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TOBI: Tumor Only Boosting Identification of Driver Mutations

Tumor-Only Boosting Identification (TOBI) is a framework for unified germline and somatic
analysis analysis using largely tumor-only samples. TOBI uses gradient booosting to learn 
features of confirmed somatic variants from a small training set of tumor-normal sampless, 
then generates a classification model that identifies variants with somatic characteristics 
in tumor-only samples.
 
First, WES files from tumor samples undergo variant calling, annotation, and filtering for 
quality. TOBI then merges variants across multiple samples. In pre-processing, TOBI labels 
variants from the training set as somatic (“som”) or non-somatic (“non_som"). Finally, in 
the machine learning step, TOBI generates and applies a somatic classifier. 


Ver. 1.2: April 12, 2016
cjmadubata & tchu modified from Alireza Roshan Ghias's code 
(Ver. 1.1: Nov 07, 2014 https://github.com/alireza202/TOBI.git TOBI)

dependencies:
	- Python 2.7.11
	- Perl v5.10.1
	- R v3.1.2
	- Java 1.7.0_25
	- samtools 0.1.19
	- bcftools 0.1.19
	- VCFtools v0.1.10.1
	- snpEff v3.6 & dbNSFP (https://sites.google.com/site/jpopgen/dbNSFP)
	- snpSift v3.6

#########################################################################################
###varCall_filtering###

inputs at each step:
	V (variant calling): indexed .bam files in a folder. Files must have .bam extension 
		and filename cannot start with a number.
	A (annotation): .vcf files in a folder. Files must have .vcf extension and filename
		cannot start with a number. If starting from this step, please format vcf to
		match bcftools output.
	F (filter): .vcf files in a folder. Files must have .vcf extension and filename 
		cannot start with a number. 
	
usage: TOBIvaf.py [-h] [--inputdir INPUTDIR] [--output OUTPUT]
                  [--config CONFIG] [--steps STEPS] [--cluster {hpc,amazon}]
                  [--debug] [--cleanup] [--ref REF] [--start START]
                  [--end END] [--snpeff SNPEFF] [--annovcf ANNOVCF]
                  [--dbnsfp DBNSFP] [--vcftype {default,TCGA}]
                  [--mergename MERGENAME]

TOBIv1.2: Tumor Only Boosting Identification of Driver Mutations All arguments
can be specified in a config file. (See included varCall.config file as an
example).

Arguments:
	General Arguments:
	
	  -h, --help            show this help message and exit
	  --inputdir INPUTDIR   [REQUIRED] directory for bam/vcf files.
	  --output OUTPUT       [REQUIRED] output directory.
	  --config CONFIG       config file specifying command line arguments.
	                        Arguments specified in the command line overwrite
	                        config file arguments.
	  --steps STEPS         [REQUIRED] Specify which steps of pipeline to run. V:
	                        variant calling A: annotate F: filter M: merge eg.
	                        --steps AF
	  --cluster {hpc,amazon}
	                        [REQUIRED] Specify which cluster to run on. hpc: run
	                        on an SGE hpc cluster amazon: CURRENTLY UNIMPLEMENTED
	  --debug               Debug/verbose flag. Default: False
	  --cleanup             Delete temporary debug files. Default True
	VCF Step Arguments:
	
	  --ref REF             [REQUIRED - VCF] Reference genome file.
	  --start START         Start index used for testing. Will not work in config.
	                        Default 1
	  --end END             End index used for testing. Will not work in config.
	                        Default 74
	Annotation Step Arguments:
	
	  --snpeff SNPEFF       [REQUIRED - ANNOTATE] Directory where snpEff is
	  --annovcf ANNOVCF     [REQUIRED - ANNOTATE] A comma separated list of .vcf
	                        files to annotate with.
	  --dbnsfp DBNSFP       [REQUIRED - ANNOTATE] Path to dbNSFP file
	Filter Step Arguments:
	
	  --vcftype {default,TCGA}
	                        Specifies vcf type specically for TCGA filtering
	Merge Step Arguments:  
	
	  --mergename MERGENAME
	                        [REQUIRED - MERGE] Name for final merged file

#########################################################################################
### machine_learning ###
Step 8. Pre-processing using R. Needs customization each time.

usage: TOBIml.py [-h] [--input INPUT] [--output OUTPUT] [--somatic SOMATIC]
                 [--log LOG] [--check_missed CHECK_MISSED] [--suffix SUFFIX]
                 [--vcftype {default,TCGA}] [--train_size TRAIN_SIZE]
                 [--verbose]
                 {preprocess,machinelearning}

TOBIv1.2: Tumor Only Boosting Identification of Driver Mutations. Machine
learning step.

positional arguments:
  {preprocess,machinelearning}
						preprocess: preprocessing step; 
						machinelearning: machine learning step
						
optional arguments:
  -h, --help            show this help message and exit
  --input INPUT         [REQUIRED] input file
  --output OUTPUT       [REQUIRED] output file for PP, output folder for ML
  --somatic SOMATIC     [REQUIRED] formatted file containing somatic variants
  --log LOG             Optional argument to specify a log to pipe stdout and
                        stderr to
  --check_missed CHECK_MISSED
                        [PP ARG] checking which mutations in important genes
                        are missed by filtering
  --suffix SUFFIX       [ML ARG] a label specific to this particular run (e.g.
                        <date>_<disease>)
  --vcftype {default,TCGA}
                        Specifies vcf type specically for TCGA filtering
  --train_size TRAIN_SIZE
                        [ML ARG] number of patients you want in the training
                        set.
  --verbose             verbose flag