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 ...
Learning the grammar of gene regulation
New AI-based tool allows to predict activity of DNA sequences Deep learning approaches have transformed how scientists predict activity and function of DNA sequences in the genome. A new AI tool called Corgi (Context-aware Regulatory Genomics Inference), developed by ...
seq2ribo – structure-aware integration of machine learning and simulation to predict ribosome location profiles from RNA sequences
Proteins are made when ribosomes move along messenger RNA (mRNA) molecules, reading their genetic instructions one codon at a time. Exactly where ribosomes pause or move quickly can influence how much protein is produced, making ribosome behavior an important ...
STGBench – sequencing-level spatial DNA-RNA simulation for multimodal and virtual cell-oriented benchmarking of genomic alterations
Spatial genomics and spatial transcriptomics are helping researchers understand how tumors grow, evolve, and respond to treatment by showing where genetic changes occur within tissues. However, developing and testing new analysis software has been difficult because researchers rarely have ...
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, ...
Mapping bacterial transcription with RNAP-seq
Gene transcription is the process cells use to copy DNA instructions into RNA. This is one of the most important steps in gene regulation, because it helps determine when genes are turned on, how strongly they are expressed, and ...
scDecorr – feature decorrelation based representation learning enables self-supervised alignment of multiple single-cell experiments
Single-cell RNA sequencing allows scientists to study how individual cells behave, revealing important differences between cells in tissues, tumors, and developing organs. However, analyzing this data is not simple. The data can be noisy, incomplete, and influenced by technical ...
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 method maps RNA binding proteins with higher precision
RNA binding proteins play a central role in how cells control gene expression. They interact with RNA molecules to influence processes such as splicing, stability, and translation. However, identifying exactly which RNA molecules these proteins bind to inside cells ...
The trRosettaRNA server for RNA structure prediction
RNA is often thought of as a simple messenger that carries genetic information, but many RNA molecules actually fold into complex three dimensional shapes that allow them to perform important biological functions. These structures influence how RNA interacts with ...
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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