Issues with the PipeAlign phylogenomics toolkit in identifying protein subfamilies
Bibliographic record
Abstract
Automated protein function annotation is extremely important in computational biology for its low cost. Standard sequence similarity comparison methods for annotation have limited specificity in identifying orthologs and paralogs. Phylogenomic methods are gaining popularity for their role in identifying orthologs and paralogs with the help of evolutionary information and sequence data. Pipelines have been developed for phylogenomic classification of proteins. Two such pipelines are PhyloFacts and PipeAlign. Given a protein of interest, these pipelines identify functional subfamilies for the protein superfamily. Subfamilies hold orthologs and paralogs and can later be used to identify orthologous groups. We evaluate the performance of PipeAlign with respect to both consistency in the generated subfamilies and phylogeny. We use the predefined subfamilies of PhyloFacts as a reference to compare the generated subfamilies of related reference sequences in PipeAlign. In the consistency analysis, we compare the compositions of the generated functional subfamilies with different related reference sequences, and use the predefined PhyloFacts subfamilies for the corresponding sequences as a measure of consistency. In the phylogenetic analysis, we compare the evolutionary distances of the members of the same and different generated subfamilies from PipeAlign.
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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 | 0.073 | 0.151 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.010 | 0.008 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 0.009 |
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".