TEcandidates: prediction of genomic origin of expressed transposable elements using RNA-seq data

Valdebenito-Maturana, Braulio; Riadi, Gonzalo

doi:10.1093/bioinformatics/bty423

Cited by 14 publications

(14 citation statements)

References 12 publications

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“…This bias may also result from Bowtie 1, the underlying mapping software recommended by RepEnrich, which cannot align discordant reads or reads with small insertions and deletions (indels), and only outputs a limited number of fraction of all possible positions for multi-mapping reads 145 . By contrast, the TEcandidates pipeline first performs de novo transcriptome assembly to identify potentially expressed TE loci, then masks nonexpressed ones in the reference genome, and finally remaps multi-mapping reads on this masked genome with less mapping ambiguity 146 . However, the ability of this pipeline to properly assemble TE transcriptomes, or to identify expressed loci among young TEs, has not yet been evaluated.…”

Section: Mappabilitymentioning

confidence: 99%

Measuring and interpreting transposable element expression

2020

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Transposable elements (TEs) are insertional mutagens that contribute greatly to the plasticity of eukaryotic genomes, influencing the evolution and adaptation of species as well as physiology or disease in individuals. Measuring TE expression helps to understand not only when and where TE mobilization can occur, but also how this process alters gene expression, chromatin accessibility or cellular signalling pathways. Although genome-wide gene expression assays such as RNA-sequencing include transposon-derived transcripts, the majority of computational analytical tools discard or misinterpret TE-derived reads. Emerging approaches are improving the identification of expressed TE loci and helping to discriminate TE transcripts that permit TE mobilization from gene-TE chimeric transcripts or pervasive transcription. Here, we review the main challenges associated with the detection of TE expression, including mappability, insertional and internal sequence polymorphisms, and the diversity of the TE transcriptional landscape, as well as the different experimental and computational strategies to solve them.

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Section: Mappabilitymentioning

confidence: 99%

Measuring and interpreting transposable element expression

2020

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“…To identify the genomic origin of expressed TEs, we used the pipeline TEcandidates [26]. In brief, TEcandidates uses de novo transcriptome assembly to assess expressed TE instances.…”

Section: Analyses Of Te Expressionmentioning

confidence: 99%

Impact of transposable elements on genome size variation between two closely related crustacean species

Becking

Gilbert

Cordaux

2020

Analytical Biochemistry

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Identifying and quantifying genome size variation among species and understanding if it evolves as a consequence of adaptive or stochastic processes is still a long-standing objective in evolutionary biology. Here, we investigated the basis of genome size variation between two closely related species of terrestrial isopods: Armadillidium vulgare and Armadillidium nasatum (Crustacea, Oniscidea). The two species diverged 25 million years ago and they substantially diverge in genome size, the A. vulgare genome being ~500 megabases larger than the A. nasatum genome (1.7 vs. 1.2 gigabases, respectively). Our analyses indicated that genome size difference is essentially attributed to transposable elements (TEs). As the two genomes largely share the same TE families, differential transpositional activity likely underlies the observed variation in TE copy numbers and genome size. Analyses of TE expression suggested that there may be ~4 times more transcribed TE copies in A. vulgare than in A. nasatum. However, the cumulative expression level of all expressed TEs was higher in A. nasatum than in A. vulgare, suggesting that the two species may have recently been experiencing different TE transposition dynamics. Overall, our results illustrate the important impact TEs can have on genome structure and evolution between closely related species.

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“…TE-centric packages include SINESFIND [73], and ERVMAP [74] which are specialized for their respective TE family of interest. Two pipelines used genome-guided de novo transcriptome assembly with TRINITY [75] to quantify TE expression at a locus-specific level: TECANDIDATES [76] and a pipeline described by Guffanti et al [77] More recently, SQUIRE [78] (software for quantifying interspersed repeat expansion), and TELESCOPE [79] adapted the EM-based read redistribution strategies described above to infer originating loci of ambiguously mapped reads, using uniquely mapped reads surrounding the locus to guide the EM read redistribution.…”

Section: Rna-seqmentioning

confidence: 99%

Mobile genomics: tools and techniques for tackling transposons

O’Neill

Brocks

Hammell

2020

Phil. Trans. R. Soc. B

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Next-generation sequencing approaches have fundamentally changed the types of questions that can be asked about gene function and regulation. With the goal of approaching truly genome-wide quantifications of all the interaction partners and downstream effects of particular genes, these quantitative assays have allowed for an unprecedented level of detail in exploring biological interactions. However, many challenges remain in our ability to accurately describe and quantify the interactions that take place in those hard to reach and extremely repetitive regions of our genome comprised mostly of transposable elements (TEs). Tools dedicated to TE-derived sequences have lagged behind, making the inclusion of these sequences in genome-wide analyses difficult. Recent improvements, both computational and experimental, allow for the better inclusion of TE sequences in genomic assays and a renewed appreciation for the importance of TE biology. This review will discuss the recent improvements that have been made in the computational analysis of TE-derived sequences as well as the areas where such analysis still proves difficult. This article is part of a discussion meeting issue ‘Crossroads between transposons and gene regulation’.

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TEcandidates: prediction of genomic origin of expressed transposable elements using RNA-seq data

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 14 publications

References 12 publications

Measuring and interpreting transposable element expression

Measuring and interpreting transposable element expression

Impact of transposable elements on genome size variation between two closely related crustacean species

Mobile genomics: tools and techniques for tackling transposons

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