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Record W2885338625 · doi:10.18805/lr-401

Investigation of IRAP transposon-based molecular markers for analysis of genetic diversity in pea germplasm

2018· article· en· W2885338625 on OpenAlexaff
Sajjad Ahmad, Rajvinder Kaur, Mark Lefsrud, Jaswinder Singh

Bibliographic record

VenueLegume Research - An International Journal · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicChromosomal and Genetic Variations
Canadian institutionsMcGill University
Fundersnot available
KeywordsBiologyTransposable elementGermplasmRetrotransposonGeneticsGenetic diversityGenetic markerAlleleLocus (genetics)GenotypeGenomeBotanyGene

Abstract

fetched live from OpenAlex

Retrotransposons diversity has been extensively studied in monocots, but it is not well documented in dicot species. Transposition activity of transposons creates DNA polymorphism and their abundant presence in genomes is making transposons a promising marker system for varietal identification and fingerprinting. In this study, four transposon-based markers (two DNA- and two RNA-transposons) were employed to evaluate the effectiveness of Inter-Retrotransposon Amplified Polymorphism (IRAP) transposon system in assessing genetic diversity in pea germplasm accessions. A total of 28 alleles were detected across the 35 pea accessions with number of alleles per locus ranged from 5 (Mutator) to 9 (Cyclops). RNA transposons produced a higher number of polymorphic alleles (Ogre: 8, Cyclops: 9) than DNA transposon markers (Mutator: 5, MITE: 6). Overall mean PIC value and D values for these transposon markers were 0.810 and 0.817 respectively. Genetic similarity values ranged from 0.143 to 0.823 with a mean similarity value of 0.403. Cluster analysis classified pea genotypes into six major groups that were somewhat consistent with their geographical origins. The molecular analyses differentiated all the 35 accessions and generated higher PIC and D values that can be useful for MAS-based breeding programs in pea.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.402
Threshold uncertainty score0.457

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.105
GPT teacher head0.349
Teacher spread0.244 · 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

Citations1
Published2018
Admission routes1
Has abstractyes

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