SonHi-C: a set of non-procedural approaches for predicting 3D genome organization from Hi-C data
Bibliographic record
Abstract
1 Abstract Background Many computational methods have been developed that leverage the results from biological experiments (such as Hi-C) to infer the 3D organization of the genome. Formally, this is referred to as the 3D genome reconstruction problem (3D-GRP). None of the existing methods for solving the 3D-GRP have utilized a non-procedural programming approach (such as constraint programming or integer programming) despite the established advantages and successful applications of such approaches for predicting the 3D structure of other biomolecules. Our objective was to develop a set of mathematical models and corresponding non-procedural implementations for solving the 3D-GRP to realize the same advantages. Results We present a set of non-procedural approaches for predicting 3D genome organization from Hi-C data (collectively referred to as SonHi-C and pronounced “sonic”). Specifically, this set is comprised of three mathematical models based on constraint programming ( CP ), graph matching ( GM ) and integer programming ( IP ). All of the mathematical models were implemented using non-procedural languages and tested with Hi-C data from Schizosaccharomyces pombe (fission yeast). The CP implementation could not optimally solve the problem posed by the fission yeast data after several days of execution time. The GM and IP implementations were able to predict a 3D model of the fission yeast genome in 1.088 and 294.44 seconds, respectively. These 3D models were then biologically validated through literature search which verified that the predictions were able to recapitulate key documented features of the yeast genome. Conclusions Overall, the mathematical models and programs developed here demonstrate the power of non-procedural programming and graph theoretic techniques for quickly and accurately modelling the 3D genome from Hi-C data. Additionally, they highlight the practical differences observed when differing non-procedural approaches are utilized to solve the 3D-GRP.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".