Software

Software Description Publication
JointSNVMix implements a probabilistic graphical model to analyse sequence data from tumour/normal pairs. PMID:22285562
APOLLOH is a hidden Markov model for predicting somatic loss of heterozygosity and allelic imbalance in whole tumour genome sequencing data. PMID:22637570
mutationSeq uses feature-based classifiers for somatic mutation prediction from paired tumour/normal next-generation sequencing data. PMID:22084253
DriverNet predicts functional important driver genes in cancer by integrating genome data (mutation and copy number variation data) and transcriptome data (gene expression data). PMID:23383675
HMMCopy makes copy number estimations for whole genome data with GC and mapability correction, then segment and classify copy number profiles with a robust Hidden Markov Model. PMID:22637570
PyClone infers the cellular frequency of point mutations from deeply sequenced data. PMID:24633410
TITAN infers subclonal copy number alterations and LOH from genome sequencing data. PMID:25060187
Auditor is a Python-implemented generative model framework for the detection of RNA-editing events from paired next-generation DNA and RNA sequence data.
HMM-dosage analyzes SNP-genotyping data of tumours to predict both somatic and germline copy number changes. PMID:22522925
PMID:23412801
SNVMix detects single nucleotide variants from next generation sequencing data. PMID:20130035
DeFuse is a software package for gene fusion discovery using RNA-Seq data. PMID:21625565
CoNAn-SNV is a probabilistic framework for the discovery of single nucleotide variants in WGSS data. PMID:22916110
PostPy is a set of tools that help you post process PyClone results.
xseq is a probabilistic model to predict the impact of somatic mutations on gene expression.
Single Cell Genotyper novel statistical model coupled with a mean field variational inference method to address the problems of: missing values, biased allelic counts, and false measurements of genotypes due to sequencing multiple cells which are common in single cell sequencing datasets.