Biology subjects

Shan, G.

Publications and source records attributed to Shan, G..

3 recordsLinked to original sources

bioRxiv2018

Using pan RNA-seq analysis to reveal the ubiquitous existence of 5′ end and 3′ end small RNAs

In this study, we used pan RNA-seq analysis to reveal the ubiquitous existence of 5 end and 3 end small RNAs. 5 and 3 sRNAs alone can be used to annotate mitochondrial with 1-bp resolution and nuclear non-coding genes and identify new steady-state RNAs, which are usually from functional genes. Using 5, 3 and intronic sRNAs, we revealed that the enzymatic dsRNA cleavage and RNAi could involve in the RNA degradation and gene expression regulation of U1 snRNA in human. The further study of 5, 3 and intronic sRNAs help rediscover double-stranded RNA (dsRNA) cleavage, RNA interference (RNAi) and the regulation of gene expression, which challenges the classical theories. In this study, we provided a simple and cost effective way for the annotation of mitochondrial and nuclear non-coding genes and the identification of new steady-state RNAs, particularly long non-coding RNAs (lncRNAs). We also provided a different point of view for cancer and virus, based on the new discoveries of dsRNA cleavage, RNAi and the regulation of gene expression.

bioinformatics↗

bioRxiv2018

NormExpression: an R package to normalize gene expression data using evaluated methods

Data normalization is a crucial step in the gene expression analysis as it ensures the validity of its downstream analyses. Although many metrics have been designed to evaluate the current normalization methods, the different metrics yield inconsistent results. In this study, we designed a new metric named Area Under normalized CV threshold Curve (AUCVC) and applied it with another metric mSCC to evaluate 14 commonly used normalization methods, achieving consistency in our evaluation results using both bulk RNA-seq and scRNA-seq data from the same library construction protocol. This consistency has validated the underlying theory that a sucessiful normalization method simultaneously maximizes the number of uniform genes and minimizes the correlation between the expression profiles of gene pairs. This consistency can also be used to analyze the quality of gene expression data. The gene expression data, normalization methods and evaluation metrics used in this study have been included in an R package named NormExpression. NormExpression provides a framework and a fast and simple way for researchers to evaluate methods (particularly some data-driven methods or their own methods) and then select a best one for data normalization in the gene expression analysis.

bioinformatics↗

bioRxiv2017

Effects of regional differences on the urinary proteomes of healthy Chinese individuals

Urine is a promising biomarker source for clinical proteomics studies. Although regional physiological differences are common in multi-center clinical studies, the presence of significant differences in the urinary proteomes of individuals from different regions remains unknown. In this study, morning urine samples were collected from healthy urban residents in three regions of China and urinary proteins were preserved using a membrane-based method (Urimem). The urine proteomes of 27 normal samples were analyzed using LC-MS/MS and compared among the three regions. We identified 1,898 proteins from Urimem samples using label-free proteome quantification, of which 62 urine proteins were differentially expressed among the three regions. Hierarchical clustering analysis showed that inter-regional differences caused less significant changes in the urine proteome than inter-sex differences. Of the 62 differentially expressed proteins, 10 have been reported to be disease biomarkers in previous clinical studies. Urimem facilitates urinary protein storage for large-scale urine sample collection, and thus accelerates biobank development and urine biomarker studies employing proteomics approaches. Regional differences are a confounding factor influencing the urine proteome and should be considered in future multi-center biomarker studies.

physiology↗