A significant number of human malignancies -- for instance, most cancers of the cervix and the liver -- are caused by infections with tumor viruses. Identification of these pathogens is now considered a critical contribution to cancer prevention. Deep sequencing enables us to systematically investi

In excess of 12% of human cancer incidents have a causal viral cofactor. Epidemiological studies of idiopathic human cancers indicate that additional tumor viruses remain to be discovered. Recent advances in sequencing technology have enabled systematic screenings of human tumor transcriptomes for viral transcripts. However, technical problems such as low abundances of viral transcripts in large volumes of sequencing data, viral sequence divergence, and homology between viral and human factors significantly confound identification of tumor viruses. We have developed a novel computational approach for detecting viral transcripts in human tumors and conducted the first systematic search for viruses in neuroblastoma, the most common cancer in infancy. The striking diversity in clinical progression of this malignancy as well as previous epidemiological and virological findings are highly suggestive of a viral cofactor. We computationally mapped deeply sequenced transcriptomes of neuroblastoma and several cancers with known viral cofactors to the human and all known viral genomes. We analyzed the homologous context of putative viral transcripts in a new and sensitive manner in order to detect both known and unknown viruses under consideration of the aforementioned confounds.