Tag Archives: Differential expression analysis

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, ...

RNA-Sequencing (RNA-Seq) provides valuable information for characterizing the molecular nature of the cells, in particular, identification of differentially expressed transcripts on a genome-wide scale. Unfortunately, cost and limited specimen availability often lead to studies with small sample sizes, and hypothesis ...

Small RNA-Seq has emerged as a powerful tool in transcriptomics, gene expression profiling and biomarker discovery. Sequencing cell-free nucleic acids, particularly microRNA (miRNA), from liquid biopsies additionally provides exciting possibilities for molecular diagnostics, and might help establish disease-specific biomarker signatures. ...

Differential expression analysis of RNA sequencing (RNA-seq) data typically relies on reconstructing transcripts or counting reads that overlap known gene structures. Researchers at Johns Hopkins University previously introduced an intermediate statistical approach called differentially expressed region (DER) finder that seeks ...

RNA sequencing (RNA-seq) is a powerful tool for genome-wide expression profiling of biological samples with the advantage of high-throughput and high resolution. There are many existing algorithms nowadays for quantifying expression levels and detecting differential gene expression, but none of ...

Differential expression analysis of RNA sequencing (RNA-seq) data typically relies on reconstructing transcripts or counting reads that overlap known gene structures. Previously, researchers at Johns Hopkins Bloomberg School of Public Health introduced an intermediate approach called differentially expressed region (DER) finder ...

High-throughput RNA-sequencing (RNA-seq) technology provides an attractive platform for gene expression analysis. In many experimental settings, RNA-seq read counts are measured from matched samples or taken from the same subject under multiple treatment conditions. The induced correlation therefore should be ...

A number of algorithms exist for analysing RNA-sequencing data to infer profiles of differential gene expression. Problems inherent in building algorithms around statistical models of over dispersed count data are formidable and frequently lead to non-uniform p-value distributions for null-hypothesis ...

RNA-Seq, or transcriptome sequencing, continues to be an exciting way to explore gene expression using next-generation sequencing (NGS). But for many researchers, the interpretation of RNA-Seq data remains a daunting task because they are using spreadsheets to read transcript names ...

RNA-Seq technique has been demonstrated as a revolutionary means for exploring transcriptome because it provides deep coverage and base pair-level resolution. RNA-Seq quantification is proven to be an efficient alternative to Microarray technique in gene expression study, and it is ...