S

Sheng Zhao

Anhui Agricultural University

ORCID: 0000-0003-2296-0603

Publishes on Genomics and Phylogenetic Studies, Cancer Genomics and Diagnostics, Molecular Biology Techniques and Applications. 68 papers and 3.7k citations.

68Publications
3.7kTotal Citations

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Top publicationsby citations

Comprehensive Algorithm for Quantitative Real-Time Polymerase Chain Reaction
Sheng Zhao, Russell D. Fernald|Journal of Computational Biology|2005
Cited by 1.3kOpen Access

Quantitative real-time polymerase chain reactions (qRT-PCR) have become the method of choice for rapid, sensitive, quantitative comparison of RNA transcript abundance. Useful data from this method depend on fitting data to theoretical curves that allow computation of mRNA levels. Calculating accurate mRNA levels requires important parameters such as reaction efficiency and the fractional cycle number at threshold (CT) to be used; however, many algorithms currently in use estimate these important parameters. Here we describe an objective method for quantifying qRT-PCR results using calculations based on the kinetics of individual PCR reactions without the need of the standard curve, independent of any assumptions or subjective judgments which allow direct calculation of efficiency and CT. We use a four-parameter logistic model to fit the raw fluorescence data as a function of PCR cycles to identify the exponential phase of the reaction. Next, we use a three-parameter simple exponent model to fit the exponential phase using an iterative nonlinear regression algorithm. Within the exponential portion of the curve, our technique automatically identifies candidate regression values using the P-value of regression and then uses a weighted average to compute a final efficiency for quantification. For CT determination, we chose the first positive second derivative maximum from the logistic model. This algorithm provides an objective and noise-resistant method for quantification of qRT-PCR results that is independent of the specific equipment used to perform PCR reactions.

Population sequencing enhances understanding of tea plant evolution
Xinchao Wang, Feng Hu, Yuxiao Chang et al.|Nature Communications|2020
Cited by 233Open Access

Tea is an economically important plant characterized by a large genome, high heterozygosity, and high species diversity. In this study, we assemble a 3.26-Gb high-quality chromosome-scale genome for the 'Longjing 43' cultivar of Camellia sinensis var. sinensis. Genomic resequencing of 139 tea accessions from around the world is used to investigate the evolution and phylogenetic relationships of tea accessions. We find that hybridization has increased the heterozygosity and wide-ranging gene flow among tea populations with the spread of tea cultivation. Population genetic and transcriptomic analyses reveal that during domestication, selection for disease resistance and flavor in C. sinensis var. sinensis populations has been stronger than that in C. sinensis var. assamica populations. This study provides resources for marker-assisted breeding of tea and sets the foundation for further research on tea genetics and evolution.