In Silico Expression Profiles of Human Endogenous Retroviruses

摘要

Human endogenous retroviruses (HERVs) are remnants of ancient retrovirus infections and now reside within the human DNA. Recently HERV expression has been detected in both normal and diseased tissues. However, the patterns of expression of individual HERV sequences are mostly unknown. In this work we use a generative mixture model, based on hidden Markov models, for estimating the activities of individual HERV sequences from databases of expressed sequences. We determine the relative activities of sixty HERVs from the HML2 group in five human tissues, I.e. we estimate the expression profile of each HERV. This allows us to gain insight into HERV function.