Researchers at Stanford have developed a novel deep-learning-based tool called CytoTRACE2 that interprets single-cell RNA sequencing (scRNA-seq) to enable the discovery of regenerative cells across all tissue types and novel targets in cancer and other diseases.
The cost of DNA and RNA sequencing have decreased in recent years to aid effective research and clinical applications; however, the labor time and throughput of preparing DNA and RNA sequencing libraries remains a challenge.
Stanford researchers have developed a new, low-cost method for tumor methylation profiling that enables tumor classification even from low amounts of fragmented DNA characteristic of liquid biopsies.
Stanford Medicine's Ji Research Group has developed a simple, quantitative method for detecting and characterizing gene fusions that uses DNA rather than RNA as analyte.
Metagenomic sequencing offers a powerful approach for the comprehensive monitoring and detection of pathogenic bacteria in food, clinical samples, and the environment.
Dr. Curt Scharfe and colleagues have developed RUSPseq, a method for next generation molecular testing originally conceived to diagnose metabolic disorders in newborns.
Researchers at Stanford have developed the SNAIL-RCA method for inexpensive and efficient multiplexed detection of single RNA molecules in single cells.
Stanford researchers at the Genome Technology Center have developed a simple, reliable, and accurate method for obtaining sequencing information for multiple sites within target nucleic acid.
Researchers in Prof. Julia Salzman's laboratory have developed a sensitive, specific algorithm for automated, high-throughput detection of RNA fusions from RNA-Seq data.
Researchers in Prof. Stephen Quake's laboratory have developed a method to measure the entire fetal genome noninvasively using materials from maternal blood.