PMDBase: a database for studying microsatellite DNA and marker development in plants
Bibliographic record
Abstract
Microsatellite DNAs (or SSRs) are important genomic components involved in many important biological functions. SSRs have been extensively exploited as molecular markers for diverse applications including genetic diversity, linkage/association mapping of gene/QTL, marker-assisted selection, variety identification and evolution analysis. However, a comprehensive database or web service for studying microsatellite DNAs and marker development in plants is lacking. Here, we developed a database, PMDBase, which integrates large amounts of microsatellite DNAs from genome sequenced plant species and includes a web service for microsatellite DNAs identification. In PMDBase, 26 230 099 microsatellite DNAs were identified spanning 110 plant species. Up to three pairs of primers were supplied for every microsatellite DNA. For 81 species, genomic features of the microsatellite DNAs (genic or non-genic) were supplied with the corresponding genes or transcripts from public databases. Microsatellite DNAs can be explored through browsing and searching modules with a user-friendly web interface and customized software. Furthermore, we developed MISAweb and embedded Primer3web to help users to identify microsatellite DNAs and design corresponding primers in their own genomic sequences online. All datasets of microsatellite DNAs can be downloaded conveniently. PMDBase will be updated regularly with new available genome data and can be accessed freely via the address http://www.sesame-bioinfo.org/PMDBase.
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 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.019 |
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".