######################### 2019 Iceland 16S ###################### ####### UPDATED 1:28 PM local time (Iceland) May 24th ########### ## find data here: http://lovelace.cluster.earlham.edu/mounts/lovelace/16S/Iceland-2019/Skalanes-Test-Pit/2024_workflow/ # ssh to smithem3@cluster.earlham.edu # EJcG5pFkGchln3 ssh lovelace cd /mounts/lovelace/16S/Iceland-2019/Skalanes-Test-Pit/2024_workflow ## created manifest file: https://docs.google.com/spreadsheets/d/1Hnu-4gPQfmLv6Fb-n5E8Gpg7dcCnQz84F9uEHxF4XnQ/edit#gid=0 ## and only test pit samples: https://docs.google.com/spreadsheets/d/1Fg6BWqgH5M5avdLudQcJ4Rufxfp23Q0iqbUz_Uxe62o/edit#gid=0 ## load qiime2 # module load qiime2/2023.9-amplicon module load qiime2/2024 source tab-qiime ## to find out about the availalbe modules use: module avail ## note that in 2023 qiime2 split and now has -amplicon and -shotgun ## get the single-end manifest file from Drive: fetch-gdrive-object.py -d 1Hnu-4gPQfmLv6Fb-n5E8Gpg7dcCnQz84F9uEHxF4XnQ -t sheet -f tsv -o STP-16S-All-Manifest.tsv fetch-gdrive-object.py -d 1Fg6BWqgH5M5avdLudQcJ4Rufxfp23Q0iqbUz_Uxe62o -t sheet -f tsv -o STP-16S-TPonly-Manifest.tsv ####################### Single-end!!! ################################# ### Re-ran ALL on 20240423 just to change the sample names so things will plot in order - aka changed STP2 to STP02 qiime tools import \ --type 'SampleData[SequencesWithQuality]' \ --input-path STP-16S-All-Manifest.tsv \ --output-path STP-16S-All-single-end-demux.qza \ --input-format SingleEndFastqManifestPhred33V2 qiime demux summarize \ --i-data STP-16S-All-single-end-demux.qza \ --o-visualization STP-16S-All-single-end-demux.qzv qiime tools import \ --type 'SampleData[SequencesWithQuality]' \ --input-path STP-16S-TPonly-Manifest.tsv \ --output-path STP-16S-TPonly-single-end-demux.qza \ --input-format SingleEndFastqManifestPhred33V2 qiime demux summarize \ --i-data STP-16S-TPonly-single-end-demux.qza \ --o-visualization STP-16S-TPonly-single-end-demux.qzv # CUTADAPT # Add the flags --p-match-read-wildcards and --p-match-adapter-wildcards to your command. # This will allow matching of IUPAC wild cards such as H, V, N, etc.. # Furthermore, I'd advise adding the flag --p-discard-untrimmed as well. # This will discard any sequences in which both primers could not be trimmed from the sequences. qiime cutadapt trim-single \ --i-demultiplexed-sequences STP-16S-All-single-end-demux.qza \ --p-cores 8 \ --p-front TATGGTAATTGTGTGCCAGCMGCCGCGGTAA \ --p-match-read-wildcards \ --p-match-adapter-wildcards \ --p-discard-untrimmed \ --o-trimmed-sequences STP-16S-All-single-end-trimmed-sequences.qza qiime demux summarize \ --i-data STP-16S-All-single-end-trimmed-sequences.qza \ --o-visualization STP-16S-All-single-end-trimmed-sequences.qzv qiime cutadapt trim-single \ --i-demultiplexed-sequences STP-16S-TPonly-single-end-demux.qza \ --p-cores 8 \ --p-front TATGGTAATTGTGTGCCAGCMGCCGCGGTAA \ --p-match-read-wildcards \ --p-match-adapter-wildcards \ --p-discard-untrimmed \ --o-trimmed-sequences STP-16S-TPonly-single-end-trimmed-sequences.qza qiime demux summarize \ --i-data STP-16S-TPonly-single-end-trimmed-sequences.qza \ --o-visualization STP-16S-TPonly-single-end-trimmed-sequences.qzv # DADA2, feature table summarize # for All: trunc at 226 where QS bottom of box drops below 25 # for Test Pit Only (TPonly): trunc at 226 where QS bottom of box drops below 25 nohup qiime dada2 denoise-single \ --p-n-threads 8 \ --i-demultiplexed-seqs STP-16S-All-single-end-trimmed-sequences.qza \ --p-trunc-len 226 \ --o-representative-sequences STP-16S-All-single-end-trimmed-repseqs.qza \ --o-table STP-16S-All-single-end-trimmed-table.qza \ --o-denoising-stats STP-16S-All-single-end-trimmed-stats.qza & qiime metadata tabulate \ --m-input-file STP-16S-All-single-end-trimmed-stats.qza \ --o-visualization STP-16S-All-single-end-trimmed-stats.qzv # metadata: https://docs.google.com/spreadsheets/d/1wQ3XPlk0CN7bIoiAifaVTu-wNjsc91IdTTFByCKgkAY/edit#gid=0 fetch-gdrive-object.py -d 1wQ3XPlk0CN7bIoiAifaVTu-wNjsc91IdTTFByCKgkAY -t sheet -f tsv -o STP-16S-Metadata.tsv ## made an updated metadata (May 22) including sequencing and DADA2 info fetch-gdrive-object.py -d 1wQ3XPlk0CN7bIoiAifaVTu-wNjsc91IdTTFByCKgkAY -t sheet -f tsv -o STP-16S-large-Metadata.tsv qiime feature-table summarize \ --i-table STP-16S-All-single-end-trimmed-table.qza \ --o-visualization STP-16S-All-single-end-trimmed-table.qzv \ --m-sample-metadata-file STP-16S-Metadata.tsv qiime feature-table tabulate-seqs \ --i-data STP-16S-All-single-end-trimmed-repseqs.qza \ --o-visualization STP-16S-All-single-end-trimmed-repseqs.qzv nohup qiime dada2 denoise-single \ --p-n-threads 8 \ --i-demultiplexed-seqs STP-16S-TPonly-single-end-trimmed-sequences.qza \ --p-trunc-len 226 \ --o-representative-sequences STP-16S-TPonly-single-end-trimmed-repseqs.qza \ --o-table STP-16S-TPonly-single-end-trimmed-table.qza \ --o-denoising-stats STP-16S-TPonly-single-end-trimmed-stats.qza & qiime metadata tabulate \ --m-input-file STP-16S-TPonly-single-end-trimmed-stats.qza \ --o-visualization STP-16S-TPonly-single-end-trimmed-stats.qzv qiime feature-table summarize \ --i-table STP-16S-TPonly-single-end-trimmed-table.qza \ --o-visualization STP-16S-TPonly-single-end-trimmed-table.qzv \ --m-sample-metadata-file STP-16S-Metadata.tsv qiime feature-table tabulate-seqs \ --i-data STP-16S-TPonly-single-end-trimmed-repseqs.qza \ --o-visualization STP-16S-TPonly-single-end-trimmed-repseqs.qzv #### Charlie noticed that we can modify the number of reads that DADA2 uses: # The number of reads to use when training the error model. Smaller numbers will result in a shorter run # time but a less reliable error model.[default: 1,000,000] # When I summed up the number of reads we got in the 2019 16S STP data, we have 1,453,471 # Somewhere on the forum (sorry I couldn't find the exact thread) the dada2 developer mentions that #the default 1M is based on some prior benchmarking and there is only modest improvements beyond that, say up to 2Mil. qiime dada2 denoise-single \ --p-n-threads 8 \ --p-n-reads-learn 3000000 \ --i-demultiplexed-seqs STP-16S-All-single-end-trimmed-sequences.qza \ --p-trunc-len 226 \ --o-representative-sequences STP-16S-All-single-end-trimmed-repseqs.qza \ --o-table STP-16S-All-single-end-trimmed-table.qza \ --o-denoising-stats STP-16S-All-single-end-trimmed-stats.qza qiime metadata tabulate \ --m-input-file STP-16S-All-single-end-trimmed-stats.qza \ --o-visualization STP-16S-All-single-end-trimmed-stats.qzv # For "all" STP data: Sampling Depth of 38914 Retained 622,624 (71.75%) features in 16 (100.00%) samples at the specifed sampling depth. # For "test pit only" data: Sampling Depth of 38891 Retained 505,583 (75.36%) features in 13 (100.00%) samples at the specifed sampling depth. qiime diversity alpha-rarefaction \ --i-table STP-16S-All-single-end-trimmed-table.qza \ --m-metadata-file STP-16S-Metadata.tsv \ --o-visualization STP-16S-All-alpha-rarefaction-38914.qzv \ --p-min-depth 10 \ --p-max-depth 38914 qiime diversity alpha-rarefaction \ --i-table STP-16S-TPonly-single-end-trimmed-table.qza \ --m-metadata-file STP-16S-Metadata.tsv \ --o-visualization STP-16S-TPonly-alpha-rarefaction-38891.qzv \ --p-min-depth 10 \ --p-max-depth 38891 # at these two levels of rarefaction we are beyond the exponential growth of the curve. ## re-upload metadata after changing the "floor" category to include "floor-dung" so the sample size is more than 2 fetch-gdrive-object.py -d 1wQ3XPlk0CN7bIoiAifaVTu-wNjsc91IdTTFByCKgkAY -t sheet -f tsv -o STP-16S-Grouped-Floors-Metadata.tsv nohup qiime phylogeny align-to-tree-mafft-fasttree \ --p-n-threads 8 \ --i-sequences STP-16S-All-single-end-trimmed-repseqs.qza \ --o-alignment STP-16S-All-single-end-trimmed-repseqs-aligned.qza \ --o-masked-alignment STP-16S-All-single-end-trimmed-repseqs-masked-aligned.qza \ --o-tree STP-16S-All-single-end-trimmed-unrooted-tree.qza \ --o-rooted-tree STP-16S-All-single-end-trimmed-rooted-tree.qza & nohup qiime phylogeny align-to-tree-mafft-fasttree \ --p-n-threads 8 \ --i-sequences STP-16S-TPonly-single-end-trimmed-repseqs.qza \ --o-alignment STP-16S-TPonly-single-end-trimmed-repseqs-aligned.qza \ --o-masked-alignment STP-16S-TPonly-single-end-trimmed-repseqs-masked-aligned.qza \ --o-tree STP-16S-TPonly-single-end-trimmed-unrooted-tree.qza \ --o-rooted-tree STP-16S-TPonly-single-end-trimmed-rooted-tree.qza & nohup qiime diversity core-metrics-phylogenetic \ --i-phylogeny STP-16S-All-single-end-trimmed-rooted-tree.qza \ --i-table STP-16S-All-single-end-trimmed-table.qza \ --p-sampling-depth 38914 \ --m-metadata-file STP-16S-Metadata.tsv \ --output-dir STP-16S-All-core-metrics-38914 & qiime diversity alpha-group-significance \ --i-alpha-diversity STP-16S-All-core-metrics-38914/faith_pd_vector.qza \ --m-metadata-file STP-16S-Metadata.tsv \ --o-visualization STP-16S-All-core-metrics-38914/faith-pd-group-significance.qzv qiime diversity alpha-group-significance \ --i-alpha-diversity STP-16S-All-core-metrics-38914/evenness_vector.qza \ --m-metadata-file STP-16S-Metadata.tsv \ --o-visualization STP-16S-All-core-metrics-38914/evenness-group-significance.qzv qiime diversity alpha-group-significance \ --i-alpha-diversity STP-16S-All-core-metrics-38914/shannon_vector.qza \ --m-metadata-file STP-16S-Metadata.tsv \ --o-visualization STP-16S-All-core-metrics-38914/shannon-group-significance.qzv nohup qiime diversity core-metrics-phylogenetic \ --i-phylogeny STP-16S-TPonly-single-end-trimmed-rooted-tree.qza \ --i-table STP-16S-TPonly-single-end-trimmed-table.qza \ --p-sampling-depth 38891 \ --m-metadata-file STP-16S-Metadata.tsv \ --output-dir STP-16S-TPonly-core-metrics-38891 & qiime diversity alpha-group-significance \ --i-alpha-diversity STP-16S-TPonly-core-metrics-38891/faith_pd_vector.qza \ --m-metadata-file STP-16S-Metadata.tsv \ --o-visualization STP-16S-TPonly-core-metrics-38891/faith-pd-group-significance.qzv qiime diversity alpha-group-significance \ --i-alpha-diversity STP-16S-TPonly-core-metrics-38891/evenness_vector.qza \ --m-metadata-file STP-16S-Metadata.tsv \ --o-visualization STP-16S-TPonly-core-metrics-38891/evenness-group-significance.qzv qiime diversity alpha-group-significance \ --i-alpha-diversity STP-16S-TPonly-core-metrics-38891/shannon_vector.qza \ --m-metadata-file STP-16S-Metadata.tsv \ --o-visualization STP-16S-TPonly-core-metrics-38891/shannon-group-significance.qzv ################################################### ### PHYLOGENY #### nohup qiime phylogeny align-to-tree-mafft-fasttree \ --p-n-threads 8 \ --i-sequences STP-16S-All-single-end-trimmed-repseqs.qza \ --o-alignment STP-16S-All-single-end-trimmed-repseqs-aligned.qza \ --o-masked-alignment STP-16S-All-single-end-trimmed-repseqs-masked-aligned.qza \ --o-tree STP-16S-All-single-end-trimmed-unrooted-tree.qza \ --o-rooted-tree STP-16S-All-single-end-trimmed-rooted-tree.qza & nohup qiime phylogeny align-to-tree-mafft-fasttree \ --p-n-threads 8 \ --i-sequences STP-16S-TPonly-single-end-trimmed-repseqs.qza \ --o-alignment STP-16S-TPonly-single-end-trimmed-repseqs-aligned.qza \ --o-masked-alignment STP-16S-TPonly-single-end-trimmed-repseqs-masked-aligned.qza \ --o-tree STP-16S-TPonly-single-end-trimmed-unrooted-tree.qza \ --o-rooted-tree STP-16S-TPonly-single-end-trimmed-rooted-tree.qza & ## ignored this next section on the second run-through on 20240423 because the new metadata file has the grouped floors ## ## on 20240423 jumped to PERMANOVA analysis ## ## re-upload metadata after changing the "floor" category to include "floor-dung" so the sample size is more than 2 fetch-gdrive-object.py -d 1wQ3XPlk0CN7bIoiAifaVTu-wNjsc91IdTTFByCKgkAY -t sheet -f tsv -o STP-16S-Grouped-Floors-Metadata.tsv ############## nohup qiime diversity core-metrics-phylogenetic \ --i-phylogeny STP-16S-All-single-end-trimmed-rooted-tree.qza \ --i-table STP-16S-All-single-end-trimmed-table.qza \ --p-sampling-depth 38914 \ --m-metadata-file STP-16S-Grouped-Floors-Metadata.tsv \ --output-dir STP-16S-All-core-metrics-grouped-floor-38914 & qiime diversity alpha-group-significance \ --i-alpha-diversity STP-16S-All-core-metrics-grouped-floor-38914/faith_pd_vector.qza \ --m-metadata-file STP-16S-Grouped-Floors-Metadata.tsv \ --o-visualization STP-16S-All-core-metrics-grouped-floor-38914/faith-pd-group-significance.qzv qiime diversity alpha-group-significance \ --i-alpha-diversity STP-16S-All-core-metrics-grouped-floor-38914/evenness_vector.qza \ --m-metadata-file STP-16S-Grouped-Floors-Metadata.tsv \ --o-visualization STP-16S-All-core-metrics-grouped-floor-38914/evenness-group-significance.qzv qiime diversity alpha-group-significance \ --i-alpha-diversity STP-16S-All-core-metrics-grouped-floor-38914/shannon_vector.qza \ --m-metadata-file STP-16S-Grouped-Floors-Metadata.tsv \ --o-visualization STP-16S-All-core-metrics-grouped-floor-38914/shannon-group-significance.qzv nohup qiime diversity core-metrics-phylogenetic \ --i-phylogeny STP-16S-TPonly-single-end-trimmed-rooted-tree.qza \ --i-table STP-16S-TPonly-single-end-trimmed-table.qza \ --p-sampling-depth 38891 \ --m-metadata-file STP-16S-Grouped-Floors-Metadata.tsv \ --output-dir STP-16S-TPonly-core-metrics-grouped-floor-38891 & qiime diversity alpha-group-significance \ --i-alpha-diversity STP-16S-TPonly-core-metrics-grouped-floor-38891/faith_pd_vector.qza \ --m-metadata-file STP-16S-Grouped-Floors-Metadata.tsv \ --o-visualization STP-16S-TPonly-core-metrics-grouped-floor-38891/faith-pd-group-significance.qzv qiime diversity alpha-group-significance \ --i-alpha-diversity STP-16S-TPonly-core-metrics-grouped-floor-38891/evenness_vector.qza \ --m-metadata-file STP-16S-Grouped-Floors-Metadata.tsv \ --o-visualization STP-16S-TPonly-core-metrics-grouped-floor-38891/evenness-group-significance.qzv qiime diversity alpha-group-significance \ --i-alpha-diversity STP-16S-TPonly-core-metrics-grouped-floor-38891/shannon_vector.qza \ --m-metadata-file STP-16S-Grouped-Floors-Metadata.tsv \ --o-visualization STP-16S-TPonly-core-metrics-grouped-floor-38891/shannon-group-significance.qzv ### PERMANOVA - with grouped floor data qiime diversity beta-group-significance \ --i-distance-matrix STP-16S-All-core-metrics-38914/unweighted_unifrac_distance_matrix.qza \ --m-metadata-file STP-16S-Metadata.tsv \ --m-metadata-column area.numeric \ --o-visualization STP-16S-All-core-metrics-38914/unweighted-unifrac-era-significance.qzv \ --p-pairwise qiime diversity beta-group-significance \ --i-distance-matrix STP-16S-All-core-metrics-38914/weighted_unifrac_distance_matrix.qza \ --m-metadata-file STP-16S-Metadata.tsv \ --m-metadata-column area.numeric \ --o-visualization STP-16S-All-core-metrics-38914/weighted-unifrac-era-significance.qzv \ --p-pairwise qiime diversity beta-group-significance \ --i-distance-matrix STP-16S-All-core-metrics-38914/bray_curtis_distance_matrix.qza \ --m-metadata-file STP-16S-Metadata.tsv \ --m-metadata-column area.numeric \ --o-visualization STP-16S-All-core-metrics-38914/bray-curtis-era-significance.qzv \ --p-pairwise qiime diversity beta-group-significance \ --i-distance-matrix STP-16S-All-core-metrics-38914/jaccard_distance_matrix.qza \ --m-metadata-file STP-16S-Metadata.tsv \ --m-metadata-column area.numeric \ --o-visualization STP-16S-All-core-metrics-38914/jaccard-era-significance.qzv \ --p-pairwise ## qiime diversity beta-group-significance \ --i-distance-matrix STP-16S-TPonly-core-metrics-grouped-floor-38891/unweighted_unifrac_distance_matrix.qza \ --m-metadata-file STP-16S-Grouped-Floors-Metadata.tsv \ --m-metadata-column area.numeric \ --o-visualization STP-16S-TPonly-core-metrics-grouped-floor-38891/unweighted-unifrac-era-significance.qzv \ --p-pairwise qiime diversity beta-group-significance \ --i-distance-matrix STP-16S-TPonly-core-metrics-grouped-floor-38891/weighted_unifrac_distance_matrix.qza \ --m-metadata-file STP-16S-Grouped-Floors-Metadata.tsv \ --m-metadata-column area.numeric \ --o-visualization STP-16S-TPonly-core-metrics-grouped-floor-38891/weighted-unifrac-era-significance.qzv \ --p-pairwise qiime diversity beta-group-significance \ --i-distance-matrix STP-16S-TPonly-core-metrics-grouped-floor-38891/bray_curtis_distance_matrix.qza \ --m-metadata-file STP-16S-Grouped-Floors-Metadata.tsv \ --m-metadata-column area.numeric \ --o-visualization STP-16S-TPonly-core-metrics-grouped-floor-38891/bray-curtis-era-significance.qzv \ --p-pairwise qiime diversity beta-group-significance \ --i-distance-matrix STP-16S-TPonly-core-metrics-grouped-floor-38891/jaccard_distance_matrix.qza \ --m-metadata-file STP-16S-Grouped-Floors-Metadata.tsv \ --m-metadata-column area.numeric \ --o-visualization STP-16S-TPonly-core-metrics-grouped-floor-38891/jaccard-era-significance.qzv \ --p-pairwise ### PERMDISP qiime diversity beta-group-significance \ --i-distance-matrix STP-16S-All-core-metrics-38914/unweighted_unifrac_distance_matrix.qza \ --m-metadata-file STP-16S-Metadata.tsv \ --m-metadata-column area.numeric \ --o-visualization STP-16S-All-core-metrics-38914/unweighted-unifrac-area-significance-disp.qzv \ --p-method permdisp qiime diversity beta-group-significance \ --i-distance-matrix STP-16S-All-core-metrics-38914/weighted_unifrac_distance_matrix.qza \ --m-metadata-file STP-16S-Metadata.tsv \ --m-metadata-column area.numeric \ --o-visualization STP-16S-All-core-metrics-38914/weighted-unifrac-area-significance-disp.qzv \ --p-method permdisp qiime diversity beta-group-significance \ --i-distance-matrix STP-16S-All-core-metrics-38914/bray_curtis_distance_matrix.qza \ --m-metadata-file STP-16S-Metadata.tsv \ --m-metadata-column area.numeric \ --o-visualization STP-16S-All-core-metrics-38914/bray_curtis_distance-area-significance-disp.qzv \ --p-method permdisp qiime diversity beta-group-significance \ --i-distance-matrix STP-16S-All-core-metrics-38914/jaccard_distance_matrix.qza \ --m-metadata-file STP-16S-Metadata.tsv \ --m-metadata-column area.numeric \ --o-visualization STP-16S-All-core-metrics-38914/jaccard_distance-area-significance_disp.qzv \ --p-method permdisp qiime diversity beta-group-significance \ --i-distance-matrix STP-16S-TPonly-core-metrics-grouped-floor-38891/unweighted_unifrac_distance_matrix.qza \ --m-metadata-file STP-16S-Grouped-Floors-Metadata.tsv \ --m-metadata-column area.numeric \ --o-visualization STP-16S-TPonly-core-metrics-grouped-floor-38891/unweighted-unifrac-area-significance-disp.qzv \ --p-method permdisp qiime diversity beta-group-significance \ --i-distance-matrix STP-16S-TPonly-core-metrics-grouped-floor-38891/weighted_unifrac_distance_matrix.qza \ --m-metadata-file STP-16S-Grouped-Floors-Metadata.tsv \ --m-metadata-column area.numeric \ --o-visualization STP-16S-TPonly-core-metrics-grouped-floor-38891/weighted-unifrac-area-significance-disp.qzv \ --p-method permdisp qiime diversity beta-group-significance \ --i-distance-matrix STP-16S-TPonly-core-metrics-grouped-floor-38891/bray_curtis_distance_matrix.qza \ --m-metadata-file STP-16S-Grouped-Floors-Metadata.tsv \ --m-metadata-column area.numeric \ --o-visualization STP-16S-TPonly-core-metrics-grouped-floor-38891/bray_curtis_distance-area-significance-disp.qzv \ --p-method permdisp qiime diversity beta-group-significance \ --i-distance-matrix STP-16S-TPonly-core-metrics-grouped-floor-38891/jaccard_distance_matrix.qza \ --m-metadata-file STP-16S-Grouped-Floors-Metadata.tsv \ --m-metadata-column area.numeric \ --o-visualization STP-16S-TPonly-core-metrics-grouped-floor-38891/jaccard_distance-area-significance-disp.qzv \ --p-method permdisp ### TAXONOMY (as in https://docs.qiime2.org/2024.2/tutorials/pd-mice/) # start with SILVA classifier qiime feature-classifier classify-sklearn \ --i-reads STP-16S-All-single-end-trimmed-repseqs.qza \ --i-classifier silva-138.1-ssu-nr99-2029-16S-lca-classifier.qza \ --o-classification STP-16S-All-single-end-SILVA-taxonomy.qza qiime metadata tabulate \ --m-input-file STP-16S-All-single-end-SILVA-taxonomy.qza \ --o-visualization STP-16S-All-single-end-SILVA-taxonomy.qzv ###### this SILVA classifier is the one I made, it is not giving a good classification # try again with the pre-made classifier - 20240445 nohup qiime feature-classifier classify-sklearn \ --i-reads STP-16S-All-single-end-trimmed-repseqs.qza \ --i-classifier silva-138-99-nb-classifier.qza \ --o-classification STP-16S-All-single-end-SILVA-untrained-taxonomy.qza & qiime metadata tabulate \ --m-input-file STP-16S-All-single-end-SILVA-untrained-taxonomy.qza \ --o-visualization STP-16S-All-single-end-SILVA-untrained-taxonomy.qzv ########## Made some more classifiers with different parameters, try them too ## this one uses shorter primer sequences to train the classifier: silva-138.1-ssu-nr99-2019-16S-lca-classifier-2.qza nohup qiime feature-classifier classify-sklearn \ --i-reads STP-16S-All-single-end-trimmed-repseqs.qza \ --i-classifier silva-138.1-ssu-nr99-2019-16S-lca-classifier-2.qza \ --o-classification STP-16S-All-single-end-SILVA-lca2-taxonomy.qza & qiime metadata tabulate \ --m-input-file STP-16S-All-single-end-SILVA-lca2-taxonomy.qza \ --o-visualization STP-16S-All-single-end-SILVA-lca2-taxonomy.qzv qiime taxa barplot \ --i-table STP-16S-All-single-end-trimmed-table.qza \ --i-taxonomy STP-16S-All-single-end-SILVA-lca2-taxonomy.qza \ --m-metadata-file STP-16S-Metadata.tsv \ --o-visualization STP-16S-All-single-end-trimmed-taxa-lca2-barplot.qzv ## this one uses a different p-mode so we can use clawback: silva-138.1-ssu-nr99-2019-uniq-classifier-2.qza nohup qiime feature-classifier classify-sklearn \ --i-reads STP-16S-All-single-end-trimmed-repseqs.qza \ --i-classifier silva-138.1-ssu-nr99-2019-uniq-classifier-2.qza \ --o-classification STP-16S-All-single-end-SILVA-uniq2-taxonomy.qza & qiime metadata tabulate \ --m-input-file STP-16S-All-single-end-SILVA-uniq2-taxonomy.qza \ --o-visualization STP-16S-All-single-end-SILVA-uniq2-taxonomy.qzv qiime taxa barplot \ --i-table STP-16S-All-single-end-trimmed-table.qza \ --i-taxonomy STP-16S-All-single-end-SILVA-uniq2-taxonomy.qza \ --m-metadata-file STP-16S-Metadata.tsv \ --o-visualization STP-16S-All-single-end-trimmed-taxa-uniq2-barplot.qzv ## this is the classifier I made with clawback: soil-nonsaline-16S-uniq-2-classifier.qza nohup qiime feature-classifier classify-sklearn \ --i-reads STP-16S-All-single-end-trimmed-repseqs.qza \ --i-classifier soil-nonsaline-16S-uniq-2-classifier.qza \ --o-classification STP-16S-All-single-end-SILVA-soil-nonsaline-taxonomy.qza & ## HERE on 1:11 pm May 22 qiime metadata tabulate \ --m-input-file STP-16S-All-single-end-SILVA-soil-nonsaline-taxonomy.qza \ --o-visualization STP-16S-All-single-end-SILVA-soil-nonsaline-taxonomy.qzv qiime taxa barplot \ --i-table STP-16S-All-single-end-trimmed-table.qza \ --i-taxonomy STP-16S-All-single-end-SILVA-soil-nonsaline-taxonomy.qza \ --m-metadata-file STP-16S-Metadata.tsv \ --o-visualization STP-16S-All-single-end-trimmed-taxa-uniq2-soil-nonsaline-barplot.qzv ### GREENGENES2 classifier nohup qiime feature-classifier classify-sklearn \ --i-reads STP-16S-All-single-end-trimmed-repseqs.qza \ --i-classifier gg2.2022.10.backbone.v4.nb.qza \ --o-classification STP-16S-All-single-end-GG2-taxonomy.qza & qiime metadata tabulate \ --m-input-file STP-16S-All-single-end-GG2-taxonomy.qza \ --o-visualization STP-16S-All-single-end-GG2-taxonomy.qzv ##### Visualize taxonomic data ##### qiime feature-table filter-samples \ --i-table STP-16S-All-single-end-trimmed-table.qza \ --p-min-frequency 10 \ --o-filtered-table STP-16S-All-single-end-trimmed-table-10.qza qiime taxa barplot \ --i-table STP-16S-All-single-end-trimmed-table-10.qza \ --i-taxonomy STP-16S-All-single-end-GG2-taxonomy.qza \ --m-metadata-file STP-16S-Metadata.tsv \ --o-visualization STP-16S-All-single-end-trimmed-10-GG2-taxa-barplot.qzv qiime feature-table filter-samples \ --i-table STP-16S-All-single-end-trimmed-table-10.qza \ --p-min-frequency 38914 \ --o-filtered-table STP-16S-All-Solo-single-end-trimmed-table-10-filtered-38914.qza qiime taxa barplot \ --i-table STP-16S-All-Solo-single-end-trimmed-table-10-filtered-38914.qza \ --i-taxonomy STP-16S-All-single-end-GG2-taxonomy.qza \ --m-metadata-file STP-16S-Metadata.tsv \ --o-visualization STP-16S-All-single-end-trimmed-table-10-filtered-38914-GG2-taxa-barplot.qzv qiime taxa barplot \ --i-table STP-16S-All-Solo-single-end-trimmed-table-10-filtered-38914.qza \ --i-taxonomy STP-16S-All-single-end-SILVA-taxonomy.qza \ --m-metadata-file STP-16S-Metadata.tsv \ --o-visualization STP-16S-All-single-end-trimmed-table-10-filtered-38914-SILVA-taxa-barplot.qzv ## Weighted Greengenes2 Classifier nohup qiime feature-classifier classify-sklearn \ --i-reads STP-16S-All-single-end-trimmed-repseqs.qza \ --i-classifier GG2-V4-soil-nonsaline-weights-classifier.qza \ --o-classification STP-16S-All-single-end-GG2-weighted-taxonomy.qza & qiime metadata tabulate \ --m-input-file STP-16S-All-single-end-GG2-weighted-taxonomy.qza \ --o-visualization STP-16S-All-single-end-GG2-weighted-taxonomy.qzv qiime taxa barplot \ --i-table STP-16S-All-Solo-single-end-trimmed-table-10-filtered-38914.qza \ --i-taxonomy STP-16S-All-single-end-GG2-weighted-taxonomy.qza \ --m-metadata-file STP-16S-Metadata.tsv \ --o-visualization STP-16S-All-single-end-trimmed-table-10-filtered-38914-GG2-weighted-taxa-barplot.qzv #### KRONA module load qiime2/2024 #qiime krona collapse-and-plot \ #--i-table table_2k.qza \ #--i-taxonomy taxonomy.qza \ #--o-krona-plot krona.qzv qiime krona collapse-and-plot \ --i-table STP-16S-All-single-end-trimmed-table.qza \ --i-taxonomy STP-16S-All-single-end-SILVA-taxonomy.qza \ --o-krona-plot STP-16S-All-single-end-SILVA-krona.qzv qiime krona collapse-and-plot \ --i-table STP-16S-All-single-end-trimmed-table.qza \ --i-taxonomy STP-16S-All-single-end-GG2-taxonomy.qza \ --o-krona-plot STP-16S-All-single-end-GG2-krona.qzv qiime krona collapse-and-plot \ --i-table STP-16S-All-Solo-single-end-trimmed-table-10-filtered-38914.qza \ --i-taxonomy STP-16S-All-single-end-SILVA-taxonomy.qza \ --o-krona-plot STP-16S-All-single-end-trimmed-10-filtered-38914-SILVA-krona.qzv qiime krona collapse-and-plot \ --i-table STP-16S-All-Solo-single-end-trimmed-table-10-filtered-38914.qza \ --i-taxonomy STP-16S-All-single-end-GG2-taxonomy.qza \ --o-krona-plot STP-16S-All-single-end-trimmed-10-filtered-38914-GG2-krona.qzv # Differential abundance with ANCOM-BC # Before we begin, we will filter out low abundance/low prevalence ASVs. # Filtering can provide better resolution and limit false discovery rate (FDR) # penalty on features that are too far below the noise threshhold to be applicable # to a statistical test. qiime feature-table filter-features \ --i-table Iceland-2023-Solo-single-end-trimmed-table-113416.qza \ --p-min-frequency 10 \ --p-min-samples 4 \ --o-filtered-table Iceland-2023-Solo-single-end-trimmed-table-113416_abund.qza qiime composition ancombc \ --i-table Iceland-2023-Solo-single-end-trimmed-table-113416_abund.qza \ --m-metadata-file Iceland-2023-Solo-Metadata.tsv \ --p-formula 'era' \ --o-differentials Iceland-2023-Solo-single-end-trimmed-table-113416-ancombc-era.qza qiime composition da-barplot \ --i-data Iceland-2023-Solo-single-end-trimmed-table-113416-ancombc-era.qza \ --p-significance-threshold 0.001 \ --o-visualization Iceland-2023-Solo-single-end-trimmed-table-113416-ancombc-era-barplot.qzv # Longitutidinal and paired sample comparisons # https://docs.qiime2.org/2024.2/tutorials/longitudinal/ # q2-longitudinal could compare the effects of different collection methods, storage methods, # DNA extraction methods, or any bioinformatic processing steps on the feature composition of # individual samples. # PCoA-based analyses ############################################################################################################## ############################################################################################################## #################################### with targeted data set!! ################################################ ############################################################################################################## ############################################################################################################## ## May 23, made a "targeted" metadata file which excludes STP09, all negative controls, riverbed and sheep pasture fetch-gdrive-object.py -d 1hTo2PGfxUjFlUXTPvaldEYgeHWzSSLkbtKO-ieq-jRc -t sheet -f tsv -o STP-16S-Targeted-Metadata.tsv fetch-gdrive-object.py -d 1Fg6BWqgH5M5avdLudQcJ4Rufxfp23Q0iqbUz_Uxe62o -t sheet -f tsv -o STP-16S-Targeted-Manifest-FWDs.tsv qiime tools import \ --type 'SampleData[SequencesWithQuality]' \ --input-path STP-16S-Targeted-Manifest-FWDs.tsv \ --output-path STP-16S-Targeted-single-end-demux.qza \ --input-format SingleEndFastqManifestPhred33V2 qiime demux summarize \ --i-data STP-16S-Targeted-single-end-demux.qza \ --o-visualization STP-16S-Targeted-single-end-demux.qzv ############################################################################################################## ############################################################################################################## # CUTADAPT # Add the flags --p-match-read-wildcards and --p-match-adapter-wildcards to your command. # This will allow matching of IUPAC wild cards such as H, V, N, etc.. # Furthermore, I'd advise adding the flag --p-discard-untrimmed as well. # This will discard any sequences in which both primers could not be trimmed from the sequences. # TATGGTAATTGTGTGCCAGCMGCCGCGGTAA - this was what I used originally, in the prior workflow # GTGYCAGCMGCCGCGGTAA - this is what I will use this time, which is the V4 primer sequenced I used for the classifier qiime cutadapt trim-single \ --i-demultiplexed-sequences STP-16S-Targeted-single-end-demux.qza \ --p-cores 8 \ --p-front GTGYCAGCMGCCGCGGTAA \ --p-match-read-wildcards \ --p-match-adapter-wildcards \ --p-discard-untrimmed \ --o-trimmed-sequences STP-16S-Targeted-single-end-trimmed-short-sequences.qza qiime demux summarize \ --i-data STP-16S-Targeted-single-end-trimmed-short-sequences.qza \ --o-visualization STP-16S-Targeted-single-end-trimmed-short-sequences.qzv qiime cutadapt trim-single \ --i-demultiplexed-sequences STP-16S-Targeted-single-end-demux.qza \ --p-cores 8 \ --p-front TATGGTAATTGTGTGCCAGCMGCCGCGGTAA \ --p-match-read-wildcards \ --p-match-adapter-wildcards \ --p-discard-untrimmed \ --o-trimmed-sequences STP-16S-Targeted-single-end-trimmed-long-sequences.qza qiime demux summarize \ --i-data STP-16S-Targeted-single-end-trimmed-long-sequences.qza \ --o-visualization STP-16S-Targeted-single-end-trimmed-long-sequences.qzv # Either way, both have similar QC scores: the first position to drop below 25 is 226 (same as used for All Samples) # the first position to drop below 20 is 269 (drop below being the bottom bound of the box) # Using the long primer sequence, we end up with 788563 reads. # Using the short primer sequence, we end up with 781231 reads - a difference of 7,332 reads. # how to know which is better??? I think I should use the "short" data because that's the same sequence I use in the # taxonomy to train the classifiers, and the feedback was that part of the "long" sequence shouldn't be present in the DNA ############################################################################################################## ############################################################################################################## # DADA2, feature table summarize # trunc at 268, sequence falls below 20QC at position 269 # don't change the number of reads for the error model from default, based on forum post. nohup qiime dada2 denoise-single \ --p-n-threads 8 \ --i-demultiplexed-seqs STP-16S-Targeted-single-end-trimmed-short-sequences.qza \ --p-trunc-len 268 \ --o-representative-sequences STP-16S-Targeted-single-end-trimmed-repseqs.qza \ --o-table STP-16S-Targeted-single-end-trimmed-table.qza \ --o-denoising-stats STP-16S-Targeted-single-end-trimmed-stats.qza & qiime metadata tabulate \ --m-input-file STP-16S-Targeted-single-end-trimmed-stats.qza \ --o-visualization STP-16S-Targeted-single-end-trimmed-stats.qzv qiime feature-table summarize \ --i-table STP-16S-Targeted-single-end-trimmed-table.qza \ --o-visualization STP-16S-Targeted-single-end-trimmed-table.qzv \ --m-sample-metadata-file STP-16S-Targeted-Metadata.tsv qiime feature-table tabulate-seqs \ --i-data STP-16S-Targeted-single-end-trimmed-repseqs.qza \ --o-visualization STP-16S-Targeted-single-end-trimmed-repseqs.qzv ### and try again truncaging at 226?? nohup qiime dada2 denoise-single \ --p-n-threads 8 \ --i-demultiplexed-seqs STP-16S-Targeted-single-end-trimmed-short-sequences.qza \ --p-trunc-len 226 \ --o-representative-sequences STP-16S-Targeted-single-end-trimmed-226-repseqs.qza \ --o-table STP-16S-Targeted-single-end-trimmed-226-table.qza \ --o-denoising-stats STP-16S-Targeted-single-end-trimmed-226-stats.qza & qiime metadata tabulate \ --m-input-file STP-16S-Targeted-single-end-trimmed-226-stats.qza \ --o-visualization STP-16S-Targeted-single-end-trimmed-226-stats.qzv ### that did it! much better results. Move forward with trunc @ 226 - replace other files qiime feature-table summarize \ --i-table STP-16S-Targeted-single-end-trimmed-226-table.qza \ --o-visualization STP-16S-Targeted-single-end-trimmed-226-table.qzv \ --m-sample-metadata-file STP-16S-Targeted-Metadata.tsv qiime feature-table tabulate-seqs \ --i-data STP-16S-Targeted-single-end-trimmed-226-repseqs.qza \ --o-visualization STP-16S-Targeted-single-end-trimmed-226repseqs.qzv ############################################################################################################## ############################################################################################################## # Diversity and Phylogeny # Sampling Depth of 38762: Retained 465,144 (73.87%) features in 12 (100.00%) samples at the specifed sampling depth. qiime diversity alpha-rarefaction \ --i-table STP-16S-Targeted-single-end-trimmed-226-table.qza \ --m-metadata-file STP-16S-Targeted-Metadata.tsv \ --o-visualization STP-16S-Targeted-alpha-rarefaction-38762.qzv \ --p-min-depth 5 \ --p-max-depth 38762 # at this level of rarefaction we are beyond the exponential growth of the curve. nohup qiime phylogeny align-to-tree-mafft-fasttree \ --p-n-threads 8 \ --i-sequences STP-16S-Targeted-single-end-trimmed-226-repseqs.qza \ --o-alignment STP-16S-Targeted-single-end-trimmed-repseqs-aligned.qza \ --o-masked-alignment STP-16S-Targeted-single-end-trimmed-repseqs-masked-aligned.qza \ --o-tree STP-16S-Targeted-single-end-trimmed-unrooted-tree.qza \ --o-rooted-tree STP-16S-Targeted-single-end-trimmed-rooted-tree.qza & nohup qiime diversity core-metrics-phylogenetic \ --i-phylogeny STP-16S-Targeted-single-end-trimmed-rooted-tree.qza \ --i-table STP-16S-Targeted-single-end-trimmed-226-table.qza \ --p-sampling-depth 38914 \ --m-metadata-file STP-16S-Targeted-Metadata.tsv \ --output-dir STP-16S-Targeted-core-metrics-rooted-38914 & qiime diversity alpha-group-significance \ --i-alpha-diversity STP-16S-Targeted-core-metrics-rooted-38914/faith_pd_vector.qza \ --m-metadata-file STP-16S-Targeted-Metadata.tsv \ --o-visualization STP-16S-Targeted-core-metrics-rooted-38914/faith-pd-group-significance.qzv qiime diversity alpha-group-significance \ --i-alpha-diversity STP-16S-Targeted-core-metrics-rooted-38914/evenness_vector.qza \ --m-metadata-file STP-16S-Targeted-Metadata.tsv \ --o-visualization STP-16S-Targeted-core-metrics-rooted-38914/evenness-group-significance.qzv qiime diversity alpha-group-significance \ --i-alpha-diversity STP-16S-Targeted-core-metrics-rooted-38914/shannon_vector.qza \ --m-metadata-file STP-16S-Targeted-Metadata.tsv \ --o-visualization STP-16S-Targeted-core-metrics-rooted-38914/shannon-group-significance.qzv ## can't use the unrooted tree? Invalid value for '--i-phylogeny': Expected an artifact of at least # type Phylogeny[Rooted]. An artifact of type Phylogeny[Unrooted] was # provided. #nohup qiime diversity core-metrics-phylogenetic \ # --i-phylogeny STP-16S-Targeted-single-end-trimmed-unrooted-tree.qza \ # --i-table STP-16S-Targeted-single-end-trimmed-226-table.qza \ # --p-sampling-depth 38914 \ # --m-metadata-file STP-16S-Targeted-Metadata.tsv \ # --output-dir STP-16S-Targeted-core-metrics-unrooted-38914 & ############################################################################################################## ############################################################################################################## ### PERMANOVA - with grouped floor data qiime diversity beta-group-significance \ --i-distance-matrix STP-16S-Targeted-core-metrics-rooted-38914/unweighted_unifrac_distance_matrix.qza \ --m-metadata-file STP-16S-Targeted-Metadata.tsv \ --m-metadata-column area.numeric \ --o-visualization STP-16S-Targeted-core-metrics-rooted-38914/unweighted-unifrac-era-significance.qzv \ --p-pairwise qiime diversity beta-group-significance \ --i-distance-matrix STP-16S-Targeted-core-metrics-rooted-38914/weighted_unifrac_distance_matrix.qza \ --m-metadata-file STP-16S-Targeted-Metadata.tsv \ --m-metadata-column area.numeric \ --o-visualization STP-16S-Targeted-core-metrics-rooted-38914/weighted-unifrac-era-significance.qzv \ --p-pairwise qiime diversity beta-group-significance \ --i-distance-matrix STP-16S-Targeted-core-metrics-rooted-38914/bray_curtis_distance_matrix.qza \ --m-metadata-file STP-16S-Targeted-Metadata.tsv \ --m-metadata-column area.numeric \ --o-visualization STP-16S-Targeted-core-metrics-rooted-38914/bray-curtis-era-significance.qzv \ --p-pairwise qiime diversity beta-group-significance \ --i-distance-matrix STP-16S-Targeted-core-metrics-rooted-38914/jaccard_distance_matrix.qza \ --m-metadata-file STP-16S-Targeted-Metadata.tsv \ --m-metadata-column area.numeric \ --o-visualization STP-16S-Targeted-core-metrics-rooted-38914/jaccard-era-significance.qzv \ --p-pairwise ############################################################################################################## ############################################################################################################## ### PERMDISP qiime diversity beta-group-significance \ --i-distance-matrix STP-16S-Targeted-core-metrics-rooted-38914/unweighted_unifrac_distance_matrix.qza \ --m-metadata-file STP-16S-Targeted-Metadata.tsv \ --m-metadata-column area.numeric \ --o-visualization STP-16S-Targeted-core-metrics-rooted-38914/unweighted-unifrac-area-significance-disp.qzv \ --p-method permdisp qiime diversity beta-group-significance \ --i-distance-matrix STP-16S-Targeted-core-metrics-rooted-38914/weighted_unifrac_distance_matrix.qza \ --m-metadata-file STP-16S-Targeted-Metadata.tsv \ --m-metadata-column area.numeric \ --o-visualization STP-16S-Targeted-core-metrics-rooted-38914/weighted-unifrac-area-significance-disp.qzv \ --p-method permdisp qiime diversity beta-group-significance \ --i-distance-matrix STP-16S-Targeted-core-metrics-rooted-38914/bray_curtis_distance_matrix.qza \ --m-metadata-file STP-16S-Targeted-Metadata.tsv \ --m-metadata-column area.numeric \ --o-visualization STP-16S-Targeted-core-metrics-rooted-38914/bray_curtis_distance-area-significance-disp.qzv \ --p-method permdisp qiime diversity beta-group-significance \ --i-distance-matrix STP-16S-Targeted-core-metrics-rooted-38914/jaccard_distance_matrix.qza \ --m-metadata-file STP-16S-Targeted-Metadata.tsv \ --m-metadata-column area.numeric \ --o-visualization STP-16S-Targeted-core-metrics-rooted-38914/jaccard_distance-area-significance_disp.qzv \ --p-method permdisp ############################################################################################################## ############################################################################################################## ### TAXONOMY (as in https://docs.qiime2.org/2024.2/tutorials/pd-mice/) # start with SILVA classifier qiime feature-classifier classify-sklearn \ --i-reads STP-16S-All-single-end-trimmed-repseqs.qza \ --i-classifier silva-138.1-ssu-nr99-2029-16S-lca-classifier.qza \ --o-classification STP-16S-All-single-end-SILVA-taxonomy.qza qiime metadata tabulate \ --m-input-file STP-16S-All-single-end-SILVA-taxonomy.qza \ --o-visualization STP-16S-All-single-end-SILVA-taxonomy.qzv ###### this SILVA classifier is the one I made, it is not giving a good classification # try again with the pre-made classifier - 20240445 nohup qiime feature-classifier classify-sklearn \ --i-reads STP-16S-All-single-end-trimmed-repseqs.qza \ --i-classifier silva-138-99-nb-classifier.qza \ --o-classification STP-16S-All-single-end-SILVA-untrained-taxonomy.qza & qiime metadata tabulate \ --m-input-file STP-16S-All-single-end-SILVA-untrained-taxonomy.qza \ --o-visualization STP-16S-All-single-end-SILVA-untrained-taxonomy.qzv ########## Made some more classifiers with different parameters, try them too ## this one uses shorter primer sequences to train the classifier: silva-138.1-ssu-nr99-2019-16S-lca-classifier-2.qza nohup qiime feature-classifier classify-sklearn \ --i-reads STP-16S-All-single-end-trimmed-repseqs.qza \ --i-classifier silva-138.1-ssu-nr99-2019-16S-lca-classifier-2.qza \ --o-classification STP-16S-All-single-end-SILVA-lca2-taxonomy.qza & qiime metadata tabulate \ --m-input-file STP-16S-All-single-end-SILVA-lca2-taxonomy.qza \ --o-visualization STP-16S-All-single-end-SILVA-lca2-taxonomy.qzv qiime taxa barplot \ --i-table STP-16S-All-single-end-trimmed-table.qza \ --i-taxonomy STP-16S-All-single-end-SILVA-lca2-taxonomy.qza \ --m-metadata-file STP-16S-Metadata.tsv \ --o-visualization STP-16S-All-single-end-trimmed-taxa-lca2-barplot.qzv ## this one uses a different p-mode so we can use clawback: silva-138.1-ssu-nr99-2019-uniq-classifier-2.qza nohup qiime feature-classifier classify-sklearn \ --i-reads STP-16S-All-single-end-trimmed-repseqs.qza \ --i-classifier silva-138.1-ssu-nr99-2019-uniq-classifier-2.qza \ --o-classification STP-16S-All-single-end-SILVA-uniq2-taxonomy.qza & qiime metadata tabulate \ --m-input-file STP-16S-All-single-end-SILVA-uniq2-taxonomy.qza \ --o-visualization STP-16S-All-single-end-SILVA-uniq2-taxonomy.qzv qiime taxa barplot \ --i-table STP-16S-All-single-end-trimmed-table.qza \ --i-taxonomy STP-16S-All-single-end-SILVA-uniq2-taxonomy.qza \ --m-metadata-file STP-16S-Metadata.tsv \ --o-visualization STP-16S-All-single-end-trimmed-taxa-uniq2-barplot.qzv ## this is the classifier I made with clawback: soil-nonsaline-16S-uniq-2-classifier.qza nohup qiime feature-classifier classify-sklearn \ --i-reads STP-16S-All-single-end-trimmed-repseqs.qza \ --i-classifier soil-nonsaline-16S-uniq-2-classifier.qza \ --o-classification STP-16S-All-single-end-SILVA-soil-nonsaline-taxonomy.qza & ## HERE on 1:11 pm May 22 qiime metadata tabulate \ --m-input-file STP-16S-All-single-end-SILVA-soil-nonsaline-taxonomy.qza \ --o-visualization STP-16S-All-single-end-SILVA-soil-nonsaline-taxonomy.qzv qiime taxa barplot \ --i-table STP-16S-All-single-end-trimmed-table.qza \ --i-taxonomy STP-16S-All-single-end-SILVA-soil-nonsaline-taxonomy.qza \ --m-metadata-file STP-16S-Metadata.tsv \ --o-visualization STP-16S-All-single-end-trimmed-taxa-uniq2-soil-nonsaline-barplot.qzv ### GREENGENES2 classifier nohup qiime feature-classifier classify-sklearn \ --i-reads STP-16S-All-single-end-trimmed-repseqs.qza \ --i-classifier gg2.2022.10.backbone.v4.nb.qza \ --o-classification STP-16S-All-single-end-GG2-taxonomy.qza & qiime metadata tabulate \ --m-input-file STP-16S-All-single-end-GG2-taxonomy.qza \ --o-visualization STP-16S-All-single-end-GG2-taxonomy.qzv ##### Visualize taxonomic data ##### qiime feature-table filter-samples \ --i-table STP-16S-All-single-end-trimmed-table.qza \ --p-min-frequency 10 \ --o-filtered-table STP-16S-All-single-end-trimmed-table-10.qza qiime taxa barplot \ --i-table STP-16S-All-single-end-trimmed-table-10.qza \ --i-taxonomy STP-16S-All-single-end-GG2-taxonomy.qza \ --m-metadata-file STP-16S-Metadata.tsv \ --o-visualization STP-16S-All-single-end-trimmed-10-GG2-taxa-barplot.qzv qiime feature-table filter-samples \ --i-table STP-16S-All-single-end-trimmed-table-10.qza \ --p-min-frequency 38914 \ --o-filtered-table STP-16S-All-Solo-single-end-trimmed-table-10-filtered-38914.qza qiime taxa barplot \ --i-table STP-16S-All-Solo-single-end-trimmed-table-10-filtered-38914.qza \ --i-taxonomy STP-16S-All-single-end-GG2-taxonomy.qza \ --m-metadata-file STP-16S-Metadata.tsv \ --o-visualization STP-16S-All-single-end-trimmed-table-10-filtered-38914-GG2-taxa-barplot.qzv qiime taxa barplot \ --i-table STP-16S-All-Solo-single-end-trimmed-table-10-filtered-38914.qza \ --i-taxonomy STP-16S-All-single-end-SILVA-taxonomy.qza \ --m-metadata-file STP-16S-Metadata.tsv \ --o-visualization STP-16S-All-single-end-trimmed-table-10-filtered-38914-SILVA-taxa-barplot.qzv ## Weighted Greengenes2 Classifier nohup qiime feature-classifier classify-sklearn \ --i-reads STP-16S-All-single-end-trimmed-repseqs.qza \ --i-classifier GG2-V4-soil-nonsaline-weights-classifier.qza \ --o-classification STP-16S-All-single-end-GG2-weighted-taxonomy.qza & qiime metadata tabulate \ --m-input-file STP-16S-All-single-end-GG2-weighted-taxonomy.qza \ --o-visualization STP-16S-All-single-end-GG2-weighted-taxonomy.qzv qiime taxa barplot \ --i-table STP-16S-All-Solo-single-end-trimmed-table-10-filtered-38914.qza \ --i-taxonomy STP-16S-All-single-end-GG2-weighted-taxonomy.qza \ --m-metadata-file STP-16S-Metadata.tsv \ --o-visualization STP-16S-All-single-end-trimmed-table-10-filtered-38914-GG2-weighted-taxa-barplot.qzv #### KRONA module load qiime2/2024 #qiime krona collapse-and-plot \ #--i-table table_2k.qza \ #--i-taxonomy taxonomy.qza \ #--o-krona-plot krona.qzv qiime krona collapse-and-plot \ --i-table STP-16S-All-single-end-trimmed-table.qza \ --i-taxonomy STP-16S-All-single-end-SILVA-taxonomy.qza \ --o-krona-plot STP-16S-All-single-end-SILVA-krona.qzv qiime krona collapse-and-plot \ --i-table STP-16S-All-single-end-trimmed-table.qza \ --i-taxonomy STP-16S-All-single-end-GG2-taxonomy.qza \ --o-krona-plot STP-16S-All-single-end-GG2-krona.qzv qiime krona collapse-and-plot \ --i-table STP-16S-All-Solo-single-end-trimmed-table-10-filtered-38914.qza \ --i-taxonomy STP-16S-All-single-end-SILVA-taxonomy.qza \ --o-krona-plot STP-16S-All-single-end-trimmed-10-filtered-38914-SILVA-krona.qzv qiime krona collapse-and-plot \ --i-table STP-16S-All-Solo-single-end-trimmed-table-10-filtered-38914.qza \ --i-taxonomy STP-16S-All-single-end-GG2-taxonomy.qza \ --o-krona-plot STP-16S-All-single-end-trimmed-10-filtered-38914-GG2-krona.qzv # Differential abundance with ANCOM-BC # Before we begin, we will filter out low abundance/low prevalence ASVs. # Filtering can provide better resolution and limit false discovery rate (FDR) # penalty on features that are too far below the noise threshhold to be applicable # to a statistical test. qiime feature-table filter-features \ --i-table Iceland-2023-Solo-single-end-trimmed-table-113416.qza \ --p-min-frequency 10 \ --p-min-samples 4 \ --o-filtered-table Iceland-2023-Solo-single-end-trimmed-table-113416_abund.qza qiime composition ancombc \ --i-table Iceland-2023-Solo-single-end-trimmed-table-113416_abund.qza \ --m-metadata-file Iceland-2023-Solo-Metadata.tsv \ --p-formula 'era' \ --o-differentials Iceland-2023-Solo-single-end-trimmed-table-113416-ancombc-era.qza qiime composition da-barplot \ --i-data Iceland-2023-Solo-single-end-trimmed-table-113416-ancombc-era.qza \ --p-significance-threshold 0.001 \ --o-visualization Iceland-2023-Solo-single-end-trimmed-table-113416-ancombc-era-barplot.qzv # Longitutidinal and paired sample comparisons # https://docs.qiime2.org/2024.2/tutorials/longitudinal/ # q2-longitudinal could compare the effects of different collection methods, storage methods, # DNA extraction methods, or any bioinformatic processing steps on the feature composition of # individual samples. # PCoA-based analyses