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Record W3010736271 · doi:10.1101/2020.03.19.999144

Identification and characterization of retro-DNAs, a new type of retrotransposons originated from DNA transposons, in primate genomes

2020· preprint· en· W3010736271 on OpenAlexafffund
Wanxiangfu Tang, Ping Liang

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2020
Typepreprint
Languageen
FieldAgricultural and Biological Sciences
TopicChromosomal and Genetic Variations
Canadian institutionsBrock University
FundersOntario Ministry of Research and InnovationNatural Sciences and Engineering Research Council of CanadaBrock UniversityCompute Canada
KeywordsRetrotransposonTransposable elementGenomeLong terminal repeatBiologyGeneticsTransposition (logic)DNAGeneComputer science

Abstract

fetched live from OpenAlex

Abstract Mobile elements (MEs) can be divided into two major classes based on their transposition mechanisms as retrotransposons and DNA transposons. DNA transposons move in the genomes directly in the form of DNA in a cut-and-paste style, while retrotransposons utilize an RNA-intermediate to transpose in a “copy-and-paste” fashion. In addition to the t arget s ite d uplications (TSDs), a hallmark of transposition shared by both classes, the DNA transposons also carry t erminal i nverted r epeats (TIRs). DNA transposons constitute ~3% of primate genomes and they are thought to be inactive in the recent primate genomes since ~37My ago despite their success during early primate evolution. Retrotransposons can be further divided into Long Terminal Repeat retrotransposons (LTRs), which are characterized by the presence of LTRs at the two ends, and non-LTRs, which lack LTRs. In the primate genomes, LTRs constitute ~9% of genomes and have a low level of ongoing activity, while non-LTR retrotransposons represent the major types of MEs, contributing to ~37% of the genomes with some members being very young and currently active in retrotransposition. The four known types of non-LTR retrotransposons include LINEs, SINEs, SVAs, and processed pseudogenes, all characterized by the presence of a polyA tail and TSDs, which mostly range from 8 to 15 bp in length. All non-LTR retrotransposons are known to utilize the L1-based target-primed reverse transcription (TPRT) machineries for retrotransposition. In this study, we report a new type of non-LTR retrotransposon, which we named as retro-DNAs, to represent DNA transposons by sequence but non-LTR retrotransposons by the transposition mechanism in the recent primate genomes. By using a bioinformatics comparative genomics approach, we identified a total of 1,750 retro-DNAs, which represent 748 unique insertion events in the human genome and nine non-human primate genomes from the ape and monkey groups. These retro-DNAs, mostly as fragments of full-length DNA transposons, carry no TIRs but longer TSDs with ~23.5% also carrying a polyA tail and with their insertion site motifs and TSD length pattern characteristic of non-LTR retrotransposons. These features suggest that these retro-DNAs are DNA transposon sequences likely mobilized by the TPRT mechanism. Further, at least 40% of these retro-DNAs locate to genic regions, presenting significant potentials for impacting gene function. More interestingly, some retro-DNAs, as well as their parent sites, show certain levels of current transcriptional expression, suggesting that they have the potential to create more retro-DNAs in the current primate genomes. The identification of retro-DNAs, despite small in number, reveals a new mechanism in propagating the DNA transposons sequences in the primate genomes with the absence of canonical DNA transposon activity. It also suggests that the L1 TPRT machinery may have the ability to retrotranspose a wider variety of DNA sequences than what we currently know.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.020
GPT teacher head0.215
Teacher spread0.195 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2020
Admission routes2
Has abstractyes

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