def get_infer_haps_params(reference_dir, reference):
referencedata = config_reference.get_cluster_reference_data(
reference_dir, reference)
docker_containers = config_reference.containers()['docker']
params = {
'memory': {
'low': 4,
'med': 6,
'high': 16
},
'max_cores': None,
'chromosomes': referencedata['chromosomes'],
'extract_seqdata': {
'genome_fasta_template': referencedata['ref_genome'],
'genome_fai_template': referencedata['ref_genome'] + '.fai',
},
'ref_data_dir': referencedata['copynumber_ref_data'],
'docker': {
'single_cell_pipeline': docker_containers['remixt'],
},
}
return {'infer_haps': params}
def get_breakpoint_params(reference_dir, reference):
referencedata = config_reference.get_cluster_reference_data(
reference_dir, reference)
docker_containers = config_reference.containers()['docker']
params = {
'memory': {
'low': 4,
'med': 6,
'high': 16
},
'ref_data_directory': referencedata['destruct_ref_data'],
'destruct_config': {
'genome_fasta': referencedata['ref_genome'],
'genome_fai': referencedata['ref_genome'] + '.fai',
'gtf_filename': referencedata['destruct_gtf_file'],
},
'docker': {
'single_cell_pipeline': docker_containers['single_cell_pipeline'],
'destruct': docker_containers['destruct'],
'lumpy': docker_containers['lumpy'],
'samtools': docker_containers['samtools'],
},
}
return {'breakpoint_calling': params}
def get_germline_calling_params(reference_dir, reference, version):
referencedata = config_reference.get_cluster_reference_data(reference_dir, reference)
docker_containers = config_reference.containers(version)['docker']
params = {
'memory': {'low': 4, 'med': 6, 'high': 16},
'max_cores': 8,
'docker': {
'single_cell_pipeline': docker_containers['single_cell_pipeline'],
'samtools': docker_containers['samtools'],
'vcftools': docker_containers['vcftools'],
'snpeff': docker_containers['snpeff'],
},
'ref_genome': referencedata['ref_genome'],
'chromosomes': referencedata['chromosomes'],
'split_size': 10000000,
'databases': {
'mappability': {
'url': 'http://hgdownload-test.cse.ucsc.edu/goldenPath/hg19/encodeDCC/wgEncodeMapability/release3'
'/wgEncodeCrgMapabilityAlign50mer.bigWig',
'local_path': referencedata['databases']['mappability']['local_path'],
},
'snpeff': {
"db": 'GRCh37.75',
"data_dir": referencedata['databases']['snpeff']['local_path']
},
},
}
return {'germline_calling': params}
def get_copy_number_calling_params(reference_dir, reference, binsize):
referencedata = config_reference.get_cluster_reference_data(
reference_dir, reference)
docker_containers = config_reference.containers()['docker']
params = {
'memory': {
'low': 4,
'med': 6,
'high': 16
},
'ref_genome': referencedata['ref_genome'],
'chromosomes': referencedata['chromosomes'],
'split_size': 10000000,
'max_cores': None,
'chromosomes': referencedata['chromosomes'],
'extract_seqdata': {},
'ref_data_dir': referencedata['copynumber_ref_data'],
'docker': {
'single_cell_pipeline': docker_containers['remixt'],
'titan': docker_containers['titan']
},
'titan_params': {
"normal_contamination": [0.2, 0.4, 0.6, 0.8],
'num_clusters': [1, 2],
'ploidy': [1, 2, 3, 4],
'chrom_info_filename': referencedata['chrom_info_filename'],
'window_size': binsize,
'gc_wig': referencedata['gc_wig_file'][binsize],
'mappability_wig': referencedata['gc_wig_file'][binsize],
}
}
return {'copy_number_calling': params}
def get_merge_bams_params(reference_dir, reference, cluster):
referencedata = config_reference.get_cluster_reference_data(
reference_dir, reference)
if cluster in ['azure', 'aws']:
one_split_job = True
else:
one_split_job = False
docker_containers = config_reference.containers()['docker']
params = {
'memory': {
'low': 4,
'med': 6,
'high': 16
},
'max_cores': 8,
'docker': {
'single_cell_pipeline': docker_containers['single_cell_pipeline'],
'samtools': docker_containers['samtools']
},
'ref_genome': referencedata['ref_genome'],
'split_size': 10000000,
'chromosomes': referencedata['chromosomes'],
'one_split_job': one_split_job
}
return {'merge_bams': params}
def get_multi_sample_params():
docker_containers = config_reference.containers()['docker']
params = {
'memory': {
'low': 4,
'med': 6,
'high': 16
},
'docker': {
'single_cell_pipeline': docker_containers['single_cell_pipeline'],
},
}
return {'multi_sample': params}
def get_sv_genotyping_params(reference_dir, reference, version):
docker_containers = config_reference.containers(version)['docker']
referencedata = config_reference.get_cluster_reference_data(reference_dir, reference)
params = {
'ref_genome': referencedata['ref_genome'],
'memory': {'low': 4, 'med': 6, 'high': 16},
'docker': {
'single_cell_pipeline': docker_containers['single_cell_pipeline'],
'svtyper': docker_containers['svtyper']
},
}
return {'sv_genotyping': params}
def get_aneufinder_params(reference_dir, reference, version):
referencedata = config_reference.get_cluster_reference_data(reference_dir, reference)
docker_containers = config_reference.containers(version)['docker']
params = {
'memory': {'med': 6},
'docker': {
'single_cell_pipeline': docker_containers['single_cell_pipeline'],
'aneufinder': docker_containers['aneufinder'],
},
'chromosomes': referencedata['chromosomes'],
'ref_genome': referencedata['ref_genome']
}
return {'aneufinder': params}
def get_annotation_params(reference_dir, reference, version):
referencedata = config_reference.get_cluster_reference_data(
reference_dir, reference)
docker_containers = config_reference.containers(version)['docker']
docker_containers = {
'single_cell_pipeline': docker_containers['single_cell_pipeline'],
'cell_cycle_classifier': docker_containers['cell_cycle_classifier'],
'corrupt_tree': docker_containers['corrupt_tree']
}
params = {
'docker':
docker_containers,
'memory': {
'med': 6
},
'classifier_training_data':
referencedata['classifier_training_data'],
'fastqscreen_training_data':
referencedata['fastqscreen_training_data'],
'reference_gc':
referencedata['reference_gc_qc'],
'chromosomes':
referencedata['chromosomes'],
'num_states':
12,
'map_cutoff':
0.9,
'ref_type':
reference,
'corrupt_tree_params': {
'neighborhood_size': 2,
'lower_fraction': 0.05,
'engine_nchains': 1,
'engine_nscans': 10000,
'model_fpr_bound': 0.1,
'model_fnr_bound': 0.5
},
'good_cells': [
['quality', 'ge', 0.75],
['experimental_condition', 'notin', ["NTC", "NCC", "gDNA", "GM"]],
['cell_call', 'in', ['C1']],
['is_contaminated', 'in', ['False', 'false', False]],
]
}
return {"annotation": params}
def get_qc_params(reference_dir, reference, version):
docker_containers = config_reference.containers(version)['docker']
referencedata = config_reference.get_cluster_reference_data(reference_dir, reference)
params = {
'ref_genome': referencedata['ref_genome'],
'vep': referencedata['vep'],
'memory': {'low': 4, 'med': 6, 'high': 16},
'docker': {
'single_cell_pipeline': docker_containers['single_cell_pipeline'],
'vcf2maf': docker_containers['vcf2maf'],
'pseudo_bulk_qc_html_report': docker_containers['pseudo_bulk_qc_html_report'],
},
}
return {'qc': params}
def get_split_bam_params(reference_dir, reference, version):
referencedata = config_reference.get_cluster_reference_data(reference_dir, reference)
docker_containers = config_reference.containers(version)['docker']
params = {
'memory': {'low': 4, 'med': 6, 'high': 16},
'max_cores': 8,
'docker': {
'single_cell_pipeline': docker_containers['single_cell_pipeline'],
'samtools': docker_containers['samtools']
},
'ref_genome': referencedata['ref_genome'],
'split_size': 10000000,
'chromosomes': referencedata['chromosomes'],
'one_split_job': True
}
return {'split_bam': params}
def get_align_params(reference_dir, reference, version):
referencedata = config_reference.get_cluster_reference_data(reference_dir, reference)
refdata_callback = config_reference.get_cluster_reference_data
docker_containers = config_reference.containers(version)['docker']
docker_containers = {
'single_cell_pipeline': docker_containers['single_cell_pipeline'],
'fastqc': docker_containers['fastqc'],
'samtools': docker_containers['samtools'],
'bwa': docker_containers['bwa'],
'picard': docker_containers['picard'],
'trimgalore': docker_containers['trimgalore'],
'fastq_screen': docker_containers['fastq_screen'],
}
params = {
'ref_genome': referencedata['ref_genome'],
'docker': docker_containers,
'memory': {'med': 6},
'adapter': 'CTGTCTCTTATACACATCTCCGAGCCCACGAGAC',
'adapter2': 'CTGTCTCTTATACACATCTGACGCTGCCGACGA',
'picard_wgs_params': {
"min_bqual": 20,
"min_mqual": 20,
"count_unpaired": False,
},
'chromosomes': referencedata['chromosomes'],
'gc_windows': referencedata['gc_windows'],
'fastq_screen_params': {
'strict_validation': True,
'filter_contaminated_reads': False,
'aligner': 'bwa',
'genomes': [
{'name': 'grch37', 'path': refdata_callback(reference_dir, 'grch37')['ref_genome']},
{'name': 'mm10', 'path': refdata_callback(reference_dir, 'mm10')['ref_genome']},
{'name': 'salmon', 'path': refdata_callback(reference_dir, 'GCF_002021735')['ref_genome']},
]
}
}
return {"alignment": params}
def get_hmmcopy_params(reference_dir, reference, binsize, smoothing_function, version):
referencedata = config_reference.get_cluster_reference_data(reference_dir, reference)
docker_containers = config_reference.containers(version)['docker']
docker_containers = {
'single_cell_pipeline': docker_containers['single_cell_pipeline'],
'hmmcopy': docker_containers['hmmcopy']
}
params = {
'multipliers': [1, 2, 3, 4, 5, 6],
'map_cutoff': 0.9,
'bin_size': binsize,
'e': 0.999999,
'eta': 50000,
'g': 3,
'lambda': 20,
'min_mqual': 20,
'nu': 2.1,
'num_states': 12,
's': 1,
'strength': 1000,
'kappa': '100,100,700,100,25,25,25,25,25,25,25,25',
'm': '0,1,2,3,4,5,6,7,8,9,10,11',
'mu': '0,1,2,3,4,5,6,7,8,9,10,11',
'smoothing_function': smoothing_function,
'exclude_list': referencedata['exclude_list'],
'gc_wig_file': referencedata['gc_wig_file'][binsize],
'map_wig_file': referencedata['map_wig_file'][binsize],
'chromosomes': referencedata['chromosomes'],
'ref_genome': referencedata['ref_genome'],
'docker': docker_containers,
'igv_segs_quality_threshold': 0.75,
'memory': {'med': 6},
'good_cells': [
['median_hmmcopy_reads_per_bin', 'ge', 50],
['is_contaminated', 'in', ['False', 'false', False]],
]
}
return {"hmmcopy": params}
def get_variant_calling_params(reference_dir, reference):
referencedata = config_reference.get_cluster_reference_data(
reference_dir, reference)
docker_containers = config_reference.containers()['docker']
status_data = {'kwargs': {'split_size': 10000000}}
params = {
'memory': {
'low': 4,
'med': 6,
'high': 16
},
'max_cores': 8,
'docker': {
'single_cell_pipeline': docker_containers['single_cell_pipeline'],
'vcftools': docker_containers['vcftools'],
'strelka': docker_containers['strelka'],
'mutationseq': docker_containers['mutationseq'],
},
'ref_genome': referencedata['ref_genome'],
'chromosomes': referencedata['chromosomes'],
'split_size': 10000000,
'cosmic_status': copy.deepcopy(status_data),
'dbsnp_status': copy.deepcopy(status_data),
'mappability': copy.deepcopy(status_data),
'snpeff': copy.deepcopy(status_data),
'tri_nucleotide_context': copy.deepcopy(status_data),
'databases': {
'cosmic': {
'download_method': 'sftp',
'user_name': '*****@*****.**',
'password': '******',
'host': 'sftp-cancer.sanger.ac.uk',
'remote_paths': {
'coding':
'/files/grch37/cosmic/v75/VCF/CosmicCodingMuts.vcf.gz',
'non_coding':
'/files/grch37/cosmic/v75/VCF/CosmicNonCodingVariants.vcf.gz',
},
'local_path':
referencedata['databases']['cosmic']['local_path'],
},
'dbsnp': {
'url':
'ftp://ftp.ncbi.nih.gov/snp/organisms/human_9606_b146_GRCh37p13/VCF/common_all_20151104.vcf.gz',
'local_path':
referencedata['databases']['dbsnp']['local_path'],
},
'mappability': {
'url':
'http://hgdownload-test.cse.ucsc.edu/goldenPath/hg19/encodeDCC/wgEncodeMapability/release3'
'/wgEncodeCrgMapabilityAlign50mer.bigWig',
'local_path':
referencedata['databases']['mappability']['local_path'],
},
'ref_genome': {
'url':
'http://www.bcgsc.ca/downloads/genomes/9606/hg19/1000genomes/bwa_ind/genome/GRCh37-lite.fa',
'local_path': referencedata['ref_genome'],
},
'snpeff': {
"db": 'GRCh37.75'
},
},
'museq_params': {
'threshold': 0.5,
'verbose': True,
'purity': 70,
'coverage': 4,
'buffer_size': '2G',
'mapq_threshold': 10,
'indl_threshold': 0.05,
'normal_variant': 25,
'tumour_variant': 2,
'baseq_threshold': 10,
}
}
return {'variant_calling': params}
