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QIIME2 Taxonomy classifier

Author: Siobhon L Egan siobhonegan@hotmail.com

Last updated Jan 2021 QIIME2 version QIIME2-2020.11

Instructions for making feature classifiers using QIIME2.

Overview of classifiers made

Trained classifiers have been done for the follow:

  • Green genes 13_8 release

    • 16S rRNA V1-2 - primers 27F-Y and 338R - 27F-Y AGAGTTTGATCCTGGCTYAG (Ref: Gofton et al. 2015, DOI: 10.1186/s13071-015-0958-3) - 338R TGCTGCCTCCCGTAGGAGT (Ref: Turner et al. 1999, DOI: 10.1111/j.1550-7408.1999.tb04612.x)
    • 16S rRNA V4 - primers 515F and 806R - 515F GTGBCAGCMGCCGCGGTAA (Caporaso et al. 2011, DOI: 10.1073/pnas.1000080107) - 806R GGACTACHVGGGTWTCTAAT (Caporaso et al. 2011, DOI: 10.1073/pnas.1000080107)
    • 16S rRNA V3-4 - primers 338F and 806R - 338F ACTCCTACGGGAGGCAGCAG (Lopez et al. 2003, DOI: 10.1128/AEM.69.11.6801-6807.2003) - 806R GGACTACHVGGGTWTCTAAT (Caporaso et al. 2011, DOI: 10.1073/pnas.1000080107)

  • Silva 132 release

    • 16S rRNA V1-2 - primers 27F-Y and 338R - 27F-Y AGAGTTTGATCCTGGCTYAG (Ref: Gofton et al. 2015, DOI: 10.1186/s13071-015-0958-3) - 338R TGCTGCCTCCCGTAGGAGT (Ref: Turner et al. 1999, DOI: 10.1111/j.1550-7408.1999.tb04612.x)
    • 16S rRNA V4 - primers 515F and 806R - 515F GTGBCAGCMGCCGCGGTAA (Caporaso et al. 2011, DOI: 10.1073/pnas.1000080107) - 806R GGACTACHVGGGTWTCTAAT (Caporaso et al. 2011, DOI: 10.1073/pnas.1000080107)
    • 16S rRNA V3-4 - primers 338F and 806R - 338F ACTCCTACGGGAGGCAGCAG (Lopez et al. 2003, DOI: 10.1128/AEM.69.11.6801-6807.2003) - 806R GGACTACHVGGGTWTCTAAT (Caporaso et al. 2011, DOI: 10.1073/pnas.1000080107)

1. Extract required files

Greengenes

Download the latest Greengenes release, currently 13_8 release

Once you unzip the file gg_13_8_otus it should contain the following:

otus/
rep_set/
rep_set_aligned/
taxonomy/
trees/
notes

Files you will need for this tutorial are

  • rep_set/99_otus.fasta
  • taxonomy/99_otu_taxonomy

SILVA

Download the latest Silva release, currently 132.

Once you unzip the file SILVA_132_QIIME_release it should contain the following:

core_alignment/
raw_data/
rep_set/
rep_set_aligned/
taxonomy/
Silva_132_notes.txt

Files you will need for this tutorial are

  • rep_set/rep_set_16S_only/99/silva_132_99_16S.fna
  • taxonomy/16S_only/99/consensus_taxonomy_7_levels.txt

3. Activate QIIME2 environment

Activate session

conda activate qiime2-2020.11

4. Greengenes classifier

4.1. Make QIIME2 artifacts

qiime tools import \
--type 'FeatureData[Sequence]' \
--input-path 99_otus.fasta \
--output-path 99_otus_16S.qza

qiime tools import \
--type 'FeatureData[Taxonomy]' \
--input-format HeaderlessTSVTaxonomyFormat \
--input-path 99_otu_taxonomy.txt \
--output-path ref-taxonomy.qza

4.2. Extract reference sequences

A. For 16S rRNA v1-2 and v4 amplicons

qiime feature-classifier extract-reads \
--i-sequences 99_otus_16S.qza \
--p-f-primer {SEQUENCE} \
--p-r-primer {SEQUENCE} \
--p-trunc-len 120 \
--p-min-length 100 \
--p-max-length 400 \
--o-reads ref-seqs.qza

Output artifacts: ref-seqs.qza

B. For 16S rRNA v3-4 amplicons

qiime feature-classifier extract-reads \
--i-sequences 99_otus_16S.qza \
--p-f-primer ACTCCTACGGGAGGCAGCAG \
--p-r-primer GGACTACHVGGGTWTCTAAT \
--p-min-length 100 \
--p-max-length 500 \
--o-reads ref-seqs.qza

Output artifacts: ref-seqs.qza

4.3. Train the classifier

qiime feature-classifier fit-classifier-naive-bayes \
--i-reference-reads ref-seqs.qza \
--i-reference-taxonomy ref-taxonomy.qza \
--o-classifier classifier.qza

Output artifacts: classifier.qza

5. Silva classifier

5.1. Make QIIME2 artifacts

qiime tools import \
--type 'FeatureData[Sequence]' \
--input-path silva_132_99_16S.fna \
--output-path 99_otus_16S.qza

qiime tools import \
--type 'FeatureData[Taxonomy]' \
--input-format HeaderlessTSVTaxonomyFormat \
--input-path consensus_taxonomy_7_levels.txt \
--output-path ref-taxonomy.qza

5.2. Extract reference sequences

A. For 16S rRNA v1-2 and v4 amplicons

qiime feature-classifier extract-reads \
--i-sequences 99_otus_16S.qza \
--p-f-primer {SEQUENCE} \
--p-r-primer {SEQUENCE} \
--p-trunc-len 120 \
--p-min-length 100 \
--p-max-length 400 \
--o-reads ref-seqs.qza

Output artifacts: ref-seqs.qza

B. For 16S rRNA v3-4 amplicons

qiime feature-classifier extract-reads \
--i-sequences 99_otus_16S.qza \
--p-f-primer ACTCCTACGGGAGGCAGCAG \
--p-r-primer GGACTACHVGGGTWTCTAAT \
--p-min-length 100 \
--p-max-length 500 \
--o-reads ref-seqs.qza

Output artifacts: ref-seqs.qza

5.3. Train the classifier

qiime feature-classifier fit-classifier-naive-bayes \
--i-reference-reads ref-seqs.qza \
--i-reference-taxonomy ref-taxonomy.qza \
--o-classifier classifier.qza

Output artifacts: classifier.qza