Transposable elements (TEs) comprise vast parts of eukaryotic genomes. In the past, TEs were seen as selfish mobile elements capable of populating a host genome to increase their chances for survival. By doing so they leave traces of junk DNA in host genomes that are usually regarded as by-products when sequencing, assembling, and annotating new genomes.
However, this picture is slowly changing (Drost & Sanchez, 2019) and TEs have been shown to be involved in generating a diverse range of novel phenotypes.
Today, the de novo detection of transposable elements is performed by annotation tools which try to detect any type of repeated sequence, TE family, or remnand DNA loci that can be associated with a known transposable element within a genome assembly. The main goal of such efforts is to retrieve a maximum amount of loci that can be associated with TEs. If successful, such annotation can then be used to mask host genomes and to perform classic (phylo-)genomics studies focusing on host genes.
More than 300 repeat and TE annotation tools have been developed so far. Most of them are designed and optimized to annotate either the entire repeat space or specific superfamilies of TEs and their DNA remnants.
The LTRpred pipeline has a different goal than all other annotation tools. It focuses particularly on LTR retrotransposons and aims to annotate only functional and potentially mobile elements. Such type of annotation is crucial for studying retrotransposon activity in eukaryotic genomes and to understand whether specific retrotransposon families can be activated artificially and harnessed to mutagenize genomes at much faster speed.
LTRpred will take any genome assembly file in
fasta format as input and will generate a detailed annotation of functional and potentially mobile LTR retrotransposons.
Users can consult a comprehensive Introduction to the
LTRpred pipeline to get familiar with the tool.
The fastest way to generate a LTR retrotransposon prediction for a genome of interest (after installing all prerequisite command line tools) is to use the
LTRpred() function and relying on the default parameters. In the following example, a LTR transposon prediction is performed for parts of the Human Y chromosome.
When running your own genome, please specify
genome.file = "path/to/your/genome.fasta instead of
system.file(..., package = "LTRpred"). The command
system.file(..., package = "LTRpred") merely references the path to the example file stored in the LTRpred package itself.
Please cite the following paper when using
LTRpred for your own research:
HG Drost. LTRpred: de novo annotation of intact retrotransposons. Journal of Open Source Software, 5(50), 2170 (2020).
This tutorial introduces users to
Users can also read the tutorials within (RStudio) :
You can also find a list of all available
LTRpred functions here: https://hajkd.github.io/LTRpred/reference/index.html
- Z Wang & D Baulcombe. Transposon age and non-CG methylation. Nature Communications, 11, 1221 (2020).
J Cho, M Benoit, M Catoni, HG Drost, A Brestovitsky, M Oosterbeek and J Paszkowski. Sensitive detection of pre-integration intermediates of LTR retrotransposons in crop plants. Nature Plants, 5, 26-33 (2019).
M Benoit, HG Drost, M Catoni, Q Gouil, S Lopez-Gomollon, DC Baulcombe, J Paszkowski. Environmental and epigenetic regulation of Rider retrotransposons in tomato. PloS Genetics, 15(9): e1008370 (2019).
- Nguinkal et al. The First Highly Contiguous Genome Assembly of Pikeperch (Sander lucioperca), an Emerging Aquaculture Species in Europe Genes, 0(9), 708 (2019).
E Cerruti, C Gisbert, HG Drost, D Valentino, E Portis, L Barchi, J Prohens, S Lanteri, C Comino, M Catoni. Epigenetic bases of grafting-induced vigour in eggplant. bioaRxiv (2019).
P Gan, R Hiroyama, A Tsushima, S Masuda et al. Subtelomeric regions and a repeat-rich chromosome harbor multicopy effector gene clusters with variable conservation in multiple plant pathogenic Colletotrichum species bioRxiv (2020)
I would be very happy to learn more about potential improvements of the concepts and functions provided in this package.
Furthermore, in case you find some bugs or need additional (more flexible) functionality of parts of this package, please let me know:
I would like to thank the Paszkowski team for incredible support and motivating discussions that led to the realization of this project.