Expression and Quantification

Alignment-free RNA quantification tools have significantly increased the speed of RNA-seq analysis. However, it is unclear whether these state-of-the-art RNA-seq analysis pipelines can quantify...

In single-cell RNA sequencing (scRNA-seq) studies, only a small fraction of the transcripts present in each cell are sequenced. This leads to unreliable...

Single-cell RNA sequencing (scRNA-seq) enables the quantification of each gene’s expression distribution across cells, thus allowing the assessment of the...

St. Jude Children’s Research Hospital investigators have developed a software package to help identify biomarkers that differentiate between cell...

The major algorithms for quantifying transcriptomics data for differential gene expression analysis were designed for analyzing data from human or human-like genomes, specifically those with single...

New research led by Holger Heyn at the Centro Nacional de Análisis Genómico of the Centre for Genomic Regulation (CNAG-CRG), presents a sophisticated computational framework to analyze single-cell gene expression levels, scalable to process...

Many tools have been developed to profile microRNA (miRNA) expression from small RNA-seq data. These tools must contend with several issues: the small...

With rapid technical advances, single cell RNA-seq (scRNA-seq) has been used to detect cell subtypes exhibiting distinct gene expression profiles and to trace cell transitions in development and disease...

The excessive amount of zeros in single-cell RNA-seq data include “real” zeros due to the on-off nature of gene transcription in single cells and “dropout”...

Counting molecules using next-generation sequencing (NGS) suffers from PCR amplification bias, which reduces the accuracy of many quantitative NGS-based experimental methods such as RNASeq. This is true even if molecules are made distinguishable using unique molecular identifiers (UMIs) before PCR ...