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 ...
PotatoRTD and TomatoRTD: comprehensive reference transcript datasets for accurate transcriptome analysis and isoform discovery
Genes can produce more than one RNA transcript, allowing a single gene to generate different versions of RNA that may ultimately produce different proteins. Accurately identifying these transcript variants, known as isoforms, is important for understanding how genes function ...
Machine learning improves RNA sequencing annotation of active genes
Understanding which parts of the genome are active is essential for studying how cells develop, specialize, and respond to disease. RNA sequencing provides researchers with information about gene activity, but turning sequencing signals into accurate maps of genes and ...
MiRformer – a dual-transformer-encoder framework for predicting microRNA-mRNA interactions from paired sequences
MicroRNAs, or miRNAs, are small RNA molecules that help control how genes are expressed. They do this by binding to messenger RNAs, or mRNAs, preventing them from producing proteins or triggering their breakdown. Because microRNAs regulate many biological processes, ...
ScPanKD – new AI framework improves T cell subtype identification in single cell RNA sequencing
Single cell RNA sequencing has become one of the most powerful tools for studying complex tissues because it allows researchers to examine the gene activity of individual cells. One of its most common uses is identifying different cell types ...
Multiple AI models improve cell type identification in single-cell RNA sequencing
Single-cell RNA sequencing has become one of the most powerful tools for studying complex biological systems. By measuring gene expression in individual cells, researchers can identify different cell types, track cellular development, and better understand disease processes. However, one ...
MUTACLASH – identifying functional small RNA target sites with crosslinking footprints
Small RNAs such as microRNAs and piRNAs help control how genes are turned on or off. They do this by binding to messenger RNAs, which carry the instructions for making proteins. However, figuring out exactly where these small RNAs ...
Improving atlas-scale single-cell annotation models with hierarchical cross-entropy loss
Single-cell RNA sequencing has transformed biology by letting scientists examine gene activity one cell at a time. A key challenge in this process is correctly identifying what type of cell each sequenced cell actually is. This step, known as ...
The GENCODE CLS project – massively expanding the lncRNA catalog through capture long-read RNA sequencing
Understanding how genomes work depends on knowing where genes are and what they produce. While protein-coding genes are relatively well annotated, long non-coding RNAs, or lncRNAs, have remained one of the least understood parts of the genome. These RNAs ...
CASSIA – a multi-agent large language model for automated and interpretable cell annotation
Cell type annotation remains one of the most critical yet time-consuming steps in single-cell RNA sequencing analysis. Researchers from the University of Wisconsin-Madison have developed CASSIA (Collaborative Agent System for Single-cell Interpretable Annotation), a novel multi-agent LLM framework that ...
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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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