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53 variations found. OsMH_04G0302400 (OsMH_04T0302400.1), ranging from 20,561,814 bp to 20,565,893 bp (including 2 kb upstream and 1 kb downstream of the gene).

Syntenic genes

Gene Structure

Five-point stars indicate variants annotated as snpEff HIGH impact or PolyPhen-2 damaging.

Chromatin accessibility landscape across tissues

Allele-specific chromatin accessibility effects predicted by Basenji are reported as the signed local sequence activity log-ratio (LSAR): log2(PALT + 1) − log2(PREF + 1), using local predictions averaged across the central eight 128-bp output bins (1,024 bp total). Positive and negative values indicate increased and reduced predicted accessibility for the alternative allele, respectively. For MH63RS3 SNPs, a light-red five-point star (★) indicates an HEV.

Detailed Variation Information

Var ID Chrom Position Primary Allele Secondary Allele Primary Allele Frequency DEL Frequency Var GWAS Results Effect
vm0420561910 chr04 20561910 A C 99.50% 0.10% A -> C,T NA
NA
vm0420561985 chr04 20561985 A G 68.50% 0.00% A -> G NA
NA
vm0420562149 chr04 20562149 T A 63.20% 0.03% T -> A NA
NA
vm0420562314 chr04 20562314 T C 96.90% 0.00% T -> C NA
NA
vm0420562414 chr04 20562414 G A 96.80% 0.00% G -> A NA
NA
vm0420562494 chr04 20562494 T C 97.10% 0.03% T -> C NA
NA
vm0420562561 chr04 20562561 G C 68.30% 0.00% G -> C NA
NA
vm0420562718 chr04 20562718 G C 96.90% 0.03% G -> C NA
NA
vm0420562899 chr04 20562899 G T 97.10% 0.17% G -> T NA
NA
vm0420562906 chr04 20562906 A T 97.10% 0.26% A -> T NA
NA
vm0420562918 chr04 20562918 T A 97.10% 0.10% T -> A NA
NA
vm0420563487 chr04 20563487 G T 99.70% 2.02% G -> T NA
NA
vm0420563868 chr04 20563868 T A 100.00% 3.67% T -> A NA
NA
vm0420563923 chr04 20563923 A T 63.00% 0.10% A -> T NA
NA
vm0420563967 chr04 20563967 G T 97.00% 0.17% G -> T NA
NA
vm0420563986 chr04 20563986 A C 99.70% 0.17% A -> C NA
NA
vm0420564006 chr04 20564006 G C 96.70% 0.26% G -> C NA
NA
vm0420564018 chr04 20564018 T G 64.50% 0.23% T -> G,A NA
NA
vm0420564035 chr04 20564035 G A 99.90% 0.46% G -> A NA
NA
vm0420564040 chr04 20564040 G A 99.40% 0.46% G -> A NA
NA
vm0420564042 chr04 20564042 C T 68.50% 1.03% C -> T NA
NA
vm0420564053 chr04 20564053 G A 72.00% 0.03% G -> A NA
NA
vm0420564070 chr04 20564070 C T 68.70% 1.26% C -> T NA
NA
vm0420564078 chr04 20564078 C G 97.90% 0.46% C -> G NA
NA
vm0420564079 chr04 20564079 C T 96.90% 0.33% C -> T NA
NA
vm0420564092 chr04 20564092 C A 96.80% 0.03% C -> A,T NA
NA
vm0420564099 chr04 20564099 C T 96.50% 0.63% C -> T NA
NA
vm0420564105 chr04 20564105 C T 68.30% 0.13% C -> T,A NA
NA
vm0420564107 chr04 20564107 A G 64.70% 0.03% A -> G NA
NA
vm0420564146 chr04 20564146 T C 62.80% 0.46% T -> C NA
NA
vm0420564162 chr04 20564162 C T 96.20% 0.60% C -> T NA
NA
vm0420564165 chr04 20564165 A G 64.60% 0.63% A -> G NA
NA
vm0420564175 chr04 20564175 C T 99.70% 0.56% C -> T NA
NA
vm0420564185 chr04 20564185 T C 96.80% 0.03% T -> C,A NA
NA
vm0420564229 chr04 20564229 A C 64.30% 0.13% A -> C NA
NA
vm0420564327 chr04 20564327 G T 96.90% 0.07% G -> T NA
NA
vm0420564348 chr04 20564348 G A 99.60% 2.45% G -> A NA
NA
vm0420564480 chr04 20564480 T C 64.20% 0.23% T -> C NA
NA
vm0420564770 chr04 20564770 G T 93.60% 0.00% G -> T NA
NA
vm0420565070 chr04 20565070 A T 64.20% 0.00% A -> T NA
NA
vm0420565072 chr04 20565072 C G 64.40% 0.00% C -> G NA
NA
vm0420565087 chr04 20565087 G T 98.40% 0.00% G -> T NA
NA
vm0420565158 chr04 20565158 G A 64.40% 0.00% G -> A NA
NA
vm0420565234 chr04 20565234 C G 99.40% 0.00% C -> G NA
NA
vm0420565294 chr04 20565294 G C 97.00% 0.03% G -> C NA
NA
vm0420565412 chr04 20565412 A C 97.00% 0.00% A -> C NA
NA
vm0420565506 chr04 20565506 A G 99.10% 0.00% A -> G NA
NA
vm0420565617 chr04 20565617 C T 71.30% 0.03% C -> T NA
NA
vm0420565646 chr04 20565646 A T 100.00% 0.00% A -> T NA
NA
vm0420565704 chr04 20565704 T C 99.70% 0.03% T -> C NA
NA
vm0420565789 chr04 20565789 A G 63.10% 0.13% A -> G NA
NA
vm0420565876 chr04 20565876 T * 72.40% 0.86% T -> A NA
NA
vm0420565892 chr04 20565892 T C 63.50% 0.20% T -> C NA
NA

Information

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: Allele-specific chromatin accessibility effects are predicted using Basenji (Kelley et al., 2018), a deep learning sequence-to-signal model trained to predict chromatin accessibility from genomic sequence. The signed local sequence activity log-ratio is LSAR = log2(PALT + 1) − log2(PREF + 1), where PALT and PREF are local predictions averaged across the central eight 128-bp output bins (1,024 bp total). Positive values indicate that the alternative allele is predicted to increase chromatin accessibility, whereas negative values indicate reduced accessibility relative to the reference allele. These scores enable systematic prioritization of non-coding variants with potential regulatory impact.

High-impact regulatory variants (HEVs): HEV cutoffs depend on both the reference genome and the variant set used for calibration. A symbol is shown for a tissue-specific score only when its absolute unrounded value reaches the applicable cutoff. Both predicted increases and decreases can therefore be marked.

  • Nipponbare (IRGSP-1.0; vg) SNP and non-SNP variants: A light-red ★ denotes an HEV at 0.052 (exact value 0.051688551903), a cutoff calibrated from the SNP-only q90 and applied to both SNP and non-SNP scores. A red ★ denotes a strong HEV at the combined SNP and non-SNP q90 cutoff of 0.106 (exact value 0.10640232). Strong HEVs are displayed as ★ only.
  • MH63RS3 (vm) SNPs: a light-red ★ denotes an HEV at the SNP-only q90 cutoff of 0.052 (exact value 0.052074253559). No strong-HEV tier is used.
  • ZS97RS3 (vz) SNPs: a light-red ★ denotes an HEV at the SNP-only q90 cutoff of 0.047 (exact value 0.047439455986). No strong-HEV tier is used.

These model-derived HEVs are distinct from snpEff HIGH-impact or PolyPhen-2 damaging annotations. “Strong” describes the predicted effect magnitude relative to the combined calibration cutoff; it is not a confidence score.

Population differentiation and diversity statistics: Population genetic statistics are displayed across the gene neighborhood to provide evolutionary context. Genetic differentiation is quantified using FST, 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 FST 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.

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