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Record W1966388395 · doi:10.1159/000097415

Identification of a human chromosome-specific interstitial telomere-like sequence (ITS) at 22q11.2 using double-strand PRINS

2007· article· en· W1966388395 on OpenAlexaff
Jun Yan, EricF. Bouchard, Oumar Samassékou, B.-Z. Chen

Bibliographic record

VenueCytogenetic and Genome Research · 2007
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCongenital heart defects research
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsTelomereBiologyLocus (genetics)GeneticsTandem repeatChromosomeDiGeorge syndromeMicrosatelliteGenomeDNAGeneAllele

Abstract

fetched live from OpenAlex

Interstitial telomeric sequences (ITSs), telomere-like repeats at intrachromosomal sites, are common in mammals and consist of tandem repeats of the canonical telomeric repeat, TTAGGG, or a repeat similar to this. We report that the ITS in human chromosome region 22q11.2 is, in the sequenced genome database, 101 tandem repeats of the sequence TTAGGGAGG. Using the primed in situ labeling (PRINS) technique and primers against the canonical telomeric repeat (TTAGGG), we illuminated telomeric sites for all chromosomes and an ITS locus at 22q11.2. Using the TTAGGGAGG sequence, we designed PRINS primers that efficiently and specifically illuminate the 22q11.2 ITS locus without illuminating telomeric and other ITS loci. The 22q11.2 locus has more repeat units than other ITSs loci enabling an unprecedented high detection frequency for this interstitial telomere locus. The 22q11.2 is associated with hot spots for disease-related chromosome breaks for multiple disorders, such as DiGeorge syndrome and chronic myeloid leukemia. We describe our findings that the ITS at 22q11.2 is in the same area of, and proximal to the common rearrangement region of multiple disorders. We suggest that the ITS might be involved in DNA repair processes in this area to protect the chromosome from more serious damage.

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)0.002
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.728

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 science0.0000.001
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 head0.146
GPT teacher head0.416
Teacher spread0.270 · 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

Citations8
Published2007
Admission routes1
Has abstractyes

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