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Record W2547546878 · doi:10.1093/gbe/evw255

<i>Blastocystis</i>Mitochondrial Genomes Appear to Show Multiple Independent Gains and Losses of Start and Stop Codons

2016· article· en· W2547546878 on OpenAlexafffund
Alison S. Jacob, Lee O’Brien Andersen, Paulina Pavinski Bitar, Vincent P. Richards, Sarah Shah, Michael J. Stanhope, Christen Rune Stensvold, C Graham Clark

Bibliographic record

VenueGenome Biology and Evolution · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomics and Phylogenetic Studies
Canadian institutionsDalhousie University
FundersLundbeckfondenCanadian Institutes of Health ResearchDalhousie University
KeywordsBiologySyntenyGeneGeneticsGenomeTransfer RNAOpen reading frameComparative genomicsStart codonGC-contentStop codonRibosomal RNARNAComputational biologyGenomicsMessenger RNAPeptide sequence

Abstract

fetched live from OpenAlex

Complete mitochondrion-related organelle (MRO) genomes of several subtypes (STs) of the unicellular stramenopile Blastocystis are presented. Complete conservation of gene content and synteny in gene order is observed across all MRO genomes, comprising 27 protein coding genes, 2 ribosomal RNA genes, and 16 transfer RNA (tRNA) genes. Despite the synteny, differences in the degree of overlap between genes were observed between subtypes and also between isolates within the same subtype. Other notable features include unusual base-pairing mismatches in the predicted secondary structures of some tRNAs. Intriguingly, the rps4 gene in some MRO genomes is missing a start codon and, based on phylogenetic relationships among STs, this loss has happened twice independently. One unidentified open reading frame (orf160) is present in all MRO genomes. However, with the exception of ST4 where the feature has been lost secondarily, orf160 contains variously one or two in-frame stop codons. The overall evidence suggests that both the orf160 and rps4 genes are functional in all STs, but how they are expressed remains unclear.

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 score0.824
Threshold uncertainty score0.442

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 studies0.0000.000
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 head0.012
GPT teacher head0.233
Teacher spread0.221 · 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

Citations12
Published2016
Admission routes2
Has abstractyes

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