Release date: 2016-04-21
Next-generation sequencing technology (NGS) greatly enhances our understanding of diseases such as cancer. However, the research object of NGS is the cell population, which will mask the heterogeneity between cells. For this reason, single cell sequencing (SCS) technology has received increasing attention.
Researchers at the University of Texas MD Anderson Cancer Center developed the first mutation detection program for single-cell DNA sequencing, Monovar. This important achievement was published in the April 18 issue of Nature Methods. The author of the article was Dr. Ken Chen and Nicholas Navin, assistant professors at the MD Anderson Cancer Center. Dr. Chen graduated from Tsinghua University and subsequently obtained his Ph.D. from the University of Illinois.
Single-cell sequencing technology has had a major impact on many biological fields, including cancer research, neurobiology, microbiology, and immunology, which have greatly promoted people's understanding of tumor characteristics. However, researchers have been lacking the tools to accurately detect single nucleotide variants (SNVs) in single-cell sequencing data. “We developed Monovar for this purpose,†said Dr. Nicholas Navin. “Monovar is a new statistical method that uses multiple single-cell data to discover SNV.â€
Researchers believe that Monovar is a major advancement in single-cell sequencing to assess SNV and can provide people with critical information on a variety of diseases. Accurate detection of SNV is important for cancer diagnosis, personalized medicine and prenatal diagnosis, and Monovar has broad application prospects in these areas.
The field of single-cell genomics has developed very rapidly in recent years, revealing many important clues to complex biological systems, including the ecological diversity of microbial communities and the genome of human cancers. An important review published by Nature Reviews Genetics on January 25 provides a comprehensive overview of the development of single-cell genome sequencing. The author of the article is Stephen R. Quake, a well-known scientist and head of the Department of Bioengineering at Stanford University.
Intercellular differences and heterogeneity are inherently characteristic of stem cell populations. If we perform a omics analysis on a group of cells, these critical information will be masked. Single-cell sequencing technology can comprehensively analyze cell heterogeneity in the same stem cells and identify cells with different phenotypes. Peking University researcher Tang Fuyue and Wen Lu published an article in the April 15 issue of Genome Biology, which introduced the single-cell sequencing technology in stem cell biology research.
Source: Biopass
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