Generation, annotation, and analysis of an extensive Aspergillus niger EST collection
Bibliographic record
Abstract
BACKGROUND: Aspergillus niger, a saprophyte commonly found on decaying vegetation, is widely used and studied for industrial purposes. Despite its place as one of the most important organisms for commercial applications, the lack of available information about its genetic makeup limits research with this filamentous fungus. RESULTS: We present here the analysis of 12,820 expressed sequence tags (ESTs) generated from A. niger cultured under seven different growth conditions. These ESTs identify about 5,108 genes of which 44.5% code for proteins sharing similarity (E < or = 1e(-5)) with GenBank entries of known function, 38% code for proteins that only share similarity with GenBank entries of unknown function and 17.5% encode proteins that do not have a GenBank homolog. Using the Gene Ontology hierarchy, we present a first classification of the A. niger proteins encoded by these genes and compare its protein repertoire with other well-studied fungal species. We have established a searchable web-based database that includes the EST and derived contig sequences and their annotation. Details about this project and access to the annotated A. niger database are available. CONCLUSION: This EST collection and its annotation provide a significant resource for fundamental and applied research with A. niger. The gene set identified in this manuscript will be highly useful in the annotation of the genome sequence of A. niger, the genes described in the manuscript, especially those encoding hydrolytic enzymes will provide a valuable source for researchers interested in enzyme properties and applications.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 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) | 0.002 | 0.003 |
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".