Operational Difference on DNA: a release ladder and a repair solver for sequence variants, with fifteen pre-registered sealed audits of three public splicing datasets (restricted research package, all rounds)
Abstract
Operational Difference on DNA: a release ladder and a repair solver for sequence variants, with fifteen pre-registered sealed audits of three public splicing datasets (restricted research package, all rounds)
Description
Eric J. Needham, Symmersion, Agios Onoufrios, Crete. 21 October 2026.
What this is
The complete record of applying the Operational Difference method to sequence-to-function data, using RNA splicing as the test domain. The method is implemented as a genomics module of the Symmersion audit software, together with the compatibility-residual repair solver carried over from field equations to data.
Two instruments. The release ladder is iterated conditional quotient refinement: starting from the exon, it asks which declared map still splits the outcome within the current refinement, releases the strongest map that survives a within-exon matched null with family-wise error control, and repeats. The repair solver treats each measured variant as a jet, with the kernel of the measured design, and returns verdicts of point, class or outside the basis against a replicate noise floor.
One protocol. Every analysis is locked before any outcome is read: the analysis, the predictions and the thresholds are written and hashed first, the results are sealed afterwards, and each is re-run from its lock to confirm it reproduces field by field. Known-answer validation on planted data precedes every lock, including scenarios written to be adversarial to the method. Disclosed errors are recorded as errata rather than corrected silently, and an aborted lock is kept on the record.
Across fifteen rounds, 123 predictions were locked in advance and 98 were met. All seventeen seals verify against their locks and input hashes, and every analysis reproduces from its lock.
The progression, round by round
Round 1 (8/12 and 10/12 met, two libraries)
Audits the known splicing maps on the public MFASS libraries: 27,733 natural variants across 2,185 exons, and 13,075 designed perturbations assayed in two intron backbones. The ladder releases graded donor strength first, then the canonical-site indicator, graded acceptor strength, the hexamer map, folding, silencers, enhancers and the translation map. Designed changes that hold the splice-site score constant move inclusion 5.2 to 11.8 times less than changes designed to weaken it, in both backbones and at both sites: absorbed at one map, consequential at another. Four misses came from one mistaken expectation, that the binary canonical map would outrank the graded one.
Round 2 (5/6 and 5/5 met)
Offers SpliceAI as a candidate new map. Given every first-round map it is still consequential within exons, in both libraries and both backbones, so it is a new map in the solver's sense; on held-out exons it shrinks the structured remainder by about seven points. Most of the remainder therefore stands.
Round 3 (11/11 met)
Blind recovery. A learner given no splicing knowledge — only the four letters, positions relative to the exon boundaries, and outcomes on half the exons — is scored against an answer key locked in advance. It recovers the donor consensus at 9 of 9 positions and the acceptor at 20 of 20, against shuffled-outcome means of 1.91 and 8.08, replicates this on the other half of the exons, and produces word weights agreeing with published enhancer and silencer scores it never saw (rho 0.352).
Round 4 (4/7 met)
Points the method at the remainder and gives it a floor. Variants remeasured in an independently built library agree within exon at r = 0.886, so 0.889 of within-exon variation is reproducible, against 0.279 reached by every declared map together. Three further candidate families — exon context, local RNA structure at the sites, position-dependent words — reach none of it.
Round 5 (2/4 met)
Asks whether that remainder is a general rule or specific to each exon, using a declared model that can learn only rules transferring between exons. On planted data it recovers about three-quarters of a rule of its own form; on held-out real exons it adds nothing. A convolutional network that failed the same planted check was set aside before any use on real data and is kept in the package as the record.
Round 6 (5/7 met)
One exon measured in every combination (FAS exon 6, 3,072 genotypes; Baeza-Centurion et al. 2019). Its own mutation effects composed by the published scaling law, plus pairwise interactions, reach a held-out R² of 0.882, leaving 0.057 against replicate noise of 0.060. Within one exon, composition is lawful to the measurement floor.
Round 7 (7/7 met)
Asks whether a new exon's effects can be read from its letters, on 309,698 substitutions in 601 exons (OpenSplice preprint). Learned blind on half the exons, the code reads 0.457 of the within-exon variation of exons it never saw, 0.543 of what is reproducible. A transferable context rule is now detectable but small. The unread part is exonic.
Round 8 (6/7 met)
Locates what the blind code adds over SpliceAI: exonic six-letter words, not letters near the splice sites, reproducible across independent halves of the training exons. The miss, R5, is informative: what is missed is NOT the classical enhancer/silencer vocabulary.
Round 9 (6/8 met)
Tests two mechanisms as declared maps. Rescoring SpliceAI over the whole construct, rather than at the two annotated splice sites only, removes 0.936 of the competing-site effect, so that component was a scoring convention rather than model error. G-quadruplex propensity removes 0.534 of the G-rich effect, specifically, without touching the other word classes.
Round 10 (4/7 met)
Uses the 394,286 deletions the earlier rounds left untouched to ask whether removing a regulatory element costs more than its letters do individually. For 3 to 6-nucleotide windows it does not, with planted controls establishing that cooperativity and redundancy would both have been detected. The strong redundancy a naive comparison appears to show is an…
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BibTeX
@misc{needham2026operational,
title = {{Operational Difference on DNA: a release ladder and a repair solver for sequence variants, with fifteen pre-registered sealed audits of three public splicing datasets (restricted research package, all rounds)}},
author = {Eric Needham},
howpublished = {Zenodo},
year = {2026},
month = oct,
doi = {10.5281/zenodo.23535523},
url = {https://zenodo.org/records/23535523},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - UNPB
TI - Operational Difference on DNA: a release ladder and a repair solver for sequence variants, with fifteen pre-registered sealed audits of three public splicing datasets (restricted research package, all rounds)
AU - Eric Needham
PY - 2026
DA - 2026/10/21
PB - Zenodo
DO - 10.5281/zenodo.23535523
UR - https://zenodo.org/records/23535523
AB - Operational Difference on DNA: a release ladder and a repair solver for sequence variants, with fifteen pre-registered sealed audits of three public splicing datasets (restricted research package, all rounds) Description Eric J. Needham, Symmersion, Agios Onoufrios, Crete. 21 October 2026. What this is The complete record of applying the Operational Difference method to sequence-to-function data, using RNA splicing as the test domain. The method is implemented as a genomics module of the Symmersion audit software, together with the compatibility-residual repair solver carried over from field equations to data. Two instruments. The release ladder is iterated conditional quotient refinement: starting from the exon, it asks which declared map still splits the outcome within the current refinement, releases the strongest map that survives a within-exon matched null with family-wise error control, and repeats. The repair solver treats each measured variant as a jet, with the kernel of the measured design, and returns verdicts of point, class or outside the basis against a replicate noise floor. One protocol. Every analysis is locked before any outcome is read: the analysis, the predictions and the thresholds are written and hashed first, the results are sealed afterwards, and each is re-run from its lock to confirm it reproduces field by field. Known-answer validation on planted data precedes every lock, including scenarios written to be adversarial to the method. Disclosed errors are recorded as errata rather than corrected silently, and an aborted lock is kept on the record. Across fifteen rounds, 123 predictions were locked in advance and 98 were met. All seventeen seals verify against their locks and input hashes, and every analysis reproduces from its lock. The progression, round by round Round 1 (8/12 and 10/12 met, two libraries) Audits the known splicing maps on the public MFASS libraries: 27,733 natural variants across 2,185 exons, and 13,075 designed perturbations assayed in two intron backbones. The ladder releases graded donor strength first, then the canonical-site indicator, graded acceptor strength, the hexamer map, folding, silencers, enhancers and the translation map. Designed changes that hold the splice-site score constant move inclusion 5.2 to 11.8 times less than changes designed to weaken it, in both backbones and at both sites: absorbed at one map, consequential at another. Four misses came from one mistaken expectation, that the binary canonical map would outrank the graded one. Round 2 (5/6 and 5/5 met) Offers SpliceAI as a candidate new map. Given every first-round map it is still consequential within exons, in both libraries and both backbones, so it is a new map in the solver's sense; on held-out exons it shrinks the structured remainder by about seven points. Most of the remainder therefore stands. Round 3 (11/11 met) Blind recovery. A learner given no splicing knowledge — only the four letters, positions relative to the exon boundaries, and outcomes on half the exons — is scored against an answer key locked in advance. It recovers the donor consensus at 9 of 9 positions and the acceptor at 20 of 20, against shuffled-outcome means of 1.91 and 8.08, replicates this on the other half of the exons, and produces word weights agreeing with published enhancer and silencer scores it never saw (rho 0.352). Round 4 (4/7 met) Points the method at the remainder and gives it a floor. Variants remeasured in an independently built library agree within exon at r = 0.886, so 0.889 of within-exon variation is reproducible, against 0.279 reached by every declared map together. Three further candidate families — exon context, local RNA structure at the sites, position-dependent words — reach none of it. Round 5 (2/4 met) Asks whether that remainder is a general rule or specific to each exon, using a declared model that can learn only rules transferring between exons. On planted data it recovers about three-quarters of a rule of its own form; on held-out real exons it adds nothing. A convolutional network that failed the same planted check was set aside before any use on real data and is kept in the package as the record. Round 6 (5/7 met) One exon measured in every combination (FAS exon 6, 3,072 genotypes; Baeza-Centurion et al. 2019). Its own mutation effects composed by the published scaling law, plus pairwise interactions, reach a held-out R² of 0.882, leaving 0.057 against replicate noise of 0.060. Within one exon, composition is lawful to the measurement floor. Round 7 (7/7 met) Asks whether a new exon's effects can be read from its letters, on 309,698 substitutions in 601 exons (OpenSplice preprint). Learned blind on half the exons, the code reads 0.457 of the within-exon variation of exons it never saw, 0.543 of what is reproducible. A transferable context rule is now detectable but small. The unread part is exonic. Round 8 (6/7 met) Locates what the blind code adds over SpliceAI: exonic six-letter words, not letters near the splice sites, reproducible across independent halves of the training exons. The miss, R5, is informative: what is missed is NOT the classical enhancer/silencer vocabulary. Round 9 (6/8 met) Tests two mechanisms as declared maps. Rescoring SpliceAI over the whole construct, rather than at the two annotated splice sites only, removes 0.936 of the competing-site effect, so that component was a scoring convention rather than model error. G-quadruplex propensity removes 0.534 of the G-rich effect, specifically, without touching the other word classes. Round 10 (4/7 met) Uses the 394,286 deletions the earlier rounds left untouched to ask whether removing a regulatory element costs more than its letters do individually. For 3 to 6-nucleotide windows it does not, with planted controls establishing that cooperativity and redundancy would both have been detected. The strong redundancy a naive comparison appears to show is an…
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