Transcriptome and methane emission dataset of indica and japonica rice cultivars (93–11, Milyang352, Milyang392) at tillering and heading stages

This dataset comprises transcriptome profiles and methane emission measurements collected from roots and stems (including leaves) of three rice cultivars: Indica 93-11, Japonica Milyang352, and Milyang392 (an anther-culture line derived from 93-11 × Milyang352 with a high Indica gene proportion). Notably, Milyang392 exhibited the lowest methane emission among the three cultivars, highlighting its potential for climate-resilient breeding. Samples were collected at the tillering stage (June 28 and July 12, 2022) and heading stage (July 26 and August 11, 2022), with three biological replicates per condition, totaling 72 samples. RNA-Seq data were generated using the Illumina HiSeq platform (paired-end, 150 bp), and low-quality reads (Phred < 20, length < 50 bp) were filtered using Trimmomatic and BBDuk. Reads were aligned to the <i>Oryza sativa</i> pan-genome (TGSRICEPAN) using HISAT2, and gene-level read counts were computed using HTSeq. Differentially expressed genes (DEGs) were identified using DESeq2 with thresholds of |log2 fold change| ≥ 1 and adjusted p-value < 0.05. Maximum detected DEGs were 19,267 (root) and 17,165 (stem). Methane emissions were measured using gas chromatography (GC, Agilent 7890B) from the same biological replicates, showing the highest emission in 93-11 (15.2 mg/m²/h), followed by Milyang352 (12.8 mg/m²/h) and Milyang392 (9.5 mg/m²/h). These values correspond to 2.00 ± 0.12 kg/ha for 93-11, 1.57 ± 0.06 kg/ha for Milyang352, and 1.17 ± 0.01 kg/ha for Milyang392. The dataset is useful for studies of methane-related gene expression, rice metabolic pathways, and climate-resilient crop breeding, providing a valuable resource for machine learning applications and understanding genotype-specific responses.