Expression and Quantification

One of the standard methods of high-throughput RNA sequencing analysis is differential expression. However, it does not detect changes in molecular regulation. In contrast to the standard differential...

Gene expression models, which are key towards understanding cellular regulatory response, underlie observations of single-cell transcriptional dynamics. Although RNA expression data encode information on gene expression models, existing computational...

RNA sequencing (RNA-seq) is gaining popularity as a complementary assay to genome sequencing for precisely identifying the molecular causes of rare...

RNA sequencing has become an increasingly affordable way to profile gene expression analyses. Researchers from the LNCC, Brazil have developed a scientific workflow implementing several open...

Digital counting individual nucleic acid molecule is of great significance for fundamental biological research and accurate diagnosis of genetic diseases, which is hard to achieve with existing single-molecule detection technologies. Researchers at the...

The rapid development of single cell transcriptomics has revolutionized the study of complex tissues. Single cell RNA-sequencing (scRNA-seq) can profile tens-of-thousands of dissociated cells from a single...

Researchers from the University of Tsukuba have designed a statistical framework that identifies regulation of gene expression by neighboring cell...

Since scientists first mapped the complete human genome, attention has now turned to the question of how cells use this master copy of genetic...

Rapid development of transcriptome sequencing technologies has resulted in a data revolution and emergence of new approaches to study transcriptomic regulation such as alternative splicing, alternative polyadenylation, CRISPR knockout...

Unsupervised clustering of single-cell RNA sequencing data (scRNA-seq) is important because it allows us to identify putative cell types. However, the large number of cells (up to millions), the high...