BackgroundHuman endogenous retroviruses (HERV) constitute approximately 8% of the human genome and have long been considered "junk". The sheer number and repetitive nature of these elements make studies of their expression methodologically challenging. Hence, little is known of transcription of genomic regions harboring such elements.ResultsApplying a recently developed technique for obtaining high resolution melting temperature data, we examined the frequency distributions of HERV-W gag element into 13 Tm categories in human tissues. Transcripts containing HERV-W gag sequences were expressed in non-random patterns with extensive variations in the expression between both tissues, including different brain regions, and individuals. Furthermore, the patterns of such transcripts varied more between individuals in brain regions than other tissues.ConclusionThus, regulated expression of non-coding regions of the human genome appears to include the HERV-W family of repetitive elements. Although it remains to be established whether such expression patterns represent leakage from transcription of functional regions or specific transcription, the current approach proves itself useful for studying detailed expression patterns of repetitive regions.
BackgroundIn addition to their use in detecting undesired real-time PCR products, melting temperatures are useful for detecting variations in the desired target sequences. Methodological improvements in recent years allow the generation of high-resolution melting-temperature (Tm) data. However, there is currently no convention on how to statistically analyze such high-resolution Tm data.ResultsMixture model analysis was applied to Tm data. Models were selected based on Akaike's information criterion. Mixture model analysis correctly identified categories in Tm data obtained for known plasmid targets. Using simulated data, we investigated the number of observations required for model construction. The precision of the reported mixing proportions from data fitted to a preconstructed model was also evaluated.ConclusionMixture model analysis of Tm data allows the minimum number of different sequences in a set of amplicons and their relative frequencies to be determined. This approach allows Tm data to be analyzed, classified, and compared in an unbiased manner.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.