Creation of a BAC resource to study the structure and evolution of the banana (<i>Musa balbisiana</i>) genome
Bibliographic record
Abstract
The first bacterial artificial chromosome (BAC) library of the banana species Musa balbisiana 'Pisang Klutuk Wulung' (PKW BAC library) was constructed and characterized. One improved and one novel protocol for nuclei isolation were employed to overcome problems caused by high levels of polyphenols and polysaccharides present in leaf tissues. The use of flow cytometry to purify cell nuclei eliminated contamination with secondary metabolites and plastid DNA. Furthermore, the usefulness of the inducible pCC1BAC vector to obtain a higher amount of BAC DNA was demonstrated. The PKW BAC library represents nine haploid genome equivalents of M. balbisiana and its mean insert size is 135 kb. It consists of two sublibraries, of which the first one (SN sublibrary with 24,960 clones) was prepared according to an improved standard nuclei isolation protocol, whereas the second (FN sublibrary with 11,904 clones) was obtained from flow-sorted nuclei. Screening with 12 RFLP probes, which were genetically anchored to 8 genetic linkage groups of the banana species Musa acuminata, revealed an average of 11 BAC clones per probe, thus confirming the genome coverage estimated based on the insert size, as well as a high level of conservation between the two species of Musa. Localization of selected BAC clones to mitotic chromosomes using FISH indicated that the BAC library represented a useful resource for cytogenetic mapping. As the first step in map-based cloning of a genetic factor that is involved in the activation of integrated pararetroviral sequences of Banana streak virus (BSV), the BSV expressed locus (BEL) was physically delimited. The PKW BAC library represents a publicly available tool, and is currently used to reveal the integration and activation mechanisms of BSV sequences and to study banana genome structure and evolution.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".