Pan-3D genome analysis reveals structural and functional differentiation of soybean genomes
Bibliographic record
Abstract
BACKGROUND: High-order chromatin structure plays important roles in gene regulation. However, the diversity of the three-dimensional (3D) genome across plant accessions are seldom reported. RESULTS: Here, we perform the pan-3D genome analysis using Hi-C sequencing data from 27 soybean accessions and comprehensively investigate the relationships between 3D genomic variations and structural variations (SVs) as well as gene expression. We find that intersection regions between A/B compartments largely contribute to compartment divergence. Topologically associating domain (TAD) boundaries in A compartments exhibit significantly higher density compared to those in B compartments. Pan-3D genome analysis shows that core TAD boundaries have the highest transcription start site (TSS) density and lowest GC content and repeat percentage. Further investigation shows that non-long terminal repeat (non-LTR) retrotransposons play important roles in maintaining TAD boundaries, while Gypsy elements and satellite repeats are associated with private TAD boundaries. Moreover, presence and absence variation (PAV) is found to be the major contributor to 3D genome variations. Nevertheless, approximately 55% of 3D genome variations are not associated with obvious genetic variations, and half of them affect the flanking gene expression. In addition, we find that the 3D genome may also undergo selection during soybean domestication. CONCLUSION: Our study sheds light on the role of 3D genomes in plant genetic diversity and provides a valuable resource for studying gene regulation and genome evolution.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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".