Novel algorithm able to detect mutations in single-cell sequencing data sets
Being able to characterise somatic mutations at single-cell resolution is essential for understanding cancer evolution…
Being able to characterise somatic mutations at single-cell resolution is essential for understanding cancer evolution…
Generative pre-trained models have achieved remarkable success in various domains such as natural language processing…
Single-cell RNA sequencing (scRNA-seq) is widely used to reveal heterogeneity in cells, which has given…
Spatially resolved transcriptomic technologies show promise in revealing complex pathophysiological processes, but developing sensitive, high-resolution,…
Biobanks are a key resource for obtaining human cell lines for biomedical research, including for…
RNA sequencing has paved the way for transcriptomics, which is the study of all the…
Long-read RNA sequencing is essential to produce accurate and exhaustive annotation of eukaryotic genomes. Despite…
RNA editing is a post-transcriptional modification with a cell-specific manner and important biological implications. Although…
Next-generation sequencing technologies have enabled many advances across diverse areas of biology, with many benefiting…
Through the magnification of an electron micrograph, particles of the 1918 pandemic’s influenza A virus…