Identifying differentially expressed genes in cDNA microarray experiments.

A major goal of microarray experiments is to determine which genes are differentially expressed between samples. Differential expression has been assessed by taking ratios of expression levels of different samples at a spot on the array and flagging spots (genes) where the magnitude of the fold difference exceeds some threshold. More recent work has attempted to incorporate the fact that the variability of these ratios is not constant. Most methods are variants of Student's t-test. These variants standardize the ratios by dividing by an estimate of the standard deviation of that ratio; spots with large standardized values are flagged. Estimating these standard deviations requires replication of the measurements, either within a slide or between slides, or the use of a model describing what the standard deviation should be. Starting from considerations of the kinetics driving microarray hybridization, we derive models for the intensity of a replicated spot, when replication is performed within and between arrays. Replication within slides leads to a beta-binomial model, and replication between slides leads to a gamma-Poisson model. These models predict how the variance of a log ratio changes with the total intensity of the signal at the spot, independent of the identity of the gene. Ratios for genes with a small amount of total signal are highly variable, whereas ratios for genes with a large amount of total signal are fairly stable. Log ratios are scaled by the standard deviations given by these functions, giving model-based versions of Studentization. An example is given.     

脱发相关差异表达基因的生物信息学分析

利用生物信息学方法分析脱发相关差异表达基因,有望帮助了解脱发发生发展的分子机制。本研究从NCBI的子数据库GEO中选择基因表达谱GSE45512和GSE45513数据集,利用R语言limma工具包,筛选出两个物种斑秃样本与正常样本的共同显著差异表达基因。对这部分基因进行功能注释和蛋白互作网络分析,同时对全部差异表达基因进行基因集富集分析。结果发现,人头皮斑秃样本共筛选出225个差异表达基因;C3H/HeJ小鼠自发斑秃皮肤样本共筛选出337个差异表达基因;两个物种的共同显著差异表达基因有23个。GO功能富集分析和蛋白互作网络分析显示,这部分差异基因显著富集于免疫相关功能,并且彼此间存在蛋白互作关系。基因集富集分析显示两个物种的差异基因都能显著富集到趋化因子信号通路、细胞因子受体相互作用、金葡菌感染及抗原加工与呈递通路;而且人的下调差异基因不仅映射到了人类表型数据库的脱发表型,也映射到皮肤附属物病理相关表型。综上所述,本研究通过生物信息方法分析脱发皮肤组织与正常皮肤组织的差异表达基因,最终筛选出23个在人和小鼠中共同存在的显著差异表达基因;此外,分析发现脱发与免疫过程及皮肤附属物病变密切相关,这些结果为脱发的诊断和治疗提供了新思路。     

Proteomic analysis of differentially expressed proteins in Penaeus vannamei hemocytes upon Taura syndrome virus infection

To understand molecular responses of crustacean hemocytes to virus infection, we applied 2-DE proteomics approach to investigate altered proteins in hemocytes of Penaeus vannamei during Taura syndrome virus (TSV) infection. At 24 h postinfection, quantitative intensity analysis and nano-LC-ESI-MS/MS revealed 11 forms of 8 proteins that were significantly up-regulated, whereas 9 forms of 5 proteins were significantly down-regulated in the infected shrimps. These altered proteins play important roles in host defense (hemocyanin, catalase, carboxylesterase, transglutaminase, and glutathione transferase), signal transduction (14-3-3 zeta), carbohydrate metabolism (acetylglucosamine pyrophosphorylase), cellular structure and integrity (beta-tubulin, beta-actin, tropomyosin, and myosin), and ER-stress response (protein disulfide isomerase). Semiquantitative RT-PCR and Western blot analysis confirmed the upregulation of 14-3-3 at both mRNA and protein levels. Interestingly, several altered protein spots were identified as fragments of hemocyanin. Mass spectrometric analysis showed that the hemocyanin spots at acidic and basic regions represented the C- and N-terminal hemocyanin fragments, respectively. As three-quarters of C-terminal fragments were up-regulated, whereas two-thirds of N-terminal hemocyanin fragments were down-regulated, we therefore hypothesize that C- and N-terminal hemocyanin fragments may have differential roles in hemocytes. Further investigation of these data may lead to better understanding of the molecular responses of crustacean hemocytes to TSV infection.     

Enrich_analysis.pl,差异表达基因富集分析的perl脚本

如何从庞大的基因表达数据库里挖掘出有价值的信息,并做出科学的生物学诠释,是基因表达分析领域的重要挑战。富集分析为解决这一问题提出了合理的方案。用Perl语言写了一个脚本——Enrich_analysis.pl,可根据基因注释信息进行基因富集分析,并利用Fisher's Exact Test做检验。富集分析先根据生物学知识将基因归类,然后进行基因差异表达分析,提高数据的可解释性。并利用3篇SCI文章的差异基因数据,对本perl脚本进行了对比测试,证明本perl脚本算法正确可靠。     

A theoretical analysis of the selection of differentially expressed genes

A great deal of recent research has focused on the challenging task of selecting differentially expressed genes from microarray data ("gene selection"). Numerous gene selection algorithms have been proposed in the literature, but it is often unclear exactly how these algorithms respond to conditions like small sample sizes or differing variances. Choosing an appropriate algorithm can therefore be difficult in many cases. In this paper we propose a theoretical analysis of gene selection, in which the probability of successfully selecting differentially expressed genes, using a given ranking function, is explicitly calculated in terms of population parameters. The theory developed is applicable to any ranking function which has a known sampling distribution, or one which can be approximated analytically. In contrast to methods based on simulation, the approach presented here is computationally efficient and can be used to examine the behavior of gene selection algorithms under a wide variety of conditions, even when the number of genes involved runs into the tens of thousands. The utility of our approach is illustrated by comparing three widely-used gene selection methods.     

Identifying differentially expressed genes in human acute leukemia and mouse brain microarray datasets utilizing QTModel

One of the essential issues in microarray data analysis is to identify differentially expressed genes (DEGs) under different experimental treatments. In this article, a statistical procedure was proposed to identify the DEGs for gene expression data with or without missing observations from microarray experiment with one- or two-treatment factors. An F statistic based on Henderson method III was constructed to test the significance of differential expression for each gene under different treatment(s) levels. The cutoff P value was adjusted to control the experimental-wise false discovery rate. A human acute leukemia dataset corrected from 38 leukemia patients was reanalyzed by the proposed method. In comparison to the results from significant analysis of microarray (SAM) and microarray analysis of variance (MAANOVA), it was indicated that the proposed method has similar performance with MAANOVA for data with one-treatment factor, but MAANOVA cannot directly handle missing data. In addition, a mouse brain dataset collected from six brain regions of two inbred strains (two-treatment factors) was reanalyzed to identify genes with distinct regional-specific expression patterns. The results showed that the proposed method could identify more distinct regional-specific expression patterns than the previous analysis of the same dataset. Moreover, a computer program was developed and incorporated in the software QTModel, which is freely available at .     

DNA microarray analysis of genes differentially expressed in adipocyte differentiation

In the present study, the human liposarcoma cell line SW872 was used to identify global changes in gene expression profiles occurring during adipogenesis. We further explored some of the genes expressed during the late phase of adipocyte differentiation. These genes may play a major role in promoting excessive proliferation and accumulation of lipid droplets, which contribute to the development of obesity. By using microarray-based technology, we examined differential gene expression in early differentiated adipocytes and late differentiated adipocytes. Validated genes exhibited a ≥10-fold increase in the late phase of adipocyte differentiation by polymerase chain reaction (RT-PCR). Compared with undifferentiated preadipocytes, we found that 763 genes were increased in early differentiated adipocytes, and 667 genes were increased in later differentiated adipocytes. Furthermore, 21 genes were found being expressed 10-fold higher in the late phase of adipocyte differentiation. The results were in accordance with the RT-PCR test, which validated 11 genes, namely, CIDEC, PID1, LYRM1, ADD1, PPARγ2, ANGPTL4, ADIPOQ, ACOX1, FIP1L1, MAP3K2 and PEX14. Most of these genes were found being expressed in the later phase of adipocyte differentiation involved in obesity-related diseases. The findings may help to better understand the mechanism of obesity and related diseases.     

RNA-Seq analysis of differentially expressed genes in rice under photooxidation

Efficient photosynthesis is critical for plant survival and growth. When plant-absorbed light exceeds the overall rate of energy conversion, it will trigger photooxidation. In this study, we selected a photooxidation mutant 812HS, it was isolated from the progeny of japonica rice (Oryza sativa L.) 812S and shows leaf yellowing and hypersensitive to photooxidation. Chloroplast ultrastructure in the leaves of 812HS showed that photooxidation resulted in significant chloroplast damage compared with 812S for changes in gene expressions in response to photooxidation stress using next-generation sequencing technologies on an Illumina HiSeq 2000 platform. A total of 88508 and 88495 genes were identified from 812S and 812HS, respectively. Expressions of 1199 genes were significantly upregulated, while 1342 genes were remarkably downregulated in 812HS. These genes were notably enriched in the 21 KEGG pathways. Based on their expression patterns, several key pathways were identified to be involved in the photooxidation of 812HS. qRT-PCR analysis further confirmed the results of RNA-Seq. This study enabled us to integrate analysis of RNA-Seq in rice and offered a deeper insight into the molecular mechanisms in response to photo-oxidative stress and provided clues for further critical gene identification in the protective mechanisms against photooxidation.     

In search of differentially expressed genes and proteins

A great challenge for modern cell biology is the successful examination of the co-expression of thousands of genes under physiological or pathological conditions and how the expression patterns define the different states of a single cell, tissue or a microorganism. Gene expression can be analyzed today on a large scale by advanced technical approaches for differential screening of proteins and mRNAs. The identification of differentially expressed mRNAs has been successfully applied to understand gene function and the underlying molecular mechanism(-s) of differentiation, development and disease state. Analysis of gene expression by the systematic mapping of thousands of proteins present in a cell or tissue can be achieved by the use of two-dimensional (2D) gel electrophoresis, quantitative computer image analysis, and protein identification techniques. In this article, we comment on some of these techniques and try to stress their advantages and drawbacks. We show how data from RNA/DNA mapping, sequence information from genome projects and protein pattern profiling can be linked with each other and annotated. These comprehensive approaches permit the study of differential gene and protein expressions in cells or tissues.     

Library subtraction of in vitro cDNA libraries to identify differentially expressed genes in scrapie infection.

We have developed a system where double-stranded cDNA can be amplified using a synthetic oligonucleotide primer and the polymerase chain reaction, generating cDNA libraries in vitro. Using a library subtraction strategy (1), scrapie and control brain in vitro cDNA libraries were used to identify sequences whose expression is modulated in scrapie infection. One of these sequences represents beta-2 microglobulin, while the other two have not been previously described. The use of in vitro libraries offers increased speed and efficiency of construction, and their subtraction is more efficient and powerful, compared with the previous system (1).