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Record W4403432522 · doi:10.1093/gbe/evae220

The Highly Repetitive Genome of <i>Myxobolus rasmusseni</i>, an Emerging Myxozoan Parasite of Fathead Minnows

2024· article· en· W4403432522 on OpenAlexafffundabout
Viraj Muthye, Aralia Leon‐Coria, Cameron P. Goater, Constance A. M. Finney, James D. Wasmuth

Bibliographic record

VenueGenome Biology and Evolution · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMyxozoan Parasites in Aquatic Species
Canadian institutionsUniversity of LethbridgeUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Calgary
KeywordsBiologyGenomeEvolutionary biologyGenome sizeMyxozoaParasite hostingMitochondrial DNAZoologyMonophylyGeneticsGenePhylogeneticsCladeFish <Actinopterygii>Fishery

Abstract

fetched live from OpenAlex

Myxozoans are a monophyletic taxon of approximately 2,400 described species of parasites from the phylum Cnidaria. The recent focus on their negative impacts on fisheries, on their evolution from free-living ancestors, and on their emergence into new fish host populations has stressed the critical need for genomic resources for this parasitic group. Here, we describe the genome assembly and annotation of Myxobolus rasmusseni, an emerging parasite of fathead minnows in Alberta, Canada. The assembly is 174.6 Mb in size, 68% of which is made up of repetitive elements, making it one of the most repetitive animal genomes sequenced to date. Through comparisons to other myxozoans, we show that widespread gene loss, a known phenomenon of this group of parasites, is consistent with closely related species. Additionally, we assembled the M. rasmusseni mitochondrial genome, which is nearly twice the size of the typical animal mitochondrial genome yet contains only five of the canonical mitochondrial protein-coding genes and open reading frames not found in other myxozoans. These results add to our understanding of the gene- and genome-level diversity observed in myxozoans.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.771
Threshold uncertainty score0.514

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 studies0.0000.001
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 head0.009
GPT teacher head0.285
Teacher spread0.276 · 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

Citations2
Published2024
Admission routes3
Has abstractyes

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