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] gmail.com or Sohrab Shah: sshah [at] bccrc.ca
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