Software on GitHub
The Gradinaru Lab maintains a GitHub account where one can openly access the laboratory software/scripts to perform various data analysis as described by authors in the published articles.
Coming soon on GitHub!
Protein Engineering Analysis Tool (PEAT)
A cloud-based platform for management and analysis of next generation sequencing viral mutant datasets.
The Gradinaru Lab engineers customized Adeno-associated viruses (AAVs) to selectively deliver genes to specific cell types and tissues. For example, a major therapeutic challenge is gene delivery to neurons, as the virus must cross the Blood Brain Barrier and further penetrate individual neurons. In 2017, the Gradinaru Lab demonstrated that a variant of the natural AAV9 could selectively penetrate the BBB, 55% of cortical neurons, and 69% of striatal neurons.
Professor Gradinaru’s group uses directed evolution to identify these virus variants. The directed evolution method involves inserting partially random DNA sequences at specific mutation sites, to create many unique variants of the virus. These variants are introduced into mice, whose tissues are subsequently sequenced using deep sequencing. If a variant of a virus has penetrated a tissue, it will be discovered in high quantities in the sequencing for that tissue. Several rounds of an experiment may be performed to narrow the pool of potential variants and to produce a more optimal virus variant for a particular cell type and/or tissue type.
The deluge of data from these deep sequencing experiments has brought about data management and analysis challenges, for which there are no current commercially available solutions. The Schmidt Academy is working with the Gradinaru lab on new software system that will accelerate streamline and accelerate research using AAVs.
Laboratory protocols on Protocols.io
The Gradinaru Lab maintains a protocols.io account where one can openly access the laboratory protocols on AAV Production and Tissue Clearing.
Plasmids on Addgene
The Gradinaru Lab deposit plasmids from published articles on Addgene.