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# ENCODE ATAC-Seq/DNase-Seq pipeline
# Author: Jin Lee ([email protected])
#CAPER docker quay.io/encode-dcc/atac-seq-pipeline:v1.5.4
#CAPER singularity docker://quay.io/encode-dcc/atac-seq-pipeline:v1.5.4
#CROO out_def https://storage.googleapis.com/encode-pipeline-output-definition/atac.croo.json
workflow atac {
# pipeline version
String pipeline_ver = 'v1.5.4'
# general sample information
String title = 'Untitled'
String description = 'No description'
# endedness for input data
Boolean? paired_end # to define endedness for all replciates
# if defined, this will override individual endedness below
Array[Boolean] paired_ends = [] # to define endedness for individual replicate
# genome TSV
# you can define any genome parameters either in this TSV
# or individually in an input JSON file
# individually defined parameters will override those defined in this TSV
File? genome_tsv # reference genome data TSV file including
# all genome-specific file paths and parameters
# individual genome parameters
String? genome_name # genome name
File? ref_fa # reference fasta (*.fa.gz)
File? ref_mito_fa # mito-only reference fasta (*.fa.gz)
File? bwa_idx_tar # bwa index tar (uncompressed .tar)
File? bwa_mito_idx_tar # bwa mito-only index tar (uncompressed .tar)
File? bowtie2_idx_tar # bowtie2 index tar (uncompressed .tar)
File? bowtie2_mito_idx_tar # bowtie2 mito-only index tar (uncompressed .tar)
File? custom_aligner_idx_tar # custom aligner's index tar (uncompressed .tar)
File? custom_aligner_mito_idx_tar # custom aligner's mito-only index tar (uncompressed .tar)
File? chrsz # 2-col chromosome sizes file
File? blacklist # blacklist BED (peaks overlapping will be filtered out)
File? blacklist2 # 2nd blacklist (will be merged with 1st one)
String? mito_chr_name
String? regex_bfilt_peak_chr_name
String? gensz # genome sizes (hs for human, mm for mouse or sum of 2nd col in chrsz)
File? tss # TSS BED file
File? dnase # open chromatin region BED file
File? prom # promoter region BED file
File? enh # enhancer region BED file
File? reg2map # file with cell type signals
File? reg2map_bed # file of regions used to generate reg2map signals
File? roadmap_meta # roadmap metedata
# parameters for pipeline
String pipeline_type = 'atac' # atac (default), dnase (same as atac but no tn5 shifting)
String aligner = 'bowtie2' # supported aligner: bowtie2
File? custom_align_py # custom align python script
String peak_caller = 'macs2'
String peak_type = 'narrowPeak'
File? custom_call_peak_py # custom call_peak python script
# important flags for pipeline
Boolean align_only = false # disable all post-align analyses (peak-calling, overlap, idr, ...)
Boolean true_rep_only = false # disable all analyses involving pseudo replicates (including overlap/idr)
Boolean enable_xcor = false # enable cross-corr analysis
Boolean enable_count_signal_track = false # generate count signal track
Boolean enable_idr = true # enable IDR analysis on raw peaks
Boolean enable_preseq = false
Boolean enable_fraglen_stat = true
Boolean enable_tss_enrich = true
Boolean enable_annot_enrich = true
Boolean enable_jsd = true # enable JSD plot generation (deeptools fingerprint)
Boolean enable_compare_to_roadmap = false
Boolean enable_gc_bias = true
# parameters for trim_adapter
Boolean auto_detect_adapter = false # automatically detect/trim adapters
String cutadapt_param = '-e 0.1 -m 5' # cutadapt parameters (err_rate=0.1, min_trim_len=5)
# parameters for aligner and filter
Int multimapping = 4 # for samples with multimapping reads
String dup_marker = 'picard' # picard, sambamba
Boolean no_dup_removal = false # keep all dups in final BAM
Int? mapq_thresh # threshold for low MAPQ reads removal
Int mapq_thresh_bwa = 30
Int mapq_thresh_bowtie2 = 30
Array[String] filter_chrs = ['chrM', 'MT']
# array of chromosomes to be removed from nodup/filt BAM
# chromosomes will be removed from both BAM header/contents
# e.g. (default: mito-chrs) ['chrM', 'MT']
Int subsample_reads = 0 # subsample TAGALIGN (0: no subsampling)
Int xcor_subsample_reads = 25000000 # subsample TAG-ALIGN for xcor only (not used for other downsteam analyses)
# parameters for peak calling
Int cap_num_peak = 300000 # cap number of raw peaks for each replicate
Float pval_thresh = 0.01 # p.value threshold for peak caller
Int smooth_win = 73 # size of smoothing window for peak caller
Float idr_thresh = 0.05 # IDR threshold
# resources
# these variables will be automatically ignored if they are not supported by platform
# "disks" is for cloud platforms (Google Cloud Platform, DNAnexus) only
Int align_cpu = 4
Int align_mem_mb = 20000
Int align_time_hr = 48
String align_disks = 'local-disk 400 HDD'
Int filter_cpu = 2
Int filter_mem_mb = 20000
Int filter_time_hr = 24
String filter_disks = 'local-disk 400 HDD'
Int bam2ta_cpu = 2
Int bam2ta_mem_mb = 10000
Int bam2ta_time_hr = 6
String bam2ta_disks = 'local-disk 100 HDD'
Int spr_mem_mb = 16000
Int jsd_cpu = 2
Int jsd_mem_mb = 12000
Int jsd_time_hr = 6
String jsd_disks = 'local-disk 200 HDD'
Int xcor_cpu = 2
Int xcor_mem_mb = 16000
Int xcor_time_hr = 6
String xcor_disks = 'local-disk 100 HDD'
Int call_peak_cpu = 1
Int call_peak_mem_mb = 16000
Int call_peak_time_hr = 24
String call_peak_disks = 'local-disk 200 HDD'
Int macs2_signal_track_mem_mb = 16000
Int macs2_signal_track_time_hr = 24
String macs2_signal_track_disks = 'local-disk 400 HDD'
Int preseq_mem_mb = 16000
String filter_picard_java_heap = '4G'
String preseq_picard_java_heap = '6G'
String fraglen_stat_picard_java_heap = '6G'
String gc_bias_picard_java_heap = '6G'
# input file definition
# supported types: fastq, bam, nodup_bam (or filtered bam), ta (tagAlign), peak
# pipeline can start from any type of inputs
# leave all other types undefined
# you can define up to 10 replicates
# fastqs and adapters
# if auto_detect_adapter == true, undefined adapters will be detected/trimmed
# otherwise, only defined adapters will be trimmed
# so you can selectively detect/trim adapters for a specific fastq
Array[File] fastqs_rep1_R1 = [] # FASTQs to be merged for rep1 R1
Array[File] fastqs_rep1_R2 = [] # do not define if single-ended
Array[File] fastqs_rep2_R1 = [] # do not define if unreplicated
Array[File] fastqs_rep2_R2 = [] # ...
Array[File] fastqs_rep3_R1 = []
Array[File] fastqs_rep3_R2 = []
Array[File] fastqs_rep4_R1 = []
Array[File] fastqs_rep4_R2 = []
Array[File] fastqs_rep5_R1 = []
Array[File] fastqs_rep5_R2 = []
Array[File] fastqs_rep6_R1 = []
Array[File] fastqs_rep6_R2 = []
Array[File] fastqs_rep7_R1 = []
Array[File] fastqs_rep7_R2 = []
Array[File] fastqs_rep8_R1 = []
Array[File] fastqs_rep8_R2 = []
Array[File] fastqs_rep9_R1 = []
Array[File] fastqs_rep9_R2 = []
Array[File] fastqs_rep10_R1 = []
Array[File] fastqs_rep10_R2 = []
String? adapter
Array[String] adapters_rep1_R1 = []
Array[String] adapters_rep1_R2 = []
Array[String] adapters_rep2_R1 = []
Array[String] adapters_rep2_R2 = []
Array[String] adapters_rep3_R1 = []
Array[String] adapters_rep3_R2 = []
Array[String] adapters_rep4_R1 = []
Array[String] adapters_rep4_R2 = []
Array[String] adapters_rep5_R1 = []
Array[String] adapters_rep5_R2 = []
Array[String] adapters_rep6_R1 = []
Array[String] adapters_rep6_R2 = []
Array[String] adapters_rep7_R1 = []
Array[String] adapters_rep7_R2 = []
Array[String] adapters_rep8_R1 = []
Array[String] adapters_rep8_R2 = []
Array[String] adapters_rep9_R1 = []
Array[String] adapters_rep9_R2 = []
Array[String] adapters_rep10_R1 = []
Array[String] adapters_rep10_R2 = []
# other input types (bam, nodup_bam, ta). they are per replicate
Array[File?] bams = []
Array[File?] nodup_bams = []
Array[File?] tas = []
# optional read length array. used it pipeline starts from BAM or TA
Array[Int?] read_len = [] # [rep_id]. read length for each rep
# other input types (peak)
Array[File?] peaks = [] # per replicate
Array[File?] peaks_pr1 = [] # per replicate. do not define if true_rep_only==true
Array[File?] peaks_pr2 = [] # per replicate. do not define if true_rep_only==true
File? peak_ppr1 # do not define if unreplicated or true_rep_only==true
File? peak_ppr2 # do not define if unreplicated or true_rep_only==true
File? peak_pooled # do not define if unreplicated or true_rep_only==true
####################### pipeline starts here #######################
# DO NOT DEFINE ANY VARIABLES DECLARED BELOW IN AN INPUT JSON FILE #
# THEY ARE TEMPORARY/INTERMEDIATE SYSTEM VARIABLES #
####################### pipeline starts here #######################
# read genome data and paths
if ( defined(genome_tsv) ) {
call read_genome_tsv { input: genome_tsv = genome_tsv }
}
File? ref_fa_ = if defined(ref_fa) then ref_fa
else read_genome_tsv.ref_fa
File? bwa_idx_tar_ = if defined(bwa_idx_tar) then bwa_idx_tar
else read_genome_tsv.bwa_idx_tar
File? bwa_mito_idx_tar_ = if defined(bwa_mito_idx_tar) then bwa_mito_idx_tar
else read_genome_tsv.bwa_mito_idx_tar
File? bowtie2_idx_tar_ = if defined(bowtie2_idx_tar) then bowtie2_idx_tar
else read_genome_tsv.bowtie2_idx_tar
File? bowtie2_mito_idx_tar_ = if defined(bowtie2_mito_idx_tar) then bowtie2_mito_idx_tar
else read_genome_tsv.bowtie2_mito_idx_tar
File? custom_aligner_idx_tar_ = if defined(custom_aligner_idx_tar) then custom_aligner_idx_tar
else read_genome_tsv.custom_aligner_idx_tar
File? custom_aligner_mito_idx_tar_ = if defined(custom_aligner_mito_idx_tar) then custom_aligner_mito_idx_tar
else read_genome_tsv.custom_aligner_mito_idx_tar
File? chrsz_ = if defined(chrsz) then chrsz
else read_genome_tsv.chrsz
String? gensz_ = if defined(gensz) then gensz
else read_genome_tsv.gensz
File? blacklist1_ = if defined(blacklist) then blacklist
else read_genome_tsv.blacklist
File? blacklist2_ = if defined(blacklist2) then blacklist2
else read_genome_tsv.blacklist2
# merge multiple blacklists
# two blacklists can have different number of columns (3 vs 6)
# so we limit merged blacklist's columns to 3
Array[File] blacklists = select_all([blacklist1_, blacklist2_])
if ( length(blacklists) > 1 ) {
call pool_ta as pool_blacklist { input:
tas = blacklists,
col = 3,
}
}
File? blacklist_ = if length(blacklists) > 1 then pool_blacklist.ta_pooled
else if length(blacklists) > 0 then blacklists[0]
else blacklist2_
String? mito_chr_name_ = if defined(mito_chr_name) then mito_chr_name
else read_genome_tsv.mito_chr_name
String? regex_bfilt_peak_chr_name_ = if defined(regex_bfilt_peak_chr_name) then regex_bfilt_peak_chr_name
else read_genome_tsv.regex_bfilt_peak_chr_name
String? genome_name_ = if defined(genome_name) then genome_name
else if defined(read_genome_tsv.genome_name) then read_genome_tsv.genome_name
else basename(select_first([genome_tsv, ref_fa_, chrsz_, 'None']))
# read additional annotation data
File? tss_ = if defined(tss) then tss
else read_genome_tsv.tss
File? dnase_ = if defined(dnase) then dnase
else read_genome_tsv.dnase
File? prom_ = if defined(prom) then prom
else read_genome_tsv.prom
File? enh_ = if defined(enh) then enh
else read_genome_tsv.enh
File? reg2map_ = if defined(reg2map) then reg2map
else read_genome_tsv.reg2map
File? reg2map_bed_ = if defined(reg2map_bed) then reg2map_bed
else read_genome_tsv.reg2map_bed
File? roadmap_meta_ = if defined(roadmap_meta) then roadmap_meta
else read_genome_tsv.roadmap_meta
### temp vars (do not define these)
String aligner_ = if defined(custom_align_py) then 'custom' else aligner
String peak_caller_ = if defined(custom_call_peak_py) then 'custom' else peak_caller
String peak_type_ = peak_type
String idr_rank_ = if peak_caller_=='spp' then 'signal.value'
else if peak_caller_=='macs2' then 'p.value'
else 'p.value'
Int cap_num_peak_ = cap_num_peak
Int mapq_thresh_ = if aligner=='bowtie2' then select_first([mapq_thresh, mapq_thresh_bowtie2])
else select_first([mapq_thresh, mapq_thresh_bwa])
# temporary 2-dim fastqs array [rep_id][merge_id]
Array[Array[File]] fastqs_R1 =
if length(fastqs_rep10_R1)>0 then
[fastqs_rep1_R1, fastqs_rep2_R1, fastqs_rep3_R1, fastqs_rep4_R1, fastqs_rep5_R1,
fastqs_rep6_R1, fastqs_rep7_R1, fastqs_rep8_R1, fastqs_rep9_R1, fastqs_rep10_R1]
else if length(fastqs_rep9_R1)>0 then
[fastqs_rep1_R1, fastqs_rep2_R1, fastqs_rep3_R1, fastqs_rep4_R1, fastqs_rep5_R1,
fastqs_rep6_R1, fastqs_rep7_R1, fastqs_rep8_R1, fastqs_rep9_R1]
else if length(fastqs_rep8_R1)>0 then
[fastqs_rep1_R1, fastqs_rep2_R1, fastqs_rep3_R1, fastqs_rep4_R1, fastqs_rep5_R1,
fastqs_rep6_R1, fastqs_rep7_R1, fastqs_rep8_R1]
else if length(fastqs_rep7_R1)>0 then
[fastqs_rep1_R1, fastqs_rep2_R1, fastqs_rep3_R1, fastqs_rep4_R1, fastqs_rep5_R1,
fastqs_rep6_R1, fastqs_rep7_R1]
else if length(fastqs_rep6_R1)>0 then
[fastqs_rep1_R1, fastqs_rep2_R1, fastqs_rep3_R1, fastqs_rep4_R1, fastqs_rep5_R1,
fastqs_rep6_R1]
else if length(fastqs_rep5_R1)>0 then
[fastqs_rep1_R1, fastqs_rep2_R1, fastqs_rep3_R1, fastqs_rep4_R1, fastqs_rep5_R1]
else if length(fastqs_rep4_R1)>0 then
[fastqs_rep1_R1, fastqs_rep2_R1, fastqs_rep3_R1, fastqs_rep4_R1]
else if length(fastqs_rep3_R1)>0 then
[fastqs_rep1_R1, fastqs_rep2_R1, fastqs_rep3_R1]
else if length(fastqs_rep2_R1)>0 then
[fastqs_rep1_R1, fastqs_rep2_R1]
else if length(fastqs_rep1_R1)>0 then
[fastqs_rep1_R1]
else []
# no need to do that for R2 (R1 array will be used to determine presense of fastq for each rep)
Array[Array[File]] fastqs_R2 =
[fastqs_rep1_R2, fastqs_rep2_R2, fastqs_rep3_R2, fastqs_rep4_R2, fastqs_rep5_R2,
fastqs_rep6_R2, fastqs_rep7_R2, fastqs_rep8_R2, fastqs_rep9_R2, fastqs_rep10_R2]
# temporary 2-dim adapters array [rep_id][merge_id]
Array[Array[String]] adapters_R1 =
[adapters_rep1_R1, adapters_rep2_R1, adapters_rep3_R1, adapters_rep4_R1, adapters_rep5_R1,
adapters_rep6_R1, adapters_rep7_R1, adapters_rep8_R1, adapters_rep9_R1, adapters_rep10_R1]
Array[Array[String]] adapters_R2 =
[adapters_rep1_R2, adapters_rep2_R2, adapters_rep3_R2, adapters_rep4_R2, adapters_rep5_R2,
adapters_rep6_R2, adapters_rep7_R2, adapters_rep8_R2, adapters_rep9_R2, adapters_rep10_R2]
# temporary variables to get number of replicates
# WDLic implementation of max(A,B,C,...)
Int num_rep_fastq = length(fastqs_R1)
Int num_rep_bam = if length(bams)<num_rep_fastq then num_rep_fastq
else length(bams)
Int num_rep_nodup_bam = if length(nodup_bams)<num_rep_bam then num_rep_bam
else length(nodup_bams)
Int num_rep_ta = if length(tas)<num_rep_nodup_bam then num_rep_nodup_bam
else length(tas)
Int num_rep_peak = if length(peaks)<num_rep_ta then num_rep_ta
else length(peaks)
Int num_rep = num_rep_peak
# sanity check for inputs
if ( num_rep == 0 ) {
call raise_exception as error_input_data { input:
msg = 'No FASTQ/BAM/TAG-ALIGN/PEAK defined in your input JSON. Check if your FASTQs are defined as "atac.fastqs_repX_RY". DO NOT MISS suffix _R1 even for single ended FASTQ.'
}
}
if ( !defined(chrsz_) ) {
call raise_exception as error_genome_database { input:
msg = 'No genome database found in your input JSON. Did you define "atac.genome_tsv" correctly?'
}
}
# align each replicate
scatter(i in range(num_rep)) {
# to override endedness definition for individual replicate
# paired_end will override paired_ends[i]
Boolean? paired_end_ = if !defined(paired_end) && i<length(paired_ends) then paired_ends[i]
else paired_end
Boolean has_input_of_align = i<length(fastqs_R1) && length(fastqs_R1[i])>0
Boolean has_output_of_align = i<length(bams) && defined(bams[i])
if ( has_input_of_align && !has_output_of_align ) {
call align { input :
fastqs_R1 = fastqs_R1[i],
fastqs_R2 = fastqs_R2[i],
adapter = adapter,
adapters_R1 = adapters_R1[i],
adapters_R2 = adapters_R2[i],
paired_end = paired_end_,
auto_detect_adapter = auto_detect_adapter,
cutadapt_param = cutadapt_param,
aligner = aligner_,
mito_chr_name = mito_chr_name_,
chrsz = chrsz_,
custom_align_py = custom_align_py,
multimapping = multimapping,
idx_tar = if aligner=='bwa' then bwa_idx_tar_
else if aligner=='bowtie2' then bowtie2_idx_tar_
else custom_aligner_idx_tar_,
# resource
cpu = align_cpu,
mem_mb = align_mem_mb,
time_hr = align_time_hr,
disks = align_disks,
}
}
File? bam_ = if has_output_of_align then bams[i] else align.bam
# mito only mapping to get frac mito qc
Boolean has_input_of_align_mito = has_input_of_align &&
(aligner=='bowtie2' && defined(bowtie2_mito_idx_tar_) ||
aligner=='bwa' && defined(bwa_mito_idx_tar_) ||
defined(custom_aligner_mito_idx_tar_))
if ( has_input_of_align_mito ) {
call align as align_mito { input :
fastqs_R1 = fastqs_R1[i],
fastqs_R2 = fastqs_R2[i],
adapter = adapter,
adapters_R1 = adapters_R1[i],
adapters_R2 = adapters_R2[i],
paired_end = paired_end_,
auto_detect_adapter = auto_detect_adapter,
cutadapt_param = cutadapt_param,
aligner = aligner_,
mito_chr_name = mito_chr_name_,
chrsz = chrsz_,
custom_align_py = custom_align_py,
multimapping = multimapping,
idx_tar = if aligner=='bwa' then bwa_mito_idx_tar_
else if aligner=='bowtie2' then bowtie2_mito_idx_tar_
else custom_aligner_mito_idx_tar_,
# resource
cpu = align_cpu,
mem_mb = align_mem_mb,
time_hr = align_time_hr,
disks = align_disks,
}
}
if ( defined(align.non_mito_samstat_qc) && defined(align_mito.samstat_qc) ) {
call frac_mito { input:
non_mito_samstat = align.non_mito_samstat_qc,
mito_samstat = align_mito.samstat_qc
}
}
Boolean has_input_of_filter = has_output_of_align || defined(align.bam)
Boolean has_output_of_filter = i<length(nodup_bams) && defined(nodup_bams[i])
# skip if we already have output of this step
if ( has_input_of_filter && !has_output_of_filter ) {
call filter { input :
bam = bam_,
paired_end = paired_end_,
dup_marker = dup_marker,
mapq_thresh = mapq_thresh_,
filter_chrs = filter_chrs,
chrsz = chrsz_,
no_dup_removal = no_dup_removal,
multimapping = multimapping,
mito_chr_name = mito_chr_name_,
cpu = filter_cpu,
mem_mb = filter_mem_mb,
picard_java_heap = filter_picard_java_heap,
time_hr = filter_time_hr,
disks = filter_disks,
}
}
File? nodup_bam_ = if has_output_of_filter then nodup_bams[i] else filter.nodup_bam
Boolean has_input_of_bam2ta = has_output_of_filter || defined(filter.nodup_bam)
Boolean has_output_of_bam2ta = i<length(tas) && defined(tas[i])
if ( has_input_of_bam2ta && !has_output_of_bam2ta ) {
call bam2ta { input :
bam = nodup_bam_,
disable_tn5_shift = if pipeline_type=='atac' then false else true,
subsample = subsample_reads,
paired_end = paired_end_,
mito_chr_name = mito_chr_name_,
cpu = bam2ta_cpu,
mem_mb = bam2ta_mem_mb,
time_hr = bam2ta_time_hr,
disks = bam2ta_disks,
}
}
File? ta_ = if has_output_of_bam2ta then tas[i] else bam2ta.ta
Boolean has_input_of_xcor = has_output_of_align || defined(align.bam)
if ( has_input_of_xcor && enable_xcor ) {
call filter as filter_no_dedup { input :
bam = bam_,
paired_end = paired_end_,
dup_marker = dup_marker,
mapq_thresh = mapq_thresh_,
filter_chrs = filter_chrs,
chrsz = chrsz_,
no_dup_removal = true,
multimapping = multimapping,
mito_chr_name = mito_chr_name_,
cpu = filter_cpu,
mem_mb = filter_mem_mb,
picard_java_heap = filter_picard_java_heap,
time_hr = filter_time_hr,
disks = filter_disks,
}
call bam2ta as bam2ta_no_dedup { input :
bam = filter_no_dedup.nodup_bam, # output name is nodup but it's not deduped
disable_tn5_shift = if pipeline_type=='atac' then false else true,
subsample = 0,
paired_end = paired_end_,
mito_chr_name = mito_chr_name_,
cpu = bam2ta_cpu,
mem_mb = bam2ta_mem_mb,
time_hr = bam2ta_time_hr,
disks = bam2ta_disks,
}
# subsample tagalign (non-mito) and cross-correlation analysis
call xcor { input :
ta = bam2ta_no_dedup.ta,
subsample = xcor_subsample_reads,
paired_end = paired_end_,
mito_chr_name = mito_chr_name_,
cpu = xcor_cpu,
mem_mb = xcor_mem_mb,
time_hr = xcor_time_hr,
disks = xcor_disks,
}
}
Boolean has_input_of_macs2_signal_track = has_output_of_bam2ta || defined(bam2ta.ta)
if ( has_input_of_macs2_signal_track ) {
# generate count signal track
call macs2_signal_track { input :
ta = ta_,
gensz = gensz_,
chrsz = chrsz_,
pval_thresh = pval_thresh,
smooth_win = smooth_win,
mem_mb = macs2_signal_track_mem_mb,
disks = macs2_signal_track_disks,
time_hr = macs2_signal_track_time_hr,
}
}
Boolean has_input_of_call_peak = has_output_of_bam2ta || defined(bam2ta.ta)
Boolean has_output_of_call_peak = i<length(peaks) && defined(peaks[i])
if ( has_input_of_call_peak && !has_output_of_call_peak && !align_only ) {
# call peaks on tagalign
call call_peak { input :
peak_caller = peak_caller_,
peak_type = peak_type_,
custom_call_peak_py = custom_call_peak_py,
ta = ta_,
gensz = gensz_,
chrsz = chrsz_,
cap_num_peak = cap_num_peak_,
pval_thresh = pval_thresh,
smooth_win = smooth_win,
blacklist = blacklist_,
regex_bfilt_peak_chr_name = regex_bfilt_peak_chr_name_,
cpu = call_peak_cpu,
mem_mb = call_peak_mem_mb,
disks = call_peak_disks,
time_hr = call_peak_time_hr,
}
}
File? peak_ = if has_output_of_call_peak then peaks[i] else call_peak.peak
Boolean has_input_of_spr = has_output_of_bam2ta || defined(bam2ta.ta)
if ( has_input_of_spr && !align_only && !true_rep_only ) {
call spr { input :
ta = ta_,
paired_end = paired_end_,
mem_mb = spr_mem_mb,
}
}
Boolean has_input_of_call_peak_pr1 = defined(spr.ta_pr1)
Boolean has_output_of_call_peak_pr1 = i<length(peaks_pr1) && defined(peaks_pr1[i])
if ( has_input_of_call_peak_pr1 && !has_output_of_call_peak_pr1 &&
!align_only && !true_rep_only ) {
# call peaks on 1st pseudo replicated tagalign
call call_peak as call_peak_pr1 { input :
peak_caller = peak_caller_,
peak_type = peak_type_,
custom_call_peak_py = custom_call_peak_py,
ta = spr.ta_pr1,
gensz = gensz_,
chrsz = chrsz_,
cap_num_peak = cap_num_peak_,
pval_thresh = pval_thresh,
smooth_win = smooth_win,
blacklist = blacklist_,
regex_bfilt_peak_chr_name = regex_bfilt_peak_chr_name_,
cpu = call_peak_cpu,
mem_mb = call_peak_mem_mb,
disks = call_peak_disks,
time_hr = call_peak_time_hr,
}
}
File? peak_pr1_ = if has_output_of_call_peak_pr1 then peaks_pr1[i]
else call_peak_pr1.peak
Boolean has_input_of_call_peak_pr2 = defined(spr.ta_pr2)
Boolean has_output_of_call_peak_pr2 = i<length(peaks_pr2) && defined(peaks_pr2[i])
if ( has_input_of_call_peak_pr2 && !has_output_of_call_peak_pr2 &&
!align_only && !true_rep_only ) {
# call peaks on 2nd pseudo replicated tagalign
call call_peak as call_peak_pr2 { input :
peak_caller = peak_caller_,
peak_type = peak_type_,
custom_call_peak_py = custom_call_peak_py,
ta = spr.ta_pr2,
gensz = gensz_,
chrsz = chrsz_,
cap_num_peak = cap_num_peak_,
pval_thresh = pval_thresh,
smooth_win = smooth_win,
blacklist = blacklist_,
regex_bfilt_peak_chr_name = regex_bfilt_peak_chr_name_,
cpu = call_peak_cpu,
mem_mb = call_peak_mem_mb,
disks = call_peak_disks,
time_hr = call_peak_time_hr,
}
}
File? peak_pr2_ = if has_output_of_call_peak_pr2 then peaks_pr2[i]
else call_peak_pr2.peak
Boolean has_input_of_count_signal_track = has_output_of_bam2ta || defined(bam2ta.ta)
if ( has_input_of_count_signal_track && enable_count_signal_track ) {
# generate count signal track
call count_signal_track { input :
ta = ta_,
chrsz = chrsz_,
}
}
# tasks factored out from ATAqC
Boolean has_input_of_tss_enrich = defined(nodup_bam_) && defined(tss_) && (
defined(align.read_len) || i<length(read_len) && defined(read_len[i]) )
if ( enable_tss_enrich && has_input_of_tss_enrich ) {
call tss_enrich { input :
read_len = if i<length(read_len) && defined(read_len[i]) then read_len[i]
else align.read_len,
nodup_bam = nodup_bam_,
tss = tss_,
chrsz = chrsz_,
}
}
if ( enable_fraglen_stat && paired_end_ && defined(nodup_bam_) ) {
call fraglen_stat_pe { input :
nodup_bam = nodup_bam_,
picard_java_heap = fraglen_stat_picard_java_heap,
}
}
if ( enable_preseq && defined(bam_) ) {
call preseq { input :
bam = bam_,
paired_end = paired_end_,
mem_mb = preseq_mem_mb,
picard_java_heap = preseq_picard_java_heap,
}
}
if ( enable_gc_bias && defined(nodup_bam_) && defined(ref_fa_) ) {
call gc_bias { input :
nodup_bam = nodup_bam_,
ref_fa = ref_fa_,
picard_java_heap = gc_bias_picard_java_heap,
}
}
if ( enable_annot_enrich && defined(ta_) && defined(blacklist_) && defined(dnase_) && defined(prom_) && defined(enh_) ) {
call annot_enrich { input :
ta = ta_,
blacklist = blacklist_,
dnase = dnase_,
prom = prom_,
enh = enh_,
}
}
if ( enable_compare_to_roadmap && defined(macs2_signal_track.pval_bw) &&
defined(reg2map_) && defined(roadmap_meta_) ) {
call compare_signal_to_roadmap { input :
pval_bw = macs2_signal_track.pval_bw,
reg2map_bed = reg2map_bed_,
reg2map = reg2map_,
roadmap_meta = roadmap_meta_,
}
}
}
# if there are TAs for ALL replicates then pool them
Boolean has_all_inputs_of_pool_ta = length(select_all(ta_))==num_rep
if ( has_all_inputs_of_pool_ta && num_rep>1 ) {
# pool tagaligns from true replicates
call pool_ta { input :
tas = ta_,
}
}
# if there are pr1 TAs for ALL replicates then pool them
Boolean has_all_inputs_of_pool_ta_pr1 = length(select_all(spr.ta_pr1))==num_rep
if ( has_all_inputs_of_pool_ta_pr1 && num_rep>1 && !align_only && !true_rep_only ) {
# pool tagaligns from pseudo replicate 1
call pool_ta as pool_ta_pr1 { input :
tas = spr.ta_pr1,
}
}
# if there are pr2 TAs for ALL replicates then pool them
Boolean has_all_inputs_of_pool_ta_pr2 = length(select_all(spr.ta_pr2))==num_rep
if ( has_all_inputs_of_pool_ta_pr1 && num_rep>1 && !align_only && !true_rep_only ) {
# pool tagaligns from pseudo replicate 2
call pool_ta as pool_ta_pr2 { input :
tas = spr.ta_pr2,
}
}
Boolean has_input_of_call_peak_pooled = defined(pool_ta.ta_pooled)
Boolean has_output_of_call_peak_pooled = defined(peak_pooled)
if ( has_input_of_call_peak_pooled && !has_output_of_call_peak_pooled &&
!align_only && num_rep>1 ) {
# call peaks on pooled replicate
call call_peak as call_peak_pooled { input :
peak_caller = peak_caller_,
peak_type = peak_type_,
custom_call_peak_py = custom_call_peak_py,
ta = pool_ta.ta_pooled,
gensz = gensz_,
chrsz = chrsz_,
cap_num_peak = cap_num_peak_,
pval_thresh = pval_thresh,
smooth_win = smooth_win,
blacklist = blacklist_,
regex_bfilt_peak_chr_name = regex_bfilt_peak_chr_name_,
cpu = call_peak_cpu,
mem_mb = call_peak_mem_mb,
disks = call_peak_disks,
time_hr = call_peak_time_hr,
}
}
File? peak_pooled_ = if has_output_of_call_peak_pooled then peak_pooled
else call_peak_pooled.peak
Boolean has_input_of_count_signal_track_pooled = defined(pool_ta.ta_pooled)
if ( has_input_of_count_signal_track_pooled && enable_count_signal_track && num_rep>1 ) {
call count_signal_track as count_signal_track_pooled { input :
ta = pool_ta.ta_pooled,
chrsz = chrsz_,
}
}
Boolean has_input_of_macs2_signal_track_pooled = defined(pool_ta.ta_pooled)
if ( has_input_of_macs2_signal_track_pooled && num_rep>1 ) {
call macs2_signal_track as macs2_signal_track_pooled { input :
ta = pool_ta.ta_pooled,
gensz = gensz_,
chrsz = chrsz_,
pval_thresh = pval_thresh,
smooth_win = smooth_win,
mem_mb = macs2_signal_track_mem_mb,
disks = macs2_signal_track_disks,
time_hr = macs2_signal_track_time_hr,
}
}
Boolean has_input_of_jsd = defined(blacklist_) &&
length(select_all(nodup_bam_))==num_rep
if ( has_input_of_jsd && num_rep > 0 && enable_jsd ) {
# fingerprint and JS-distance plot
call jsd { input :
nodup_bams = nodup_bam_,
blacklist = blacklist_,
mapq_thresh = mapq_thresh_,
cpu = jsd_cpu,
mem_mb = jsd_mem_mb,
time_hr = jsd_time_hr,
disks = jsd_disks,
}
}
Boolean has_input_of_call_peak_ppr1 = defined(pool_ta_pr1.ta_pooled)
Boolean has_output_of_call_peak_ppr1 = defined(peak_ppr1)
if ( has_input_of_call_peak_ppr1 && !has_output_of_call_peak_ppr1 &&
!align_only && !true_rep_only && num_rep>1 ) {
# call peaks on 1st pooled pseudo replicates
call call_peak as call_peak_ppr1 { input :
peak_caller = peak_caller_,
peak_type = peak_type_,
custom_call_peak_py = custom_call_peak_py,
ta = pool_ta_pr1.ta_pooled,
gensz = gensz_,
chrsz = chrsz_,
cap_num_peak = cap_num_peak_,
pval_thresh = pval_thresh,
smooth_win = smooth_win,
blacklist = blacklist_,
regex_bfilt_peak_chr_name = regex_bfilt_peak_chr_name_,
cpu = call_peak_cpu,
mem_mb = call_peak_mem_mb,
disks = call_peak_disks,
time_hr = call_peak_time_hr,
}
}
File? peak_ppr1_ = if has_output_of_call_peak_ppr1 then peak_ppr1
else call_peak_ppr1.peak
Boolean has_input_of_call_peak_ppr2 = defined(pool_ta_pr2.ta_pooled)
Boolean has_output_of_call_peak_ppr2 = defined(peak_ppr2)
if ( has_input_of_call_peak_ppr2 && !has_output_of_call_peak_ppr2 &&
!align_only && !true_rep_only && num_rep>1 ) {
# call peaks on 2nd pooled pseudo replicates
call call_peak as call_peak_ppr2 { input :
peak_caller = peak_caller_,
peak_type = peak_type_,
custom_call_peak_py = custom_call_peak_py,
ta = pool_ta_pr2.ta_pooled,
gensz = gensz_,
chrsz = chrsz_,
cap_num_peak = cap_num_peak_,
pval_thresh = pval_thresh,
smooth_win = smooth_win,
blacklist = blacklist_,
regex_bfilt_peak_chr_name = regex_bfilt_peak_chr_name_,
cpu = call_peak_cpu,
mem_mb = call_peak_mem_mb,
disks = call_peak_disks,
time_hr = call_peak_time_hr,
}
}
File? peak_ppr2_ = if has_output_of_call_peak_ppr2 then peak_ppr2
else call_peak_ppr2.peak
# do IDR/overlap on all pairs of two replicates (i,j)
# where i and j are zero-based indices and 0 <= i < j < num_rep
Array[Pair[Int, Int]] pairs_ = cross(range(num_rep),range(num_rep))
scatter( pair in pairs_ ) {
Pair[Int, Int]? null_pair
Pair[Int, Int]? pairs__ = if pair.left<pair.right then pair else null_pair
}
Array[Pair[Int, Int]] pairs = select_all(pairs__)
if ( !align_only ) {
scatter( pair in pairs ) {
# pair.left = 0-based index of 1st replicate
# pair.right = 0-based index of 2nd replicate
# Naive overlap on every pair of true replicates
call overlap { input :
prefix = 'rep'+(pair.left+1)+'_vs_rep'+(pair.right+1),
peak1 = peak_[pair.left],
peak2 = peak_[pair.right],
peak_pooled = peak_pooled_,
peak_type = peak_type_,
blacklist = blacklist_,
chrsz = chrsz_,
regex_bfilt_peak_chr_name = regex_bfilt_peak_chr_name_,
ta = pool_ta.ta_pooled,
}
}
}
if ( enable_idr && !align_only ) {
scatter( pair in pairs ) {
# pair.left = 0-based index of 1st replicate
# pair.right = 0-based index of 2nd replicate
# IDR on every pair of true replicates
call idr { input :
prefix = 'rep'+(pair.left+1)+'_vs_rep'+(pair.right+1),
peak1 = peak_[pair.left],
peak2 = peak_[pair.right],
peak_pooled = peak_pooled_,
idr_thresh = idr_thresh,
peak_type = peak_type_,
rank = idr_rank_,
blacklist = blacklist_,
chrsz = chrsz_,
regex_bfilt_peak_chr_name = regex_bfilt_peak_chr_name_,
ta = pool_ta.ta_pooled,
}
}
}
# overlap on pseudo-replicates (pr1, pr2) for each true replicate
if ( !align_only && !true_rep_only ) {
scatter( i in range(num_rep) ) {
call overlap as overlap_pr { input :
prefix = 'rep'+(i+1)+'-pr1_vs_rep'+(i+1)+'-pr2',
peak1 = peak_pr1_[i],
peak2 = peak_pr2_[i],
peak_pooled = peak_[i],
peak_type = peak_type_,
blacklist = blacklist_,
chrsz = chrsz_,
regex_bfilt_peak_chr_name = regex_bfilt_peak_chr_name_,
ta = ta_[i],
}
}
}
if ( !align_only && !true_rep_only && enable_idr ) {
scatter( i in range(num_rep) ) {
# IDR on pseduo replicates
call idr as idr_pr { input :
prefix = 'rep'+(i+1)+'-pr1_vs_rep'+(i+1)+'-pr2',
peak1 = peak_pr1_[i],
peak2 = peak_pr2_[i],
peak_pooled = peak_[i],
idr_thresh = idr_thresh,
peak_type = peak_type_,
rank = idr_rank_,
blacklist = blacklist_,
chrsz = chrsz_,
regex_bfilt_peak_chr_name = regex_bfilt_peak_chr_name_,
ta = ta_[i],
}
}
}
if ( !align_only && !true_rep_only && num_rep>1 ) {
# Naive overlap on pooled pseudo replicates
call overlap as overlap_ppr { input :
prefix = 'pooled-pr1_vs_pooled-pr2',
peak1 = peak_ppr1_,
peak2 = peak_ppr2_,
peak_pooled = peak_pooled_,
peak_type = peak_type_,
blacklist = blacklist_,
chrsz = chrsz_,
regex_bfilt_peak_chr_name = regex_bfilt_peak_chr_name_,
ta = pool_ta.ta_pooled,
}
}
if ( !align_only && !true_rep_only && num_rep>1 ) {
# IDR on pooled pseduo replicates
call idr as idr_ppr { input :
prefix = 'pooled-pr1_vs_pooled-pr2',
peak1 = peak_ppr1_,
peak2 = peak_ppr2_,
peak_pooled = peak_pooled_,
idr_thresh = idr_thresh,
peak_type = peak_type_,
rank = idr_rank_,
blacklist = blacklist_,
chrsz = chrsz_,
regex_bfilt_peak_chr_name = regex_bfilt_peak_chr_name_,
ta = pool_ta.ta_pooled,
}
}
# reproducibility QC for overlap/IDR peaks
if ( !align_only && !true_rep_only && num_rep > 0 ) {
# reproducibility QC for overlapping peaks
call reproducibility as reproducibility_overlap { input :
prefix = 'overlap',
peaks = overlap.bfilt_overlap_peak,
peaks_pr = overlap_pr.bfilt_overlap_peak,
peak_ppr = overlap_ppr.bfilt_overlap_peak,
peak_type = peak_type_,
chrsz = chrsz_,
}
}
if ( !align_only && !true_rep_only && num_rep > 0 && enable_idr ) {
# reproducibility QC for IDR peaks
call reproducibility as reproducibility_idr { input :
prefix = 'idr',
peaks = idr.bfilt_idr_peak,
peaks_pr = idr_pr.bfilt_idr_peak,
peak_ppr = idr_ppr.bfilt_idr_peak,
peak_type = peak_type_,
chrsz = chrsz_,
}
}
# Generate final QC report and JSON
call qc_report { input :
pipeline_ver = pipeline_ver,
title = title,
description = description,
genome = genome_name_,
multimapping = multimapping,
paired_ends = paired_end_,
pipeline_type = pipeline_type,
aligner = aligner_,
peak_caller = peak_caller_,
cap_num_peak = cap_num_peak_,
idr_thresh = idr_thresh,
pval_thresh = pval_thresh,
xcor_subsample_reads = xcor_subsample_reads,
samstat_qcs = align.samstat_qc,
nodup_samstat_qcs = filter.samstat_qc,
frac_mito_qcs = frac_mito.frac_mito_qc,
dup_qcs = filter.dup_qc,
lib_complexity_qcs = filter.lib_complexity_qc,
xcor_plots = xcor.plot_png,
xcor_scores = xcor.score,