RNA-Seq Blog Transcriptome Research & Industry News
Single-cell RNA sequencing (scRNA-seq) enables the quantification of each gene’s expression distribution across cells, thus allowing the assessment of the...
Read More »Gene expression distribution deconvolution in single-cell RNA sequencing
Researchers develop a better method to compare gene expression in single cells
St. Jude Children’s Research Hospital investigators have developed a software package to help identify biomarkers that differentiate between cell...
Read More »FADU – a feature counting tool for prokaryotic RNA-Seq analysis
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...
Read More »New tool enables big-scale analysis of single-cell RNA-Seq data
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...
Read More »miR-MaGiC improves quantification accuracy for small RNA-seq
Many tools have been developed to profile microRNA (miRNA) expression from small RNA-seq data. These tools must contend with several issues: the small...
Read More »Bias correction for single cell RNA-seq quantification
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...
Read More »DEsingle – detecting three types of differential expression in single-cell RNA-seq data
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”...
Read More »TRUmiCount – Correctly counting absolute numbers of molecules using unique molecular identifiers
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 ...
Read More »CirComPara – A Multi-Method Comparative Bioinformatics Pipeline to Detect and Study circRNAs from RNA-seq Data
Circular RNAs (circRNAs) are generated by backsplicing of immature RNA forming covalently closed loops of intron/exon RNA molecules. Pervasiveness, evolutionary conservation, massive and regulated expression, and posttranscriptional regulatory roles of circRNAs in eukaryotes have been...
Read More »FateID infers cell fate bias in multipotent progenitors from single-cell RNA-seq data
To understand stem cell differentiation along multiple lineages, it is necessary to resolve heterogeneous cellular states and the ancestral relationships between them. Researchers from the Max Planck Institute of Immunobiology and Epigenetics developed a robotic miniaturized CEL-Seq2 implementation to carry ...
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