Update README.md

KChen-lab · Apr 23, 2023 · 4476a1d · 4476a1d
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diff --git a/README.md b/README.md
@@ -60,12 +60,12 @@ We provide one example dataset provided the `example/` folder, which includes:
   The bam file storing read alignment for sample A.
 * `B.bam (.bai)`  
   The bam file storing read alignment for sample B. 
-* `CCDG_14151_B01_GRM_WGS_2020-08-05_chr20.filtered.shapeit2-duohmm-phased.vcf.gz` 
+* `CCDG_14151_B01_GRM_WGS_2020-08-05_chr20.filtered.shapeit2-duohmm-phased.vcf.gz`  
   The reference panel with over 3,000 samples in 1000 Genome database. Only SNVs located in chr20: 0-2Mb were extracted in this vcf file. 
 * `chr20_2Mb.hg38.fa (.fai)`  
   The genome reference used for read aligments. Only seuqences in chr20:0-20Mb were extracted in this fasta file.
 
-There is a bash script `./test/runPreprocess.sh` to run above example in the folder `test`. You need to prepare the bam file list for option `-b`. If you have multiple sample in the list file, `Monopogen` will run the joint calling which can increase the SNV calling accuracy and sensitivity. Run the test script as following:
+There is a bash script `./test/runPreprocess.sh` to run above example in the folder `test`. You need to prepare the bam file list for option `-b`. If you have multiple sample in this file, you can use more CPUs by setting `-t` to make `Monopogen` faster.  Run the test script as following:
 
 ```
 path="XXy/Monopogen"
@@ -74,66 +74,87 @@ export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:${path}/apps
 python  ${path}/src/Monopogen.py  preProcess -b bam.lst -o out  -a ${path}/apps -t 8
 
 ```
-
+After running the `preProcess` module, there will be bam files after quality controls in the folder `out/Bam/` which will be used for downstream SNV calling.
 
+## 3.2 Germline SNV calling  
+
+You can type the following command to get the help information.
 
-```
-path="XX/Monopogen"
-export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:${path}/apps
-
-python  ../src/Monopogen.py    germline  \
-      -b  ../example/chr20_2Mb.rh.filter.sort.bam  \
-      -y  single  \
-      -t  all  \
-      -a  ../apps  \
-      -c  chr20  \
-      -o  out \
-      -d  10 \
-      -p  ../example/CCDG_14151_B01_GRM_WGS_2020-08-05_chr20.filtered.shapeit2-duohmm-phased.vcf.gz  \
-      -r  ../example/chr20_2Mb.hg38.fa   -m 3 -s 5
-
+`python ./src/Monopogen.py  germline --help`
 
 ```
+usage: Monopogen.py germline [-h] -r REGION -s
+                             {varScan,varImpute,varPhasing,all} [-o OUT] -g
+                             REFERENCE -p IMPUTATION_PANEL
+                             [-m MAX_SOFTCLIPPED] -a APP_PATH [-t NTHREADS]
 
-## Output
+optional arguments:
+  -h, --help            show this help message and exit
+  -r REGION, --region REGION
+                        The genome regions for variant calling (default: None)
+  -s {varScan,varImpute,varPhasing,all}, --step {varScan,varImpute,varPhasing,all}
+                        Run germline variant calling step by step (default:
+                        all)
+  -o OUT, --out OUT     The output director (default: None)
+  -g REFERENCE, --reference REFERENCE
+                        The human genome reference used for alignment
+                        (default: None)
+  -p IMPUTATION_PANEL, --imputation-panel IMPUTATION_PANEL
+                        The population-level variant panel for variant
+                        imputation refinement, such as 1000 Genome 3 (default:
+                        None)
+  -a APP_PATH, --app-path APP_PATH
+                        The app library paths used in the tool (default: None)
+  -t NTHREADS, --nthreads NTHREADS
+                        Number of threads used for SNVs calling (default: 1)
+ ```
 
-The most important results are in the folder `out/germline`: 
+There is a bash script `./test/runGermline.sh` to run above example. You need to prepare the genome region file list for option `-r` with an example shown in `test/region.lst`. Each region is in one row. If you want to call the whole chromosome, you can only specficy the chromosome ID in each row.  Also you can use more CPUs by setting `-t` to make `Monopogen` faster when there are many genome regions.  Run the test script as following:
+
+```
+python  ${path}/src/Monopogen.py  germline  \
+    -a   ${path}/apps -t 8   -r  region.lst \
+    -p  ../example/CCDG_14151_B01_GRM_WGS_2020-08-05_chr20.filtered.shapeit2-duohmm-phased.vcf.gz  \
+    -g  ../example/chr20_2Mb.hg38.fa   -s all  -o out
 
-* `chr20.germline.vcf`  
-In this vcf file, the germline SNVs were genotyped for the study sample 
+```
+The `germline` module will generate the phased VCF files with name `*.phased.vcf.gz` in the folder `out/germline`. If there are multiple samples in the bam file list from `-b` option in `preProcess` module, the phased VCF files will contain genotypes from multiple samples. The output of phased genotypes are as following:
+
 ```
 ##fileformat=VCFv4.2
-##filedate=20220808
+##filedate=20230422
 ##source="beagle.27Jul16.86a.jar (version 4.1)"
 ##INFO=<ID=AF,Number=A,Type=Float,Description="Estimated ALT Allele Frequencies">
-##INFO=<ID=AR2,Number=1,Type=Float,Description="Allelic R-Squared: estimated squared correlation between most probable REF dose and true REF dose">
-##INFO=<ID=DR2,Number=1,Type=Float,Description="Dosage R-Squared: estimated squared correlation between estimated REF dose [P(RA) + 2*P(RR)] and true REF dose">
+##INFO=<ID=AR2,Number=1,Type=Float,Description="Allelic R-Squared: estimated squared correlation betwe
+##INFO=<ID=DR2,Number=1,Type=Float,Description="Dosage R-Squared: estimated squared correlation betwee
 ##INFO=<ID=IMP,Number=0,Type=Flag,Description="Imputed marker">
 ##FORMAT=<ID=GT,Number=1,Type=String,Description="Genotype">
 ##FORMAT=<ID=DS,Number=A,Type=Float,Description="estimated ALT dose [P(RA) + P(AA)]">
 ##FORMAT=<ID=GP,Number=G,Type=Float,Description="Estimated Genotype Probability">
-#CHROM  POS     ID      REF     ALT     QUAL    FILTER  INFO    FORMAT  test1   test2
-chr20   60291   .       G       T       .       PASS    AR2=0.00;DR2=0.00;AF=0.50       GT:DS:GP        0/1:1:0,1,0     0/1:1:0,1,0
-chr20   64506   .       C       T       .       PASS    AR2=0.00;DR2=0.00;AF=0.0051     GT:DS:GP        0/0:0.01:0.99,0.01,0    0/0:0.01:0.99,0.01,0
-chr20   68303   .       T       C       .       PASS    AR2=0.00;DR2=0.00;AF=0.50       GT:DS:GP        0/1:1:0,1,0     0/1:1:0,1,0
-chr20   75250   .       C       T       .       PASS    AR2=0.00;DR2=0.00;AF=0.50       GT:DS:GP        0/1:1:0,1,0     0/1:1:0,1,0
-chr20   88108   .       T       C       .       PASS    AR2=0.00;DR2=0.00;AF=0.87       GT:DS:GP        1/1:1.73:0,0.26,0.73    1/1:1.73:0,0.26,0.73
-chr20   159104  .       T       C       .       PASS    AR2=0.00;DR2=0.00;AF=0.9960     GT:DS:GP        1/1:1.99:0,0.01,0.99    1/1:1.99:0,0.01,0.99
-chr20   175269  .       T       C       .       PASS    AR2=0.00;DR2=0.00;AF=0.9903     GT:DS:GP        1/1:1.98:0,0.02,0.98    1/1:1.98:0,0.02,0.98
-chr20   186086  .       G       A       .       PASS    AR2=0.00;DR2=0.00;AF=0.983      GT:DS:GP        1/1:1.97:0,0.03,0.97    1/1:1.97:0,0.03,0.97
-chr20   186183  .       G       A       .       PASS    AR2=0.00;DR2=0.00;AF=0.983      GT:DS:GP        1/1:1.97:0,0.03,0.97    1/1:1.97:0,0.03,0.97
-chr20   198814  .       A       T       .       PASS    AR2=0.00;DR2=0.00;AF=0.50       GT:DS:GP        0/1:1:0.05,0.89,0.06    0/1:1:0.05,0.89,0.06
-chr20   231710  .       T       G       .       PASS    AR2=0.00;DR2=0.00;AF=0.70       GT:DS:GP        0/1:1.4:0,0.6,0.4       0/1:1.4:0,0.6,0.4
-chr20   240166  .       T       G       .       PASS    AR2=0.00;DR2=0.00;AF=0.49       GT:DS:GP        0/1:0.97:0.03,0.97,0    0/1:0.97:0.03,0.97,0
-chr20   240377  .       T       G       .       PASS    AR2=0.00;DR2=0.00;AF=0.49       GT:DS:GP        0/1:0.97:0.03,0.97,0    0/1:0.97:0.03,0.97,0
-chr20   247326  .       G       A       .       PASS    AR2=0.00;DR2=0.00;AF=0.9964     GT:DS:GP        1/1:1.99:0,0.01,0.99    1/1:1.99:0,0.01,0.99
-chr20   248854  .       T       C       .       PASS    AR2=0.00;DR2=0.00;AF=0.73       GT:DS:GP        0/1:1.47:0,0.53,0.47    0/1:1.47:0,0.53,0.47
-chr20   254891  .       C       T       .       PASS    AR2=0.00;DR2=0.00;AF=0.51       GT:DS:GP        0/1:1.02:0,0.97,0.03    0/1:1.02:0,0.97,0.03
-chr20   255081  .       G       A       .       PASS    AR2=0.00;DR2=0.00;AF=0.85       GT:DS:GP        1/1:1.7:0,0.3,0.7       1/1:1.7:0,0.3,0.7
-chr20   260997  .       G       A       .       PASS    AR2=0.00;DR2=0.00;AF=0.40       GT:DS:GP        0/1:0.8:0.2,0.8,0       0/1:0.8:0.2,0.8,0
-chr20   265294  .       G       T       .       PASS    AR2=0.00;DR2=0.00;AF=0.918      GT:DS:GP        1/1:1.84:0,0.16,0.84    1/1:1.84:0,0.16,0.84
+#CHROM  POS     ID      REF     ALT     QUAL    FILTER  INFO    FORMAT  19D013_European_F_78    19D014_European_M_84
+chr20   68303   .       T       C       .       PASS    .       GT      1|0     1|1
+chr20   88108   .       T       C       .       PASS    .       GT      1|1     0|1
+chr20   127687  .       A       C       .       PASS    .       GT      1|1     1|1
+chr20   153835  .       T       C       .       PASS    .       GT      1|0     1|1
+chr20   154002  .       C       T       .       PASS    .       GT      1|1     1|1
+chr20   159104  .       T       C       .       PASS    .       GT      1|1     1|1
+chr20   167839  .       T       C       .       PASS    .       GT      1|1     1|1
+chr20   198814  .       A       T       .       PASS    .       GT      1|0     1|1
+chr20   231710  .       T       G       .       PASS    .       GT      1|1     1|1
+chr20   237210  .       T       C       .       PASS    .       GT      1|1     1|1
+chr20   247326  .       G       A       .       PASS    .       GT      1|1     1|0
+chr20   248854  .       T       C       .       PASS    .       GT      1|1     1|0
+chr20   255081  .       G       A       .       PASS    .       GT      1|1     1|0
+chr20   274893  .       G       C       .       PASS    .       GT      0|1     1|1
+chr20   275122  .       G       T       .       PASS    .       GT      0|1     1|1
+chr20   275241  .       G       A       .       PASS    .       GT      0|1     1|0
+chr20   275361  .       C       T       .       PASS    .       GT      0|1     1|0
+chr20   275932  .       A       G       .       PASS    .       GT      0|1     1|0
+chr20   276086  .       T       A       .       PASS    .       GT      0|1     1|0
 
 ```
+
+
 ## Run on multiple chromosomes and multiple samples 
 
 Users can submit jobs with multiple chromosomes in the parallele fashion as following: