Mining and survey of simple sequence repeats in expressed sequence tags of dicotyledonous species
Why is this work in the frame?
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.
Full frame distilled prediction
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.
- Candidate categories
- none
- Consensus categories
- none
- Domain
- Candidate signal: noneConsensus signal: none
- Study design
- Candidate signal: Bench or experimentalConsensus signal: none
- Genre
- Candidate signal: EmpiricalConsensus signal: Empirical
- Teacher disagreement score
- 0.658
- Threshold uncertainty score
- 0.299
- Validation status
machine_predicted_unvalidated·codex-gemma-dda1882f352a
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 | 0.000 |
| 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)
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
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.
- Teacher spread
- 0.210 · how far apart the two teachers sit on this one work
- Validation status
score_only:v0-immature-baseline· verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it
Abstract
Simple sequence repeat (SSR) markers are widely used in many plant and animal genomes due to their abundance, hypervariability, and suitability for high-throughput analysis. Development of SSR markers using molecular methods is time consuming, laborious, and expensive. Use of computational approaches to mine ever-increasing sequences such as expressed sequence tags (ESTs) in public databases permits rapid and economical discovery of SSRs. Most of such efforts to date focused on mining SSRs from monocotyledonous ESTs. In this study, we have computationally mined and examined the abundance of SSRs in more than 1.54 million ESTs belonging to 55 dicotyledonous species. The frequency of ESTs containing SSRs among species ranged from 2.65% to 16.82%. Dinucleotide repeats were found to be the most abundant followed by tri- or mono-nucleotide repeats. The motifs A/T, AG/GA/CT/TC, and AAG/AGA/GAA/CTT/TTC/TCT were the predominant mono-, di-, and tri-nucleotide SSRs, respectively. Most of the mononucleotide SSRs contained 15-25 repeats, whereas the majority of the di- and tri-nucleotide SSRs contained 5-10 repeats. The comprehensive SSR survey data presented here demonstrates the potential of in silico mining of ESTs for rapid development of SSR markers for genetic analysis and applications in dicotyledonous crops.
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.
The record
- Venue
- Genome
- Topic
- Genetic Mapping and Diversity in Plants and Animals
- Field
- Biochemistry, Genetics and Molecular Biology
- Canadian institutions
- not available
- Funders
- Lilly EndowmentDow AgroSciencesNational Science Foundation
- Keywords
- BiologySequence (biology)GeneticsExpressed sequence tagComputational biologyEvolutionary biologyRepeated sequenceGenomeGene
- Has abstract in OpenAlex
- yes