Faculty Use Cases

Below are some example use cases of projects using AWS. 

Emory Clinical Biomarkers Laboratory

The Emory Clinical Biomarkers Laboratory, directed by Dr. Dean P. Jones, operates a high-resolution metabolomics platform to analyze clinical samples, e.g. plasma and tissue samples from HIV patients, for identification of metabolic biomarkers and to improve the understanding of disease mechanisms. Under the technical guidance of Karan Uppal, PhD, Director of Computational Metabolomics of the laboratory, AWS is used to support various data processing workflows including: 1) data extraction from instrument files using R packages apLCMS, xcms, and xMSanalyzer; 2) quality evaluation using xMSanalyzer; 3) annotation using xMSannotator; 4) statistical, network and pathway analysis using R and Python based tools. The lab generally uses AWS EC2 spot instances (with low risk of out-bidding) for computing to maximize cost saving and uses S3 for data transfer and sharing.  Overall, the lab is enjoying the flexibility and scalability of AWS for most of their computation needs.

Emory Integrated Computational Core

Rich Johnston, PhD, director of the Emory Integrated Computation Core (EICC) has recently mapped and called 1027 genomes in less than a week with AWS using PEMapper and PECaller.  Each of these 30x genomes was about 60 to 100 GB compressed in size, and the mapping would typically take 12 hours on a 64-core workstation. While the on-premises Emory Department of Genetics’ HPC compute cluster could only handle 2 genomes per day, Rich was able to launch 200 AWS EC2 instances in parallel to process 200 genomes per day. The whole workflow was scripted and took advantage of AWS APIs to enable the automation. S3 was used to stage the genomes and handles the transfer of the results. In short, AWS’s scalability allows Rich to meet his project deadline fast. For additional information, Rich is available to share his AWS experience. You can contact him at eicc@emory.edu.

Citation

Richard Johnston, Pankaj Chopra, Thomas S. Wingo, Viren Patel, International Consortium on Brain and Behavior in 22q11.2 Deletion Syndrome, Michael P. Epstein, Jennifer G. Mulle, Stephen T. Warren,Michael E. Zwick, and David J. Cutlera, “PEMapper and PECaller provide a simplified approach to whole-genome sequencing”. Proc Natl Acad Sci U S A. 2017 Mar 7;114(10) 

Machine Learning to Predict Psychosis

Using a Machine Learning approach on AWS at Emory, Dr. Phil Wolff, Professor in Emory's Psychology Department, studies the prediction of psychosis using semantic density and latent content analysis. Click this link for more information. 

Citation

Rezaii, N., Walker, E. & Wolff, P. A machine learning approach to predicting psychosis using semantic density and latent content analysis. npj Schizophr 5, 9 (2019). https://doi.org/10.1038/s41537-019-0077-9

Understanding Transplant Rejection and Infection

The goal of a transplant is having one transplant last a lifetime. While most patients do well with their transplants, there are those whose bodies get infections or reject the transplant completely. This story highlights how Dr. Chris Larsen, Emory transplant surgeon and immunologist, and his team use AWS at Emory to help predict those outcomes.

Studying the Microbiome through Genomic Analysis

Studying the microbiome involves the analysis of complex gene data of bacteria and other microorganisms, often by processing a large number of gene sequences with powerful compute resources. Learn how Dr. Irene Yang, Assistant Professor in the Nell Hodgson Woodruff School of Nursing, utilized AWS at Emory for her microbiome research at this link.