This module provides a gene-centered integrative view of genetic variation and regulatory features in rice. It summarizes local genetic variants, functional and phenotypic annotations, chromatin accessibility, model-predicted regulatory effects of non-coding variants, and population genetic statistics within the genomic neighborhood of a queried gene. Quantitative chromatin accessibility and transcriptome profiles across multiple tissues, developmental stages, and three reference varieties are jointly presented to support functional, regulatory, and evolutionary interpretation.

Gene Expression Profiles: Quantitative gene expression levels across multiple tissues and developmental stages are shown where available. These profiles enable direct comparison between transcriptional output and local regulatory activity, facilitating interpretation of tissue-specific gene regulation.

Chromatin Accessibility Landscape: Tissue-resolved chromatin accessibility profiles are visualized across the queried genomic region using ATAC-seq data. Accessibility signals are quantified from Tn5 insertion events aggregated into fixed genomic windows of 250 bp with a sliding step of 100 bp. Heatmap-style visualization highlights spatial and tissue-specific patterns of chromatin openness, revealing putative cis-regulatory elements and their tissue specificity.

Regulatory Effect Predictions for Non-coding Variants: Predicted regulatory effect scores for non-coding variants are generated using Basenji (Kelley et al., 2018), a deep learning sequence-to-signal model trained to predict chromatin accessibility from genomic sequence. For each variant, the effect score is defined as the difference in predicted local chromatin accessibility (±1 kb around the variant) between the alternative (alt) and reference (ref) alleles (ΔPCA = PCA_alt - PCA_ref, where PCA denotes Predicted Chromatin Accessibility). Positive values indicate that the alternative allele is predicted to increase chromatin accessibility, whereas negative values indicate a predicted reduction in accessibility relative to the reference allele. These scores enable systematic prioritization of non-coding variants with potential regulatory impact.

High-impact regulatory variants (HEVs): Basenji predicts a separate effect score for each variant in each tissue. HEVs are defined using a standardized procedure: for each scored variant allele, the maximum absolute predicted effect across the available tissues is calculated, and the 90th percentile across all variant-allele records is used as the reference-specific cutoff. Thus the top 10% refers to variant alleles ranked by their cross-tissue maximum, before alternate alleles at the same genomic position are collapsed. A genomic position is classified as an HEV if at least one allele passes. In the tissue tracks, a star is shown only where the variant's absolute effect reaches the resulting reference-specific HEV cutoff. Both predicted increases and decreases in chromatin accessibility can be marked as HEVs, and the same variant may be starred in some tissues but not in others. These model-derived HEVs are distinct from snpEff HIGH-impact or PolyPhen-2 damaging annotations.

Population differentiation and diversity statistics: Population genetic statistics are displayed across the gene neighborhood to provide evolutionary context. Genetic differentiation is quantified using F_ST, calculated in sliding windows of 10 kb with a step size of 1 kb between predefined rice populations. Nucleotide diversity (π) is computed using the same window and step size to measure local genetic variation within populations. Joint visualization of F_ST and π highlights genomic regions under potential selection and links regulatory variation with population-level evolutionary processes.

Genetic Variants in the Gene Neighborhood: All genetic variants located within the gene body and flanking regions are listed and visualized along the genomic coordinate system. Variants are annotated with sequence context, functional annotations, and population-level allele statistics, enabling detailed inspection of local genetic diversity and variant distribution.