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 are provided for non-coding variants based on a Basenji (Kelley et al., 2018): sequence-to-signal model. deep learning model. 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.

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.