다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.

/pdf/limma-powers-differential-expression-analyses-for-rna-4yl7ae3arh.pdf

limma powers differential expression analyses for RNA-sequencing and microarray studies

When small RNA is sequenced on current sequencing machines, the resulting reads are usually longer than the RNA and therefore contain parts of the 3' adapter. That adapter must be found and removed error-tolerantly from each read before read mapping. Previous solutions are either hard to use or do not offer required features, in particular support for color space data. As an easy to use alternative, we developed the command-line tool cutadapt, which supports 454, Illumina and SOLiD (color space) data, offers two adapter trimming algorithms, and has other useful features. Cutadapt, including its MIT-licensed source code, is available for download at http://code.google.com/p/cutadapt/

https://journal.embnet.org/index.php/embnetjournal/article/download/200/458

Cutadapt removes adapter sequences from high-throughput sequencing reads

抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

Gene-expression analysis is increasingly important in biological research, with real-time reverse transcription PCR (RT-PCR) becoming the method of choice for high-throughput and accurate expression profiling of selected genes. Given the increased sensitivity, reproducibility and large dynamic range of this methodology, the requirements for a proper internal control gene for normalization have become increasingly stringent. Although housekeeping gene expression has been reported to vary considerably, no systematic survey has properly determined the errors related to the common practice of using only one control gene, nor presented an adequate way of working around this problem. We outline a robust and innovative strategy to identify the most stably expressed control genes in a given set of tissues, and to determine the minimum number of genes required to calculate a reliable normalization factor. We have evaluated ten housekeeping genes from different abundance and functional classes in various human tissues, and demonstrated that the conventional use of a single gene for normalization leads to relatively large errors in a significant proportion of samples tested. The geometric mean of multiple carefully selected housekeeping genes was validated as an accurate normalization factor by analyzing publicly available microarray data. The normalization strategy presented here is a prerequisite for accurate RT-PCR expression profiling, which, among other things, opens up the possibility of studying the biological relevance of small expression differences.

/pdf/accurate-normalization-of-real-time-quantitative-rt-pcr-data-34rwzsvnu5.pdf

Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes

Background: Due to the heterogenic biology of neuroblastoma (NB), current risk assessment schemes sometimes lead to misclassification of the patient with respect to therapy protocols. To reduce unnecessary under- or overtreatment and to improve the power of predictive outcome, additional tumor-specific prognostic markers are required. As several genes have already been shown to be silenced by hypermethylation of their promoter region in NB, this study aimed at identifying tumor DNA-methylation markers with prognostic power in order to improve NB patient stratification.
Methods: To identify genes silenced by promoter methylation we applied two genome-wide methylation screening methodologies on 8 NB cell lines: a re-expression analysis using 5-aza-2'-deoxycytidine (DAC) and massively parallel sequencing after capturing with a methyl-CpG-binding domain (MBD). To select for prognostic markers, 212 genes upregulated after DAC-treatment were subsequently screened using methylation-specific PCR (MSP) on primary tumors. After integrated data analysis, 43 unique candidate biomarkers were further analyzed with MSP on 89 primary tumors, representing low- and high-risk surviving and deceased patients.
Results: We identified 12 novel DNA-methylation markers (COL6A3, miR-1225, miR-3177, PCDHA6, PLXNC1, ANKRD43, ADRB2, APOE, miR-671, QPCT, KCND2 and PRPH), methylated in >50% of the primary tumors. Promoter methylation of DPP4, HIST1H3C and GNAS was found to be associated with overall and event-free survival, and the methylation status of TGFBI, TNFRSF10D, KRT19, TNFRSF10A, RARRES1, FAS, PRPH, CNR1, QPCT, HIST1H3C, ACSS3, GNAS and GRB10 with at least one of the classical risk factors, such as age, stage and MYCN status.
Conclusions: This study, being the most extensive methylation study in NB performed thus far, has led to the discovery of several novel prognostic DNA-methylation markers, and is of great value for the development of a DNA-methylation-based prognostic classifier. In addition, methylation in microRNA (miRNA) promoter regions could be demonstrated in primary NB tumor samples for the first time.

Promoter methylation analysis identifies prognostic methylation biomarkers in neuroblastoma

The aim of our study was to find factors that modify the phenotype of the normal peroxisomal compartment (number, size, shape, membrane proteins and enzyme content of the matrix) and/or the transcription and translation of genes involved in peroxisome biogenesis and function in normal wild-type cells.

Regulation of peroxisomal genes by DHEA and vitamin D.

T he perceived ease of use of real-time quantitative PCR (qPCR) and reverse transcription PCR (RT-qPCR) technology has revolutionized life science research. Its effectiveness at amplification and quantification of low levels of nucleic acids has driven the emergence of numerous applications, including cellular mRNA and miRNA quantification, biomarker discovery and validation, microbial quantification, cancer risk assessment, gene dosage determination, and detection of extremely low copy targets for forensic investigations. This, in turn, has resulted in an abundance of publications utilizing qPCR data obtained with diverse reagents, protocols, analysis methods, and reporting formats. Unfortunately, few papers report in detail how these results were obtained. This lack of clarity and transparency has led to concern in the research community over the reliability of qPCR data interpretation and the real danger of the scientific literature being corrupted with publications reporting erroneous and conflicting results. This has already occurred in some cases, resulting, for example, in retraction of a Science “Breakthrough of the Year 2005” report1. Now that qPCR has come of age, standardization is needed to ensure its validity, prompting the recent formulation of guidelines to increase experimental transparency, promote consistency between laboratoReprint from

Jo Vandesompele

Papers

Promoter methylation analysis identifies prognostic methylation biomarkers in neuroblastoma

Regulation of peroxisomal genes by DHEA and vitamin D.

New Guidelines Seek to Promote Accurate Interpretation of Data and Reliable Results Standardization of qPCR and RT- qPCR

Reply: Direct Detection of Circulating MicroRNAs Unveiled the Absence of MicroRNA-218-5p in Smoker Subjects.

Real-time PCR quality control for gene expression profiling on the LightCycler 480 System