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. 2020 Dec 22;36(20):4991-4999.
doi: 10.1093/bioinformatics/btaa632.

TE-greedy-nester: structure-based detection of LTR retrotransposons and their nesting

Matej Lexa  1   2 , Pavel Jedlicka  1 , Ivan Vanat  2 , Michal Cervenansky  1   2 , Eduard Kejnovsky  1
Affiliations

TE-greedy-nester: structure-based detection of LTR retrotransposons and their nesting

Matej Lexa et al. Bioinformatics. .

Abstract

Motivation: Transposable elements (TEs) in eukaryotes often get inserted into one another, forming sequences that become a complex mixture of full-length elements and their fragments. The reconstruction of full-length elements and the order in which they have been inserted is important for genome and transposon evolution studies. However, the accumulation of mutations and genome rearrangements over evolutionary time makes this process error-prone and decreases the efficiency of software aiming to recover all nested full-length TEs.

Results: We created software that uses a greedy recursive algorithm to mine increasingly fragmented copies of full-length LTR retrotransposons in assembled genomes and other sequence data. The software called TE-greedy-nester considers not only sequence similarity but also the structure of elements. This new tool was tested on a set of natural and synthetic sequences and its accuracy was compared to similar software. We found TE-greedy-nester to be superior in a number of parameters, namely computation time and full-length TE recovery in highly nested regions.

Availability and implementation: http://gitlab.fi.muni.cz/lexa/nested.

Supplementary information: Supplementary data are available at Bioinformatics online.

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Figures

Fig. 1.
Fig. 1.
Nesting algorithm essentials. (A) Algorithm overview showing data processing in TE-greedy-nester; (B) Scoring graph structure used to evaluate structural completeness of LTR retrotransposon candidates (shown as SCORE CANDIDATES in panel A). Any deviation from prescribed order of structural components (full arrows) is penalized (dotted arrows)
Fig. 2.
Fig. 2.
An example of TE-greedy-nester output visualized using Annotation Sketch from the Genome Tools (Gremme et al., 2013) software suite (Command: gt sketch output.png example.gff)
Fig. 3.
Fig. 3.
Sensitivity, specificity, accuracy and precision of TE-greedy-nester and comparable software on synthetic and biological data; (A) zea_10%; (B) zea_90%; (C) zea_matryoshka; (D) oryza_10%, (E) oryza_90% and (F) oryza_matryoshka
Fig. 4.
Fig. 4.
Number of correctly identified TEs as a function of length tolerance by the four software tools; (A) zea_10%; (B) zea_90%; (C) zea_matryoshka; (D) oryza_10%, (E) oryza_90% and (F) oryza_matryoshka
Fig. 5.
Fig. 5.
Number of correctly identified TEs at different nesting levels by the four software tools; (A) zea_10%; (B) zea_90%; (C) zea_matryoshka; (D) oryza_10%, (E) oryza_90% and (F) oryza_matryoshka
Fig. 6.
Fig. 6.
Number of correctly identified TEs at different nesting levels by the four software tools in biological data; (A) zea_adh1 and (B) zea_2MB
See this image and copyright information in PMC

References

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