Automating RNA sequencing analysis with AutoRNAseq
As RNA sequencing becomes more widely used, researchers are generating massive amounts of data. While this creates new opportunities to understand biology and disease, it also introduces a challenge, how to process all of this data in a consistent ...
RNA sequencing without references uncovers hidden transcriptome features
Single-cell RNA sequencing has become a powerful tool for studying how genes behave in individual cells. Most current approaches rely on comparing sequencing data to known reference genomes, which can limit discovery, especially when studying less characterized organisms or ...
Improving small RNA analysis with a new miRNA alignment tool
Small non-coding RNAs, often abbreviated as sncRNAs, are short RNA molecules that play important roles in regulating gene expression. Among them, microRNAs, or miRNAs, are especially well known for controlling how messenger RNA is translated into proteins. Because of ...
Evaluating semi supervised methods for single cell RNA sequencing integration
Single cell RNA sequencing allows scientists to study gene expression in individual cells, helping reveal how different cell types function and interact. However, combining datasets from different experiments can be difficult because of technical differences known as batch effects, ...
A chatbot simplifies RNA sequencing data analysis
RNA sequencing has become a standard method for measuring gene expression across thousands of genes at once. While the technology itself is powerful, analyzing the resulting data can be complex. Researchers often need bioinformatics expertise to select appropriate statistical ...
A user friendly tool brings RNA sequencing analysis to the classroom
RNA sequencing, often called RNA-seq, has become one of the most important tools for studying how genes are turned on and off in cells. It allows researchers to measure gene expression across the entire genome, helping scientists understand diseases, ...
Simple methods rival ai models in single cell RNA analysis
Single-cell RNA sequencing, often called scRNA-seq, allows scientists to measure gene activity in thousands or even millions of individual cells. Because these datasets are large and complex, researchers have recently developed powerful artificial intelligence systems, including transformer-based foundation models ...
scHiCAR – trimodal single-cell profiling of transcriptome, epigenome and 3D genome
Understanding how genes are turned on and off requires more than simply measuring RNA levels. Inside the nucleus, DNA is folded in three dimensions, and this folding helps bring regulatory DNA elements, called cis regulatory elements, into contact with ...
Hierarchical analysis of RNA secondary structures with pseudoknots based on sections
RNA molecules do much more than carry genetic messages. They fold into specific shapes that help control how genes are regulated, how proteins are made, and how enzymes function. One of the most challenging features to predict in RNA ...
An agentic AI framework for ingestion and standardization of single-cell RNA-seq data analysis
The rapid growth of publicly available single-cell RNA sequencing data has opened the door to answering many new biological questions. Researchers can now explore how individual cells behave in health and disease across many tissues and conditions. However, actually ...
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