MASiVEdb: the Sirevirus Plant Retrotransposon Database

PlantLTRdb: An interactive database for 195 plant species LTR-retrotransposons

LTR-retrotransposons (LTR-RTs) are a large group of transposable elements that replicate through an RNA intermediate and alter genome structure. The activities of LTR-RTs in plant genomes provide helpful information about genome evolution and gene function. LTR-RTs near or within genes can directly alter gene function. This work introduces PlantLTRdb, an intact LTR-RT database for 195 plant species. Using homology- and de novo structure-based methods, a total of 150.18 Gbp representing 3,079,469 pseudomolecules/scaffolds were analyzed to identify, characterize, annotate LTR-RTs, estimate insertion ages, detect LTR-RT-gene chimeras, and determine nearby genes. Accordingly, 520,194 intact LTR-RTs were discovered, including 29,462 autonomous and 490,732 nonautonomous LTR-RTs. The autonomous LTR-RTs included 10,286 Gypsy and 19,176 Copia, while the nonautonomous were divided into 224,906 Gypsy, 218,414 Copia, 1,768 BARE-2, 3,147 TR-GAG and 4,2497 unknown. Analysis of the identified LTR-RTs located within genes showed that a total of 36,236 LTR-RTs were LTR-RT-gene chimeras and 11,619 LTR-RTs were within pseudo-genes. In addition, 50,026 genes are within 1 kbp of LTR-RTs, and 250,587 had a distance of 1 to 10 kbp from LTR-RTs. PlantLTRdb allows researchers to search, visualize, BLAST and analyze plant LTR-RTs. PlantLTRdb can contribute to the understanding of structural variations, genome organization, functional genomics, and the development of LTR-RT target markers for molecular plant breeding. PlantLTRdb is available at https://bioinformatics.um6p.ma/PlantLTRdb.

Diverse and mobile: eccDNA-based identification of carrot low-copy-number LTR retrotransposons active in callus cultures

Long terminal repeat retrotransposons (LTR-RTs) are mobilized via an RNA intermediate using a 'copy and paste' mechanism, and account for the majority of repetitive DNA in plant genomes. As a side effect of mobilization, the formation of LTR-RT-derived extrachromosomal circular DNAs (eccDNAs) occurs. Thus, high-throughput sequencing of eccDNA can be used to identify active LTR-RTs in plant genomes. Despite the release of a reference genome assembly, carrot LTR-RTs have not yet been thoroughly characterized. LTR-RTs are abundant and diverse in the carrot genome. We identified 5976 carrot LTR-RTs, 2053 and 1660 of which were attributed to Copia and Gypsy superfamilies, respectively. They were further classified into lineages, families and subfamilies. More diverse LTR-RT lineages, i.e. lineages comprising many low-copy-number subfamilies, were more frequently associated with genic regions. Certain LTR-RT lineages have been recently active in Daucus carota. In particular, low-copy-number LTR-RT subfamilies, e.g. those belonging to the DcAle lineage, have significantly contributed to carrot genome diversity as a result of continuing activity. We utilized eccDNA sequencing to identify and characterize two DcAle subfamilies, Alex1 and Alex3, active in carrot callus. We documented 14 and 32 de novo insertions of Alex1 and Alex3, respectively, which were positioned in non-repetitive regions.

Patterns of selection in the evolution of a transposable element

Transposable elements are a major component of most eukaryotic genomes. Here, we present a new approach which allows us to study patterns of natural selection in the evolution of transposable elements over short time scales. The method uses the alignment of all elements with intact gag/pol genes of a transposable element family from a single genome. We predict that the ratio of nonsynonymous to synonymous variants in the alignment should decrease as a function of the frequency of the variants, because elements with nonsynonymous variants that reduce transposition will have fewer progeny. We apply our method to Sirevirus long-terminal repeat retrotransposons that are abundant in maize and other plant species and show that nonsynonymous to synonymous variants declines as variant frequency increases, indicating that negative selection is acting strongly on the Sirevirus genome. The asymptotic value of nonsynonymous to synonymous variants suggests that at least 85% of all nonsynonymous mutations in the transposable element reduce transposition. Crucially, these patterns in nonsynonymous to synonymous variants are only predicted to occur if the gene products from a particular transposable element insertion preferentially promote the transposition of the same insertion. Overall, by using large numbers of intact elements, this study sheds new light on the selective processes that act on transposable elements.

Finding and Characterizing Repeats in Plant Genomes

Plant genomes contain a particularly high proportion of repeated structures of various types. This chapter proposes a guided tour of the available software that can help biologists to scan automatically for these repeats in sequence data or check hypothetical models intended to characterize their structures. Since transposable elements (TEs) are a major source of repeats in plants, many methods have been used or developed for this broad class of sequences. They are representative of the range of tools available for other classes of repeats and we have provided two sections on this topic (for the analysis of genomes or directly of sequenced reads), as well as a selection of the main existing software. It may be hard to keep up with the profusion of proposals in this dynamic field and the rest of the chapter is devoted to the foundations of an efficient search for repeats and more complex patterns. We first introduce the key concepts of the art of indexing and mapping or querying sequences. We end the chapter with the more prospective issue of building models of repeat families. We present the Machine Learning approach first, seeking to build predictors automatically for some families of ET, from a set of sequences known to belong to this family. A second approach, the linguistic (or syntactic) approach, allows biologists to describe themselves and check the validity of models of their favorite repeat family.

Computational Analysis of Transposable Elements and CircRNAs in Plants

This chapter provides two main contributions: (1) a description of computational tools and databases used to identify and analyze transposable elements (TEs) and circRNAs in plants; and (2) data analysis on public TE and circRNA data. Our goal is to highlight the primary information available in the literature on circular noncoding RNAs and transposable elements in plants. The exploratory analysis performed on publicly available circRNA and TEs data help discuss four sequence features. Finally, we investigate the association on circRNAs:TE in plants in the model organism Arabidopsis thaliana.

Gene capture by transposable elements leads to epigenetic conflict in maize

Transposable elements (TEs) regularly capture fragments of genes. When the host silences these TEs, siRNAs homologous to the captured regions may also target the genes. This epigenetic crosstalk establishes an intragenomic conflict: silencing the TEs has the cost of silencing the genes. If genes are important, however, natural selection may maintain function by moderating the silencing response, which may also advantage the TEs. In this study, we examined this model by focusing on Helitrons, Pack-MULEs, and Sirevirus LTR retrotransposons in the maize genome. We documented 1263 TEs containing exon fragments from 1629 donor genes. Consistent with epigenetic conflict, donor genes mapped more siRNAs and were more methylated than genes with no evidence of capture. However, these patterns differed between syntelog versus translocated donor genes. Syntelogs appeared to maintain function, as measured by gene expression, consistent with moderation of silencing for functionally important genes. Epigenetic marks did not spread beyond their captured regions and 24nt crosstalk siRNAs were linked with CHH methylation. Translocated genes, in contrast, bore the signature of silencing. They were highly methylated and less expressed, but also overrepresented among donor genes and located away from chromosomal arms, which suggests a link between capture and gene movement. Splitting genes into potential functional categories based on evolutionary constraint supported the synteny-based findings. TE families captured genes in different ways, but the evidence for their advantage was generally less obvious; nevertheless, TEs with captured fragments were older, mapped fewer siRNAs, and were slightly less methylated than TEs without captured fragments. Collectively, our results argue that TE capture triggers an intragenomic conflict that may not affect the function of important genes but may lead to the pseudogenization of less-constrained genes.

Measuring Performance Metrics of Machine Learning Algorithms for Detecting and Classifying Transposable Elements

Because of the promising results obtained by machine learning (ML) approaches in several fields, every day is more common, the utilization of ML to solve problems in bioinformatics. In genomics, a current issue is to detect and classify transposable elements (TEs) because of the tedious tasks involved in bioinformatics methods. Thus, ML was recently evaluated for TE datasets, demonstrating better results than bioinformatics applications. A crucial step for ML approaches is the selection of metrics that measure the realistic performance of algorithms. Each metric has specific characteristics and measures properties that may be different from the predicted results. Although the most commonly used way to compare measures is by using empirical analysis, a non-result-based methodology has been proposed, called measure invariance properties. These properties are calculated on the basis of whether a given measure changes its value under certain modifications in the confusion matrix, giving comparative parameters independent of the datasets. Measure invariance properties make metrics more or less informative, particularly on unbalanced, monomodal, or multimodal negative class datasets and for real or simulated datasets. Although several studies applied ML to detect and classify TEs, there are no works evaluating performance metrics in TE tasks. Here, we analyzed 26 different metrics utilized in binary, multiclass, and hierarchical classifications, through bibliographic sources, and their invariance properties. Then, we corroborated our findings utilizing freely available TE datasets and commonly used ML algorithms. Based on our analysis, the most suitable metrics for TE tasks must be stable, even using highly unbalanced datasets, multimodal negative class, and training datasets with errors or outliers. Based on these parameters, we conclude that the F1-score and the area under the precision-recall curve are the most informative metrics since they are calculated based on other metrics, providing insight into the development of an ML application.

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

Background

Transposable 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.

Methodology

We 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.

Results

Several 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.

Conclusions

ML 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.

Systematic survey of plant LTR-retrotransposons elucidates phylogenetic relationships of their polyprotein domains and provides a reference for element classification

BACKGROUND

Plant LTR-retrotransposons are classified into two superfamilies, Ty1/copia and Ty3/gypsy. They are further divided into an enormous number of families which are, due to the high diversity of their nucleotide sequences, usually specific to a single or a group of closely related species. Previous attempts to group these families into broader categories reflecting their phylogenetic relationships were limited either to analyzing a narrow range of plant species or to analyzing a small numbers of elements. Furthermore, there is no reference database that allows for similarity based classification of LTR-retrotransposons.

RESULTS

We have assembled a database of retrotransposon encoded polyprotein domains sequences extracted from 5410 Ty1/copia elements and 8453 Ty3/gypsy elements sampled from 80 species representing major groups of green plants (Viridiplantae). Phylogenetic analysis of the three most conserved polyprotein domains (RT, RH and INT) led to dividing Ty1/copia and Ty3/gypsy retrotransposons into 16 and 14 lineages respectively. We also characterized various features of LTR-retrotransposon sequences including additional polyprotein domains, extra open reading frames and primer binding sites, and found that the occurrence and/or type of these features correlates with phylogenies inferred from the three protein domains.

CONCLUSIONS

We have established an improved classification system applicable to LTR-retrotransposons from a wide range of plant species. This system reflects phylogenetic relationships as well as distinct sequence and structural features of the elements. A comprehensive database of retrotransposon protein domains (REXdb) that reflects this classification provides a reference for efficient and unified annotation of LTR-retrotransposons in plant genomes. Access to REXdb related tools is implemented in the RepeatExplorer web server (https://repeatexplorer-elixir.cerit-sc.cz/) or using a standalone version of REXdb that can be downloaded seaparately from RepeatExplorer web page (http://repeatexplorer.org/).

Considerations and complications of mapping small RNA high-throughput data to transposable elements

BACKGROUND

High-throughput sequencing (HTS) has revolutionized the way in which epigenetic research is conducted. When coupled with fully-sequenced genomes, millions of small RNA (sRNA) reads are mapped to regions of interest and the results scrutinized for clues about epigenetic mechanisms. However, this approach requires careful consideration in regards to experimental design, especially when one investigates repetitive parts of genomes such as transposable elements (TEs), or when such genomes are large, as is often the case in plants.

RESULTS

Here, in an attempt to shed light on complications of mapping sRNAs to TEs, we focus on the 2,300 Mb maize genome, 85% of which is derived from TEs, and scrutinize methodological strategies that are commonly employed in TE studies. These include choices for the reference dataset, the normalization of multiply mapping sRNAs, and the selection among sRNA metrics. We further examine how these choices influence the relationship between sRNAs and the critical feature of TE age, and contrast their effect on low copy genomic regions and other popular HTS data.

CONCLUSIONS

Based on our analyses, we share a series of take-home messages that may help with the design, implementation, and interpretation of high-throughput TE epigenetic studies specifically, but our conclusions may also apply to any work that involves analysis of HTS data.

Mechanistic and evolutionary questions about epigenetic conflicts between transposable elements and their plant hosts

Transposable elements (TEs) constitute the majority of plant genomes, but most are epigenetically inactivated by their host. Research over the last decade has elucidated many of the molecular components that are required for TE silencing. In contrast, the evolutionary dynamics between TEs and silencing pathways are less clear. Here, we discuss current information about these dynamics from both mechanistic and evolutionary perspectives. We highlight new evidence that palindromic sequences within TEs may act as signals for host recognition and that cis-regulatory regions of TEs may be sites of ongoing arms races with host defenses. We also discuss patterns of TE aging after they are silenced; while there is not yet a consensus, it appears that TEs are removed more rapidly near genes, such that older TE insertions tend to be farther from genes. We conclude by discussing the energetic costs for maintaining silencing pathways, which appear to be substantive. The maintenance of silencing pathways across many species suggests that epigenetic emergencies are frequent.

Finding and Characterizing Repeats in Plant Genomes

Plant genomes contain a particularly high proportion of repeated structures of various types. This chapter proposes a guided tour of available software that can help biologists to look for these repeats and check some hypothetical models intended to characterize their structures. Since transposable elements are a major source of repeats in plants, many methods have been used or developed for this large class of sequences. They are representative of the range of tools available for other classes of repeats and we have provided a whole section on this topic as well as a selection of the main existing software. In order to better understand how they work and how repeats may be efficiently found in genomes, it is necessary to look at the technical issues involved in the large-scale search of these structures. Indeed, it may be hard to keep up with the profusion of proposals in this dynamic field and the rest of the chapter is devoted to the foundations of the search for repeats and more complex patterns. The second section introduces the key concepts that are useful for understanding the current state of the art in playing with words, applied to genomic sequences. This can be seen as the first stage of a very general approach called linguistic analysis that is interested in the analysis of natural or artificial texts. Words, the lexical level, correspond to simple repeated entities in texts or strings. In fact, biologists need to represent more complex entities where a repeat family is built on more abstract structures, including direct or inverted small repeats, motifs, composition constraints as well as ordering and distance constraints between these elementary blocks. In terms of linguistics, this corresponds to the syntactic level of a language. The last section introduces concepts and practical tools that can be used to reach this syntactic level in biological sequence analysis.

A role for palindromic structures in the cis-region of maize Sirevirus LTRs in transposable element evolution and host epigenetic response

Transposable elements (TEs) proliferate within the genome of their host, which responds by silencing them epigenetically. Much is known about the mechanisms of silencing in plants, particularly the role of siRNAs in guiding DNA methylation. In contrast, little is known about siRNA targeting patterns along the length of TEs, yet this information may provide crucial insights into the dynamics between hosts and TEs. By focusing on 6456 carefully annotated, full-length Sirevirus LTR retrotransposons in maize, we show that their silencing associates with underlying characteristics of the TE sequence and also uncover three features of the host–TE interaction. First, siRNA mapping varies among families and among elements, but particularly along the length of elements. Within the cis-regulatory portion of the LTRs, a complex palindrome-rich region acts as a hotspot of both siRNA matching and sequence evolution. These patterns are consistent across leaf, tassel, and immature ear libraries, but particularly emphasized for floral tissues and 21- to 22-nt siRNAs. Second, this region has the ability to form hairpins, making it a potential template for the production of miRNA-like, hairpin-derived small RNAs. Third, Sireviruses are targeted by siRNAs as a decreasing function of their age, but the oldest elements remain highly targeted, partially by siRNAs that cross-map to the youngest elements. We show that the targeting of older Sireviruses reflects their conserved palindromes. Altogether, we hypothesize that the palindromes aid the silencing of active elements and influence transposition potential, siRNA targeting levels, and ultimately the fate of an element within the genome.

Automated update, revision, and quality control of the maize genome annotations using MAKER-P improves the B73 RefGen_v3 gene models and identifies new genes

The large size and relative complexity of many plant genomes make creation, quality control, and dissemination of high-quality gene structure annotations challenging. In response, we have developed MAKER-P, a fast and easy-to-use genome annotation engine for plants. Here, we report the use of MAKER-P to update and revise the maize (Zea mays) B73 RefGen_v3 annotation build (5b+) in less than 3 h using the iPlant Cyberinfrastructure. MAKER-P identified and annotated 4,466 additional, well-supported protein-coding genes not present in the 5b+ annotation build, added additional untranslated regions to 1,393 5b+ gene models, identified 2,647 5b+ gene models that lack any supporting evidence (despite the use of large and diverse evidence data sets), identified 104,215 pseudogene fragments, and created an additional 2,522 noncoding gene annotations. We also describe a method for de novo training of MAKER-P for the annotation of newly sequenced grass genomes. Collectively, these results lead to the 6a maize genome annotation and demonstrate the utility of MAKER-P for rapid annotation, management, and quality control of grasses and other difficult-to-annotate plant genomes.

Sirevirus LTR retrotransposons: phylogenetic misconceptions in the plant world

Sireviruses are an ancient and plant-specific LTR retrotransposon genus. They possess a unique genome structure that is characterized by a plethora of highly conserved sequence motifs in key domains of the non-coding genome, and often, by the presence of an envelope-like gene. Recently, their crucial role in the organization of the maize genome, where Sireviruses occupy approximately 21% of its nuclear content, was revealed, followed by an analysis of their distribution across the plant kingdom. It is now suggested that Sireviruses have been a major mediator of the evolution of many plant genomes. However, the name ‘Sirevirus’ has caused confusion in the scientific community in regards to their classification within the LTR retrotransposon order and their relationship with viruses - a situation that is not unique to Sireviruses, but also affects other LTR retrotransposon genera. Here, we clarify the phylogenetic position of Sireviruses as typical LTR retrotransposons of the Copia superfamily and explain that the confusion stems from the discrepancy in the categorization of LTR retrotransposons by the two main classification systems: the International Committee on the Taxonomy of Viruses (ICTV) system and the unified classification system for eukaryotic transposable elements. While the name ‘Sirevirus’ has been given by ICTV, we show that the transposable element system, which is more suitable for eukaryotic genome studies, lacks an appropriate taxonomic level for describing them. We urge for this inconsistency to be addressed. Finally, we provide data suggesting that of the three ICTV-proposed genera of the Pseudoviridae (that is, Copia) family, only Sireviruses form a monophyletic group, while the phylogenetic distinction between Pseudoviruses and Hemiviruses is unclear. We conclude that because of their ongoing important contribution to the classification of transposable elements, these schemes need to be frequently revisited and revised - as shown by the example of the Sirevirus LTR retrotransposon genus.