version 1.0
task qiime_import_from_bam {
meta {
description: "Parsing demultiplexed fastq BAM files into qiime readable files."
}
input {
Array[File] reads_bam
Int memory_mb = 7000
Int cpu = 5
Int disk_size_gb = ceil(2*20) + 5
String docker = "quay.io/broadinstitute/qiime2:latest"
}
parameter_meta {
reads_bam: {
description: "Unaligned reads in BAM format, one sample per BAM file.",
category: "required"
}
reads_qza: {
description: "All unaligned reads in a single QZA (QIIME) file.",
category: "other"
}
samplename_master_sheet: {
description: "File contains all samples names.",
category: "other"
}
}
command <<<
set -ex -o pipefail
#Part 1A | BAM -> FASTQ [Simple samtools command]
manifest_TSV=manifest.tsv
echo -e "sample-id\tforward-absolute-filepath\treverse-absolute-filepath" > manifest.tsv
for bam in ~{sep=' ' reads_bam}; do
#making new bash variable | regex: (_) -> (-)
NEWSAMPLENAME=$(basename $bam .bam | perl -lape 's/[_]/-/g')
echo $NEWSAMPLENAME
samtools fastq -1 $NEWSAMPLENAME.R1.fastq.gz -2 $NEWSAMPLENAME.R2.fastq.gz -0 /dev/null $bam
#All names added to one giant file
echo $NEWSAMPLENAME >> NEWSAMPLENAME.txt
#>=replaces
#>>= appends
#\t= tabs next value
echo -e "$NEWSAMPLENAME\t$(pwd)/$NEWSAMPLENAME.R1.fastq.gz\t$(pwd)/$NEWSAMPLENAME.R2.fastq.gz"
echo -e "$NEWSAMPLENAME\t$(pwd)/$NEWSAMPLENAME.R1.fastq.gz\t$(pwd)/$NEWSAMPLENAME.R2.fastq.gz" >> manifest.tsv
done
# debug
cat manifest.tsv
#fastq -> bam (provided by qiime tools import fxn)
qiime tools import \
--type 'SampleData[PairedEndSequencesWithQuality]' \
--input-path manifest.tsv \
--input-format PairedEndFastqManifestPhred33V2 \
--output-path "batch.qza"
>>>
output {
File reads_qza = "batch.qza"
String samplename_master_sheet = read_string("NEWSAMPLENAME.txt")
}
runtime {
docker: docker
memory: "~{memory_mb} MiB"
cpu: cpu
disk: disk_size_gb + " GB"
disks: "local-disk " + disk_size_gb + " HDD"
}
}
#Part 1 | Step 2:cutadapt: Trim sequences
#trimreads_trim
#trim = default
task trim_reads {
meta {
description:"Removing adapter sequences, primers, and other unwanted sequence from sequence data."
}
input {
File reads_qza
String forward_adapter = "CTGCTGCCTCCCGTAGGAGT"
String reverse_adapter = "AGAGTTTGATCCTGGCTCAG"
Int min_length = 1
Boolean keep_untrimmed_reads = false
Int memory_mb = 2000
Int cpu = 4
Int disk_size_gb = ceil(2*size(reads_qza, "GiB")) + 5
String docker = "quay.io/broadinstitute/qiime2:latest"
}
parameter_meta {
reads_qza: {
description: "All unaligned reads in a single QZA (QIIME) file.",
cateogry: "required"
}
forward_adapter: {
description: "Forward amplicon primer sequence.",
category: "advanced"
}
reverse_adapter: {
description: "Reverse amplicon primer sequence.",
cateogry: "advanced"
}
min_length: {
description: "Minimum length of the read, cutadapt will discard anything that is shorter than n bp AFTER trimming.Set to default.",
category: "other"
}
keep_untrimmed_reads: {
description: "Allows you to choose whether or not to discard untrimmed reads.",
category: "advanced"
}
trimmed_reads_qza: {
description: "Trimmed reads data file.",
category: "advanced"
}
trimmed_visualization: {
description: "A diagram that compares your demuxed reads before and after cutting (i.e. length of reads, how many reads were retained).",
category: "advanced"
}
}
command <<<
set -ex -o pipefail
qiime cutadapt trim-paired \
--i-demultiplexed-sequences "~{reads_qza}" \
--p-front-f "~{forward_adapter}" \
--p-front-r "~{reverse_adapter}" \
~{"--p-minimum-length " + min_length} \
~{true='--p-no-discard-untrimmed' false='--p-discard-untrimmed' keep_untrimmed_reads} \
--o-trimmed-sequences "trimmed.qza"
#trim_visual
qiime demux summarize \
--i-data "trimmed.qza" \
--o-visualization "trim_summary.qzv"
>>>
output {
#trimmed_sequences = paired ends for vsearch
File trimmed_reads_qza = "trimmed.qza"
File trimmed_visualization = "trim_summary.qzv"
}
runtime {
docker: docker
memory: "${memory_mb} MiB"
cpu: cpu
disk: disk_size_gb + " GB"
disks: "local-disk " + disk_size_gb + " HDD"
}
}
#Part 1 | Step 3:VSEARCH: Merge sequences
task join_paired_ends {
meta {
description: "Join paired-end sequence reads using vseach's merge_pairs function."
}
input {
#Input File: Merge paired reads
File trimmed_reads_qza
Int memory_mb = 2000
Int cpu = 1
Int disk_size_gb = ceil(2*size(trimmed_reads_qza, "GiB")) + 50
String docker = "quay.io/broadinstitute/qiime2:latest"
}
parameter_meta{
trimmed_reads_qza: {
description:"Trimmed reads data file.",
category: "required"
}
joined_end_reads_qza:{
description: "Merge paired read file.",
category: "other"
}
joined_end_visualization: {
description: "This summary is especially useful for assessing the length of linked reads and the quality scores at each sequence base position. ",
category: "other"
}
}
command <<<
set -ex -o pipefail
qiime vsearch join-pairs \
--i-demultiplexed-seqs ~{trimmed_reads_qza} \
--o-joined-sequences "joined.qza"
qiime demux summarize \
--i-data "joined.qza" \
--o-visualization "visualization.qzv"
>>>
output {
File joined_end_reads_qza = "joined.qza"
File joined_end_visualization = "visualization.qzv"
}
runtime {
docker: docker
memory: "${memory_mb} MiB"
cpu: cpu
disk: disk_size_gb + " GB"
disks: "local-disk " + disk_size_gb + " HDD"
}
}
task deblur {
meta {
description: "Perform sequence quality control for Illumina data using the Deblur workflow with a 16S reference as a positive filter."
}
input {
File joined_end_reads_qza
Int trim_length_var = 300
Int memory_mb = 2000
Int cpu = 1
Int disk_size_gb = ceil(2*size(joined_end_reads_qza, "GiB")) + 5
String docker = "quay.io/broadinstitute/qiime2:latest"
}
parameter_meta {
joined_end_reads_qza: {
description: "Merge paired read file.",
category: "required"
}
trim_length_var: {
description: "Length that all seqeuences will be trimmed, and discard any sequences that are not at least this long.",
category: "advanced"
}
representative_seqs_qza: {
description: "Generate a list of the representative sequences. May be useful to the user if they want to blast these sequences or check for correct trimming.",
category: "other"
}
representative_table_qza: {
description: "Generate a table of the representaitve sequences.",
category: "other"
}
feature_table: {
description: "A table that represent the number of of features per sample, the number of samples a given feature is found in.",
category: "other"
}
visualize_stats:{
description: "Generate visualization of deblur stats.",
category: "other"
}
}
command <<<
set -ex -o pipefail
qiime deblur denoise-16S \
--i-demultiplexed-seqs ~{joined_end_reads_qza}\
~{"--p-trim-length " + trim_length_var} \
--p-sample-stats \
--o-representative-sequences "rep_seqs.qza" \
--o-table "table.qza" \
--o-stats "stats.qza"
#Generate feature table- give you the number of features per sample
qiime feature-table summarize \
--i-table "table.qza" \
--o-visualization "table.qzv"
#Generate visualization of deblur stats
qiime deblur visualize-stats \
--i-deblur-stats "stats.qza" \
--o-visualization "stats.qzv"
>>>
output {
File representative_seqs_qza = "rep_seqs.qza"
File representative_table_qza = "table.qza"
File feature_table = "table.qzv"
File visualize_stats = "stats.qzv"
}
runtime {
docker: docker
memory: "${memory_mb} MiB"
cpu: cpu
disk: disk_size_gb + " GB"
disks: "local-disk " + disk_size_gb + " HDD"
}
}
task train_classifier {
meta {
descrription: " Upload a classidier trained to classify v1-2 amplicon sequences"
}
input {
File otu_ref
File taxanomy_ref
String forward_adapter
String reverse_adapter
Int min_length = 100
Int max_length = 500
String otu_basename = basename(otu_ref, '.qza')
Int memory_mb = 2000
Int cpu = 1
Int disk_size_gb = ceil(2*size(otu_ref, "GiB")) + 5
String docker = "quay.io/broadinstitute/qiime2:latest"
}
parameter_meta{
otu_ref: {
description: "Operational taxonomic units (OTUs) sequences imported as FASTA file.",
category:"required"
}
taxanomy_ref: {
description: "Reference taxonomy file.",
category: "required"
}
forward_adapter: {
description: "The forward primer sequence for the amplicon target.",
category: "advanced"
}
reverse_adapter: {
description: "The reverse primer sequence for the amplicon target.",
category:"advanced"
}
min_length: {
description: "Minimum length of amplicon sequences.",
category: "advanced"
}
max_length: {
description: "Maximum length of amplicon sequences.",
category:"advanced"
}
trained_classifier: {
description: "Trained taxonomic classifier on target amplicon sequences.",
category: "other"
}
}
command <<<
set -ex -o pipefail
CONDA_ENV_NAME=$(conda info --envs -q | awk -F" " '/qiime.*/{ print $1 }')
conda activate ${CONDA_ENV_NAME}
qiime tools import \
--type 'FeatureData[Sequence]' \
--input-path ~{otu_ref} \
--output-path "~{otu_basename}_seqs.qza"
qiime tools import \
--type 'FeatureData[Taxonomy]'
--input-format HeaderlessTSVTaxonomyFormat \
--input-path ~{taxanomy_ref} \
--output-path "~{otu_basename}_tax.qza"
qiime feature-classifier extract-reads\
--i-sequeunces "~{otu_basename}_seqs.qza"\
--p-f-primer "~{forward_adapter}" \
--p-r-primer "~{reverse_adapter}" \
~{"--p-min-length " + min_length} \
~{"--p-max-length " + max_length} \
--o-reads "~{otu_basename}_v1-2-ref-seqs.qza"
qiime feature-classifier fit-classifier-naive-bayes \
--i-reference-reads "~{otu_basename}_v1-2-ref-seqs.qza" \
--i-reference-taxonomy "~{otu_basename}_tax.qza" \
--o-classifier "~{otu_basename}_v1-2-classifier.qza"
>>>
output {
File trained_classifier = "~{otu_basename}_v1-2-classifier.qza"
}
runtime {
docker: docker
memory: "${memory_mb} MiB"
cpu: cpu
disk: disk_size_gb + " GB"
disks: "local-disk " + disk_size_gb + " HDD"
}
}
task tax_analysis {
meta {
description: "Protocol describes performing a taxonomic classification with a naive bayes classifier that has been trained on the V1-2 regions amplified by our primers."
}
input {
File trained_classifier
File representative_seqs_qza
File representative_table_qza
Int memory_mb = 5
Int cpu = 1
Int disk_size_gb = 375
String docker = "quay.io/broadinstitute/qiime2:latest"
}
parameter_meta{
trained_classifier: {
description: "Trained taxonomic classifier on target amplicon sequences.",
category: "required"
}
representative_seqs_qza: {
description: "List of representative sequences.",
category:"required"
}
representative_table_qza: {
description: "Table of representative sequences.",
category:"other"
}
rep_seq_list: {
description: "Generate list of representative sequences.",
category:"other"
}
tax_classification_graph: {
description: "Create a bar graph of your taxonomic classification.",
category:"other"
}
}
command <<<
set -ex -o pipefail
qiime feature-classifier classify-sklearn \
--i-classifier ~{trained_classifier} \
--i-reads ~{representative_seqs_qza} \
--o-classification "taxonomy.qza"
qiime feature-table tabulate-seqs \
--i-data ~{representative_seqs_qza} \
--o-visualization "list_rep_seqs.qzv"
qiime taxa barplot \
--i-table ~{representative_table_qza} \
--i-taxonomy "taxonomy.qza" \
--o-visualization "taxa_bar_plots.qzv"
>>>
output {
File rep_seq_list = "list_rep_seqs.qzv"
File tax_classification_graph = "taxa_bar_plots.qzv"
}
runtime {
docker: docker
memory: "10 GB"
cpu: cpu
disk: disk_size_gb + " GB"
disks: "local-disk " + disk_size_gb + " HDD"
}
}
