title: Self-organizing map-based discovery and visualization of human endogenous retroviral sequence groups.
abstract: About 8 per cent of the human genome consists of human endogenous retroviral sequences (HERVs), which are remains from ancient infections. The HERVs may give rise to transcripts or affect the expression of human genes. The first step in understanding HERV function is to classify HERVs into families. In this work we study the relationships of existing HERV families and detect potentially new HERV families. A Median Self-Organizing Map (SOM), a SOM for non-vectorial data, is used to group and visualize a collection of 3661 HERVs. The SOM-based analysis is complemented with estimates of the reliability of the results. A novel trustworthiness visualization method is used to estimate which parts of the SOM visualization are reliable and which not. The reliability of extracted interesting HERV groups is verified by a bootstrap procedure suitable for SOM visualization-based analysis. The SOM detects a group of epsilonretroviral sequences and a group of ERV9, HERVW, and HUERSP3 sequences which suggests that ERV9 and HERVW sequences may have a common origin.
authors: Department of Computer Science, University of Helsinki, P.O. Box 68, FI-00014 University of Helsinki, Finland. merja.oja@hut.fi
authors: Merja
authors: M
authors: Oja
authors: Göran O
authors: GO
authors: Sperber
authors: Jonas
authors: J
authors: Blomberg
authors: Samuel
authors: S
authors: Kaski
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D002874:Chromosome Mapping / Q000379:methods*; D004247:DNA / Q000235:genetics*; D020077:Endogenous Retroviruses / Q000235:genetics*; D015894:Genome, Human; D006801:Humans; D010802:Phylogeny; D015203:Reproducibility of Results
chemical_list: D004247:DNA