with the advent of relatively affordable high throughput technologies dna sequencing of cancers is now common practice in cancer research projects and will be increasingly used in clinical practice to inform diagnosis and treatment somatic cancer only single nucleotide variants snvs are the simplest class of mutation yet their identification in dna sequencing data is confounded by germline polymorphisms tumour heterogeneity and sequencing and analysis errors four recently published algorithms for the detection of somatic snv sites in matched cancer normal sequencing datasets are varscan somaticsniper jointsnvmix and strelka in this analysis we apply these four snv calling algorithms to cancer normal illumina exome sequencing of a chronic myeloid leukaemia cml patient the candidate snv sites returned by each algorithm are filtered to remove likely false positives then characterized and compared to investigate the strengths and weaknesses of each snv calling algorithm comparing the candidate snv sets returned by varscan somaticsniper jointsnvmix2 and strelka revealed substantial differences with respect to the number and character of sites returned the somatic probability scores assigned to the same sites their susceptibility to various sources of noise and their sensitivities to low allelic fraction candidates data accession number sra081939 code at http code google com p snv caller review david adelson adelaide edu au supplementary data are available at bioinformatics online.