Microsatellite markers developed in the stingless bee <i>Melipona fasciculata</i> by next-generation sequencing and an exploratory analysis of geographic genetic variation
Bibliographic record
Abstract
Background. Native meliponines are currently threatened by increased human impacts. The assessment of their genetic variation by microsatellite DNA markers can assist in the conservation of populations and help in the planning and establishment of efficient management strategies. Next generation sequencing has proven to be useful for identifying microsatellite loci from the large amounts of sequence data generated. Methods. The purpose of this study was to develop the first set of microsatellite markers for Melipona fasciculata, selected from partial genome assembly of Illumina paired-end reads. Contigs were created from the resulting paired-end sequence data and these were analyzed with specialized software to extract those reads that contained microsatellite loci. Primer pairs were designed for each detected locus at their flanking regions. Bee samples were genotyped from two different locations for markers characterization and validation. Results. A total of 17 microsatellite loci displayed polymorphism from two different populations of Northeastern Brazil. Mean HE and HO heterozygosities were 0.453 and 0.536, respectively. PIC across all loci ranged from 0.108 to 0.714. A genetic diversity analysis revealed high values for population differentiation estimates (FST = 0.194, RST = 0.230, and Dest = 0.162). PCoA and Bayesian clustering showed a separation of the species into two distinct clusters. Discussion. The Illumina paired-end sequencing system provided a large number of microsatellite loci from the M. fasciculata genome. From the genotyped data this study was able to reveal high FST and RST estimates and suggest the existence of genetic structure. These microsatellite markers have demonstrated strong potential for population-level genetic studies and can be used effectively as a molecular tool. Moreover, the exploratory analysis of the genetic diversity in M. fasciculata provides provisional evidence of significant population differentiation between the two studied populations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".