Physical Maps for Genome Analysis of Serotype A and D Strains of the Fungal Pathogen <i>Cryptococcus neoformans</i>
Bibliographic record
Abstract
The basidiomycete fungus Cryptococcus neoformans is an important opportunistic pathogen of humans that poses a significant threat to immunocompromised individuals. Isolates of C. neoformans are classified into serotypes (A, B, C, D, and AD) based on antigenic differences in the polysaccharide capsule that surrounds the fungal cells. Genomic and EST sequencing projects are underway for the serotype D strain JEC21 and the serotype A strain H99. As part of a genomics program for C. neoformans, we have constructed fingerprinted bacterial artificial chromosome (BAC) clone physical maps for strains H99 and JEC21 to support the genomic sequencing efforts and to provide an initial comparison of the two genomes. The BAC clones represented an estimated 10-fold redundant coverage of the genomes of each serotype and allowed the assembly of 20 contigs each for H99 and JEC21. We found that the genomes of the two strains are sufficiently distinct to prevent coassembly of the two maps when combined fingerprint data are used to construct contigs. Hybridization experiments placed 82 markers on the JEC21 map and 102 markers on the H99 map, enabling contigs to be linked with specific chromosomes identified by electrophoretic karyotyping. These markers revealed both extensive similarity in gene order (conservation of synteny) between JEC21 and H99 as well as examples of chromosomal rearrangements including inversions and translocations. Sequencing reads were generated from the ends of the BAC clones to allow correlation of genomic shotgun sequence data with physical map contigs. The BAC maps therefore represent a valuable resource for the generation, assembly, and finishing of the genomic sequence of both JEC21 and H99. The physical maps also serve as a link between map-based and sequence-based data, providing a powerful resource for continued genomic studies
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".