EM Methods

Our most recent work has focused on developing an tools that make it possible for cryo-EM users to easily access the computational capabilities in the cloud.

We began by creating an environment within Amazon Web Services (AWS) where users could create their own computer clusters to process cryo-EM data. The flexibility of this "pay-as-you-go" approach can be particularly helpful to new labs that don't have their own clusters or easy access to high-performance computing resources.

Most recently, we developed a set of tools that submit data processing jobs to AWS directly from the user's local computer or laptop, without requiring the user to interact with AWS itself. This platform also allows users to submit Rosetta modeling building jobs to AWS. This work is now being led by Mike Cianfrocco, a former postdoc in the lab, in his own group at the University of Michigan. 



Cianfrocco MA, Lahiri I, DiMaio F, Leschziner AE (2018). A software platform to deploy and manage cryo-EM jobs in the cloud. J.Struct Biol.. doi: https://doi.org/10.1016/s.jsb.2018.05.014 [Epub ahead of print]

Cianfrocco MA, Leschziner AE (2015). Low cost, high performance processing of single particle cryo-electron microscopy data in the cloud. eLIFE 06664 (link)

Chandramouli P, Hernandez-Lopez R, Wang HW, Leschziner AE (2011). Validation of the ortogonal tilt reconstruction method with a biological test sample. J Struct Biol. 175:85-96 {download PDF}

Leschziner A E (2010). The ortogonal tilt reconstruction method. Methods in Enzymology. 2010 482:237-262 {download PDF}

Leschziner AE and Nogales E (2006). The Orthogonal Tilt Reconstruction method: an approach to generating single-class volumes with no missing cone for ab initio reconstruction of asymmetric particles. J Struct Biol. 153(3):284-99. {download PDF}