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Self-organizing map-based discovery and visualization of human endogenous retroviral sequence groups
Merja Oja1, Göran O Sperber, Jonas Blomberg
1Department of Computer Science, University of Helsinki, P.O. Box 68, FI-00014 University of Helsinki, Finland. merja.oja@hut.fi
International Journal of Neural Systems
|July 14, 2005
Summary
Human endogenous retroviral sequences (HERVs) comprise 8% of the human genome. This study classifies HERVs, identifying potential new families and suggesting a common origin for ERV9 and HERVW sequences.
Area of Science:
- Genomics
- Bioinformatics
- Evolutionary Biology
Background:
- Human endogenous retroviral sequences (HERVs) constitute a significant portion of the human genome, originating from ancient retroviral infections.
- Understanding HERV classification is crucial for elucidating their potential roles in gene expression and cellular function.
Purpose of the Study:
- To analyze the relationships among existing human endogenous retroviral sequence (HERV) families.
- To identify potentially novel HERV families within the human genome.
- To visualize and group a large dataset of HERVs for relationship analysis.
Main Methods:
- Utilized a Median Self-Organizing Map (SOM) for grouping and visualizing 3661 HERVs.
- Employed a novel trustworthiness visualization method to assess the reliability of SOM results.
- Applied a bootstrap procedure to validate the reliability of identified HERV groups.
Main Results:
- The SOM analysis successfully grouped diverse HERV sequences.
- Identified a distinct group of epsilonretroviral sequences.
- Detected a cluster of ERV9, HERVW, and HUERSP3 sequences, indicating a potential common evolutionary origin.
Conclusions:
- The study provides a robust classification framework for HERVs using SOM.
- The findings suggest a shared ancestry for ERV9 and HERVW HERV families.
- This research lays the groundwork for further investigation into HERV functions and evolutionary history.