JointSNVMix

Usage: pileup_to_jcnt.py NORMAL_PILEUP_FILE TUMOUR_PILEUP_FILE OUTPUT_FILE <options>
Options:
-h, –help            show this help message and exit
-b                    Set this flag if file is in bz2 format.
–bpil                Set this flag if files are already in bpil format.
–min_base_qual=MIN_BASE_QUAL
Minimum base quality to use, value is inclusive.
Default is 5.
–min_map_qual=MIN_MAP_QUAL
Minimum mapping quality to use, value is inclusive.
Default is 30.

joint_snv_mix

Description

JointSNVMix implements a probabilistic graphical model to analyse sequence data from tumour/normal pairs. The model draws statistical strength by analysing both genome jointly to more accurately classify germline and somatic mutations.

Download:

Please visit our google code page to get the latest version http://code.google.com/p/joint-snv-mix/.

Usage:

JointSNVMix effectively reduces false positive somatic mutation predictions in tumour-normal pair sequencing data. It is highly recommended to post-process results with mutationSeq in order to filter technical artifacts.
 

Contact:

For more information or for additional questions/comments , please contact Andrew Roth: andrewjlroth [at] gmail.com or Sohrab Shah: sshah [at] bccrc.ca

JointSNVMix paper published: a statistical model for somatic point mutation detection

JointSNVMix paper published: a statistical model for somatic point mutation detection

Andrew Roth, Ryan Morin, Jiarui Ding, Anamaria Crisan, Gavin Ha, Ryan Giuliany, Ali Bashashati, Martin Hirst, Gulisa Turashvili, Arusha Oloumi, Marco A. Marra, Samuel Aparicio and Sohrab P. Shah. JointSNVMix : A Probabilistic Model For Accurate Detection Of Somatic Mutations In Normal/Tumour Paired Next Generation Sequencing Data. Bioinformatics. 2012 doi: 10.1093/bioinformatics/bts053 SOFTWARE