HAMRLNC – a comprehensive and scalable pipeline for integrated epitranscriptomic analysis
As RNA sequencing becomes faster, cheaper, and more widely used, scientists are increasingly asking more sophisticated questions about how cells work. Traditionally, RNA sequencing has been used to measure which genes are turned on or off, helping researchers compare ...
scKGBERT – a knowledge-enhanced foundation model for single-cell transcriptomics
Understanding how individual cells behave is essential for advancing modern biology and medicine. Scientists often turn to single-cell transcriptomics, a technique that measures gene activity one cell at a time, to uncover hidden differences between cells that look similar ...
Helixer: ab initio prediction of primary eukaryotic gene models
Understanding where genes are located within a genome is a critical part of biology, because genes act as the instruction manual for how organisms grow, function, and adapt. Yet as scientists sequence more species, especially those that have not ...
HydraRNA – a hybrid architecture based full-length RNA language model
Researchers from the Southern University of Science and Technology, Shenzhen have developed an innovative artificial intelligence model called HydraRNA that can interpret the complex biological “language” of RNA. RNA, a key molecule that helps convert genetic information from DNA ...
T-CellAnnoTator – reproducible single-cell annotation of programs underlying T cell subsets, activation states and functions
Researchers are gaining a clearer picture of how T cells, the body’s key defenders in the immune system, actually work. T cells are known for recognizing threats like viruses or cancer cells and then turning on specific genetic programs ...
IC2Bert – an AI-powered model that improves prediction of cancer immunotherapy success using RNA sequencing
Predicting which cancer patients will respond to immunotherapy is a major goal in precision medicine. While RNA sequencing has become a powerful tool to help with this, results can vary a lot between studies because of differences in patient ...
mLLMCelltype: A Multi-Model Framework for Cell Type Annotation in Single-Cell RNA-seq Data
### mLLMCelltype: A Multi-Model Framework for Cell Type Annotation in Single-Cell RNA-seq Data We present mLLMCelltype, a computational framework that employs multiple large language models (LLMs) to perform automated cell type annotation in single-cell RNA sequencing (scRNA-seq) data. The ...
LMD – cluster-independent multiscale marker identification in single-cell RNA-seq data
With the help of single-cell RNA sequencing (scRNA-seq), scientists try to understand what makes individual cells unique. This is important in everything from tracking immune cell behavior to identifying early disease markers or monitoring how cells respond to treatment. ...
scGPT – end-to-end protocol for fine-tuned retinal cell type annotation
Single-cell RNA sequencing (scRNA-seq) has changed the way researchers study cells by allowing them to examine gene expression at the level of individual cells. This technology helps uncover subtle differences between cell types, identify rare cells, and track how ...
ScInfeR – an efficient method for annotating cell types and sub-types in single-cell RNA-seq, ATAC-seq, and spatial omics
Accurately identifying different types of cells within tissues is essential for understanding how organs function, how diseases progress, and how treatments might work. With the rise of single-cell and spatial omics technologies, researchers can now study individual cells in ...
RNA-Seq Products & Services
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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