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...
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, ...
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 ...
Ultrafast and reference-free sequence discovery in single-cell data
Single-cell RNA sequencing has made it possible to measure gene activity in millions of individual cells. Large projects such as the Human Cell Atlas are using these technologies to build detailed maps of healthy and diseased tissues. However, most ...
ARCADIA combines RNA sequencing and spatial proteomics to reveal how tissue location shapes cell behavior
Cells are strongly influenced by their surroundings. Their behavior depends not only on which genes are active, but also on neighboring cells and the specific tissue environment they occupy. Single-cell RNA sequencing, or scRNA-seq, can measure gene activity in ...
An end-to-end computational framework for “Record-seq” transcriptional recording data
RNA sequencing, or RNA-seq, provides a snapshot of which genes are active in a biological sample at the time it is collected. Record-seq takes a different approach. Instead of measuring RNA directly at one moment, it allows engineered bacteria ...
ExoShorkie – predicting RNA-seq coverage of exogenous genomes in yeast by transfer learning
RNA sequencing, or RNA-seq, allows researchers to measure which parts of a genome are being transcribed into RNA and how strongly those regions are expressed. Being able to predict RNA-seq coverage directly from DNA sequence could help scientists better ...
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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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