Using RNA-Seq Data to Evaluate Reference Genes Suitable for Gene Expression Studies in Soybean.
Differential gene expression profiles often provide important clues for gene functions. While reverse transcription quantitative real-time polymerase chain reaction (RT-qPCR) is an important tool, the validity of the results depends heavily on the choice of proper reference genes. In this study, we employed new and published RNA-sequencing (RNA-Seq) datasets (26 sequencing libraries in total) to evaluate reference genes reported in previous soybean studies. In silico PCR showed that 13 out of 37 previously reported primer sets have multiple targets, and 4 of them have amplicons with different sizes. Using a probabilistic approach, we identified new and improved candidate reference genes. We further performed 2 validation tests (with 26 RNA samples) on 8 commonly used reference genes and 7 newly identified candidates, using RT-qPCR. In general, the new candidate reference genes exhibited more stable expression levels under the tested experimental conditions. The three newly identified candidate reference genes Bic-C2, F-box protein2, and VPS-like gave the best overall performance, together with the commonly used ELF1b. It is expected that the proposed probabilistic model could serve as an important tool to identify stable reference genes when more soybean RNA-Seq data from different growth stages and treatments are used.
School of Life Sciences and Center for Soybean Research of the Partner State Key Laboratory of Agrobiotechnology, The Chinese University of Hong Kong, Shatin, Hong Kong, SAR, China; Hong Kong Bioinformatics Centre, The Chinese University of Hong Kong, Shatin, Hong Kong, SAR, China.
Aldrin Kay-Yuen
AK
Yim
School of Life Sciences and Center for Soybean Research of the Partner State Key Laboratory of Agrobiotechnology, The Chinese University of Hong Kong, Shatin, Hong Kong, SAR, China.
Johanna Wing-Hang
JW
Wong
School of Life Sciences and Center for Soybean Research of the Partner State Key Laboratory of Agrobiotechnology, The Chinese University of Hong Kong, Shatin, Hong Kong, SAR, China.
Yee-Shan
YS
Ku
School of Life Sciences and Center for Soybean Research of the Partner State Key Laboratory of Agrobiotechnology, The Chinese University of Hong Kong, Shatin, Hong Kong, SAR, China; Hong Kong Bioinformatics Centre, The Chinese University of Hong Kong, Shatin, Hong Kong, SAR, China.
Hao
H
Qin
School of Life Sciences and Center for Soybean Research of the Partner State Key Laboratory of Agrobiotechnology, The Chinese University of Hong Kong, Shatin, Hong Kong, SAR, China; Hong Kong Bioinformatics Centre, The Chinese University of Hong Kong, Shatin, Hong Kong, SAR, China.
Ting-Fung
TF
Chan
School of Life Sciences and Center for Soybean Research of the Partner State Key Laboratory of Agrobiotechnology, The Chinese University of Hong Kong, Shatin, Hong Kong, SAR, China.
Hon-Ming
HM
Lam
D003198:Computer Simulation; D020869:Gene Expression Profiling; D018506:Gene Expression Regulation, Plant; D010940:Plant Proteins / Q000096:biosynthesis* / Q000235:genetics; D012313:RNA / Q000235:genetics; D017423:Sequence Analysis, RNA; D013025:Glycine max / Q000235:genetics*
D010940:Plant Proteins; D012313:RNA