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Record W1991119060 · doi:10.1089/cmb.2004.11.933

The Role of Unequal Crossover in Alpha-Satellite DNA Evolution: A Computational Analysis

2004· article· en· W1991119060 on OpenAlexaff
Can Alkan, Evan E. Eichler, Jeffrey A. Bailey, S. Cenk Şahinalp, Eray Tüzün

Bibliographic record

VenueJournal of Computational Biology · 2004
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicChromosomal and Genetic Variations
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsCrossoverSatelliteAlpha (finance)Computational biologyComputer scienceBiologyEvolutionary biologyMathematicsAerospace engineeringArtificial intelligenceEngineeringStatistics

Abstract

fetched live from OpenAlex

Human DNA consists of a large number of tandem repeat sequences. Such sequences are usually called satellites, with the primary example being the centromeric alpha-satellite DNA. The basic repeat unit of the alpha-satellite DNA is a 171 bp monomer. Arbitrary monomer pairs usually have considerable sequence divergence (20-40%). However, with the exception of peripheral alpha-satellite DNA, monomers can be grouped into blocks of k-monomers (4 < or = k < or = 20) between which the divergence rate is much smaller (e.g., 5%). Perhaps the simplest and best understood mechanism for tandem repeat array evolution is unequal crossover. Although it is possible that alpha-satellite sequences developed as a result of subsequent unequal crossovers only, no formal computational framework seems to have been developed to verify this possibility. In this paper, we develop such a framework and report on experiments which imply that pericentromeric alpha-satellite segments (which are devoid of higher order structure) are evolutionarily distinct from the higher order repeat segments. It is likely that the higher order repeats developed independently in distinct regions of the genome and were carried into their current locations through an unknown mechanism of transposition.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.755
Threshold uncertainty score0.119

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.009
GPT teacher head0.238
Teacher spread0.229 · 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 designSimulation or modeling
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

Citations31
Published2004
Admission routes1
Has abstractyes

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