Clustering Highly Divergent Homologous Proteins: An Alignment‐Free Method
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Bibliographic record
Abstract
The comparative analysis of amino acid sequences is an important tool in molecular biology that often requires multiple sequence alignments. In comparisons between less closely related genomes, however, it becomes more difficult to accurately align protein-coding sequences, or even to identify homologous regions in different genomes. In this article, we describe an alignment-free method for the classification of homologous protein-coding regions from different genomes. This methodology was originally developed for comparing genomes within virus families, but may be adapted for other organisms. We quantify sequence homology from the overlap (intersection distance) of the k-mer (word) frequency distributions for different protein sequences. Next, we extract groups of homologous sequences from the resulting distance matrix using a combination of dimensionality reduction and hierarchical clustering methods. Finally, we demonstrate how to generate visualizations of the composition of clusters with respect to protein annotations, and by coloring protein-coding regions of genomes by cluster assignments. These provide a useful means to quickly assess the reliability of the clustering results based on the distribution of homologous genes among genomes. © 2023 Wiley Periodicals LLC. Basic Protocol 1: Data collection and processing Basic Protocol 2: Calculating k-mer distances Basic Protocol 3: Extracting clusters of homology Support Protocol: Genome plot based on clustering results.
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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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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 it