# download svf files
$ wget https://github.com/xueyeduzhuo/svf/archive/master.zip
# decompress it on your server
$ unzip svf-master.zip
# enter svf master
$ cd svf-master
# under svf master directory
$ ./svf.sh
or
$ python svf.py feature -b <*.bam> -c <*.vcf.gz> -v <*.vcf.gz> -o <*_feature.txt> -g hg19 -f <*.fa>
svf requires aligned files(.bam format), SV files(.vcf.gz format or .bed format) , SNV files(.vcf.gz format).
$ python svf.py pipeline -b <*.bam> -c <*.vcf.gz> -v <*.vcf.gz ...> -o <*.txt> -g hg19 -f <*.fa>
Output is generated in the current working directory by default.
$ svf ... -o <*.txt>
-
python 2.7
- numpy
- pybedtools
- pysam 0.9+
- scikit-learn v0.19+
- multiprocessing
- vcf
-
bedtools 2.25.0 or later