Characterizing　copy　number　variations　(CNVs)　from　sequenced　genomes　is　a　both　feasible　and　cost-effective　way　to　search　for　driver　genes　in　cancer　diagnosis.　A　number　of　existing　algorithms　for　CNV　detection　only　explored　part　of　the　features　underlying　sequence　data　and　copy　number　structures,　resulting　in　limited　performance.　Here,　we　describe　CONDEL,　a　method　for　detecting　CNVs　from　single　tumor　samples　using　high-throughput　sequence　data.　CONDEL　utilizes　a　novel　statistic　in　combination　with　a　peel-off　scheme　to　assess　the　statistical　significance　of　genome　bins,　and　adopts　a　Bayesian　approach　to　infer　copy　number　gains,　losses,　and　deletion　zygosity　based　on　statistical　mixture　models.　We　compare　CONDEL　to　six　peer　methods　on　a　large　number　of　simulation　datasets,　showing　improved　performance　in　terms　of　true　positive　and　false　positive　rates,　and　further　validate　CONDEL　on　three　real　datasets　derived　from　the　1000　Genomes　Project　and　the　EGA　archive.　CONDEL　obtained　higher　consistent　results　in　comparison　with　other　three　single　sample-based　methods,　and　exclusively　identified　a　number　of　CNVs　that　were　previously　associated　with　cancers.　We　conclude　that　CONDEL　is　a　powerful　tool　for　detecting　copy　number　variations　on　single　tumor　samples　even　if　these　are　sequenced　at　low-coverage.　IEEE