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Record W2166243950 · doi:10.1093/nar/gkq883

Systematically fragmented genes in a multipartite mitochondrial genome

2010· article· en· W2166243950 on OpenAlexafffund
Čestmı́r Vlček, William Marande, Shona Teijeiro, Julius Lukeš, Gertraud Burger

Bibliographic record

VenueNucleic Acids Research · 2010
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomics and Phylogenetic Studies
Canadian institutionsUniversité de Montréal
FundersCanadian Institutes of Health ResearchGrantová Agentura České Republiky
KeywordsBiologyGeneticsGeneMitochondrial DNATrans-splicingRibosomal RNARibosomal proteinGenomeRNA splicingComputational biologyRibosomeRNA

Abstract

fetched live from OpenAlex

Arguably, the most bizarre mitochondrial DNA (mtDNA) is that of the euglenozoan eukaryote Diplonema papillatum. The genome consists of numerous small circular chromosomes none of which appears to encode a complete gene. For instance, the cox1 coding sequence is spread out over nine different chromosomes in non-overlapping pieces (modules), which are transcribed separately and joined to a contiguous mRNA by trans-splicing. Here, we examine how many genes are encoded by Diplonema mtDNA and whether all are fragmented and their transcripts trans-spliced. Module identification is challenging due to the sequence divergence of Diplonema mitochondrial genes. By employing most sensitive protein profile search algorithms and comparing genomic with cDNA sequence, we recognize a total of 11 typical mitochondrial genes. The 10 protein-coding genes are systematically chopped up into three to 12 modules of 60-350 bp length. The corresponding mRNAs are all trans-spliced. Identification of ribosomal RNAs is most difficult. So far, we only detect the 3'-module of the large subunit ribosomal RNA (rRNA); it does not trans-splice with other pieces. The small subunit rRNA gene remains elusive. Our results open new intriguing questions about the biochemistry and evolution of mitochondrial trans-splicing in Diplonema.

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 score0.670
Threshold uncertainty score0.547

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 head0.028
GPT teacher head0.317
Teacher spread0.289 · 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

Citations92
Published2010
Admission routes2
Has abstractyes

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