RNA-Seq Blog Transcriptome Research & Industry News
Tumor heterogeneity—the complex microenvironment of different cell subpopulations within a single tumor—remains a major challenge...
Read More »Navigating the unCHARTed – web tool explores public sequencing data for cancer research
BARseq2 – maps neuronal projections paths and gene expression simultaneously
Functional circuits consist of neurons with diverse axonal projections and gene expression. Understanding the molecular signature of...
Read More »Rank-in – enabling integrative analysis across microarray and RNA-seq for cancer
Though transcriptomics technologies evolve rapidly in the past decades, integrative analysis of mixed data between microarray and RNA-seq remains...
Read More »SuperFreq – detecting copy number alterations in RNA-Seq using
Calling copy number alterations (CNAs) from RNA sequencing (RNA-Seq) is challenging, because of the marked variability in coverage across genes and...
Read More »TIMEOR- a web-based tool to uncover temporal regulatory mechanisms from multi-omics data
Uncovering how transcription factors regulate their targets at DNA, RNA and protein levels over time is critical to define gene regulatory networks (GRNs)...
Read More »CellMap – characterizing the types and composition of iPSC-derived cells from RNA-seq data
Induced pluripotent stem cell (iPSC) derived cell types are increasingly employed as in vitro model systems for drug discovery. For these studies to...
Read More »RNAcmap – a fully automatic pipeline for predicting contact maps of RNAs by evolutionary coupling analysis
The accuracy of RNA secondary and tertiary structure prediction can be significantly improved by using structural restraints derived from evolutionary coupling or direct coupling analysis. Currently, these...
Read More »Novel guidelines help select optimal deconvolution method
Biomedical scientists are increasingly using deconvolution methods, those used to computationally analyze the composition of complex mixtures of cells. One of their challenges is to...
Read More »scDHA – fast and precise single-cell data analysis using a hierarchical autoencoder
A primary challenge in single-cell RNA sequencing (scRNA-seq) studies comes from the massive amount of data and the excess noise level...
Read More »CellMixS – quantifying and visualizing batch effects in single-cell RNA-seq data
A key challenge in single-cell RNA-sequencing (scRNA-seq) data analysis is batch effects that can obscure the biological signal of interest. Although...
Read More »