Setup_Qiime2Picrust
qiime2-2019.10  q2-picrust2


Step1

qiime fragment-insertion sepp --i-representative-sequences rep-seqs.qza --p-threads 1 --i-reference-database picrust2_default_sepp_ref.qza   --output-dir kangdata_out

Saved Phylogeny[Rooted] to: kangdata_out/tree.qza
Saved Placements to: kangdata_out/placements.qza

Step2
qiime picrust2 custom-tree-pipeline --i-table table.qza --i-tree kangdata_out/tree.qza --p-threads 1 --p-hsp-method pic --p-max-nsti 2 --o-ko-metagenome ko_metagenome_kangdata.qza    --o-ec-metagenome ec_metagenome_kangdata.qza  --o-pathway-abundance pathway_abundance_kangdata.qza   --output-dir q2-picrust2_output_kangdata --verbose

(qiime2-2019.10) msiddiq7@enggpz1p23:~/Kang_dataset_picrust$ qiime picrust2 custom-tree-pipeline --i-table table.qza --i-tree kangdata_out/tree.qza --p-threads 1 --p-hsp-method pic --p-max-nsti 2 --o-ko-metagenome ko_metagenome_kangdata.qza    --o-ec-metagenome ec_metagenome_kangdata.qza  --o-pathway-abundance pathway_abundance_kangdata.qza   --output-dir q2-picrust2_output_kangdata --verbose
Running the below commands:
hsp.py -i 16S -t /tmp/tmpvbdzbv0s/placed_seqs.tre -p 1 -n -o /tmp/tmpvbdzbv0s/picrust2_out/16S_predicted.tsv.gz -m pic

hsp.py -i EC -t /tmp/tmpvbdzbv0s/placed_seqs.tre -p 1 -n -o /tmp/tmpvbdzbv0s/picrust2_out/EC_predicted.tsv.gz -m pic

hsp.py -i KO -t /tmp/tmpvbdzbv0s/placed_seqs.tre -p 1 -n -o /tmp/tmpvbdzbv0s/picrust2_out/KO_predicted.tsv.gz -m pic

metagenome_pipeline.py -i /tmp/tmpvbdzbv0s/intable.biom -f /tmp/tmpvbdzbv0s/picrust2_out/EC_predicted.tsv.gz -o /tmp/tmpvbdzbv0s/picrust2_out/EC_metagenome_out --max_nsti 2.0 -m /tmp/tmpvbdzbv0s/picrust2_out/16S_predicted.tsv.gz
97 of 2393 ASVs were above the max NSTI cut-off of 2.0 and were removed.
97 of 2393 ASVs were above the max NSTI cut-off of 2.0 and were removed.

metagenome_pipeline.py -i /tmp/tmpvbdzbv0s/intable.biom -f /tmp/tmpvbdzbv0s/picrust2_out/KO_predicted.tsv.gz -o /tmp/tmpvbdzbv0s/picrust2_out/KO_metagenome_out --max_nsti 2.0 -m /tmp/tmpvbdzbv0s/picrust2_out/16S_predicted.tsv.gz
97 of 2393 ASVs were above the max NSTI cut-off of 2.0 and were removed.
97 of 2393 ASVs were above the max NSTI cut-off of 2.0 and were removed.

pathway_pipeline.py -i /tmp/tmpvbdzbv0s/picrust2_out/EC_metagenome_out/pred_metagenome_unstrat.tsv.gz -o /tmp/tmpvbdzbv0s/picrust2_out/pathways_out -p 1

Saved FeatureTable[Frequency] to: ko_metagenome_kangdata.qza
Saved FeatureTable[Frequency] to: ec_metagenome_kangdata.qza
Saved FeatureTable[Frequency] to: pathway_abundance_kangdata.qza
(qiime2-2019.10) msiddiq7@enggpz1p23:~/Kang_dataset_picrust$ 

Step3
qiime feature-table summarize --i-table pathway_abundance_kangdata.qza    --o-visualization pathway_abundance_kangdata.qzv

Saved Visualization to: pathway_abundance_kangdata.qzv
(qiime2-2019.10) msiddiq7@enggpz1p23:~/Kang_dataset_picrust$ 











Repeat_11-19-2020


Step4

The above metagenome predictions can be integrated into a number of QIIME2 analysis. For instance, you can quickly calculate diversity metrics based on these tables. The minimum sample pathway abundance found above was 226702, so we will rarify to this cut-off when calculating the core diversity metrics:
qiime diversity core-metrics    --i-table pathway_abundance_kangdata.qza --p-sampling-depth 1653    --m-metadata-file meta_LC.tsv    --output-dir kangdata_core_metrics1653_out    --p-n-jobs 1


Saved FeatureTable[Frequency] to: kangdata_core_metrics1653_out/rarefied_table.qza
Saved SampleData[AlphaDiversity] to: kangdata_core_metrics1653_out/observed_otus_vector.qza
Saved SampleData[AlphaDiversity] to: kangdata_core_metrics1653_out/shannon_vector.qza
Saved SampleData[AlphaDiversity] to: kangdata_core_metrics1653_out/evenness_vector.qza
Saved DistanceMatrix to: kangdata_core_metrics1653_out/jaccard_distance_matrix.qza
Saved DistanceMatrix to: kangdata_core_metrics1653_out/bray_curtis_distance_matrix.qza
Saved PCoAResults to: kangdata_core_metrics1653_out/jaccard_pcoa_results.qza
Saved PCoAResults to: kangdata_core_metrics1653_out/bray_curtis_pcoa_results.qza
Saved Visualization to: kangdata_core_metrics1653_out/jaccard_emperor.qzv
Saved Visualization to: kangdata_core_metrics1653_out/bray_curtis_emperor.qzv
(qiime2-2019.10) msiddiq7@enggpz1p23:~/Kang_dataset_picrust$ 




Step5
The final output QZA files are QIIME 2 FeatureTable[Frequency] files, which can be used with existing QIIME 2 programs. Users are typically most interested in the predicted KEGG orthologs and MetaCyc pathways. If you want to use the tables outside of QIIME 2 you can convert the files to be BIOM format. For example, you can run this command to convert the pathway abundance table to BIOM format:

qiime tools export    --input-path pathway_abundance_kangdata.qza    --output-path pathabun_exported_1653kangdata

Exported pathway_abundance_kangdata.qza as BIOMV210DirFmt to directory pathabun_exported_1653kangdata
(qiime2-2019.10) msiddiq7@enggpz1p23:~/Kang_dataset_picrust$ 

Step6

This command will convert a BIOM file to plain-text, which for the pathway abundance table would look like this:

 biom convert    -i pathabun_exported_1653kangdata/feature-table_1653.biom    -o pathabun_exported_1653kangdata/feature-table.biom_1653kangdata.tsv    --to-tsv


(qiime2-2019.10) msiddiq7@enggpz1p23:~/Kang_dataset_picrust$