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Record W2989242366 · doi:10.1038/s41598-019-52840-z

Expansion of LINEs and species-specific DNA repeats drives genome expansion in Asian Gypsy Moths

2019· article· en· W2989242366 on OpenAlexafffund
François Hébert, Luca Freschi, Gwylim S. Blackburn, Catherine Béliveau, Ken Dewar, Brian Boyle, Dawn E. Gundersen‐Rindal, Michael E. Sparks, Michel Cusson, Richard C. Hamelin, Roger C. Lévesque

Bibliographic record

VenueScientific Reports · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicLepidoptera: Biology and Taxonomy
Canadian institutionsUniversity of British ColumbiaMcGill UniversityUniversité du Québec à Trois-RivièresCanadian Forest ServiceNatural Resources CanadaUniversité Laval
FundersAnimal and Plant Health Inspection ServiceGenome British ColumbiaGenome CanadaU.S. Department of Agriculture
KeywordsSubspeciesGenomeLymantria disparBiologyGypsy mothJaponicaGenome sizeEvolutionary biologyDisparGeneticsGenePEST analysisLepidoptera genitaliaZoologyEcologyBotany

Abstract

fetched live from OpenAlex

Two subspecies of Asian gypsy moth (AGM), Lymantria dispar asiatica and L. dispar japonica, pose a serious alien invasive threat to North American forests. Despite decades of research on the ecology and biology of this pest, limited AGM-specific genomic resources are currently available. Here, we report on the genome sequences and functional content of these AGM subspecies. The genomes of L.d. asiatica and L.d. japonica are the largest lepidopteran genomes sequenced to date, totaling 921 and 999 megabases, respectively. Large genome size in these subspecies is driven by the accumulation of specific classes of repeats. Genome-wide metabolic pathway reconstructions suggest strong genomic signatures of energy-related pathways in both subspecies, dominated by metabolic functions related to thermogenesis. The genome sequences reported here will provide tools for probing the molecular mechanisms underlying phenotypic traits that are thought to enhance AGM invasiveness.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.329
Threshold uncertainty score0.550

Codex and Gemma teacher scores by category

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

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 head0.011
GPT teacher head0.222
Teacher spread0.211 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations13
Published2019
Admission routes2
Has abstractyes

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