Figmop: a profile HMM to identify genes and bypass troublesome gene models in draft genomes
Bibliographic record
Abstract
MOTIVATION: Gene models from draft genome assemblies of metazoan species are often incorrect, missing exons or entire genes, particularly for large gene families. Consequently, labour-intensive manual curation is often necessary. We present Figmop (Finding Genes using Motif Patterns) to help with the manual curation of gene families in draft genome assemblies. The program uses a pattern of short sequence motifs to identify putative genes directly from the genome sequence. Using a large gene family as a test case, Figmop was found to be more sensitive and specific than a BLAST-based approach. The visualization used allows the validation of potential genes to be carried out quickly and easily, saving hours if not days from an analysis. AVAILABILITY AND IMPLEMENTATION: Source code of Figmop is freely available for download at https://github.com/dave-the-scientist, implemented in C and Python and is supported on Linux, Unix and MacOSX. CONTACT: curran.dave.m@gmail.com SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. 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.
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)
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 teacher head, 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".