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Methods for estimating human endogenous retrovirus activities from EST databases.

Merja Oja1, Jaakko Peltonen, Jonas Blomberg

  • 1Department of Computer Science, University of Helsinki, University of Helsinki, Finland. merja.oja@tkk.fi

BMC Bioinformatics
|May 12, 2007
PubMed
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This study developed novel methods to measure human endogenous retrovirus (HERV) activity, revealing that 7% of HERVs are active, with env gene presence increasing activity. These findings shed light on the functional landscape of HERVs in human DNA.

Area of Science:

  • Genomics
  • Virology
  • Bioinformatics

Background:

  • Human endogenous retroviruses (HERVs) are remnants of ancient retroviral infections integrated into human DNA.
  • Recent studies indicate HERV expression in both healthy and diseased human tissues.
  • The specific activity levels of individual HERV sequences remain largely uncharacterized.

Purpose of the Study:

  • To develop and apply computational methods for estimating the activity of individual HERV sequences.
  • To quantify the proportion and characteristics of active HERVs within the human genome.

Main Methods:

  • Utilized a generative mixture model based on Hidden Markov Models (HMMs) for HERV activity estimation from expressed sequence tag (EST) databases.
  • Developed and validated a faster heuristic method for large-scale HERV activity assessment.

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Main Results:

  • Estimated the relative activities of 181 HERVs using the HMM-based model.
  • Assessed the activities of 2450 HERVs using the heuristic method, with most activities previously unknown.
  • Identified that approximately 7% of HERVs are active in human DNA.

Conclusions:

  • The developed methods accurately estimate HERV activity, validated on simulated and real data.
  • Active HERVs are unevenly distributed among HERV groups but uniformly distributed by estimated age.
  • HERVs possessing the retroviral env gene exhibit higher activity rates compared to those without, and few active HERVs encode complete retroviral proteins.