Tag Archives: Stanford University

RNA-Seq measures gene expression by counting sequence reads belonging to unique cDNA fragments. Molecular barcodes commonly in the form of random nucleotides were recently introduced to improve gene expression measures by detecting amplification duplicates, but are susceptible to errors generated ...

Description The Department of Genetics at Stanford University is seeking a Bioinformatician/Research Data Analyst to study the mechanisms of aging and rejuvenation in a systematic manner (http://web.stanford.edu/group/brunet/). This is a unique opportunity to independently conduct a portion of a research ...

Signaling proteins display remarkable cell-to-cell heterogeneity in their dynamic responses to stimuli, but the consequences of this heterogeneity remain largely unknown. For instance, the contribution of the dynamics of the innate immune transcription factor nuclear factor κB (NF-κB) to gene ...

Dr Nolan is immersed at the cutting edge of the RNA-Seq industry, developing and overseeing a variety of revolutionary projects namely, SCTS, split-pool synthesis combinatorial chemistry as an alternative approach to SCTS, and most recently a single cell analysis advanced ...

High-throughput single-cell technologies provide an unprecedented view into cellular heterogeneity, yet they pose new challenges in data analysis and interpretation. In this protocol, researchers from Stanford University describe the use of Spanning-tree Progression Analysis of Density-normalized Events (SPADE), a density-based ...

Single-cell RNA-seq technologies enable high throughput gene expression measurement of individual cells, and allow the discovery of heterogeneity within cell populations. Measurement of cell-to-cell gene expression similarity is critical to identification, visualization and analysis of cell populations. However, single-cell data ...

Serafim Batzoglou, Stanford University Regulatory Genomics and Epigenomics https://simons.berkeley.edu/talks/serafim-batzoglu-03-07-2016

July 11, 2016 The 2016 GTEx Project Community Meeting will be held at Stanford University. The meeting will highlight current data sets and types available, various tools being developed for these data types, and results from applying them to the ...

Current approaches to single-cell transcriptomic analysis are computationally intensive and require assay-specific modeling which limit their scope and generality. Researchers from UC Berkely and Stanford University have developed a novel method that departs from standard analysis pipelines, comparing and clustering ...

Michael Snyder, Ascherman Professor, chair of the Department of Genetics and director of the Center of Genomics and Personalized Medicine at Stanford University, will present “Personalized Medicine: New Directions to Revolutionize Management of Health and Disease,” on Thursday, Feb. 25. ...