CANT-HYD: A curated database of phylogeny-derived Hidden Markov Models for annotation of marker genes involved in hydrocarbon degradation
Bibliographic record
Abstract
ABSTRACT Discovery of microbial hydrocarbon degradation pathways has traditionally relied on laboratory isolation and characterization of microorganisms. Although many metabolic pathways for hydrocarbon degradation have been discovered, the absence of tools dedicated to their annotation makes it difficult to identify the relevant genes and predict the hydrocarbon degradation potential of microbial genomes and metagenomes. Furthermore, sequence homology between hydrocarbon degradation genes and genes with other functions often results in misannotation. A tool that systematically identifies hydrocarbon metabolic potential is therefore needed. We present the Calgary approach to ANnoTating HYDrocarbon degradation genes (CANT-HYD), a database containing HMMs of 37 marker genes involved in anaerobic and aerobic degradation pathways of aliphatic and aromatic hydrocarbons. Using this database, we show that hydrocarbon metabolic potential is widespread in the tree of life and identify understudied or overlooked hydrocarbon degradation potential in many phyla. We also demonstrate scalability by analyzing large metagenomic datasets for the prediction of hydrocarbon utilization in diverse environments. To the best of our knowledge, CANT-HYD is the first comprehensive tool for robust and accurate identification of marker genes associated with aerobic and anaerobic hydrocarbon degradation.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".