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Record W636618501 · doi:10.7717/peerj-cs.6

Reconstructing the history of a WD40 beta-propeller tandem repeat using a phylogenetically informed algorithm

2015· article· en· W636618501 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePeerJ Computer Science · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomics and Phylogenetic Studies
Canadian institutionsUniversité de SherbrookeUniversité du Québec à Montréal
Fundersnot available
KeywordsBETA (programming language)AlgorithmComputer scienceComputational biologyBiology

Abstract

fetched live from OpenAlex

Tandem repeat sequences have been found in great numbers in proteins that are conserved in a wide range of living species. In order to reconstruct the evolutionary history of such sequences, it is necessary to develop algorithms and methods that can work with highly divergent motifs. Here we propose a reconstruction algorithm that uses, in parallel, ortholog tandem repeat sequences from n species whose phylogeny is known, allowing it to distinguish mutations that occurred before and after the first speciation. At each step of the reconstruction, both the boundaries and the length of the duplicated segment are recalculated, making the approach suitable for sequences for which the fixed boundary hypothesis may not hold. We use this algorithm to reconstruct a 4-bladed ancestor of the 7-bladed WD40 beta-propeller, using orthologs of the GNB1 human protein in plants, yeasts, nematodes, insects and fishes. The results obtained for the WD40 repeats are very encouraging, as the noise in the duplication reconstruction is significantly reduced.

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.

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 score0.509
Threshold uncertainty score0.379

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 science0.0010.001
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 head0.052
GPT teacher head0.260
Teacher spread0.209 · 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