Patient-derived xenograft (PDX) and circulating tumor cell-derived explant (CDX) models are powerful methods for the study of human disease. In cancer research, these methods have been applied to multiple questions, including the study of metastatic progression, genetic evolution, and therapeutic drug responses. As PDX and CDX models can recapitulate the highly heterogeneous characteristics of a patient tumor, as well as their response to chemotherapy, there is considerable interest in combining them with next-generation sequencing to monitor the genomic, transcriptional, and epigenetic changes that accompany oncogenesis. When used for this purpose, their reliability is highly dependent on being able to accurately distinguish between sequencing reads that originate from the host, and those that arise from the xenograft itself. Here, we demonstrate that failure to correctly identify contaminating host reads when analyzing DNA- and RNA-sequencing (DNA-Seq and RNA-Seq) data from PDX and CDX models is a major confounding factor that can lead to incorrect mutation calls and a failure to identify canonical mutation signatures associated with tumorigenicity. In addition, a highly sensitive algorithm and open source software tool for identifying and removing contaminating host sequences is described. Importantly, when applied to PDX and CDX models of melanoma, these data demonstrate its utility as a sensitive and selective tool for the correction of PDX- and CDX-derived whole-exome and RNA-Seq data. This study describes a sensitive method to identify contaminating host reads in xenograft and explant DNA- and RNA-Seq data and is applicable to other forms of deep sequencing. .