Towards Improved Assessment of Functional Similarity in Large-Scale Screens: A Study on Indel Length
Bibliographic record
Abstract
Although insertions and deletions are a common type of evolutionary sequence variation, their origins and their functional consequences have not been comprehensively understood. Most alignment algorithms/programs only roughly reflect the evolutionary processes that result in gaps--which typically require further evaluation. Interestingly, it is widely believed that gaps are the predominant form of sequence variation resulting in structural and functional changes. Thus it is desirable to distinguish between gaps that reflect true point mutations and alignment artifacts when it comes to assessing the functional similarity of proteins based on computational alignments. Here we introduce pair hidden Markov model-based solutions to rapidly assess the statistical significance of gaps in alignments resulting from classical Needleman-Wunsch-like alignment procedures which implement affine gap penalty scoring schemes. Surprisingly, although it has a natural formulation, the emanating Markov chain problem had no known efficient solution thus far. In this article, we present the first efficient algorithm to solve it. We demonstrate that, when comparing paralogous protein pairs (from Escherichia coli) of equal alignment identity and similarity, alignments that contain gaps of significant length are significantly less similar in terms of functionality, as measured with respect to Gene Ontology (GO) term similarity. This demonstrates for the first time, in a formally sound manner, that insertions and deletions cause more severe functional changes between proteins than substitutions. Our method can be reliably employed to quickly filter alignment outputs for protein pairs that are more likely to be functionally similar and/or divergent and establishes a sound and useful add-on for large-scale alignment studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".