LTRclassifier: A website for fast structural LTR retrotransposons classification in plants

The good, the bad and the ugly of transposable elements annotation tools

Transposable elements are repetitive and mobile DNA segments that can be found in virtually all organisms investigated to date. Their complex structure and variable nature are particularly challenging from the genomic annotation point of view. Many softwares have been developed to automate and facilitate TEs annotation at the genomic level, but they are highly heterogeneous regarding documentation, usability and methods. In this review, we revisited the existing software for TE genomic annotation, concentrating on the most often used ones, the methodologies they apply, and usability. Building on the state of the art of TE annotation software we propose best practices and highlight the strengths and weaknesses from the available solutions.

High Nucleotide Substitution Rates Associated with Retrotransposon Proliferation Drive Dynamic Secretome Evolution in Smut Pathogens

Transposable elements (TEs) play a pivotal role in shaping diversity in eukaryotic genomes. The covered smut pathogen on barley, Ustilago hordei, encountered a recent genome expansion. Using long reads, we assembled genomes of 6 U. hordei strains and 3 sister species, to study this genome expansion. We found that larger genome sizes can mainly be attributed to a higher genome fraction of long terminal repeat retrotransposons (LTR-RTs). In the studied smut genomes, LTR-RTs fractions are the largest in U. hordei and are positively correlated with the mating-type locus sizes, which is up to ~560 kb in U. hordei. Furthermore, LTR-RTs were found to be associated with higher nucleotide substitution levels, as these occur in specific genome regions of smut species with a recent LTR-RT proliferation. Moreover, genes in genome regions with higher nucleotide substitution levels generally reside closer to LTR-RTs than other genome regions. Genome regions with many nucleotide substitutions encountered an especially high fraction of CG substitutions, which is not observed for LTR-RT sequences. The high nucleotide substitution levels particularly accelerate the evolution of secretome genes, as their more accessory nature results in substitutions that often lead to amino acid alterations. IMPORTANCE Genomic alteration can be generated through various means, in which transposable elements (TEs) can play a pivotal role. Their mobility causes mutagenesis in itself and can disrupt the function of the sequences they insert into. They also impact genome evolution as their repetitive nature facilitates nonhomologous recombination. Furthermore, TEs have been linked to specific epigenetic genome organizations. We report a recent TE proliferation in the genome of the barley covered smut fungus, Ustilago hordei. This proliferation is associated with a distinct nucleotide substitution regime that has a higher rate and a higher fraction of CG substitutions. This different regime shapes the evolution of genes in subjected genome regions. We hypothesize that TEs may influence the error-rate of DNA polymerase in a hitherto unknown fashion.

Assembled and annotated 26.5 Gbp coast redwood genome: a resource for estimating evolutionary adaptive potential and investigating hexaploid origin

Sequencing, assembly, and annotation of the 26.5 Gbp hexaploid genome of coast redwood (Sequoia sempervirens) was completed leading toward discovery of genes related to climate adaptation and investigation of the origin of the hexaploid genome. Deep-coverage short-read Illumina sequencing data from haploid tissue from a single seed were combined with long-read Oxford Nanopore Technologies sequencing data from diploid needle tissue to create an initial assembly, which was then scaffolded using proximity ligation data to produce a highly contiguous final assembly, SESE 2.1, with a scaffold N50 size of 44.9 Mbp. The assembly included several scaffolds that span entire chromosome arms, confirmed by the presence of telomere and centromere sequences on the ends of the scaffolds. The structural annotation produced 118,906 genes with 113 containing introns that exceed 500 Kbp in length and one reaching 2 Mb. Nearly 19 Gbp of the genome represented repetitive content with the vast majority characterized as long terminal repeats, with a 2.9:1 ratio of Copia to Gypsy elements that may aid in gene expression control. Comparison of coast redwood to other conifers revealed species-specific expansions for a plethora of abiotic and biotic stress response genes, including those involved in fungal disease resistance, detoxification, and physical injury/structural remodeling and others supporting flavonoid biosynthesis. Analysis of multiple genes that exist in triplicate in coast redwood but only once in its diploid relative, giant sequoia, supports a previous hypothesis that the hexaploidy is the result of autopolyploidy rather than any hybridizations with separate but closely related conifer species.

ClassifyTE: A stacking based prediction of hierarchical classification of transposable elements

MOTIVATION

Transposable Elements (TEs) or jumping genes are DNA sequences that have an intrinsic capability to move within a host genome from one genomic location to another. Studies show that the presence of a TE within or adjacent to a functional gene may alter its expression. TEs can also cause an increase in the rate of mutation and can even mediate duplications and large insertions and deletions in the genome, promoting gross genetic rearrangements. The proper classification of identified jumping genes is important for analyzing their genetic and evolutionary effects. An effective classifier, which can explain the role of TEs in germline and somatic evolution more accurately, is needed. In this study, we examine the performance of a variety of machine learning (ML) techniques and propose a robust method, ClassifyTE, for the hierarchical classification of TEs with high accuracy, using a stacking-based ML method.

RESULTS

We propose a stacking-based approach for the hierarchical classification of TEs. When trained on three different benchmark datasets, our proposed system achieved 4%, 10.68%, and 10.13% average percentage improvement (using the hF measure) compared to several state-of-the-art methods. We developed an end-to-end automated hierarchical classification tool based on the proposed approach, ClassifyTE, to classify TEs up to the super-family level. We further evaluated our method on a new TE library generated by a homology-based classification method and found relatively high concordance at higher taxonomic levels. Thus, ClassifyTE paves the way for a more accurate analysis of the role of TEs.

AVAILABILITY

The source code and data are available at https://github.com/manisa/ClassifyTE.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

The Pontastacus leptodactylus (Astacidae) Repeatome Provides Insight Into Genome Evolution and Reveals Remarkable Diversity of Satellite DNA

Pontastacus leptodactylus is a native European crayfish species found in both freshwater and brackish environments. It has commercial importance for fisheries and aquaculture industries. Up till now, most studies concerning P. leptodactylus have focused onto gaining knowledge about its phylogeny and population genetics. However, little is known about the chromosomal evolution and genome organization of this species. Therefore, we performed clustering analysis of a low coverage genomic dataset to identify and characterize repetitive DNA in the P. leptodactylus genome. In addition, the karyogram of P. leptodactylus (2n = 180) is presented here for the first time consisting of 75 metacentric, 14 submetacentric, and a submetacentric/metacentric heteromorphic chromosome pair. We determined the genome size to be at ~18.7 gigabase pairs. Repetitive DNA represents about 54.85% of the genome. Satellite DNA repeats are the most abundant type of repetitive DNA, making up to ~28% of the total amount of repetitive elements, followed by the Ty3/Gypsy retroelements (~15%). Our study established a surprisingly high diversity of satellite repeats in P. leptodactylus. The genome of P. leptodactylus is by far the most satellite-rich genome discovered to date with 258 satellite families described. Of the five mapped satellite DNA families on chromosomes, PlSAT3-411 co-localizes with the AT-rich DAPI positive probable (peri)centromeric heterochromatin on all chromosomes, while PlSAT14-79 co-localizes with the AT-rich DAPI positive (peri)centromeric heterochromatin on one chromosome and is also located subterminally and intercalary on some chromosomes. PlSAT1-21 is located intercalary in the vicinity of the (peri)centromeric heterochromatin on some chromosomes, while PlSAT6-70 and PlSAT7-134 are located intercalary on some P. leptodactylus chromosomes. The FISH results reveal amplification of interstitial telomeric repeats (ITRs) in P. leptodactylus. The prevalence of repetitive elements, especially the satellite DNA repeats, may have provided a driving force for the evolution of the P. leptodactylus genome.

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.

Inpactor, Integrated and Parallel Analyzer and Classifier of LTR Retrotransposons and Its Application for Pineapple LTR Retrotransposons Diversity and Dynamics

One particular class of Transposable Elements (TEs), called Long Terminal Repeats (LTRs), retrotransposons, comprises the most abundant mobile elements in plant genomes. Their copy number can vary from several hundreds to up to a few million copies per genome, deeply affecting genome organization and function. The detailed classification of LTR retrotransposons is an essential step to precisely understand their effect at the genome level, but remains challenging in large-sized genomes, requiring the use of optimized bioinformatics tools that can take advantage of supercomputers. Here, we propose a new tool: Inpactor, a parallel and scalable pipeline designed to classify LTR retrotransposons, to identify autonomous and non-autonomous elements, to perform RT-based phylogenetic trees and to analyze their insertion times using High Performance Computing (HPC) techniques. Inpactor was tested on the classification and annotation of LTR retrotransposons in pineapple, a recently-sequenced genome. The pineapple genome assembly comprises 44% of transposable elements, of which 23% were classified as LTR retrotransposons. Exceptionally, 16.4% of the pineapple genome assembly corresponded to only one lineage of the Gypsy superfamily: Del, suggesting that this particular lineage has undergone a significant increase in its copy numbers. As demonstrated for the pineapple genome, Inpactor provides comprehensive data of LTR retrotransposons’ classification and dynamics, allowing a fine understanding of their contribution to genome structure and evolution. Inpactor is available at https://github.com/simonorozcoarias/Inpactor.

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.

Over the years, with the increase of the acquisition of biological data, the extraction of knowledge from this data is getting more important. To understand how biology works is very important to increase the quality of the products and services which use biological data. This directly influences companies and governments, which need to remain in the knowledge frontier of an increasing competitive economy. Transposable Elements (TEs) are an example of very important biological data, and to understand their role in the genomes of organisms is very important for the development of products based on biological data. As an example, we can cite the production biofuels such as the sugar-cane-based ones. Many studies have revealed the presence of active TEs in this plant, which has gained economic importance in many countries. To understand how TEs influence the plant should help researchers to develop more resistant varieties of sugar-cane, increasing the production. Thus, the development of computational methods able to help biologists in the correct identification and classification of TEs is very important from both theoretical and practical perspectives.