Unlabelled

Human papillomavirus (HPV) causes a number of neoplastic diseases in humans. Here, we show a complex normal HPV community in a cohort of 103 healthy human subjects, by metagenomics analysis of the shotgun sequencing data generated from the NIH Human Microbiome Project. The overall HPV prevalence was 68.9% and was highest in the skin (61.3%), followed by the vagina (41.5%), mouth (30%), and gut (17.3%). Of the 109 HPV types as well as additional unclassified types detected, most were undetectable by the widely used commercial kits targeting the vaginal/cervical HPV types. These HPVs likely represent true HPV infections rather than transitory exposure because of strong organ tropism and persistence of the same HPV types in repeat samples. Coexistence of multiple HPV types was found in 48.1% of the HPV-positive samples. Networking between HPV types, cooccurrence or exclusion, was detected in vaginal and skin samples. Large contigs assembled from short HPV reads were obtained from several samples, confirming their genuine HPV origin. This first large-scale survey of HPV using a shotgun sequencing approach yielded a comprehensive map of HPV infections among different body sites of healthy human subjects.

Importance

This nonbiased survey indicates that the HPV community in healthy humans is much more complex than previously defined by widely used kits that are target selective for only a few high- and low-risk HPV types for cervical cancer. The importance of nononcogenic viruses in a mixed HPV infection could be for stimulating or inhibiting a coexisting oncogenic virus via viral interference or immune cross-reaction. Knowledge gained from this study will be helpful to guide the designing of epidemiological and clinical studies in the future to determine the impact of nononcogenic HPV types on the outcome of HPV infections.

Abstract Background Terabyte-scale collections of string-encoded data are expected from consortia efforts such as the Human Microbiome Project http://nihroadmap.nih.gov/hmp. Intra- and inter-project data similarity searches are enabled by rapid k-mer matching strategies. Software applications for sequence database partitioning, guide tree estimation, molecular classification and alignment acceleration have benefited from embedded k-mer searches as sub-routines. However, a rapid, general-purpose, open-source, flexible, stand-alone k-mer tool has not been available. Results Here we present a stand-alone utility, Simrank, which allows users to rapidly identify database strings the most similar to query strings. Performance testing of Simrank and related tools against DNA, RNA, protein and human-languages found Simrank 10X to 928X faster depending on the dataset. Conclusions Simrank provides molecular ecologists with a high-throughput, open source choice for comparing large sequence sets to find similarity.