Benchmarking RNA sequencing for more accurate alternative splicing analysis
RNA sequencing benchmarking across 42 laboratories identifies data quality, sequencing depth, and bioinformatics choices that improve alternative splicing and isoform analysis...
POND-seq enables non-destructive RNA sequencing in living cells
RNA sequencing is widely used to measure gene activity, but most methods require cells to be broken open or fixed before their RNA can be collected. This means researchers usually get only a single snapshot of gene expression from ...
New method allows scientists to follow gene activity over time in the same cells
Live-cell RNA sequencing uses engineered virus-like particles to repeatedly sample transcriptomes without destroying cells, enabling researchers to track gene expression changes over time...
Single-cell and single-embryo RNA sequencing
Single-cell and single-embryo RNA sequencing enables detailed analysis of gene expression and cell identity during the earliest stages of embryonic development...
Deep learning improves microRNA target prediction from sequence
MicroRNAs, or miRNAs, are small RNA molecules that help control gene expression. They work by guiding Argonaute proteins to specific RNA targets, where they can reduce the production of proteins from those genes. Predicting which RNAs a miRNA will ...
Atlas of the brain’s striatum could guide researchers to new drug treatments
A new study reveals insights into populations of neurons affected by Huntington’s disease, schizophrenia, addiction, and other disorders. A region of the brain called the striatum is critical for many cognitive and motor functions, including decision-making, control of movement, ...
scLS – a computationally efficient differentially expressed gene detection algorithm
The proposed algorithm enables pseudotime-based dynamics without the need for explicit regression models or branch assignment Single-cell RNA sequencing (scRNA-seq) is a method to measure gene expression of individual cells, allowing observation of various cellular processes, including cell differentiation, ...
Spatial mapping of RNA turnover kinetics in the mouse brain
Gene expression is often described by measuring how much RNA is present in a cell at a given moment. But RNA levels are constantly changing as new molecules are produced and older ones are broken down. Measuring both processes ...
SPIDER improves spatial transcriptomics data using single-cell RNA sequencing
Spatial transcriptomics is a powerful technology that allows researchers to measure gene activity while preserving information about where cells are located within a tissue. Unlike traditional RNA sequencing, which can lose this spatial information when tissue is broken apart, ...
CellTypeAI – cell annotation for scRNA-seq using local generative-AI
Single-cell RNA sequencing, or scRNA-seq, allows researchers to examine gene activity in individual cells rather than averaging signals across thousands or millions of cells. This level of detail can reveal important differences between cell populations, but it also creates ...
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Most Shared Posts
- Upcoming Conference – The Festival of Genomics & Biodata
- Gene trajectory inference for single-cell data by optimal transport metrics
- Upcoming Workshop – Advanced Topics in Single Cell RNA-Seq Analysis: Multiomics ATAC-Seq & RNA-Seq
- BAP-seq – bioorthogonal masked acylating agents for proximity-dependent RNA labelling
- Single-cell RNA-seq of the rare virosphere reveals the native hosts of giant viruses in the marine environment
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