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Record W2048360413 · doi:10.1186/1471-2164-9-200

Genome sequence surveys of Brachiola algerae and Edhazardia aedis reveal microsporidia with low gene densities

2008· article· en· W2048360413 on OpenAlexafffund
Ben Williams, R. C. H. Lee, James J. Becnel, Louis M. Weiss, Naomi M. Fast, Patrick J. Keeling

Bibliographic record

VenueBMC Genomics · 2008
Typearticle
Languageen
FieldImmunology and Microbiology
TopicParasitic Infections and Diagnostics
Canadian institutionsUniversity of British ColumbiaCanadian Institute for Advanced Research
FundersNational Institute of Allergy and Infectious DiseasesMichael Smith Health Research BCNatural Sciences and Engineering Research Council of CanadaCanadian Institute for Advanced Research
KeywordsBiologyGenomeMicrosporidiaRetrotransposonIntergenic regionMicrosporidiosisGenome sizeTransposable elementGeneticsEvolutionary biologyGene densityGeneGenomicsGenome evolution

Abstract

fetched live from OpenAlex

BACKGROUND: Microsporidia are well known models of extreme nuclear genome reduction and compaction. The smallest microsporidian genomes have received the most attention, but genomes of different species range in size from 2.3 Mb to 19.5 Mb and the nature of the larger genomes remains unknown. RESULTS: Here we have undertaken genome sequence surveys of two diverse microsporidia, Brachiola algerae and Edhazardia aedis. In both species we find very large intergenic regions, many transposable elements, and a low gene-density, all in contrast to the small, model microsporidian genomes. We also find no recognizable genes that are not also found in other surveyed or sequenced microsporidian genomes. CONCLUSION: Our results demonstrate that microsporidian genome architecture varies greatly between microsporidia. Much of the genome size difference could be accounted for by non-coding material, such as intergenic spaces and retrotransposons, and this suggests that the forces dictating genome size may vary across the phylum.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.617
Threshold uncertainty score0.686

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.017
GPT teacher head0.223
Teacher spread0.206 · 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 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

Citations75
Published2008
Admission routes2
Has abstractyes

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