Tag Archives: Differential expression analysis

Differential expression analysis on the basis of RNA-Seq count data has become a standard tool in transcriptomics. Several studies have shown that prior normalization of the data is crucial for a reliable detection of transcriptional differences. Until now it has ...

RNA sequencing (RNA-seq) is a high throughput technology that profiles gene expression in a genome-wide manner. RNA-seq has been mainly used for testing differential expression (DE) of transcripts between two conditions and has recently been used for testing differential alternative ...

Many biases and spurious effects are inherent in RNA-seq technology, resulting in a non-uniform distribution of sequencing read counts for each base position in a gene. Therefore, a base-level strategy is required to model the non-uniformity. Also, the properties of ...

Sequence read alignment to a reference genome is a fundamental step in many genomics studies. Accuracy in this fundamental step is crucial for correct interpretation of biological data. In cases where two or more closely related bacterial strains are being ...

RNA sequencing (RNA-seq) is a powerful approach for measuring gene expression levels in cells and tissues, but it relies on high-quality RNA. Researchers at the  Johns Hopkins School of Medicine demonstrate here that statistical adjustment using existing quality measures largely fails to ...

RNA sequencing (RNA-seq) and microarrays are two transcriptomics techniques aimed at the quantification of transcribed genes and their isoforms. Here researchers from the Luxembourg Institute of Health compare the latest Affymetrix HTA 2.0 microarray with Illumina 2000 RNA-seq for the ...

In differential expression analysis of RNA-sequencing (RNA-seq) read count data for two sample groups, it is known that highly expressed genes (or longer genes) are more likely to be differentially expressed which is called read count bias (or gene length ...

RNA-Sequencing (RNA-Seq) has become a routine technology for investigating gene expression differences in comparative transcriptomic studies. Differential expression (DE) analysis of the isoforms of genes is just emerging now that expression (read counts) can be estimated with higher accuracy at ...

The individual sample heterogeneity is one of the biggest obstacles in biomarker identification for complex diseases such as cancers. Current statistical models to identify differentially expressed genes between disease and control groups often overlook the substantial human sample heterogeneity. Meanwhile, ...

Heatmaps are an indispensible visualization tool for examining large-scale snapshots of genomic activity across various types of next-generation sequencing datasets. However, traditional heatmap software do not typically offer multi-scale insight across multiple layers of genomic analysis (e.g., differential expression analysis, ...