs leaf qiime feature-table merge \ --i-tables final_Adelaide_human_green_space_merged_table_postExposure_nasal_morningRemoved.qza \ --i-tables final_Adelaide_human_green_space_merged_table_leaf.qza \ --o-merged-table CoreMicrobiome/afterRemoval/final_Adelaide_human_green_space_merged_table_nasal_leaf.qza qiime feature-table summarize \ --i-table CoreMicrobiome/afterRemoval/final_Adelaide_human_green_space_merged_table_nasal_leaf.qza \ --o-visualization CoreMicrobiome/afterRemoval/final_Adelaide_human_green_space_merged_table_nasal_leaf.qzv \ --m-sample-metadata-file Playford_UK_India_mapping_file.txt # post-exposure nasal vs soil qiime feature-table merge \ --i-tables final_Adelaide_human_green_space_merged_table_postExposure_nasal_morningRemoved.qza \ --i-tables final_Adelaide_human_green_space_merged_table_soil.qza \ --o-merged-table CoreMicrobiome/afterRemoval/final_Adelaide_human_green_space_merged_table_nasal_soil.qza qiime feature-table summarize \ --i-table CoreMicrobiome/afterRemoval/final_Adelaide_human_green_space_merged_table_nasal_soil.qza \ --o-visualization CoreMicrobiome/afterRemoval/final_Adelaide_human_green_space_merged_table_nasal_soil.qzv \ --m-sample-metadata-file Playford_UK_India_mapping_file.txt ### okay, now that we have created feature tables with environmental samples and post-exposure samples with pre-exposure ASVs removed, we can now perform core microbiome analyses # post-exposure skin vs air qiime feature-table core-features \ --i-table CoreMicrobiome/afterRemoval/final_Adelaide_human_green_space_merged_table_skin_air.qza \ --p-min-fraction 0.5 \ --p-max-fraction 1.0 \ --o-visualization CoreMicrobiome/afterRemoval/skin_air_afterRemoval_core_microbiome.qzv # post-exposure skin vs leaf qiime feature-table core-features \ --i-table CoreMicrobiome/afterRemoval/final_Adelaide_human_green_space_merged_table_skin_leaf.qza \ --p-min-fraction 0.5 \ --p-max-fraction 1.0 \ --o-visualization CoreMicrobiome/afterRemoval/skin_leaf_afterRemoval_core_microbiome.qzv # post-exposure skin vs soil qiime feature-table core-features \ --i-table CoreMicrobiome/afterRemoval/final_Adelaide_human_green_space_merged_table_skin_soil.qza \ --p-min-fraction 0.5 \ --p-max-fraction 1.0 \ --o-visualization CoreMicrobiome/afterRemoval/skin_soil_afterRemoval_core_microbiome.qzv # post-exposure nasal vs air qiime feature-table core-features \ --i-table CoreMicrobiome/afterRemoval/final_Adelaide_human_green_space_merged_table_nasal_air.qza \ --p-min-fraction 0.5 \ --p-max-fraction 1.0 \ --o-visualization CoreMicrobiome/afterRemoval/nasal_air_afterRemoval_core_microbiome.qzv # post-exposure nasal vs leaf qiime feature-table core-features \ --i-table CoreMicrobiome/afterRemoval/final_Adelaide_human_green_space_merged_table_nasal_leaf.qza \ --p-min-fraction 0.5 \ --p-max-fraction 1.0 \ --o-visualization CoreMicrobiome/afterRemoval/nasal_leaf_afterRemoval_core_microbiome.qzv # post-exposure nasal vs soil qiime feature-table core-features \ --i-table CoreMicrobiome/afterRemoval/final_Adelaide_human_green_space_merged_table_nasal_soil.qza \ --p-min-fraction 0.5 \ --p-max-fraction 1.0 \ --o-visualization CoreMicrobiome/afterRemoval/nasal_soil_afterRemoval_core_microbiome.qzv ### So there were no ASVs shared between post-exposure samples and environmental samples at an 80% core ### Therefore, we will repeat this analysis at a genus level (level 6) ### To do this, we have to collapse the feature tables to level 6 before performing the core microbiome analysis # post-exposure skin vs air qiime taxa collapse \ --i-table CoreMicrobiome/afterRemoval/final_Adelaide_human_green_space_merged_table_skin_air.qza \ --i-taxonomy silva_human_green_space_taxonomy.qza \ --p-level 6 \ --o-collapsed-table CoreMicrobiome/afterRemoval/l6-final_Adelaide_human_green_space_merged_table_skin_air.qza qiime feature-table core-features \ --i-table CoreMicrobiome/afterRemoval/l6-final_Adelaide_human_green_space_merged_table_skin_air.qza \ --p-min-fraction 0.5 \ --p-max-fraction 1.0 \ --o-visualization CoreMicrobiome/afterRemoval/l6_skin_air_afterRemoval_core_microbiome.qzv # post-exposure skin vs leaf qiime taxa collapse \ --i-table CoreMicrobiome/afterRemoval/final_Adelaide_human_green_space_merged_table_skin_leaf.qza \ --i-taxonomy silva_human_green_space_taxonomy.qza \ --p-level 6 \ --o-collapsed-table CoreMicrobiome/afterRemoval/l6-final_Adelaide_human_green_space_merged_table_skin_leaf.qza qiime feature-table core-features \ --i-table CoreMicrobiome/afterRemoval/l6-final_Adelaide_human_green_space_merged_table_skin_leaf.qza \ --p-min-fraction 0.5 \ --p-max-fraction 1.0 \ --o-visualization CoreMicrobiome/afterRemoval/l6_skin_leaf_afterRemoval_core_microbiome.qzv # post-exposure skin vs soil qiime taxa collapse \ --i-table CoreMicrobiome/afterRemoval/final_Adelaide_human_green_space_merged_table_skin_soil.qza \ --i-taxonomy silva_human_green_space_taxonomy.qza \ --p-level 6 \ --o-collapsed-table CoreMicrobiome/afterRemoval/l6-final_Adelaide_human_green_space_merged_table_skin_soil.qza qiime feature-table core-features \ --i-table CoreMicrobiome/afterRemoval/l6-final_Adelaide_human_green_space_merged_table_skin_soil.qza \ --p-min-fraction 0.5 \ --p-max-fraction 1.0 \ --o-visualization CoreMicrobiome/afterRemoval/l6_skin_soil_afterRemoval_core_microbiome.qzv # post-exposure nasal vs air qiime taxa collapse \ --i-table CoreMicrobiome/afterRemoval/final_Adelaide_human_green_space_merged_table_nasal_air.qza \ --i-taxonomy silva_human_green_space_taxonomy.qza \ --p-level 6 \ --o-collapsed-table CoreMicrobiome/afterRemoval/l6-final_Adelaide_human_green_space_merged_table_nasal_air.qza qiime feature-table core-features \ --i-table CoreMicrobiome/afterRemoval/l6-final_Adelaide_human_green_space_merged_table_nasal_air.qza \ --p-min-fraction 0.5 \ --p-max-fraction 1.0 \ --o-visualization CoreMicrobiome/afterRemoval/l6_nasal_air_afterRemoval_core_microbiome.qzv # post-exposure nasal vs leaf qiime taxa collapse \ --i-table CoreMicrobiome/afterRemoval/final_Adelaide_human_green_space_merged_table_nasal_leaf.qza \ --i-taxonomy silva_human_green_space_taxonomy.qza \ --p-level 6 \ --o-collapsed-table CoreMicrobiome/afterRemoval/l6-final_Adelaide_human_green_space_merged_table_nasal_leaf.qza qiime feature-table core-features \ --i-table CoreMicrobiome/afterRemoval/l6-final_Adelaide_human_green_space_merged_table_nasal_leaf.qza \ --p-min-fraction 0.5 \ --p-max-fraction 1.0 \ --o-visualization CoreMicrobiome/afterRemoval/l6_nasal_leaf_afterRemoval_core_microbiome.qzv # post-exposure nasal vs soil qiime taxa collapse \ --i-table CoreMicrobiome/afterRemoval/final_Adelaide_human_green_space_merged_table_nasal_soil.qza \ --i-taxonomy silva_human_green_space_taxonomy.qza \ --p-level 6 \ --o-collapsed-table CoreMicrobiome/afterRemoval/l6-final_Adelaide_human_green_space_merged_table_nasal_soil.qza qiime feature-table core-features \ --i-table CoreMicrobiome/afterRemoval/l6-final_Adelaide_human_green_space_merged_table_nasal_soil.qza \ --p-min-fraction 0.5 \ --p-max-fraction 1.0 \ --o-visualization CoreMicrobiome/afterRemoval/l6_nasal_soil_afterRemoval_core_microbiome.qzv ### Information for an 80% core microbiome analysis was collated and presented as Table 2 in the manuscript