Assessment of genetic variation for the LINE-1 retrotransposon from next generation sequence data

Eric Rouchka, Diego E. Montoya-Durango, Vilius Stribinskis, Kenneth Ramos, Ted Kalbfleisch

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


Background: In humans, copies of the Long Interspersed Nuclear Element 1 (LINE-1) retrotransposon comprise 21% of the reference genome, and have been shown to modulate expression and produce novel splice isoforms of transcripts from genes that span or neighbor the LINE-1 insertion site.Results: In this work, newly released pilot data from the 1000 Genomes Project is analyzed to detect previously unreported full length insertions of the retrotransposon LINE-1. By direct analysis of the sequence data, we have identified 22 previously unreported LINE-1 insertion sites within the sequence data reported for a mother/father/daughter trio.Conclusions: It is demonstrated here that next generation sequencing data, as well as emerging high quality datasets from individual genome projects allow us to assess the amount of heterogeneity with respect to the LINE-1 retrotransposon amongst humans, and provide us with a wealth of testable hypotheses as to the impact that this diversity may have on the health of individuals and populations.

Original languageEnglish
Article numberS12
JournalBMC Bioinformatics
Issue numberSUPPL. 9
StatePublished - Oct 28 2010

Bibliographical note

Funding Information:
All authors would like to acknowledge the support of NIEHS Center Grant P30ES01444. TK and ER would like to acknowledge support from the DOE grant DE-EM0000197, and the KY IDeA Networks of Biomedical Research Excellence grant 2P20RR016481-09. TK would like to acknowledge support from the 2008 NARSAD Young Investigator Award. This article has been published as part of BMC Bioinformatics Volume 11 Supplement 9, 2010: Selected Proceedings of the 2010 AMIA Summit on Translational Bioinformatics. The full contents of the supplement are available online at

ASJC Scopus subject areas

  • Structural Biology
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Applied Mathematics


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