Deterministic DNA-first discovery reveals recurrent genomic architectures
- 1Department of Mathematical Sciences, Tsinghua University
Abstract
Genome mining typically begins with known genes or protein families, which can make systems defined primarily by genomic organization difficult to detect. We developed a DNA-first framework combining asymmetric-covariance natural-vector representations with generic positional recurrence, joining separately calibrated signals through genomic coordinates. In benchmarks of array-associated reverse transcriptase (ART) loci, the target-free mathematical arm localized seven of eight predeclared loci, including all seven with exact-array coordinates. Dual-arm signals occurred in none of three non-array RT controls and in one of five UG27 systems, indicating selectivity within these small control sets. A separate profile-guided search of 6.12 Gb identified five candidate array-associated RT-like systems. In an independent 54.38-Gb evaluation of this profile-guided arm, structural compatibility was 24/32 under a historical-rule mirror, 12/32 after novelty-only selection, and 0/17 for architecture-selected complete ORFs; the two 32-member panels shared six exact sequences. Together, these results suggest that mathematical features of raw DNA can complement protein-directed searches in nominating genomic architectures for experimental RNA-to-DNA studies.
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BibTeX
@misc{hu2026deterministic,
title = {{Deterministic DNA-first discovery reveals recurrent genomic architectures}},
author = {G. Hu and H. Wang and S. S.-T. Yau},
howpublished = {bioRxiv},
year = {2026},
month = nov,
doi = {10.64898/2026.11.01.763506},
url = {https://www.biorxiv.org/content/10.64898/2026.11.01.763506v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Deterministic DNA-first discovery reveals recurrent genomic architectures
AU - G. Hu
AU - H. Wang
AU - S. S.-T. Yau
PY - 2026
DA - 2026/11/01
PB - bioRxiv
DO - 10.64898/2026.11.01.763506
UR - https://www.biorxiv.org/content/10.64898/2026.11.01.763506v1
AB - Genome mining typically begins with known genes or protein families, which can make systems defined primarily by genomic organization difficult to detect. We developed a DNA-first framework combining asymmetric-covariance natural-vector representations with generic positional recurrence, joining separately calibrated signals through genomic coordinates. In benchmarks of array-associated reverse transcriptase (ART) loci, the target-free mathematical arm localized seven of eight predeclared loci, including all seven with exact-array coordinates. Dual-arm signals occurred in none of three non-array RT controls and in one of five UG27 systems, indicating selectivity within these small control sets. A separate profile-guided search of 6.12 Gb identified five candidate array-associated RT-like systems. In an independent 54.38-Gb evaluation of this profile-guided arm, structural compatibility was 24/32 under a historical-rule mirror, 12/32 after novelty-only selection, and 0/17 for architecture-selected complete ORFs; the two 32-member panels shared six exact sequences. Together, these results suggest that mathematical features of raw DNA can complement protein-directed searches in nominating genomic architectures for experimental RNA-to-DNA studies.
ER -