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Deep sequencing of small RNAs(sR NA) is widely used in sR NAs studies in plants. In order to investigate the sequencing frequency variation of sR NAs, the same sR NA samples from rice grains were sequenced twice using deep sequencing technique. The sR NAs were classified into three categories, high abundance(> 100 RPM), medium abundance(10–100 RPM) and low abundance(1–10 RPM). According to the repeat sequencing data of the same sample, highly expressed sR NAs(> 100 RPM) were less subject to random drift, and 95% of the sR NAs Log2 ratio between two samples fell between-0.649 and 0.558. The same trend was observed in mediumly expressed sR NAs(10–100 RPM), and 95% of the Log2 ratio fell between-0.535 and 0.759. As to lowly expressed sR NAs(1–10 RPM), 95% of the Log2 ratio varied between-1.009 and 1.011. These results can be used as a theoretical guide to find differentially expressed s RNAs in sR NA studies in plants.
Deep sequencing of small RNAs (sR NA) is widely used in sR NAs studies in plants. In order to investigate the sequencing frequency variation of sR NAs, the same sR NA samples from rice grains sequenced twice using deep sequencing technique. The sR NAs were classified into three categories, high abundance (> 100 RPM), medium abundance (10-100 RPM) and low abundance (1-10 RPM). According to the repeat sequencing data of the same sample, highly expressed sR NAs RPM) were less subject to random drift, and 95% of the sR NAs Log2 ratio between two samples fell between-0.649 and 0.558. The same trend was observed in mediumly expressed sR NAs (10-100 RPM), and 95% of the As to lowly expressed sR NAs (1-10 RPM), 95% of the Log2 ratio varied between-1.009 and 1.011. These results can be used as a theoretical guide to find differentially expressed s RNAs in sR NA studies in plants.