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bioRxiv2021

Selection and Validation of Reference Genes Desirable for Gene Expression Analysis by qRT-PCR on Seed Germination of Castanea henryi

Seed germination is the beginning of the plants life cycle, and seed biology is one of the most extensively researched areas in plant physiology, however, Castanea henryi as an important seed plant, the stable internal reference gene during germination is not clear. In this study, seven candidate genes (TUA, TUB, TIF, UBC, RPL21, RPL30, RPL34) were screened out from transcriptome data, we analyzed the expression of seven candidate reference genes in C. henryi at different germination stages with RT-qPCR, and using common algorithms including NormFinder, geNorm and BestKeeper to evaluate the candidate genes stability. The results showed that RPL34 and RPL30 were selected as the most stable genes by NormFinder; TIF was the most stable gene identified by BestKeeper; RPL34 and RPL21 were the most stable genes ranked by geNorm, and TUB was the most unstable gene identified by all of the three software. The RPL34 gene was used as the reference gene, to detected the expression trend of two starch synthetase genes SS1 and SS2 during germination by RT-qPCR, the results of RT-qPCR and transcriptome sequencing were basically consistent, which verified the stability of RPL34 candidate gene. Our result is not only showed functional genes for germination of C. henryi seeds and provide useful guidelines for the selection of reliable reference genes for the normalization of RT- qPCR data for germination of seed plants.

molecular biology↗

bioRxiv2020

A Reference Feature based method for Quantification and Identification of LC-MS based untargeted metabolomics

Batch inconsistency is a major problem when applying LC-MS based untargeted metabolomics in real-time analysis situation such as clinical diagnosis or health monitoring. And inefficiency of collecting MS2 is a major problem for metabolite identification. Here, we developed a reference-feature based quantification and identification strategy (RFQI). In RFQI, samples are individually profiled using a pre-fixed reference feature table. Quantification results show that RFQI improves features overlap rate and reduce variance across batches significantly in real-time-analysis mode, and can find more than 4-fold numbers of features. Besides, RFQI collects MS2 from consecutive increasing samples for metabolite identification of pre-fixed features, thus it can effectively compensate for the poor efficiency of MS2 collection in data-dependent acquisition mode. In summary, RFQI can make full advantage of consecutive increasing samples in real-time analysis situation, both for quantification and identification.

bioinformatics↗