In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html
 .

/pdf/moderated-estimation-of-fold-change-and-dispersion-for-rna-3ywnbkcan0.pdf

Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.

/pdf/gene-set-enrichment-analysis-a-knowledge-based-approach-for-mbpti6qljw.pdf

Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

Summary: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data. Availability: The package is freely available under the LGPL licence from the Bioconductor web site (http://bioconductor.org).

http://dmrocke.ucdavis.edu/Class/BST226.2014.Winter/edgeR%20bioinformatics%202010.pdf

edgeR: a Bioconductor package for differential expression analysis of digital gene expression data.

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

In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-Seq data, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data. DESeq2 uses shrinkage estimation for dispersions and fold changes to improve stability and interpretability of the estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression and facilitates downstream tasks such as gene ranking and visualization. DESeq2 is available as an R/Bioconductor package.

/pdf/moderated-estimation-of-fold-change-and-dispersion-for-rna-59glcgpwlk.pdf

Wingless (Wg)/Wnt signaling is conserved in all metazoan animals and plays critical roles in development. The Wg/Wnt morphogen reception is essential for signal activation, whose activity is mediated through the receptor complex and a scaffold protein Dishevelled (Dsh). We report here that the exon junction complex (EJC) activity is indispensable for Wg signaling by maintaining an appropriate level of Dsh protein for Wg ligand reception in Drosophila. Transcriptome analyses in Drosophila wing imaginal discs indicate that the EJC controls the splicing of the cell polarity gene discs large 1 (dlg1), whose coding protein directly interacts with Dsh. Genetic and biochemical experiments demonstrate that Dlg1 protein acts independently from its role in cell polarity to protect Dsh protein from lysosomal degradation. More importantly, human orthologous Dlg protein is sufficient to promote Dvl protein stabilization and Wnt signaling activity, thus revealing a conserved regulatory mechanism of Wg/Wnt signaling by Dlg and EJC.

/pdf/the-exon-junction-complex-regulates-the-splicing-of-cell-3r0xgg6v48.pdf

The exon junction complex regulates the splicing of cell polarity gene DLG1 to control Wingless signaling in development

In terms of pre-safety assessment of a potential site for high-level radioactive wastes disposal in China, the geochemical behavior of key radionuclides which tend to be released from the repository must be thoroughly investigated 99Tc is a long-lived fission product with appreciable productivity in nuclear fuel, and Tc (+7) has unlimited solubility in near-field geochemical environments In this study, the effects of ionic strength and humic acid on 99TcO4
 − sorption and diffusion in Beishan granite were investigated with through-diffusion and batch sorption experiments Results indicated that the effective diffusion coefficients (D
 e) of 99TcO4
 − in Beishan granite varied from 107 × 10−12 to 128 × 10−12 m2/s without change with ionic strength, while the distribution coefficients (K
 d) negatively correlated with ionic strength of the rock/water system This study also indicates that there is no evident influence of humic acid concentration on the diffusion behavior of 99TcO4
 − in Beishan granite, due to the limited interaction between humic acid and 99TcO4
 −

Effects of ionic strength and humic acid on 99TcO4− sorption and diffusion in Beishan granite

Genome-wide expression signatures are emerging as potential marker for overall survival and disease recurrence risk as evidenced by recent commercialization of gene expression based biomarkers in breast cancer. Similar predictions have recently been carried out using genome-wide copy number alterations and microRNAs. Existing software packages for microarray data analysis provide functions to define expression-based survival gene signatures. However, there is no software that can perform survival analysis using SNP array data or draw survival curves interactively for expression-based sample clusters. We have developed the survival analysis module in the dChip software that performs survival analysis across the genome for gene expression and copy number microarray data. Built on the current dChip software's microarray analysis functions such as chromosome display and clustering, the new survival functions include interactive exploring of Kaplan-Meier (K-M) plots using expression or copy number data, computing survival p-values from the log-rank test and Cox models, and using permutation to identify significant chromosome regions associated with survival. The dChip survival module provides user-friendly way to perform survival analysis and visualize the results in the context of genes and cytobands. It requires no coding expertise and only minimal learning curve for thousands of existing dChip users. The implementation in Visual C++ also enables fast computation. The software and demonstration data are freely available at http://dchip-surv.chenglilab.org
 .

/pdf/the-dchip-survival-analysis-module-for-microarray-data-5i3h7cw1fo.pdf

The dChip survival analysis module for microarray data

Adjusting Batch Effects in Microarray Experiments with Small Sample Size Using Empirical Bayes Methods

Microrna Expression Profile Identifies Distinct Clinically Relevant Sub-Groups in Multiple Myeloma: Novel Prognostic Markers and Potential Targets for Therapy

Cheng Li

Papers

The exon junction complex regulates the splicing of cell polarity gene DLG1 to control Wingless signaling in development

Effects of ionic strength and humic acid on 99TcO4− sorption and diffusion in Beishan granite

The dChip survival analysis module for microarray data

Adjusting Batch Effects in Microarray Experiments with Small Sample Size Using Empirical Bayes Methods

Microrna Expression Profile Identifies Distinct Clinically Relevant Sub-Groups in Multiple Myeloma: Novel Prognostic Markers and Potential Targets for Therapy