TIR-Learner, a New Ensemble Method for TIR Transposable Element Annotation, Provides Evidence for Abundant New Transposable Elements in the Maize Genome

Abstract

Transposable elements (TEs) make up a large and rapidly evolving proportion of plant genomes. Among Class II DNA TEs, TIR elements are flanked by characteristic terminal inverted repeat sequences (TIRs). TIR TEs may play important roles in genome evolution, including generating allelic diversity, inducing structural variation, and regulating gene expression. However, TIR TE identification and annotation has been hampered by the lack of effective tools, resulting in erroneous TE annotations and a significant underestimation of the proportion of TIR elements in the maize genome. This problem has largely limited our understanding of the impact of TIR elements on plant genome structure and evolution. In this paper, we propose a new method of TIR element detection and annotation. This new pipeline combines the advantages of current homology-based annotation methods with powerful de novo machine-learning approaches, resulting in greatly increased efficiency and accuracy of TIR element annotation. The results show that the copy number and genome proportion of TIR elements in maize is much larger than that of current annotations. In addition, the distribution of some TIR superfamily elements is reduced in centromeric and pericentromeric positions, while others do not show a similar bias. Finally, the incorporation of machine-learning techniques has enabled the identification of large numbers of new DTA (hAT) family elements, which have all the hallmarks of bona fide TEs yet which lack high homology with currently known DTA elements. Together, these results provide new tools for TE research and new insight into the impact of TIR elements on maize genome diversity.

Benchmarking Transposable Element Annotation Methods for Creation of a Streamlined, Comprehensive Pipeline

“…These elements are highly abundant in eukaryotic genomes, and, as such, there are a large number of annotation programs designed to identify them. We tested P-MITE [31], a specialized database of curated plant MITEs, and IRF [50], TIR-Learner [17], and GRF ( grf-main -c 0 ) (https://github.com/bioinfolabmu/GenericRepeatFinder), which structurally identify TIR elements, and finally MITE-Hunter [51], detectMITE [52], MUSTv2 [53], miteFinderII [54], MITE-Tracker [55], and GRF ( grf-mite ), which structurally identify MITEs specifically.…”

“…We found less than half of the novel TIR elements with novel TIRs had more than three copies in the rice genome (Figure 5D). This is because TIR candidates were not filtered based on copy number in TIR-Learner [17], given that some TEs may share similar TIRs but different internal regions (Figure 5D). Still, some of these could be contaminants such as LTR sequences.…”

“…For example, hAT elements typically have an 8-bp TSD, 12-28 bp terminal inverted repeat sequence (TIRs), and contain 5’-C/TA…TA/G-3’ terminal sequences. Each Class II superfamily has different structural features that need to be considered when TE annotation programs are being developed and deployed [16, 17]. Helitrons are a unique subclass of Class II elements that replicate through a rolling-circle mechanism and, as such, do not generate a TSD sequence and do not have TIRs, but do have a signature 5’-TC…CTRR-3’ terminal repeat sequence and frequently a short GC-rich stem-loop structure near the 3’ end of the element [16,18,19].…”

Transposable elements contribute to dynamic genome content in maize

“…However, this approach can be difficult to implement for TEs and other repetitive sequences. Prior studies of several loci had suggested high levels of variation in TE content among maize haplotypes (Fu and Dooner, ; Yao et al ., ; Brunner et al ., ), and genomic level comparisons using whole‐genome assemblies have been limited to assessing annotated copy number per family without resolution at the level of individual TEs (Springer et al ., ; Su et al ., ). In this study we used an approach to assess the shared and non‐shared nature of individual TEs within collinear homologous blocks of four assembled maize genomes.…”

Peer Review #1 of "A systematic review of the application of machine learning in the detection and classification of transposable elements (v0.1)"

“…There is much literature about applications of machine learning in bioinformatics (for example, reviewed in (Larrañaga et al, 2006)), showing improvements in many aspects such as genome annotation (Arango-López et al, 2017). In recent years, much bioinformatics software has been developed to detect TEs (Girgis, 2015) and, although they follow different strategies (such as homology-based, structure-based, de novo, and using comparative genomics), these lack sensitivity and specificity due to the polymorphic structures of TEs (Su, Gu & Peterson, 2019). Loureiro et al (Loureiro et al, 2013a) proved that ML could be used to improve the accuracy of TEs detection by combining results obtained by several conventional software and training a classifier using these results (Schietgat et al, 2018), (Loureiro et al, 2013b).…”