Protocols
QIIME 2 Pipline

QIIME 2 Pipline

Nucleic Acid Quantification

INSTALL MINICONDA

Download Distribution

https://docs.conda.io/en/latest/miniconda.html

Install Distribution

bash Miniconda3-latest-MacOSX-x86_64.sh

Follow the prompts on the installer screens.

INSTALL QIIME 2

Update Miniconda

conda update conda

Install `wget`

conda install wget

Install QIIME 2 within a `conda` environment

wget https://data.qiime2.org/distro/core/qiime2-2022.2-py38-osx-conda.yml
conda env create -n qiime2-2022.2 --file qiime2-2022.2-py38-osx-conda.yml

Activate the `conda` environment

conda activate qiime2-2022.2

Test your installation

qiime --help

QIIME ANALYSIS

Based on "Comprehensive end-to-end microbiome analysis using QIIME 2"

Create Metadata File

https://docs.qiime2.org/2022.2/tutorials/metadata/

sample-id	subject	week
RD01	ROLAND	1
JS01	JULIAN	1

Create the Manifest File

echo -e "sample-id\tabsolute-filepath" > manifest.tsv

Add FASTQ Files to Manifest

#!/usr/bin/env python3

from collections import defaultdict
import glob
import os
import send2trash

def multi_dict(K, type):
    if K == 1:
        return defaultdict(type)
    else:
        return defaultdict(lambda: multi_dict(K-1, type))

def delete_file(file_in):
    file_exists = os.path.isfile(file_in)
    if file_exists == True:
        send2trash.send2trash(file_in)

dir_abs = os.path.abspath("DATADIRECTORY")
file_paths_rel = glob.glob(dir_abs + "/*.fastq.gz")
file_paths_abs = []
for path in file_paths_rel:
  path_abs = os.path.abspath(path)
  file_paths_abs.append(path_abs)

files = multi_dict(3, int)
for path in file_paths_abs:
  file_dir = os.path.split(path)
  file_name_parts = file_dir[1].split("_")
  sample_ID = file_name_parts[0]
  if file_name_parts[2] == "R1":
    direction = "forward-absolute-filepath"
  elif file_name_parts[2] == "R2":
    direction = "reverse-absolute-filepath"
  files[sample_ID][direction]=path

lines = []
for i in files.keys():
  forward = files[i]["forward-absolute-filepath"]
  reverse = files[i]["reverse-absolute-filepath"]
  data = [i,forward,reverse]
  lines.append(data)

writer_name = "manifest.tsv"
delete_file(writer_name)
writer = open(writer_name, "w")
header = "sample-id\tforward-absolute-filepath\treverse-absolute-filepath\n"
writer.write(header)
for line in lines:
  line =  str(line[0]) + "\t" + str(line[1]) + "\t" + str(line[2]) + "\n"
  writer.write(line)
writer.close()

Use the manifest file to import the sequences into QIIME 2

qiime tools import \
--input-path manifest.tsv \
--type 'SampleData[PairedEndSequencesWithQuality]' \
--input-format PairedEndFastqManifestPhred33V2 \
--output-path se-demux.qza

qiime demux summarize \
--i-data se-demux.qza \
--o-visualization se-demux.qzv

qiime tools view se-demux.qzv

qiime quality-filter q-score \
--i-demux se-demux.qza \
--o-filtered-sequences demux-filtered.qza \
--o-filter-stats demux-filter-stats.qza

qiime deblur denoise-16S \
--i-demultiplexed-seqs demux-filtered.qza \
--p-trim-length 200 \
--p-sample-stats \
--p-jobs-to-start 4 \
--o-stats deblur-stats.qza \
--o-representative-sequences rep-seqs-deblur.qza \
--o-table table-deblur.qza

qiime deblur visualize-stats \
--i-deblur-stats deblur-stats.qza \
--o-visualization deblur-stats.qzv

qiime feature-table tabulate-seqs \
--i-data rep-seqs-deblur.qza \
--o-visualization rep-seqs-deblur.qzv

qiime feature-table summarize \
--i-table table-deblur.qza \
--m-sample-metadata-file metadata.tsv \
--o-visualization table-deblur.qzv

wget -O "sepp-refs-gg-13-8.qza" \
"https://data.qiime2.org/2019.10/common/sepp-refs-gg-13-8.qza"

qiime fragment-insertion sepp \
--i-representative-sequences rep-seqs-deblur.qza \
--i-reference-database sepp-refs-gg-13-8.qza \
--p-threads 4 \
--o-tree insertion-tree.qza \
--o-placements insertion-placements.qza

qiime fragment-insertion filter-features \
--i-table table-deblur.qza \
--i-tree insertion-tree.qza \
--o-filtered-table filtered-table-deblur.qza \
--o-removed-table removed-table.qza

qiime empress tree-plot \
    --i-tree insertion-tree.qza \
    --m-feature-metadata-file bespoke-taxonomy2.qza \
    --o-visualization tree-viz.qzv

qiime taxa barplot \
--i-table filtered-table-deblur.qza \
--i-taxonomy bespoke-taxonomy2.qza \
--m-metadata-file metadata.tsv \
--o-visualization bar-plots.qzv

Classifying

For V3-V4 region sequences

wget \
-O "human-stool.qza" \
"https://github.com/BenKaehler/readytowear/raw/master/data/gg_13_8/515f-806r/human-stool.qza"

wget \
-O "ref-tax.qza" \
"https://github.com/BenKaehler/readytowear/raw/master/data/gg_13_8/515f-806r/ref-tax.qza"

wget \
-O "ref-seqs-v4.qza" \
"https://github.com/BenKaehler/readytowear/raw/master/data/gg_13_8/515f-806r/ref-seqs.qza"

qiime feature-classifier fit-classifier-naive-bayes \
--i-reference-reads ref-seqs-v4.qza \
--i-reference-taxonomy ref-tax.qza \
--i-class-weight human-stool.qza \
--o-classifier gg138_v4_human-stool_classifier.qza

qiime feature-classifier classify-sklearn \
--i-reads rep-seqs-deblur.qza \
--i-classifier gg138_v4_human-stool_classifier.qza \
--o-classification bespoke-taxonomy.qza

For V2-V3 region sequences

# OUR V2-V3 SEQENCES FROM UB GENOME CORE DON'T OVERLAP FULLY WITH 515f-806r, SO YOU COULD SKIP TRAINING AND USE THE FULL-LENGTH WEIGHTED PRE-TRAINED CLASSIFIER FROM https://zenodo.org/record/6395539. OTHERWISE YOUR CLASSIFICATION WILL BE LOW_RES

wget "https://zenodo.org/record/6395539/files/full-length-human-stool-classifier.qza?download=1"

qiime feature-classifier classify-sklearn \
--i-reads rep-seqs-deblur.qza \
--i-classifier full-length-human-stool-classifier.qza \
--o-classification bespoke-taxonomy2.qza

# YOU CAN ALSO DOWNLOAD THE REF-SEQS and REF-TAX FILES AND WEIGHT FILES

wget \
-O "human-stool.qza" \
"https://github.com/BenKaehler/readytowear/raw/master/data/silva_138_1/full_length/human-stool.qza"

wget \
-O "ref-seqs.qza" \
"https://zenodo.org/record/6395539/files/ref-seqs.qza?download=1"

wget \
-O "ref-tax.qza" \
"https://zenodo.org/record/6395539/files/ref-tax.qza?download=1"

# AND THEN TRAIN THE CLASSIFIER

qiime feature-classifier fit-classifier-naive-bayes \
--i-reference-reads ref-seqs.qza \
--i-reference-taxonomy ref-tax.qza \
--i-class-weight human-stool.qza \
--o-classifier gg138_v4_human-stool_classifier.qza

qiime metadata tabulate \
--m-input-file bespoke-taxonomy.qza \
--m-input-file rep-seqs-deblur.qza \
--o-visualization bespoke-taxonomy.qzv

qiime feature-table summarize \
--i-table filtered-table-deblur.qza \
--m-sample-metadata-file metadata.tsv \
--o-visualization feature-table-summary.qzv

qiime diversity alpha-rarefaction \
--i-table filtered-table-deblur.qza \
--i-phylogeny insertion-tree.qza \
--p-max-depth 10000 \
--m-metadata-file metadata.tsv \
--o-visualization feature-table-alpha-rarefaction.qzv

qiime feature-table filter-samples \
--i-table filtered-table-deblur.qza \
--m-metadata-file metadata.tsv \
--p-where "[subject]='JULIAN'" \
--o-filtered-table child-table-j.qza

qiime feature-table summarize \
--i-table child-table-j.qza \
--m-sample-metadata-file metadata.tsv \
--o-visualization child-table-j.qzv

qiime diversity alpha-rarefaction \
--i-table child-table-j.qza \
--i-phylogeny insertion-tree.qza \
--p-max-depth 10000 \
--m-metadata-file metadata.tsv \
--o-visualization child-alpha-rarefaction-j.qzv

https://github.com/biocore/empress

pip install cython "numpy >= 1.12.0"
pip install empress

qiime dev refresh-cache
qiime empress --help

qiime empress tree-plot \
    --i-tree insertion-tree.qza \
    --o-visualization tree-viz.qzv

QIIME 2 Pipline

INSTALL MINICONDA

Download Distribution

Install Distribution

INSTALL QIIME 2

Update Miniconda

Install wget

Install QIIME 2 within a conda environment

Activate the conda environment

Test your installation

QIIME ANALYSIS

Create Metadata File

Create the Manifest File

Add FASTQ Files to Manifest

Use the manifest file to import the sequences into QIIME 2

Classifying

For V3-V4 region sequences

For V2-V3 region sequences

Procedure

Install `wget`

Install QIIME 2 within a `conda` environment

Activate the `conda` environment