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Record W4412494107 · doi:10.1126/sciadv.ads9164

A phylogenetic approach uncovers cryptic endogenous retrovirus subfamilies in the primate lineage

2025· article· en· W4412494107 on OpenAlexaff
Xun Chen, Zicong Zhang, Yi-Zhi Yan, Clément Goubert, Guillaume Bourque, Fumitaka Inoue

Bibliographic record

VenueScience Advances · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicChromosomal and Genetic Variations
Canadian institutionsMcGill University
FundersJapan Society for the Promotion of Science
KeywordsEndogenous retrovirusPhylogenetic treeBiologyGenomeAnnotationLong terminal repeatPhylogeneticsRetrovirusLineage (genetic)GeneticsEvolutionary biologyComputational biologyGene

Abstract

fetched live from OpenAlex

Current approaches for classifying and annotating endogenous retroviruses (ERVs) and their long terminal repeats (LTRs) have limited resolution and are inaccurate. Here, we developed an annotation approach based on phylogenetic analysis and cross-species conservation. Focusing on the evolutionarily young LTR subfamilies known as MER11A/B/C, we revealed the presence of four "new subfamilies," suggesting a new annotation for 412 (19.8%) of these repeat elements. We then validated their regulatory potential using a massively parallel reporter assay. We further identified motifs associated with their differential activities including an ape-specific gain of SOX-related motifs through a single-nucleotide deletion. By applying our approach across 53 simian-enriched LTR subfamilies, we defined 75 new subfamilies and found a novel annotation for a total of 3807 (30.0%) instances from 26 subfamilies. With this refined annotation of simian-enriched LTRs, it will be possible to better understand the evolution in primate genomes and potentially identify critical roles for ERVs and their LTRs in the hosts.

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)0.001
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 score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.247
Teacher spread0.227 · 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".

Quick stats

Citations7
Published2025
Admission routes1
Has abstractyes

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