<jupyter_start><jupyter_text>Table of Contents1&nbsp;&nbsp;How to open this notebook?2&nbsp;&nbsp;7143&nbsp;&nbsp;16094&nbsp;&nbsp;101415&nbsp;&nbsp;101436&nbsp;&nbsp;Merge all7&nbsp;&nbsp;Extract pmi_control biom8&nbsp;&nbsp;Merge 99_out_taxonomy.txt  How to open this notebook? Need to use Qiime2, so need to open a notebook inside a conda environment:```[nathalia] $  conda activate qiime2-2020.11(qiime2-2020.11) [nathalia] $  jnotebook ```Open this notebook - Bash kernel.<jupyter_code>pwd<jupyter_output>/Volumes/NGG_TFAILY_LAB_1/PMI<jupyter_text>714<jupyter_code>qiime tools import \
    --input-path 714/BIOM/46682/otu_table.biom \
    --type 'FeatureTable[Frequency]' \
    --input-format BIOMV210Format \
    --output-path 714/feature-table.qza
qiime feature-table filter-samples \
  --i-table 714/feature-table.qza \
  --m-metadata-file 714/templates/samples_to_keep_714.tsv \
  --o-filtered-table 714/feature-table_filtered.qza<jupyter_output>[32mSaved FeatureTable[Frequency] to: 714/feature-table_filtered.qza[0m<jupyter_text>1609<jupyter_code>qiime tools import \
    --input-path 1609/BIOM/45905/otu_table.biom \
    --type 'FeatureTable[Frequency]' \
    --input-format BIOMV210Format \
    --output-path 1609/feature-table.qza
qiime feature-table filter-samples \
  --i-table 1609/feature-table.qza \
  --m-metadata-file 1609/templates/samples_to_keep_1609.tsv \
  --o-filtered-table 1609/feature-table_filtered.qza<jupyter_output>[32mSaved FeatureTable[Frequency] to: 1609/feature-table_filtered.qza[0m<jupyter_text>10141<jupyter_code>qiime tools import \
    --input-path 10141/BIOM/44878/otu_table.biom \
    --type 'FeatureTable[Frequency]' \
    --input-format BIOMV210Format \
    --output-path 10141/feature-table_44878.qza
qiime tools import \
    --input-path 10141/BIOM/44906/otu_table.biom \
    --type 'FeatureTable[Frequency]' \
    --input-format BIOMV210Format \
    --output-path 10141/feature-table_44906.qza
qiime feature-table merge \
    --i-tables 10141/feature-table_44878.qza \
    --i-tables 10141/feature-table_44906.qza \
    --p-overlap-method sum \
    --o-merged-table 10141/feature-table_merged.qza
qiime feature-table filter-samples \
  --i-table 10141/feature-table_merged.qza \
  --m-metadata-file 10141/templates/samples_to_keep_10141.tsv \
  --o-filtered-table 10141/feature-table_filtered.qza<jupyter_output>[32mSaved FeatureTable[Frequency] to: 10141/feature-table_filtered.qza[0m<jupyter_text>10143<jupyter_code>qiime tools import \
    --input-path 10143/BIOM/45990/otu_table.biom \
    --type 'FeatureTable[Frequency]' \
    --input-format BIOMV210Format \
    --output-path 10143/feature-table_45990.qza
qiime tools import \
    --input-path 10143/BIOM/46002/otu_table.biom \
    --type 'FeatureTable[Frequency]' \
    --input-format BIOMV210Format \
    --output-path 10143/feature-table_46002.qza
qiime feature-table merge \
    --i-tables 10143/feature-table_45990.qza \
    --i-tables 10143/feature-table_46002.qza \
    --p-overlap-method sum \
    --o-merged-table 10143/feature-table_merged.qza
qiime feature-table filter-samples \
  --i-table 10143/feature-table_merged.qza \
  --m-metadata-file 10143/templates/samples_to_keep_10143.tsv \
  --o-filtered-table 10143/feature-table_filtered.qza<jupyter_output>[32mSaved FeatureTable[Frequency] to: 10143/feature-table_filtered.qza[0m<jupyter_text>Merge all<jupyter_code>qiime feature-table merge \
    --i-tables 714/feature-table_filtered.qza \
    --i-tables 1609/feature-table_filtered.qza \
    --i-tables 10141/feature-table_filtered.qza \
    --i-tables 10143/feature-table_filtered.qza \
    --p-overlap-method sum \
    --o-merged-table pmi_control_feature-table.qza<jupyter_output>[32mSaved FeatureTable[Frequency] to: pmi_control_feature-table.qza[0m<jupyter_text>Extract pmi_control biom<jupyter_code>unzip -K pmi_control_feature-table.qza
mv 95347b54-fc9f-4f83-b219-4cae5039cd7f pmi_control_feature-table_directory
biom convert -i pmi_control_feature-table_directory/data/feature-table.biom \
             -o pmi_control_feature-table.tsv --to-tsv<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.12855407047387607, 'fraction_numerical': 0.0896719319562576, 'mean_word_length': 3.995145631067961, 'pattern_counts': {'":': 0, '<': 23, '<?xml version=': 0, '>': 23, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '7', 'dir': 'jupyter-structured-clean-dedup', 'id': '289387', 'license': 'MIT', 'n_tokens_mistral': 1862, 'n_tokens_neox': 1537, 'n_words': 204, 'path': '3_filter-samples_and_merge_biom.ipynb', 'repo_name': 'nathaliagg/pmi', 'size': '4115'}