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.


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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.


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



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