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 600 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.
# retrieve docker image from dockerhub docker pull drostlab/ltrpred # run ltrpred container docker run --rm -ti drostlab/ltrpred # start R prompt within ltrpred container ~:/app# R
# retrieve docker image from dockerhub docker pull drostlab/ltrpred_rstudio # run ltrpred container docker run -e PASSWORD=ltrpred --rm -p 8787:8787 -ti drostlab/ltrpred_rstudio
RStudio and interact with the container go to your standard web browser and type in the following URL:
http://localhost:8787 Username: rstudio Password: ltrpred
Users can choose a custom password if they wish.
Within RStudio you can now run the example:
Users can exit the container by pressing
Ctrl + c multiple times.
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).
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.
# Perform de novo LTR transposon prediction for the Human Y chromosome LTRpred::LTRpred(genome.file = system.file("Hsapiens_ChrY.fa", package = "LTRpred"))
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.
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).
JH Collins, KW Keating, TR Jones, S Balaji, CB Marsan et al. Engineered yeast genomes accurately assembled from pure and mixed samples. Nature communications, 12, 1485 (2021).
H Kundariya et al. MSH1-induced heritable enhanced growth vigor through grafting is associated with the RdDM pathway in plants Nature Communications, 11, 5343 (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)
J Wang et al. Gigantic Genomes Can Provide Empirical Tests of TE Dynamics Models–An Example from Amphibians. bioRxiv, (2020).
Y Ayukawa et al. A pair of effectors encoded on a conditionally dispensable chromosome of Fusarium oxysporum suppress host-specific immunity. bioRxiv, (2020).
C Meguerditchian, A Ergun, V Decroocq, M Lefebvre et al. Pipeline to detect the relationship between transposable elements and adjacent genes in host genome bioRxiv, (2021).
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: