GINDEL Files

1.Feature collection:

1.1 Option description

Usage: ./GINDEL [options] 

Required options:
                  -D/I      call genotype of deletions/insertions
                  -r FILE   reference file(indexed)
                  -i FILE   input bam file(index and sorted) list
                  -p FILE   deletion/insertion positions(sorted and non-overlap) in vcf format.
                  -o FILE   output file name
                  -l INT    read length
Optimal options
                  -s INT    slack value for split position with default 15
                  -b        .bas file is provided
                  -m DOUBLE mean insert size
                  -v DOUBLE standard variation of insert size
		  -g FILE   genotype file in vcf format for training data
	          -h        help

1.2 Example:

For deletion:

(1)Collect Features with given constant insert size:
./GINDEL -D -r ./human_g1k_v37.fasta -i ./simulated_data_input.6.4.list -p ./simulation_del_sites.vcf -o test_sim.txt -l 100 -m 400 -v 50

(2)Collect Features with given insert size contained in .bas file:
./GINDEL -D -r ./human_g1k_v37.fasta -i ./simulated_data_input.6.4.list -p ./simulation_del_sites.vcf -o test_sim.txt -l 100 -b

(3)Collect Features without insert size:
./GINDEL -D -r ./human_g1k_v37.fasta -i ./simulated_data_input.6.4.list -p ./simulation_del_sites.vcf -o test_sim.txt -l 100


Get Trained data:
./GINDEL -D -r ./human_g1k_v37.fasta -i ./simulated_data_input.6.4.list -p ./simulation_del_sites.vcf -o test_sim.txt -l 100 -g ./genotype.vcf


For insertion:
./GINDEL -I -r ./human_g1k_v37.fasta -i ./simulated_data_input.6.4.list -p ./simulation_ins_sites.vcf -o test_sim.txt -l 100


2. Training and prediction

2.1. Training

python easy.py training_data


2.2 Predicting

..\windows\svm-predict data.scale Trained.model predictResult



Contact: chong.chu@engr.uconn.edu