上传文件至 ''

defaults

+{
+    "hg38_CDS":"oss://hcc1395/reference_genome/TMB_dir/genecode.v36.CDS_merged.bed",
+    "regions":"",
+    "docker_tmb":"registry.cn-shanghai.aliyuncs.com/hcc1395_aliyun/tmb:1.1.0",
+    "cluster_config":"OnDemand ecs.g6.large img-ubuntu-vpc",
+    "disk_size":"50",
+    "AF_filter":true
+}

inputs

+{
+    "{{ project_name }}.sample": "{{ sample }}",
+    "{{ project_name }}.hg38_CDS": "{{ hg38_CDS }}",
+    "{{ project_name }}.regions": "{{ regions }}",
+    "{{ project_name }}.snpindel_txt": "{{ snpindel_txt }}",
+    "{{ project_name }}.docker_tmb": "{{docker_tmb}}",
+    "{{ project_name }}.cluster_config": "{{ cluster_config }}",
+    "{{ project_name }}.disk_size": "{{ disk_size }}",
+    "{{ project_name }}.AF_filter": {{ AF_filter | tojson }}
+
+
+}

tmb.py

+import sys
+import pandas as pd
+
+snpindel_txt=sys.argv[1]
+size=sys.argv[2]
+sample=sys.argv[3]
+#AF_filter:yes/no(yes:Filter out AF value < 0.05)
+AF_filter=sys.argv[4]
+
+size = int(size)
+print('Target region: %.2f Mb' % (size/1000000))
+# Read annotated txt
+df = pd.read_csv(snpindel_txt, sep="\t")
+if 'Otherinfo14' in df.columns:
+  df = df.rename(columns={'AF':'AF_all', 'Otherinfo4':'#CHROM', 'Otherinfo5':'POS', 'Otherinfo6':'ID', 'Otherinfo7':'REF', 'Otherinfo8':'ALT', 'Otherinfo9':'QUAL', 'Otherinfo10':'FILTER', 'Otherinfo11':'INFO', 'Otherinfo12':'FORMAT', 'Otherinfo13':'Normal', 'Otherinfo14':'Tumor'})
+else:
+  df = df.rename(columns={'AF':'AF_all', 'Otherinfo4':'#CHROM', 'Otherinfo5':'POS', 'Otherinfo6':'ID', 'Otherinfo7':'REF', 'Otherinfo8':'ALT', 'Otherinfo9':'QUAL', 'Otherinfo10':'FILTER', 'Otherinfo11':'INFO', 'Otherinfo12':'FORMAT', 'Otherinfo13':'Tumor'})
+
+df = df[df.columns.drop(list(df.filter(regex='Otherinfo')))]
+
+# Extract AF value
+
+df.insert(df.shape[1], 'AF', df.Tumor.str.split(':', expand = True)[2])
+df['AF'] = df['AF'].apply(lambda x:float(x))
+if str(AF_filter) == 'yes':
+    #Filter out AF value < 0.05
+    df = df[df['AF'] >= 0.05]
+df.to_csv('%s.snp_indel.txt' % sample, sep='\t', index=False)
+
+
+# TMB is measured by counting the total number of somatic, non-synonymous exonic variants per the total number of genes surveyed by the product.
+df_include = df[(df['Func.refGene'] == 'exonic') & (df['ExonicFunc.refGene'] != 'synonymous SNV') & (df['FILTER'] == 'PASS')]
+total_mb = '%.2f' % (size/1000000)
+num_muts = df_include.shape[0]
+tmb = '%.2f' % (num_muts/(size/1000000))
+res = pd.DataFrame({'Total_Mb': [total_mb], 'Number_of_Muts': [num_muts], 'TMB': [tmb]})
+res.to_csv('%s.TMB.txt' % sample, sep='\t', index=False)

workflow.wdl

+import "./tasks/tmb.wdl" as tmb
+
+workflow {{ project_name }}{
+    String sample
+    File hg38_CDS
+    File? regions
+    File snpindel_txt
+    String docker_tmb
+    String cluster_config
+    String disk_size
+    Boolean AF_filter
+
+    call tmb.TMB as TMB{
+        input:
+            sample=sample,
+            hg38_CDS=hg38_CDS,
+            regions=regions,
+            snpindel_txt=snpindel_txt,
+            docker=docker_tmb,
+            cluster_config=cluster_config,
+            disk_size=disk_size,
+            AF_filter=AF_filter
+    }
+
+}