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.     

A Population Proportion approach for ranking differentially expressed genes

Background  

DNA microarrays are used to investigate differences in gene expression between two or more classes of samples. Most currently used approaches compare mean expression levels between classes and are not geared to find genes whose expression is significantly different in only a subset of samples in a class. However, biological variability can lead to situations where key genes are differentially expressed in only a subset of samples. To facilitate the identification of such genes, a new method is reported.     

A comparison on effects of normalisations in the detection of differentially expressed genes

Background  

Various normalisation techniques have been developed in the context of microarray analysis to try to correct expression measurements for experimental bias and random fluctuations. Major techniques include: total intensity normalisation; intensity dependent normalisation; and variance stabilising normalisation. The aim of this paper is to discuss the impact of normalisation techniques for two-channel array technology on the process of identification of differentially expressed genes.     

Isolation of differentially expressed genes in human heart tissues

We applied RNA arbitrarily primed-PCR (RAP-PCR) to screen the genes differentially expressed between common congenital heart defects (CHD) [atrial septal defect, ventricular septal defect, Tetrology of Fallot (TOF)] and normal human heart samples. Three of these differentially amplified fragments matched cDNA sequences coding for proteins of unknown function in humans: hCALO (human homologue of calossin), NP79 (coding for a nuclear protein of 79KD) and SUN2 (Sad-1 unc-84 domain protein 2). The other four fragments were from known human genes: apolipoprotein J, titin, dystrophin and protein kinase C-delta. Northern blot analysis confirmed that all of these genes are expressed in the human heart. The results of RAP-PCR were reconfirmed by quantitative RT-PCR in TOF and control heart samples. Both techniques showed the levels of expression of hCALO, NP79 and SUN2 to be comparable in TOF and control samples and the level of expression of dystrophin and titin, both coding for cytoskeletal proteins, to be significantly upregulated in TOF samples. In summary, we have shown that the RAP-PCR technique is useful in the identification of differentially expressed gene from biopsy samples of human CHD tissues. In this manner, we have identified three novel genes implicated in the normal function of the human heart and two known genes upregulated in TOF samples.     

Efficacy of SSH PCR in isolating differentially expressed genes

Background  

Suppression Subtractive Hybridization PCR (SSH PCR) is a sophisticated cDNA subtraction method to enrich and isolate differentially expressed genes. Despite its popularity, the method has not been thoroughly studied for its practical efficacy and potential limitations.     

The computational detection of functional nucleotide sequence motifs in the coding regions of organisms

A new algorithm has been constructed for finding under- and overrepresented oligonucleotide motifs in the protein coding regions of genomes that have been normalized for G/C content, codon usage, and amino acid order. This Robins-Krasnitz algorithm has been employed to compare the oligonucleotide frequencies between many different prokaryotic genomes. Evidence is presented demonstrating that at least some of these sequence motifs are functionally important and selected for or against during the evolution of these prokaryotes. The applications of this method include the optimization of protein expression for synthetic genes in foreign organisms, identification of novel oligonucleotide signals used by the organism and the examination of evolutionary relationships not dependent upon different gene sequence trees.     

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。     

A powerful approach for effective finding of significantly differentially expressed genes

The problem of identifying significantly differentially expressed genes for replicated microarray experiments is accepted as significant and has been tackled by several researchers. Patterns from Gene Expression (PaGE) and q-values are two of the well-known approaches developed to handle this problem. This paper proposes a powerful approach to handle this problem. We first propose a method for estimating the prior probabilities used in the first version of the PaGE algorithm. This way, the problem definition of PaGE stays intact and we just estimate the needed prior probabilities. Our estimation method is similar to Storey's estimator without being its direct extension. Then, we modify the problem formulation to find significantly differentially expressed genes and present an efficient method for finding them. This formulation increases the power by directly incorporating Storey's estimator. We report the preliminary results on the BRCA data set to demonstrate the applicability and effectiveness of our approach.     

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.     

The identification of Mycobacterium marinum genes differentially expressed in macrophage phagosomes using promoter fusions to green fluorescent protein

Mycobacterium marinum , like Mycobacterium tuberculosis , is a slow-growing pathogenic mycobacteria that is able to survive and replicate in macrophages. Using the promoter-capture vector pFPV27, we have constructed a library of 200–1000 bp fragments of M. marinum genomic DNA inserted upstream of a promoterless green fluorescent protein (GFP) gene. Only those plasmids that contain an active promoter will express GFP. Macrophages were infected with this fusion library, and phagosomes containing fluorescent bacteria were isolated. Promoter constructs that were more active intracellularly were isolated with a fluorescence-activated cell sorter, and inserts were partially sequenced. The promoter fusions expressed intracellularly exhibited homology to mycobacterial genes encoding, among others, membrane proteins and biosynthetic enzymes. Intracellular expression of GFP was 2–20 times that of the same clones grown in media. Several promoter constructs were transformed into Mycobacterium smegmatis , Mycobacterium bovis BCG and Mycobacterium tuberculosis . These constructs were positive for GFP expression in all mycobacterial strains tested. Sorting fluorescent bacteria in phagosomes circumvents the problem of isolating a single clone from macrophages, which may contain a mixed bacterial population. This method has enabled us to isolate 12 M. marinum clones that contain promoter constructs differentially expressed in the macrophage.