SIB Bioinformatics Awards 2019: RNA Velocity and the Velocyto tool
Channel: SIB - Swiss Institute of Bioinformatics
Date: 2019-10-10

What you will discover: how to analyze the expression dynamics in single cell RNAseq data, and to estimate RNA velocity of single cells using the Velocyto package (http://velocyto.org/)

Target audience: computational biologists, users of single-cell RNA sequencing, developmental biologists

Speaker: Sten Linnarsson, Professor of Molecular Systems Biology, Karolinska Institutet, Sweden (see biography below)

Click to access specific sections of the talk:
- Overview of single-cell analysis 01:11
- Going from static to dynamic: introducing RNA Velocity 03:32
- The mouse hippocampus development as an example 11:12
- Summary of RNA Velocity 19:11
- The velocyto package for Python and R 21:09

Reference paper
La Manno G et al. RNA velocity of single cells. Nature 2018 https://doi.org/10.1038/s41586-018-0414-6

Any question about this talk? Contact Sten: sten.linnarsson@ki.se 
@slinnarsson 
linnarssonlab.org


More about the speaker: 
Sten Linnarsson took his PhD in 2001, studying neurotrophic factors regulating neuronal survival, growth and plasticity. Instead of a postdoc, he founded a company to develop methods for gene expression analysis and single-molecule DNA sequencing. In 2007, he was appointed assistant professor and in 2015 Professor of Molecular Systems Biology at Karolinska Institute. He was awarded the 2015 Erik K. Fernström Prize for his work in single-cell biology, and is a member of the European Molecular Biology Organization (EMBO), and the Organizing Committee of the Human Cell Atlas initiative. Since 2007, Linnarsson has pursued single-cell biology with the ultimate aim to discover the complete branching manifold of cell states in the developing human nervous system. To achieve this goal, his group pioneered single-cell RNA sequencing, RNA single-molecule FISH, and advanced computational methods. In 2011, Linnarsson showed that cell types could be directly discovered and distinguished de novo, by unbiased sampling and sequencing of many single cells, without use of previously known markers, thus laying the conceptual foundation for the single-cell genomics field. He has made important contributions to single-cell technology: unique molecular identifiers (UMIs) for accurate quantification; Patch-seq for combined electrophysiology, morphology and transcriptomics; RNA velocity to extract dynamic information from snapshot measurements, and more. In a series of recent papers he has used these methods to explore the mammalian brain, culminating in a complete single-cell atlas of the mouse nervous system.


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