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Record W2006192575 · doi:10.1186/1471-2164-15-708

Genome wide SNP identification in chickpea for use in development of a high density genetic map and improvement of chickpea reference genome assembly

2014· article· en· W2006192575 on OpenAlexafffund
Amit Deokar, Larissa Ramsay, Andrew Sharpe, Marwan Diapari, Anoop Sindhu, Kirstin E. Bett, Thomas D. Warkentin, Bunyamin Tar’an

Bibliographic record

VenueBMC Genomics · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicGenetic and Environmental Crop Studies
Canadian institutionsNational Research Council CanadaPlant Biotechnology InstituteSaskatchewan Research Council (Canada)University of Saskatchewan
FundersAgriculture and Agri-Food CanadaSaskatchewan Pulse Growers
KeywordsBiologyGeneticsSingle-nucleotide polymorphismTag SNPGenotypingGenomeSNP genotypingReference genomePopulationMolecular Inversion ProbeSNP arrayDNA sequencingSequence assemblyGenotypeGeneTranscriptome

Abstract

fetched live from OpenAlex

BACKGROUND: In the whole genome sequencing, genetic map provides an essential framework for accurate and efficient genome assembly and validation. The main objectives of this study were to develop a high-density genetic map using RAD-Seq (Restriction-site Associated DNA Sequencing) genotyping-by-sequencing (RAD-Seq GBS) and Illumina GoldenGate assays, and to examine the alignment of the current map with the kabuli chickpea genome assembly. RESULTS: Genic single nucleotide polymorphisms (SNPs) totaling 51,632 SNPs were identified by 454 transcriptome sequencing of Cicer arietinum and Cicer reticulatum genotypes. Subsequently, an Illumina GoldenGate assay for 1,536 SNPs was developed. A total of 1,519 SNPs were successfully assayed across 92 recombinant inbred lines (RILs), of which 761 SNPs were polymorphic between the two parents. In addition, the next generation sequencing (NGS)-based GBS was applied to the same population generating 29,464 high quality SNPs. These SNPs were clustered into 626 recombination bins based on common segregation patterns. Data from the two approaches were used for the construction of a genetic map using a population derived from an intraspecific cross. The map consisted of 1,336 SNPs including 604 RAD recombination bins and 732 SNPs from Illumina GoldenGate assay. The map covered 653 cM of the chickpea genome with an average distance between adjacent markers of 0.5 cM. To date, this is the most extensive genetic map of chickpea using an intraspecific population. The alignment of the map with the CDC Frontier genome assembly revealed an overall conserved marker order; however, a few local inconsistencies within the Cicer arietinum pseudochromosome 1 (Ca1), Ca5 and Ca8 were detected. The map enabled the alignment of 215 unplaced scaffolds from the CDC Frontier draft genome assembly. The alignment also revealed varying degrees of recombination rates and hotspots across the chickpea genome. CONCLUSIONS: A high-density genetic map using RAD-Seq GBS and Illumina GoldenGate assay was developed and aligned with the existing kabuli chickpea draft genome sequence. The analysis revealed an overall conserved marker order, although some localized inversions between draft genome assembly and the genetic map were detected. The current analysis provides an insight of the recombination rates and hotspots across the chickpea genome.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.037
GPT teacher head0.206
Teacher spread0.169 · 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 source (direct Gemma or distilled Codex), 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

Citations115
Published2014
Admission routes2
Has abstractyes

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