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You should see several output directories and files have been created in your ‘ampliseq_test’ directory. These contain the test analysis results. Have a look through these, as they are similar to the output from a full ampliseq run (i.e. on your dataset).
Need instructions on setting up NextFlow tower
Q for Craig:
Do we need to add any of this to .nextflow/config file? Perhaps just for Tower?
process {
executor = 'pbspro'
scratch = 'true'
beforeScript = {
"""
mkdir -p /data1/whatmorp/singularity/mnt/session
source $HOME/.bashrc
source $HOME/.profile
"""
}
}
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nextflow run nf-core/ampliseq -profile singularity --manifest manifest.txt |
Block: takes a very long time and often fails during classifier step.
Solution: run classifier manually with QIIME and point to output file in nextflow.config
https://docs.qiime2.org/2020.11/tutorials/feature-classifier/
This is the NexFlow script for the job:
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export HOME="${PWD}/HOME"
unzip -qq Silva_132_release.zip
fasta="SILVA_132_QIIME_release/rep_set/rep_set_16S_only/99/silva_132_99_16S.fna"
taxonomy="SILVA_132_QIIME_release/taxonomy/16S_only/99/consensus_taxonomy_7_levels.txt"
if [ "false" = "true" ]; then
sed 's/#//g' $taxonomy >taxonomy-99_removeHash.txt
taxonomy="taxonomy-99_removeHash.txt"
echo "
######## WARNING! The taxonomy file was altered by removing all hash signs!"
fi
### Import
qiime tools import --type 'FeatureData[Sequence]' --input-path $fasta --output-path ref-seq-99.qza
qiime tools import --type 'FeatureData[Taxonomy]' --input-format HeaderlessTSVTaxonomyFormat --input-path $taxonomy --output-path ref-taxonomy-99.qza
#Extract sequences based on primers
qiime feature-classifier extract-reads --i-sequences ref-seq-99.qza --p-f-primer TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGCCTACGGGNGGCWGCAG --p-r-primer GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGGACTACHVGGGTATCTAATCC --o-reads TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGCCTACGGGNGGCWGCAG-GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGGACTACHVGGGTATCTAATCC-99-ref-seq.qza --quiet
#Train classifier
qiime feature-classifier fit-classifier-naive-bayes --i-reference-reads TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGCCTACGGGNGGCWGCAG-GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGGACTACHVGGGTATCTAATCC-99-ref-seq.qza --i-reference-taxonomy ref-taxonomy-99.qza --o-classifier TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGCCTACGGGNGGCWGCAG-GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGGACTACHVGGGTATCTAATCC-99-classifier.qza --quiet |
The input fast and taxonomy files the above script are pointing to are in the reference database file, Silva_132_release.zip
So to generate the classifier file manually:
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# Install QIIME2 using conda (only need to do this once per user)
# https://docs.qiime2.org/2019.10/install/native/
wget https://data.qiime2.org/distro/core/qiime2-2019.10-py36-linux-conda.yml
conda env create -n qiime2-2019.10 --file qiime2-2019.10-py36-linux-conda.yml
conda activate qiime2-2019.10
# You can test the QIIME2 installation by running: qiime --help
# Make sure you are in an interactive PBS session with sufficient RAM (lots of RAM needed)
qsub -I -S /bin/bash -l walltime=72:00:00 -l select=1:ncpus=16:mem=256gb
# Unzip Silva_132_release.zip to access the required databases
unzip Silva_132_release.zip
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Running analysis on QUTs HPC
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If you haven’t been set up or have used the HPC previously, click on this link for information on how to get access to and use the HPC:
Need a link here for HPC access and usage
Creating a shared workspace on the HPC
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To request a node using PBS, submit a shell script containing your RAM/CPU/analysis time requirements and the code needed to run your analysis. For an overview of submitting a PBS job, see here:
Need a link here for creating PBS jobs
Alternatively, you can start up an ‘interactive’ node, using the following:
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