Genetic diversity of cultivated Nigella sativa L. germplasm based on EST-SSR markers and agro-morphological traits
Bibliographic record
Abstract
Nigella sativa L. (Black cumin) has been extensively utilized in traditional medicine since ancient times. Researching genetic resources to identify the best germplasm for enhancing pharmaceutical properties is an important step in maximizing the potential of this beneficial plant. In this study, EST-SSR markers were designed and investigated alongside thymoquinone content and some important agro-morphological traits e.g., seed yield. After de novo transcriptome sequencing using the Illumina platform, data mining was performed to identify unigenes containing microsatellites using MISA software. Among 50 designed EST-SSR primers, 19 pairs reproduced polymorphic bands which were employed to estimate the genetic diversity of 32 accessions of black cumin provided from across the world. A total of 117 bands (75%) were polymorphic among 156 amplified bands. The PIC values varied from 0.47 (NS-587 and NS-183) to 0.69 (NS-242). The assessment of phytochemical variability and yield traits revealed a thymoquinone content of 0.1–1.38 %, a thousand-seed weight of 1.7–3.78 g, and a seed yield per plant of 0.28–1.74 g. Classification of accessions based on morphological traits resulted in three distinct clusters, while genetic analysis yielded four clusters. Notably, the high concordance between the morphological and genetic dendrograms indicated that the genetic loci targeted by EST-SSRs in this study can effectively represent key morphological traits. This study yielded valuable insights into the genetic diversity status of Nigella sativa , while also introduced some accessions for further breeding endeavors. • EST-SSR markers were identified by transcriptome sequences of Nigella sativa. • The genetic diversity of 32 Nigella sativa accessions was evaluated. • A high concordance between genetic and agro-morphological dendrograms was found. • The highest thymoquinone content (1.38%) was recorded in accession N45. • Accessions (N71 and RZ) with high seed yield were identified.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 teacher head, 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".