Recent progress, developments, and issues in comparative fungal genomics
Bibliographic record
Abstract
Biologists face an overwhelming richness of nucleotide and protein sequence data. As of the end of 2003, there were over 100 complete or almost complete nonviral genomes in publicly available databases. Most of these were bacterial, since prokaryotic genomes are generally much smaller in size than eukaryotic genomes. Among eukaryotes, fungi have some of the smallest genome sizes and, hence, represent the highest number of complete or almost complete genomes sequenced, with most of these released within the last 2 years. What are the genes that fungi have in common? Among these genes, which ones have homologs in plants, animals, or bacteria, and which ones are only found in fungi? Researchers are just beginning to be able to address these types of questions with data from high-throughput genomic sequencing. This paper examines some recent and possible future uses of fungal genomic data in comparative genome analyses, particularly as they relate to the study of fungal plant pathogens. Comparative genomics can facilitate research into the following areas: phylogenetics (via whole genome comparisons), targeted drugs (via unique target sites in pests), gene discovery (via conserved sequences), and gene function (via guilt by association). Each of these is discussed as well as the availability and ownership of the genomic data, and the concepts of homology and similarity.
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 | 0.030 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| 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".