RNA-Seq Blog Transcriptome Research & Industry News
A recent publication from researchers at the University of Kentucky explains the importance of identifying and understanding how differences...
Read More »Researchers develop online portal to show how biases in RNA sequences affect gene expression
ActiveSVM – single-cell mRNA-seq data analysis with a minimal number of genes
Sequencing costs currently prohibit the application of single-cell mRNA-seq to many biological and clinical analyses. Targeted single-cell mRNA-sequencing reduces sequencing costs by profiling reduced gene sets that capture biological information with a minimal number of genes. Caltech researchers have developed ...
Read More »DestVI – identification of continuums of cell types in spatial transcriptomics data
Most spatial transcriptomics technologies are limited by their resolution, with spot sizes larger than that of a single cell. Although joint analysis with single-cell...
Read More »Single-cell gene expression analyses of human cerebrovascular cells can help reveal new drug targets for Huntington’s disease and other neurodegenerative diseases
While neurons and glial cells are by far the most numerous cells in the brain, many other types of cells play important roles. Among those are cerebrovascular cells, which form the blood vessels that deliver oxygen...
Read More »RNA-Seq-Pop – Exploiting the sequence in RNA-Seq – a Snakemake workflow
Researchers at the Liverpool School of Tropical Medicine have developed a reproducible and scalable Snakemake workflow, called RNA-Seq-Pop, which...
Read More »Inference of cell state transitions and cell fate plasticity from single-cell with MARGARET
Single-cell RNA sequencing (scRNA-seq) data allows us to quantify the biological heterogeneity in developmental processes at a fine-grained level. Despite recent advances in inferring cellular dynamics from the underlying developmental process, existing computational trajectory inference (TI) methods face several critical ...
Read More »scCAN – single-cell clustering using autoencoder and network fusion
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...
Read More »MURP – a downsampling method that enables robust clustering and integration of single-cell transcriptome data
The random noises, sampling biases, and batch effects often confound true biological variations in single-cell RNA-sequencing (scRNA-seq) data. Adjusting such biases is key to the robust discoveries in...
Read More »CF-Seq – rapid re-analysis of cystic fibrosis pathogen RNA sequencing studies
Researchers studying cystic fibrosis (CF) pathogens have produced numerous RNA-seq datasets which are available in the gene expression omnibus (GEO).
Read More »Single-cell atlas of drug treatments in a mouse model of diabetic kidney disease
Diabetic kidney disease (DKD) occurs in ∼40% of patients with diabetes and causes kidney failure, cardiovascular disease, and premature death...
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