Expression and Quantification

Perturbations in the environment lead to distinctive gene expression changes within a cell. Observed over time, those variations can be characterized by single impulse-like progression patterns. Researchers at the University of Bonn have developed ImpulseDE, an R package suited to ...

The ability to quantify cellular heterogeneity is a major advantage of single-cell technologies. However, statistical methods often treat cellular heterogeneity as a nuisance. Researchers from the Dana-Farber Cancer Institute and the University of Wisconsin, Madison present a novel method to ...

Recent innovation of RNA-seq technology has shed insightful light on the transcriptomic evolution studies, especially on researches of tissue-specific expression evolution. Phylogenetic analysis of transcriptome data may help to identify causal gene expression differences underlying the evolutionary changes in morphological, ...

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, ...

Although a large number of clustering algorithms have been proposed to identify groups of co-expressed genes from microarray data, the question of if and how such methods may be applied to RNA-seq data remains unaddressed. In this work, researchers from ...

Current computational methods for estimating transcript abundance from RNA-seq data can lead to hundreds of false-positive results. Researchers from the Dana-Farber Cancer Institute show that these systematic errors stem largely from a failure to model fragment GC content bias. Sample-specific ...

The yield obtained from next generation sequencers has increased almost exponentially in recent years, making sample multiplexing common practice. While barcodes (known sequences of fixed length) primarily encode the sample identity of sequenced DNA fragments, barcodes made of random sequences ...

Current computational methods for estimating transcript abundance from RNA-seq data can lead to hundreds of false-positive results. Researchers from the Dana-Farber Cancer Institute show that these systematic errors stem largely from a failure to model fragment GC content bias. Sample-specific ...

Using single-cell RNA-seq (scRNA-seq), the full transcriptome of individual cells can be acquired, enabling a quantitative cell-type characterisation based on expression profiles. However, due to the large variability in gene expression, identifying cell types based on the transcriptome remains challenging. ...

The advancement of the next-generation sequencing technology enables mapping gene expression at the single-cell level, capable of tracking cell heterogeneity and determination of cell subpopulations using single-cell RNA sequencing (scRNA-seq). Unlike the objectives of conventional RNA-seq where differential expression analysis ...