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Section: Genomics
RESEARCH ARTICLE
Published
2021-11-30
Cite as
Caroline Meguerditchian, Ayse
Ergun, Veronique Decroocq,
Marie Lefebvre and
Quynh-Trang Bui (2021)
Pipeline to detect the positional
and directional relationship
between transposable elements
and adjacent genes in host
genome, Peer Community
Journal, 1: e48.
Correspondence
marie.lefebvre@inrae.fr
quynh-trang.bui@inrae.fr
Peer-review
Peer reviewed and
recommended by
PCI Genomics,
https://doi.org/10.24072/pci.
genomics.100010
This article is licensed
under the Creative Commons
Attribution 4.0 License.
Pipeline to detect the positional
and directional relationship
between transposable elements
and adjacent genes in host
genome
Caroline Meguerditchian1, Ayse Ergun1, Veronique
Decroocq ,1, Marie Lefebvre ,1, and Quynh-Trang
Bui ,1
Volume 1 (2021), article e48
https://doi.org/10.24072/pcjournal.57
Abstract
Understanding the relationship between transposable elements (TEs) and their clos-
est positional genes in the host genome is a key point to explore their poten-
tial role in genome evolution. Transposable elements can regulate and affect gene
expression not only because of their mobility within the genome but also be-
cause of their transcriptional activity. A comprehensive knowledge of structural or-
ganization between transposable elements and neighboring genes is important to
study TE functional role in gene regulation. We implemented a pipeline which is
capable to reveal the positional and directional relationship between TEs and ad-
jacent gene distribution in the host genome. Our tool is freely available here:
https://github.com/marieBvr/TEs_genes_relationship_pipeline
1UMR 1332 Biologie du Fruit et Pathologie, INRAE, University of Bordeaux, UMR BFP, Villenave-d’Ornon,
France
Introduction 
Transposable elements (TEs) were first discovered in maize by Barbara McClintock in 1948 (McClintock 
1948). They are mobile repetitive DNA sequences, and correspond to a significant part of eukaryotic 
genomes. These elements make up nearly half of the human genome (Koning et al. 2011), and about 85% 
of the maize genome (Anderson et al. 2019). There are many different types and structures of TEs (Tollis, 
Boissinot 2012), but they can be divided into two major classes: Class I contains elements called 
retrotransposons and Class II groups DNA transposon elements. These two classes are distinguished by 
their transposition mechanisms. The retrotransposons are firstly transcribed into RNA then 
retrotranscribed into double-stranded sequences to integrate into the genome, whereas the DNA 
transposons achieve their mobility based on an enzyme called transposase, which helps to catalyze DNA 
elements from its original location to insert into a new site within the genome. Due to their transposition 
and the act of transcription itself, the TEs can regulate gene expression by modifying genes into 
pseudogenes, by triggering alternative splicing or by modifying epigenetic marks (Bui, Grandbastien 2012; 
Cho, Paszkowski 2017; Percharde et al. 2018; Petri et al. 2019). Experimental findings hint that TE insertions 
are targeted beyond the apparent mechanistic necessity of open chromatin (Mourier et al. 2014). Other 
examples have argued the possibility that more complex molecular mechanisms have evolved and made 
evident that TEs are an integral part of the regulatory toolkit of the genome (Slotkin, Martienssen 2007; 
Drongitis et al. 2019). Studying the relationship between TEs and the surrounding genes will help determine 
the role of TEs in the many layers of gene expression regulation as well as their contribution to the plasticity 
of eukaryotic genomes and the evolution and adaptation of species. Many existing tools allow the users to 
predict TE locations in the genome, such as LTRpred (Drost 2020), PiRATE (Berthelier et al. 2018) or REPET 
(Quesneville et al. 2005; Flutre et al. 2011). Others tools can reveal the relationship between TEs and host 
sequences at transcriptome level. LIONS (Babaian et al. 2019) is one of them that detects and quantifies 
transposable element initiated transcription from RNA-seq. Another software that can provide the 
screening and selection of potentially important genomic repeats is GREAM (Genomic Repeat Element 
Analyzer for Mammals) (Chandrashekar et al. 2015). However, this web-server can offer the TE-gene 
neighborhood only within mammalian species. There are no tools for revealing positional relationship 
between TEs and host coding sequences that can be applied for any species. In order to help biologists in 
studying TE impacts upon gene expression, we developed a pipeline allowing users to report the 
association between TEs and their closest genes, such as its location and distance to adjacent genes within 
the genome. Our dedicated command line tool can be used with TE annotation from any prediction tool as 
well as any genome assembly and annotation. This pipeline also provides a graph visualization in R. 
Materials and methods 
1.1 General workflow 
Two input files are needed, a GFF file annotation of the host genome and a TSV file containing 
information about TE annotation. The Apricot dataset (Prunus mandshurica) and two different TE 
annotations were used to implement our pipeline: TE annotation from the REPET package and LTR 
retrotransposon annotation from LTRpred software. The gene and TE annotation files are firstly optimized 
for data analysis, by removing duplicate lines and sorting out by chromosome and by start position in 
ascending order. Then, the files are processed with Python in order to analyze the relationship between 
nearest genes. Our pipeline could detect and report all scenarios that could occur between TEs and the 
closest genes in the host genome as shown in the Figure 1. The Python output result is then used in R to 
create tables and graphs to visualize TE-gene relationships. The whole workflow is descripted in Figure 2. 
 
2 Caroline Meguerditchian et al.
Peer Community Journal, Vol. 1 (2021), article e48 https://doi.org/10.24072/pcjournal.57
 
Figure 1. Scenarios of relationship between TEs and the closest genes in the host genome. 
 
 
Figure 2. The workflow of the pipeline comprises three different steps: preprocessing, processing and 
statistical analysis. 
1.2 Implementation 
The first part of the algorithm was written using Python 3.7. The program contains six functions aiming 
to detect genes surrounding any TE, as well as other functions allowing to read the input data and to write 
the output in a TSV file. Here is a summary of those functions: 
• check upstream gene: this function returns the closest gene to the TE positioned in an upstream 
location. Examples from Figure 1: Gene1-TE1; Gene4-TE5/TE6. 
Caroline Meguerditchian et al. 3
Peer Community Journal, Vol. 1 (2021), article e48 https://doi.org/10.24072/pcjournal.57
• check downstream gene: this function returns the closest gene to the TE positioned in a 
downstream location. Examples from Figure 1: Gene9-TE10; Gene11-TE11. 
• check upstream overlap gene: this function returns genes with at least one base overlapping the 
upstream part of the TE. Example from Figure 1: Gene1-TE1. 
• check downstream overlap gene: this function returns genes with at least one base overlapping 
the downstream part of the TE. Example from Figure 1: Gene9-TE10. 
• check subset superset gene: this function searches for genes, which are either a subset (Gene7-
TE9) or a superset (Gene6-TE7/TE8) of the TE. 
• calculate distance: this function allows to calculate the distance between a TE and its closest gene. 
• write data: this function writes an output file containing all the information about the genes 
detected by the previous functions. 
The python output is a TSV file containing the TE-gene relationship information based on the physical 
structure of the genome as descripted above. For each case, the start position, end position, ID and strand 
information are recorded. The TE ID, type, strand and position are also reported. Graphical visualization of 
the data is obtained by R scripts with the 4.0.2 version. Using the output file from the Python script, 
descriptive statistics of the relation between TEs and genes in the genome are presented. The output of 
the R scripts is graphs and CSV files containing the values counted by the algorithm. There are three R 
scripts allowing users to report three different statistics: 
• TE statistics, which show how many TEs and what types of TEs are present in the file and the 
distribution of TEs between those types. 
• Overlap statistics, which show how many TEs have an overlap with genes, both upstream and 
downstream. 
• Distance statistics, which show the number of TEs with an upstream or downstream gene within 
0-500 bp, 500-1000 bp, 1000-2000 bp and more than 2000 bp intervals. 
The genes reported as upstream or downstream of TEs in R scripts were defined based on the TEs’ 
strand as described in Figure 3. 
 
Figure 3. Representation of downstream and upstream genes based on autonomous TEs’ strand. 
1.3 Operation 
Our workflow requires R 4.0.2 or upper, Python 3.7 and can be run on any operating system with 
common specifications (1Go disk space, 4Go RAM, multicore CPU is recommended). 
Use case 
In order to explain how the program works, we will use two example files named gene_testing_data 
and transposon_testing_data, which gather all possible scenarios of the position of TEs relative to their 
closest genes. These two input files can be downloaded from the github page. The following command 
must be run in order to execute the program: 
(1) python3 Multiprocessing/Create_Data_multipro.py \ 
-g data/Gene_testing_data.tsv \ 
-te data/Transposon_testing_data.tsv \ 
-o result/output_TE.tsv 
4 Caroline Meguerditchian et al.
Peer Community Journal, Vol. 1 (2021), article e48 https://doi.org/10.24072/pcjournal.57
 
The -g argument is used to provide the gene file, the -te is used to come up with the TE file and the -o 
to supply the name of the result file. The output file is in TSV format. Once this file has been obtained, the 
R analysis can be run by adapting the following script line:  
(2) Rscript Rscript/number_te.r \ 
-f result/output_TE.tsv \ 
-o result/count_TE_transposons.pdf 
 
(3) Rscript Rscript/Overlap_counting.r \ 
-f result/output_TE.tsv \ 
-p result/overlap_TE_results.pdf \ 
-o result/overlap_TE_results.csv 
 
(4) Rscript Rscript/Distance_counting.r \ 
-f result/output_TE.tsv \ 
-p result/distance_TE_results.pdf \ 
-o result/distance_TE_results.csv 
The R scripts will provide output files with the statistics regarding overlapping genes and LTR 
retrotransposons as shown in Figure 4, or distance between LTR retrotransposons and genes as shown in 
Figure 5, in table format as well as graph visualization. 
 
Figure 4. Number of sense LTR retransposons (strand+) and antisense LTR retransposons (strand-) 
with a downstream-overlap gene and upstream-overlap gene in Prunus mandshurica. 
Caroline Meguerditchian et al. 5
Peer Community Journal, Vol. 1 (2021), article e48 https://doi.org/10.24072/pcjournal.57
 
Figure 5. Number of sense LTR retransposons (strand+) and antisense LTR retransposons (strand-) 
with an adjacent upstream gene in Prunus mandshurica. 
Conclusion 
Transposable elements are repetitive DNA sequences that have the ability to move within the genome. 
These mobile elements can play an important role in gene regulation and have a large impact on genome 
evolution. We have developed the first pipeline, which can report directly the relationship between TEs 
and its nearest genes among the genome. The accuracy of the tool has been verified by using a test dataset 
and running on two different TE annotation softwares. This pipeline could be useful to subsequently 
analyze potential effects of TEs on gene expression as well as on specific gene function. 
Acknowledgements 
We thank the INRAE BFP and the University of Bordeaux for the administrative support. Version 4 of 
this preprint has been peer-reviewed and recommended by Peer Community In Genomics 
(https://doi.org/10.24072/pci.genomics.100010). 
Conflict of interest disclosure 
No competing interests were disclosed. 
Data, script and code availability 
Up-to-date source code and tutorials are available at:  
https://github.com/marieBvr/TEs_genes_relationship_pipeline. Archived source code as at time of 
publication are available from: https://doi.org/10.5281/zenodo.4442377. 
The raw data of the Apricot genome, Prunus mandshurica, are available on the European Nucleotide 
Archive (ENA) under the project name PRJEB42606: 
https://www.ebi.ac.uk/ena/browser/view/PRJEB42606 and the assembled genome is available at 
https://www.rosaceae.org/node/10811682. 
6 Caroline Meguerditchian et al.
Peer Community Journal, Vol. 1 (2021), article e48 https://doi.org/10.24072/pcjournal.57
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