A method for analyzing censored survival phenotype with gene expression data

Background  

Survival time is an important clinical trait for many disease studies. Previous works have shown certain relationship between patients' gene expression profiles and survival time. However, due to the censoring effects of survival time and the high dimensionality of gene expression data, effective and unbiased selection of a gene expression signature to predict survival probabilities requires further study.     

A new unsupervised feature ranking method for gene expression data based on consensus affinity

Feature selection is widely established as one of the fundamental computational techniques in mining microarray data. Due to the lack of categorized information in practice, unsupervised feature selection is more practically important but correspondingly more difficult. Motivated by the cluster ensemble techniques, which combine multiple clustering solutions into a consensus solution of higher accuracy and stability, recent efforts in unsupervised feature selection proposed to use these consensus solutions as oracles. However,these methods are dependent on both the particular cluster ensemble algorithm used and the knowledge of the true cluster number. These methods will be unsuitable when the true cluster number is not available, which is common in practice. In view of the above problems, a new unsupervised feature ranking method is proposed to evaluate the importance of the features based on consensus affinity. Different from previous works, our method compares the corresponding affinity of each feature between a pair of instances based on the consensus matrix of clustering solutions. As a result, our method alleviates the need to know the true number of clusters and the dependence on particular cluster ensemble approaches as in previous works. Experiments on real gene expression data sets demonstrate significant improvement of the feature ranking results when compared to several state-of-the-art techniques.     

A new method for analyzing protein sequence relationships based on Sammon maps.

Recent advances in gene sequencing and rational drug design have re-emphasized the need for new methods for protein analysis, classification, and structure and function prediction. In this article, we introduce a new method for analyzing protein sequences based on Sammon''s non-linear mapping algorithm. When applied to a family of homologous sequences, the method is able to capture the essential features of the similarity matrix, and provides a faithful representation of chemical or evolutionary distance in a simple and intuitive way. The merits of the new algorithm are demonstrated using examples from the protein kinase family.     

Multiplex relative RT-PCR method for verification of differential gene expression

Differential display, suppression subtractive hybridization and other techniques for identification of differentially expressed genes produce fragments of cDNA from mRNAs whose differences in abundance must be verified. This report describes a relative multiplex RT-PCR assay that facilitates the analysis of large numbers of samples for differences in mRNA abundance without the use of radioactivity or blotting. The species of interest is co-amplified with 18S rRNA over a range of cycles followed by electrophoresis through ethidium bromide-agarose gels. Intensities of the bands of interest, normalized for 18S band intensities, are plotted as a function of cycle number. Regression equations fitted to the curves are used to calculate the number of cycles necessary for each sample's normalized signal to reach a threshold intensity. Differences between samples in the number of cycles required to reach that threshold reflect differences in the original abundances of those species. A comparison with results previously obtained using northern blots showed that relative differences as small as 20% and as large as an order of magnitude are accurately detected. The simplicity of the assay allows its routine application in both research and teaching laboratories.     

A new oxidase method for analyzing L-lactate

A new oxidase-coupled colorimetric method for analysis of L-lactate in biological fluids has been developed without use of peroxidase. The method is based on lactate oxidase-catalysed transformation of lactate to pyruvate which is determined photometrically in the next dye-producing reaction of 3-methyl-2-benzothiazolinone hydrazone (MBTH) in the presence of ferric ions. Sensitivity of the method is estimated as 0.1 micromole of analyte in 4-ml of reaction mixture. Linearity is observed in the range 0.1-1.0 micromole of L-lactate in sample (r = 0.99943; p < 0.0001). The developed method has been adapted for assay of L-lactic acid in kefirs and yogurts.     

基于荧光通用引物的多重定量RT-PCR基因表达谱分析平台的构建、优化及其应用

文章建立并优化了一种基于荧光通用引物的多重定量RT-PCR技术, 该技术采用了嵌合特异引物引导荧光通用引物的扩增方案, 多个目的基因被一对通用引物等比例扩增, 从而实现多重定量检测。该技术实现了经济可靠的中通量基因表达定量研究, 弥补了基因表达分析平台中cDNA芯片定量准确性低和Real-time quanti-tative PCR通量小的缺点, 完善了整个基因表达的分析过程。文章以小鼠X染色体上影响性发育启动的QTL区段为例, 选择11个目的基因进行了技术构建及优化, 确定了该技术的检测灵敏度为102拷贝, 通用引物与上游嵌合特异引物的比例以1:1为佳, 并且验证了该技术的重复性和准确性。降落式(Touchdown)PCR结合通用引物补加实验表明, 该优化步骤可大大改善低丰度表达基因的扩增。通过对2个品系(C3H/HeJ和C57BL/6J)15日龄小鼠的下丘脑和睾丸组织中的11个基因的表达分析, 在下丘脑中找到了一个差异表达的基因PHF6可用于进一步的基因功能研究。     

A new method for RAPD primers selection based on primer bias in nucleotide sequence data

Sequence analysis has proved that decamer nucleotides, used as primers of RAPD (random amplified polymorphic DNA), differ with each other greatly in number of annealing sites in the Arabidopsis thaliana genome. It is called the 'primer bias' by the authors. The biased primers produce a highly variable number of amplicons by polymerase chain reaction (PCR). The number of amplicons is proved to correlate with the number of annealing sites. Therefore, a statistical method is proposed for selecting efficient primers based on the primer bias in the genomic sequence. The method was tested by experiment in A. thaliana genome, and the results demonstrate that the method outperforms routine methods and can substantially increase the efficiency of RAPD methodologies. We also proved that the expressed sequence tags (ESTs) show a highly coincident bias pattern with that of the whole genomic sequence, and can therefore be used to assess efficiencies of primers for species whose genomic sequence data are currently unknown.     

A polymerase chain reaction-based method for cloning novel members of a gene family using a combination of degenerate and inhibitory primers

We have developed a novel method for cloning gene family members by using a polymerase chain reaction technique. The method is based on the amplification of a broad range of homologous genes in combination with the specific inhibition of already cloned genes. To accomplish this, we designed degenerate primers to highly conserved regions among the gene family members, and inhibitory primers to the divergent region at the 3'-margin of each degenerate primer. The 5'-end of the inhibitory primer, the 3'-end of which was aminated, had 3-4 bases overlapping the 3'-end of the degenerate primer. The potential of this method was demonstrated by the successful cloning of a novel member of the yeast MKC7/YAP3 gene family homologue from a filamentous fungus, Aspergillus oryzae, by inhibiting amplification of an already cloned homologue, opsB.     

Improved biplex quantitative real-time polymerase chain reaction with modified primers for gene expression analysis

Stabilizing modified bases incorporated in primers allows the reduction of housekeeping gene primer concentration not possible with regular primers without sacrificing amplification efficiency. Low primer concentration allows coamplification of the most abundant housekeeping genes with very rare templates without mutual inhibition. Real-time polymerase chain reaction (PCR) coamplification of 18S ribosomal RNA with several genes of interest was used in this study with MGB Eclipse (Nanogen, San Diego, CA) hybridization probes. The results may be useful for high throughput gene expression studies as they simplify validation experiments.     

A method for cross-species gene expression analysis with high-density oligonucleotide arrays

DNA microarrays have been widely used in gene expression analysis of biological processes. Due to a lack of sequence information, the applications have been largely restricted to humans and a few model organisms. Presented within this study are results of the cross-species hybridization with Affymetrix human high-density oligonucleotide arrays or GeneChip® using distantly related mammalian species; cattle, pig and dog. Based on the unique feature of the Affymetrix GeneChip® where every gene is represented by multiple probes, we hypothesized that sequence conservation within mammals is high enough to generate sufficient signals from some of the probes for expression analysis. We demonstrated that while overall hybridization signals are low for cross-species hybridization, a few probes of most genes still generated signals equivalent to the same-species hybridization. By masking the poorly hybridized probes electronically, the remaining probes provided reliable data for gene expression analysis. We developed an algorithm to select the reliable probes for analysis utilizing the match/mismatch feature of GeneChip®. When comparing gene expression between two tissues using the selected probes, we found a linear correlation between the cross-species and same-species hybridization. In addition, we validated cross-species hybridization results by quantitative PCR using randomly selected genes. The method shown herein could be applied to both plant and animal research.     

A new measure for functional similarity of gene products based on Gene Ontology

Background  

Gene Ontology (GO) is a standard vocabulary of functional terms and allows for coherent annotation of gene products. These annotations provide a basis for new methods that compare gene products regarding their molecular function and biological role.     

基于线性判别分析的基因表达数据分类方法研究

由于基因表达数据高属性维、低样本维的特点,Fisher分类器对该种数据分类性能不是很高。本文提出了Fisher的改进算法Fisher-List。该算法独特之处在于为每个类别确定一个决策阀值,每个阀值既包含总体样本信息,又含有某些对分类至关重要的个体样本信息。本文用实验证明新算法在基因表达数据分类方面比Fisher、LogitBoost、AdaBoost、k-近邻法、决策树和支持向量机具有更高的性能。     

A rapid method for analyzing the ligation products of synthetic oligodeoxyribonucleotides

A method is described for the rapid analysis of DNA ligation products in the assembly of synthetic genes and gene fragments. The method is based on the simultaneous analysis of multiple ligation reactions where a single but different DNA oligomer is radiolabelled per ligation reaction. After each ligation the reaction mixture is electrophoresed on a denaturing, as well as a non-denaturing, polyacrylamide gel allowing one to monitor the ligation reaction products. In addition, a unique method for generating single stranded DNA sizing standards up to approximately 300 nucleotides in length is described.     

A new method for nonhomogeneous time course expression analysis

Time course expression analysis constitutes a large portion of applications of microarray experiments. One primary goal of such experiments is to detect genes with the temporal changes over a period of time or at some interested time points. Difficulties arising from data with small number of replicates over only a few unaligned time points in multiple groups pose challenges for efficient statistical analysis. Some known methods are limited by the unverifiable assumptions or by the scope of applications for only two groups. We present a new method for detecting differentially expressed genes under nonhomogeneous time course experiments in multiple groups. The new method first models the time course curve of one gene by a Gaussian process to align the nonhomogeneous time course data and to compute the gradient of the time course curve as well, the latter of which is used as directional information to enhance the sensitivity of detection for temporal changes. Second, we adopt a nonparametric method to test a surrogate hypothesis based on the augmented data from the Gaussian process model. The proposed method is robust in terms of model fitting and testing. It does not require any distributional assumption for the observations or the test statistic and the method works for the case with as few as triplicate samples over four or five time points under multiple groups. We show the effectiveness and superiority of the new method in comparison with some existing methods using simulated models and two real data sets.