Identifying repeats and transposable elements in sequenced genomes: how to find your way through the dense forest of programs

The production of genome sequences has led to another important advance in their annotation, which is closely linked to the exact determination of their content in terms of repeats, among which are transposable elements (TEs). The evolutionary implications and the presence of coding regions in some...

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Bibliographic Details
Published in:Heredity 2010-06, Vol.104 (6), p.520-533
Main Author: Lerat, E
Format: Article
Language:English
Subjects:
Algorithms
Animals
Bioinformatics
Biologists
Biomedical and Life Sciences
Biomedicine
Cytogenetics
DNA Transposable Elements
Ecology
Eukaryota - genetics
Evolutionary Biology
Genome
Genomics
Genomics - methods
Human Genetics
Humans
Plant Genetics and Genomics
Plants - genetics
Repetitive Sequences, Nucleic Acid
review
Software
Online Access:Get full text
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Summary:The production of genome sequences has led to another important advance in their annotation, which is closely linked to the exact determination of their content in terms of repeats, among which are transposable elements (TEs). The evolutionary implications and the presence of coding regions in some TEs can confuse gene annotation, and also hinder the process of genome assembly, making particularly crucial to be able to annotate and classify them correctly in genome sequences. This review is intended to provide an overview as comprehensive as possible of the automated methods currently used to annotate and classify TEs in sequenced genomes. Different categories of programs exist according to their methodology and the repeat, which they can identify. I describe here the main characteristics of the programs, their main goals and the difficulties they can entail. The drawbacks of the different methods are also highlighted to help biologists who are unfamiliar with algorithmic methods to understand this methodology better. Globally, using several different programs and carrying out a cross comparison of their results has the best chance of finding reliable results as any single program. However, this makes it essential to verify the results provided by each program independently. The ideal solution would be to test all programs against the same data set to obtain a true comparison of their actual performance.
ISSN:0018-067X
1365-2540
DOI:10.1038/hdy.2009.165