Classification Of Transposable Elements Research Articles

Earl Grey: A Fully Automated User-Friendly Transposable Element Annotation and Analysis Pipeline.

Transposable elements (TEs) are major components of eukaryotic genomes and are implicated in a range of evolutionary processes. Yet, TE annotation and characterization remain challenging, particularly for nonspecialists, since existing pipelines are typically complicated to install, run, and extract data from. Current methods of automated TE annotation are also subject to issues that reduce overall quality, particularly (i) fragmented and overlapping TE annotations, leading to erroneous estimates of TE count and coverage, and (ii) repeat models represented by short sections of total TE length, with poor capture of 5' and 3' ends. To address these issues, we present Earl Grey, a fully automated TE annotation pipeline designed for user-friendly curation and annotation of TEs in eukaryotic genome assemblies. Using nine simulated genomes and an annotation of Drosophila melanogaster, we show that Earl Grey outperforms current widely used TE annotation methodologies in ameliorating the issues mentioned above while scoring highly in benchmarking for TE annotation and classification and being robust across genomic contexts. Earl Grey provides a comprehensive and fully automated TE annotation toolkit that provides researchers with paper-ready summary figures and outputs in standard formats compatible with other bioinformatics tools. Earl Grey has a modular format, with great scope for the inclusion of additional modules focused on further quality control and tailored analyses in future releases.

Comparative Analysis of Transposable Elements Reveals the Diversity of Transposable Elements in Decapoda and Their Effects on Genomic Evolution.

Transposable elements (TEs) are mobile genetic elements that exist in the host genome and exert considerable influence on the evolution of the host genome. Since crustaceans, including decapoda, are considered ideal models for studying the relationship between adaptive evolution and TEs, TEs were identified and classified in the genomes of eight decapoda species and one diplostraca species (as the outgroup) using two strategies, namely homology-based annotation and de novo annotation. The statistics and classification of TEs showed that their proportion in the genome and their taxonomic composition in decapoda were different. Moreover, correlation analysis and transcriptome data demonstrated that there were more PIF-Harbinger TEs in the genomes of Eriocheir sinensis and Scylla paramamosain, and the expression patterns of PIF-Harbingers were significantly altered under air exposure stress conditions. These results signaled that PIF-Harbingers expanded in the genome of E. sinensis and S. paramamosain and might be related to their air exposure tolerance levels. Meanwhile, sequence alignment revealed that some Jockey-like sequences (JLSs) with high similarity to specific regions of the White spot syndrome virus (WSSV) genome existed in all eight decapod species. At the same time, phylogenetic comparison exposed that the phylogenetic tree constructed by JLSs was not in agreement with that of the species tree, and the distribution of each branch was significantly different. The abovementioned results signaled that these WSSV-specific JLSs might transfer horizontally and contribute to the emergence of WSSV. This study accumulated data for expanding research on TEs in decapod species and also provided new insights and future direction for the breeding of stress-resistant and disease-resistant crab breeds.

Genomic object detection: An improved approach for transposable elements detection and classification using convolutional neural networks.

Analysis of eukaryotic genomes requires the detection and classification of transposable elements (TEs), a crucial but complex and time-consuming task. To improve the performance of tools that accomplish these tasks, Machine Learning approaches (ML) that leverage computer resources, such as GPUs (Graphical Processing Unit) and multiple CPU (Central Processing Unit) cores, have been adopted. However, until now, the use of ML techniques has mostly been limited to classification of TEs. Herein, a detection-classification strategy (named YORO) based on convolutional neural networks is adapted from computer vision (YOLO) to genomics. This approach enables the detection of genomic objects through the prediction of the position, length, and classification in large DNA sequences such as fully sequenced genomes. As a proof of concept, the internal protein-coding domains of LTR-retrotransposons are used to train the proposed neural network. Precision, recall, accuracy, F1-score, execution times and time ratios, as well as several graphical representations were used as metrics to measure performance. These promising results open the door for a new generation of Deep Learning tools for genomics. YORO architecture is available at https://github.com/simonorozcoarias/YORO.

An Overview of Best Practices for Transposable Element Identification, Classification, and Annotation in Eukaryotic Genomes.

Transposable elements (TEs) exert an increasingly diverse spectrum of influences on eukaryotic genome structure, function, and evolution. A deluge of genomic, transcriptomic, and proteomic data provides the foundation for turning essentially any non-model eukaryotic species into an emerging model to study any and all aspects of organismal biology, ultimately shaping future directions for biomedical, environmental, and biodiversity research. However, identification and annotation of the mobile genome component still lags behind the standards accepted for host gene annotation. To achieve the objective of providing every genome project with a comprehensive description of its mobilome component in addition to the standard genic and transcriptomic datasets, each step of TE identification, classification, and annotation should be focused on improving TE boundary designation, reducing identification error rates, and providing accurate information on the type and integrity of TE insertions. Here, we offer practical advice for generating TE models in de novo assemblies for non-model organisms, provide step-by-step instructions to guide inexperienced TE annotators through some of the commonly utilized TE analysis pipelines, and entertain suggestions for tool improvement which could be implemented by interested developers.

Assembly-Free Detection and Quantification of Transposable Elements with dnaPipeTE.

The detection and quantification of transposable elements (TE) are notoriously challenging despite their relevance in evolutionary genomics and molecular ecology. The main hurdle is caused by the dependence of numerous tools on genome assemblies, whose level of completion directly affects the comparability of the results across species or populations. dnaPipeTE, whose use is demonstrated here, tackles this issue by directly performing TE detection, classification, and quantification from unassembled short reads. This chapter details all the required steps to perform a comparative analysis of the TE content between two related species, starting from the installation of a recently containerized version of the program to the post-processing of the outputs.

Revealing Landscapes of Transposable Elements in Apis Species by Meta-Analysis

Simple SummaryStudies on the detection of transposable elements and their annotations have posed several challenges. For example, simple comparisons of transposable elements in different species using different methods can lead to misinterpretations. Thus, assembling data for transposable elements analyzed by unified methods is important for comparison purposes. Therefore, we performed a meta-analysis of transposable elements identified using genome datasets from five Apis species (11 sets of genome data) and specific software to detect the transposable elements, which revealed the landscapes of transposable elements. We examined the types and locations of transposable elements in the Apis genomes. The landscapes of transposable elements showed that four to seven transposable element families among 13 and 15 families of TEs detected in classes I and II, respectively, consisted mainly of Apis-associated transposable elements. These families include DNA/TcMar-Mariner and DNA/CMC-EnSpm. In addition, more DNA/TcMar-Mariner consensus sequences and copies were detected in Apis mellifera than in other Apis species. These data suggest that TcMar-Mariner might exert A. mellifera-specific effects in the host A. mellifera species. Our landscape data provide new insights into Apis transposable elements; furthermore, detailed analyses of our data could pave the way for new biological insights in this field.Transposable elements (TEs) are grouped into several families with diverse sequences. Owing to their diversity, studies involving the detection, classification, and annotation of TEs are difficult tasks. Moreover, simple comparisons of TEs among different species with different methods can lead to misinterpretations. The genome data of several honey bee (Apis) species are available in public databases. Therefore, we conducted a meta-analysis of TEs, using 11 sets of genome data for Apis species, in order to establish data of “landscape of TEs”. Consensus TE sequences were constructed and their distributions in the Apis genomes were determined. Our results showed that TEs belonged to four to seven TE families among 13 and 15 families of TEs detected in classes I and II respectively mainly consisted of Apis TEs and that more DNA/TcMar-Mariner consensus sequences and copies were present in all Apis genomes tested. In addition, more consensus sequences and copy numbers of DNA/TcMar-Mariner were detected in Apis mellifera than in other Apis species. These results suggest that TcMar-Mariner might exert A. mellifera-specific effects on the host A. mellifera species. In conclusion, our unified approach enabled comparison of Apis genome sequences to determine the TE landscape, which provide novel evolutionary insights into Apis species.

Specificities and Dynamics of Transposable Elements in Land Plants.

Simple SummaryTransposable elements are dynamic components of plant genomes, and display a high diversity of lineages and distribution as the result of evolutionary driving forces and overlapping mechanisms of genetic and epigenetic regulation. They are now regarded as main contributors for genome evolution and function, and important regulators of endogenous gene expression. In this review, we survey recent progress and current challenges in the identification and classification of transposon lineages in complex plant genomes, highlighting the molecular specificities that may explain the expansion and diversification of mobile genetic elements in land plants.Transposable elements (TEs) are important components of most plant genomes. These mobile repetitive sequences are highly diverse in terms of abundance, structure, transposition mechanisms, activity and insertion specificities across plant species. This review will survey the different mechanisms that may explain the variability of TE patterns in land plants, highlighting the tight connection between TE dynamics and host genome specificities, and their co-evolution to face and adapt to a changing environment. We present the current TE classification in land plants, and describe the different levels of genetic and epigenetic controls originating from the plant, the TE itself, or external environmental factors. Such overlapping mechanisms of TE regulation might be responsible for the high diversity and dynamics of plant TEs observed in nature.

Comparative Genomics: Insights on the Pathogenicity and Lifestyle of Rhizoctonia solani.

Proper management of agricultural disease is important to ensure sustainable food security. Staple food crops like rice, wheat, cereals, and other cash crops hold great export value for countries. Ensuring proper supply is critical; hence any biotic or abiotic factors contributing to the shortfall in yield of these crops should be alleviated. Rhizoctonia solani is a major biotic factor that results in yield losses in many agriculturally important crops. This paper focuses on genome informatics of our Malaysian Draft R. solani AG1-IA, and the comparative genomics (inter- and intra- AG) with four AGs including China AG1-IA (AG1-IA_KB317705.1), AG1-IB, AG3, and AG8. The genomic content of repeat elements, transposable elements (TEs), syntenic genomic blocks, functions of protein-coding genes as well as core orthologous genic information that underlies R. solani’s pathogenicity strategy were investigated. Our analyses show that all studied AGs have low content and varying profiles of TEs. All AGs were dominant for Class I TE, much like other basidiomycete pathogens. All AGs demonstrate dominance in Glycoside Hydrolase protein-coding gene assignments suggesting its importance in infiltration and infection of host. Our profiling also provides a basis for further investigation on lack of correlation observed between number of pathogenicity and enzyme-related genes with host range. Despite being grouped within the same AG with China AG1-IA, our Draft AG1-IA exhibits differences in terms of protein-coding gene proportions and classifications. This implies that strains from similar AG do not necessarily have to retain similar proportions and classification of TE but must have the necessary arsenal to enable successful infiltration and colonization of host. In a larger perspective, all the studied AGs essentially share core genes that are generally involved in adhesion, penetration, and host colonization. However, the different infiltration strategies will depend on the level of host resilience where this is clearly exhibited by the gene sets encoded for the process of infiltration, infection, and protection from host.

TERL: classification of transposable elements by convolutional neural networks.

Transposable elements (TEs) are the most represented sequences occurring in eukaryotic genomes. Few methods provide the classification of these sequences into deeper levels, such as superfamily level, which could provide useful and detailed information about these sequences. Most methods that classify TE sequences use handcrafted features such as k-mers and homology-based search, which could be inefficient for classifying non-homologous sequences. Here we propose an approach, called transposable elements pepresentation learner (TERL), that preprocesses and transforms one-dimensional sequences into two-dimensional space data (i.e., image-like data of the sequences) and apply it to deep convolutional neural networks. This classification method tries to learn the best representation of the input data to classify it correctly. We have conducted six experiments to test the performance of TERL against other methods. Our approach obtained macro mean accuracies and F1-score of 96.4% and 85.8% for superfamilies and 95.7% and 91.5% for the order sequences from RepBase, respectively. We have also obtained macro mean accuracies and F1-score of 95.0% and 70.6% for sequences from seven databases into superfamily level and 89.3% and 73.9% for the order level, respectively. We surpassed accuracy, recall and specificity obtained by other methods on the experiment with the classification of order level sequences from seven databases and surpassed by far the time elapsed of any other method for all experiments. Therefore, TERL can learn how to predict any hierarchical level of the TEs classification system and is about 20 times and three orders of magnitude faster than TEclass and PASTEC, respectively https://github.com/muriloHoracio/TERL. Contact:murilocruz@alunos.utfpr.edu.br.

Genomic re-assessment of the transposable element landscape of the potato genome.

We provide a comprehensive and reliable potato TE landscape, based on a wide variety of identification tools and integrative approaches, producing clear and ready-to-use outputs for the scientific community. Transposable elements (TEs) are DNA sequences with the ability to autoreplicate and move throughout the host genome. TEs are major drivers in stress response and genome evolution. Given their significance, the development of clear and efficient TE annotation pipelines has become essential for many species. The latest de novo TE discovery tools, along with available TEs from Repbase and sRNA-seq data, allowed us to perform a reliable potato TEs detection, classification and annotation through an open-source and freely available pipeline ( https://github.com/DiegoZavallo/TE_Discovery ). Using a variety of tools, approaches and rules, we were able to provide a clearly annotated of characterized TEs landscape. Additionally, we described the distribution of the different types of TEs across the genome, where LTRs and MITEs present a clear clustering pattern in pericentromeric and subtelomeric/telomeric regions respectively. Finally, we analyzed the insertion age and distribution of LTR retrotransposon families which display a distinct pattern between the two major superfamilies. While older Gypsy elements concentrated around heterochromatic regions, younger Copia elements located predominantly on euchromatic regions. Overall, we delivered not only a reliable, ready-to-use potato TE annotation files, but also all the necessary steps to perform de novo detection for other species.

A systematic review of the application of machine learning in the detection and classification of transposable elements.

BackgroundTransposable elements (TEs) constitute the most common repeated sequences in eukaryotic genomes. Recent studies demonstrated their deep impact on species diversity, adaptation to the environment and diseases. Although there are many conventional bioinformatics algorithms for detecting and classifying TEs, none have achieved reliable results on different types of TEs. Machine learning (ML) techniques can automatically extract hidden patterns and novel information from labeled or non-labeled data and have been applied to solving several scientific problems.MethodologyWe followed the Systematic Literature Review (SLR) process, applying the six stages of the review protocol from it, but added a previous stage, which aims to detect the need for a review. Then search equations were formulated and executed in several literature databases. Relevant publications were scanned and used to extract evidence to answer research questions.ResultsSeveral ML approaches have already been tested on other bioinformatics problems with promising results, yet there are few algorithms and architectures available in literature focused specifically on TEs, despite representing the majority of the nuclear DNA of many organisms. Only 35 articles were found and categorized as relevant in TE or related fields.ConclusionsML is a powerful tool that can be used to address many problems. Although ML techniques have been used widely in other biological tasks, their utilization in TE analyses is still limited. Following the SLR, it was possible to notice that the use of ML for TE analyses (detection and classification) is an open problem, and this new field of research is growing in interest.

Retrotransposons in Plant Genomes: Structure, Identification, and Classification through Bioinformatics and Machine Learning.

Transposable elements (TEs) are genomic units able to move within the genome of virtually all organisms. Due to their natural repetitive numbers and their high structural diversity, the identification and classification of TEs remain a challenge in sequenced genomes. Although TEs were initially regarded as “junk DNA”, it has been demonstrated that they play key roles in chromosome structures, gene expression, and regulation, as well as adaptation and evolution. A highly reliable annotation of these elements is, therefore, crucial to better understand genome functions and their evolution. To date, much bioinformatics software has been developed to address TE detection and classification processes, but many problematic aspects remain, such as the reliability, precision, and speed of the analyses. Machine learning and deep learning are algorithms that can make automatic predictions and decisions in a wide variety of scientific applications. They have been tested in bioinformatics and, more specifically for TEs, classification with encouraging results. In this review, we will discuss important aspects of TEs, such as their structure, importance in the evolution and architecture of the host, and their current classifications and nomenclatures. We will also address current methods and their limitations in identifying and classifying TEs.

RepetDB: a unified resource for transposable element references

BackgroundThanks to their ability to move around and replicate within genomes, transposable elements (TEs) are perhaps the most important contributors to genome plasticity and evolution. Their detection and annotation are considered essential in any genome sequencing project. The number of fully sequenced genomes is rapidly increasing with improvements in high-throughput sequencing technologies. A fully automated de novo annotation process for TEs is therefore required to cope with the deluge of sequence data.However, all automated procedures are error-prone, and an automated procedure for TE identification and classification would be no exception. It is therefore crucial to provide not only the TE reference sequences, but also evidence justifying their classification, at the scale of the whole genome. A few TE databases already exist, but none provides evidence to justify TE classification. Moreover, biological information about the sequences remains globally poor.ResultsWe present here the RepetDB database developed in the framework of GnpIS, a genetic and genomic information system. RepetDB is designed to store and retrieve detected, classified and annotated TEs in a standardized manner. RepetDB is an implementation with extensions of InterMine, an open-source data warehouse framework used here to store, search, browse, analyze and compare all the data recorded for each TE reference sequence. InterMine can display diverse information for each sequence and allows simple to very complex queries. Finally, TE data are displayed via a worldwide data discovery portal. RepetDB is accessible at urgi.versailles.inra.fr/repetdb.ConclusionsRepetDB is designed to be a TE knowledge base populated with full de novo TE annotations of complete (or near-complete) genome sequences. Indeed, the description and classification of TEs facilitates the exploration of specific TE families, superfamilies or orders across a large range of species. It also makes possible cross-species searches and comparisons of TE family content between genomes.

A machine learning based framework to identify and classify long terminal repeat retrotransposons.

Transposable elements (TEs) are repetitive nucleotide sequences that make up a large portion of eukaryotic genomes. They can move and duplicate within a genome, increasing genome size and contributing to genetic diversity within and across species. Accurate identification and classification of TEs present in a genome is an important step towards understanding their effects on genes and their role in genome evolution. We introduce TE-Learner, a framework based on machine learning that automatically identifies TEs in a given genome and assigns a classification to them. We present an implementation of our framework towards LTR retrotransposons, a particular type of TEs characterized by having long terminal repeats (LTRs) at their boundaries. We evaluate the predictive performance of our framework on the well-annotated genomes of Drosophila melanogaster and Arabidopsis thaliana and we compare our results for three LTR retrotransposon superfamilies with the results of three widely used methods for TE identification or classification: RepeatMasker, Censor and LtrDigest. In contrast to these methods, TE-Learner is the first to incorporate machine learning techniques, outperforming these methods in terms of predictive performance, while able to learn models and make predictions efficiently. Moreover, we show that our method was able to identify TEs that none of the above method could find, and we investigated TE-Learner’s predictions which did not correspond to an official annotation. It turns out that many of these predictions are in fact strongly homologous to a known TE.

Cut-and-Paste Transposons in Fungi with Diverse Lifestyles.

Transposable elements (TEs) shape genomes via recombination and transposition, lead to chromosomal rearrangements, create new gene neighborhoods, and alter gene expression. They play key roles in adaptation either to symbiosis in Amanita genus or to pathogenicity in Pyrenophora tritici-repentis. Despite growing evidence of their importance, the abundance and distribution of mobile elements replicating in a “cut-and-paste” fashion is barely described so far. In order to improve our knowledge on this old and ubiquitous class of transposable elements, 1,730 fungal genomes were scanned using both de novo and homology-based approaches. DNA TEs have been identified across the whole data set and display uneven distribution from both DNA TE classification and fungal taxonomy perspectives. DNA TE content correlates with genome size, which confirms that many transposon families proliferate simultaneously. In contrast, it is independent from intron density, average gene distance and GC content. TE count is associated with species’ lifestyle and tends to be elevated in plant symbionts and decreased in animal parasites. Lastly, we found that fungi with both RIP and RNAi systems have more total DNA TE sequences but less elements retaining a functional transposase, what reflects stringent control over transposition.

Inferring transposons activity chronology by TRANScendence \u2013 TEs database and de-novo mining tool

BackgroundThe constant progress in sequencing technology leads to ever increasing amounts of genomic data. In the light of current evidence transposable elements (TEs for short) are becoming useful tools for learning about the evolution of host genome. Therefore the software for genome-wide detection and analysis of TEs is of great interest.ResultsHere we describe the computational tool for mining, classifying and storing TEs from newly sequenced genomes. This is an online, web-based, user-friendly service, enabling users to upload their own genomic data, and perform de-novo searches for TEs. The detected TEs are automatically analyzed, compared to reference databases, annotated, clustered into families, and stored in TEs repository. Also, the genome-wide nesting structure of found elements are detected and analyzed by new method for inferring evolutionary history of TEs.We illustrate the functionality of our tool by performing a full-scale analyses of TE landscape in Medicago truncatula genome.ConclusionsTRANScendence is an effective tool for the de-novo annotation and classification of transposable elements in newly-acquired genomes. Its streamlined interface makes it well-suited for evolutionary studies.

The genomes and transposable elements in plants: are they friends or foes?

Transposable elements (TEs) are mobile genetic elements that are present in prokaryotes and eukaryotes. The ubiquity and abundance of these self-replicating entities, bereft of cellular function, had earned them the label of ‘genomic parasites’. However, the status of TEs has been revised, with ample genomic and biological evidence now portraying them as “genomic gold”. They are perceived as a major participant in the evolution of species. This review addresses the classification of TEs as well as their role and significance in the evolution of genomes, genetic diversity, gene regulation, and exaptation of contemporary species of the plant and animal kingdoms.

Transposome: a toolkit for annotation of transposable element families from unassembled sequence reads.

Transposable elements (TEs) can be found in virtually all eukaryotic genomes and have the potential to produce evolutionary novelty. Despite the broad taxonomic distribution of TEs, the evolutionary history of these sequences is largely unknown for many taxa due to a lack of genomic resources and identification methods. Given that most TE annotation methods are designed to work on genome assemblies, we sought to develop a method to provide a fine-grained classification of TEs from DNA sequence reads. Here, we present a toolkit for the efficient annotation of TE families from low-coverage whole-genome shotgun (WGS) data, enabling the rapid identification of TEs in a large number of taxa. We compared our software, Transposome, with other approaches for annotating repeats from WGS data, and we show that it offers significant improvements in run time and produces more precise estimates of genomic repeat abundance. Transposome may also be used as a general toolkit for working with Next Generation Sequencing (NGS) data, and for constructing custom genome analysis pipelines. The source code for Transposome is freely available (http://sestaton.github.io/Transposome), implemented in Perl and is supported on Linux.

PASTEC: an automatic transposable element classification tool.

SummaryThe classification of transposable elements (TEs) is key step towards deciphering their potential impact on the genome. However, this process is often based on manual sequence inspection by TE experts. With the wealth of genomic sequences now available, this task requires automation, making it accessible to most scientists. We propose a new tool, PASTEC, which classifies TEs by searching for structural features and similarities. This tool outperforms currently available software for TE classification. The main innovation of PASTEC is the search for HMM profiles, which is useful for inferring the classification of unknown TE on the basis of conserved functional domains of the proteins. In addition, PASTEC is the only tool providing an exhaustive spectrum of possible classifications to the order level of the Wicker hierarchical TE classification system. It can also automatically classify other repeated elements, such as SSR (Simple Sequence Repeats), rDNA or potential repeated host genes. Finally, the output of this new tool is designed to facilitate manual curation by providing to biologists with all the evidence accumulated for each TE consensus.AvailabilityPASTEC is available as a REPET module or standalone software (http://urgi.versailles.inra.fr/download/repet/REPET_linux-x64-2.2.tar.gz). It requires a Unix-like system. There are two standalone versions: one of which is parallelized (requiring Sun grid Engine or Torque), and the other of which is not.

Computational approaches for identification and classification of transposable elements in eukaryotic genomes

Repetitive sequences (repeats) represent a significant fraction of the eukaryotic genomes and can be divided into tandem repeats, segmental duplications, and interspersed repeats on the basis of their sequence characteristics and how they are formed. Most interspersed repeats are derived from transposable elements (TEs). Eukaryotic TEs have been subdivided into two major classes according to the intermediate they use to move. The transposition and amplification of TEs have a great impact on the evolution of genes and the stability of genomes. However, identification and classification of TEs are complex and difficult due to the fact that their structure and classification are complex and diverse compared with those of other types of repeats. Here, we briefly introduced the function and classification of TEs, and summarized three different steps for identification, classification and annotation of TEs in eukaryotic genomes: (1) assembly of a repeat library, (2) repeat correction and classification, and (3) genome annotation. The existing computational approaches for each step were summarized and the advantages and disadvantages of the approaches were also highlighted in this review. To accurately identify, classify, and annotate the TEs in eukaryotic genomes requires combined methods. This review provides useful information for biologists who are not familiar with these approaches to find their way through the forest of programs.