Email updates

Keep up to date with the latest news and content from BMC Bioinformatics and BioMed Central.

Open Access Highly Accessed Methodology article

An automated homology-based approach for identifying transposable elements

Ryan C Kennedy12*, Maria F Unger13, Scott Christley4, Frank H Collins123 and Gregory R Madey12

Author Affiliations

1 Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN, USA

2 Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN, USA

3 Department of Biological Sciences, University of Notre Dame, Notre Dame, IN, USA

4 Department of Surgery, University of Chicago, Chicago, IL, USA

For all author emails, please log on.

BMC Bioinformatics 2011, 12:130  doi:10.1186/1471-2105-12-130

Published: 3 May 2011

Abstract

Background

Transposable elements (TEs) are mobile sequences found in nearly all eukaryotic genomes. They have the ability to move and replicate within a genome, often influencing genome evolution and gene expression. The identification of TEs is an important part of every genome project. The number of sequenced genomes is rapidly rising, and the need to identify TEs within them is also growing. The ability to do this automatically and effectively in a manner similar to the methods used for genes is of increasing importance. There exist many difficulties in identifying TEs, including their tendency to degrade over time and that many do not adhere to a conserved structure. In this work, we describe a homology-based approach for the automatic identification of high-quality consensus TEs, aimed for use in the analysis of newly sequenced genomes.

Results

We describe a homology-based approach for the automatic identification of TEs in genomes. Our modular approach is dependent on a thorough and high-quality library of representative TEs. The implementation of the approach, named TESeeker, is BLAST-based, but also makes use of the CAP3 assembly program and the ClustalW2 multiple sequence alignment tool, as well as numerous BioPerl scripts. We apply our approach to newly sequenced genomes and successfully identify consensus TEs that are up to 99% identical to manually annotated TEs.

Conclusions

While TEs are known to be a major force in the evolution of genomes, the automatic identification of TEs in genomes is far from mature. In particular, there is a lack of automated homology-based approaches that produce high-quality TEs. Our approach is able to generate high-quality consensus TE sequences automatically, requiring the user to only provide a few basic parameters. This approach is intentionally modular, allowing researchers to use components separately or iteratively. Our approach is most effective for TEs with intact reading frames. The implementation, TESeeker, is available for download as a virtual appliance, while the library of representative TEs is available as a separate download.