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Google has announced announced a new partnership with University of Chicago Medicine which will take advantage of the CRI’s experience and expertise in working with biomedical data and predictive algorithms.

The Google team will work with University researchers to apply advanced machine learning techniques to de-identified patient data. These models may detect patterns that enable doctors to predict future health events — meaning they could anticipate patients’ needs before they arise, improve outcomes, and save lives. The partnership will expand on work in this area that is already in progress at UCM, such as the eCART model for predicting cardiac arrest.

Read more about the partnership in the Chicago Tribune and on Google’s blog.

This fall, the CRI will launch our new high performance computing cluster, named Gardner. Available to all BSD/UCM researchers and their collaborators, the new cluster will allow us to accommodate more users and run analyses even more quickly and powerfully.

Gardner will feature:

• 2.0 GHz Intel Haswell processors across all nodes
• Infiniband FDR interconnect (56 Gbps)
• 97 TFLOPs  Actual Performance (Rmax)
• 88 standard compute nodes (2464 total cores;  128 GB RAM per node)
• 28 mid-tier compute nodes (784 total cores; 512 GB RAM per node)
• 4 large memory nodes (112 total cores; 1.28 TB RAM per node)
• 5 GPU nodes with NVidia Tesla K80 GPUs
• 1 Xeon Phi nodes with 2 Knight’s Corner coprocessors
• 350 TB Scratch Space

Read More

With a recent lawsuit drawing attention to inaccuracies in heart rate data gathered by Fitbits, do wearable health tracking devices have a place in clinical research? CRI Director Sam Volchenboum and Daphne Kis, writing in TechCrunch, look at both sides of the issue and advocate for a path forward that prioritizes both accuracy and usability.

Wearables and other forms of real-time tracking can transform large-scale studies of disease, giving researchers data that is more extensive and more accurate than what patients are able to remember weeks or months later. For example, this UChicago study of IBD patients uses Fitbits to track physical activity, allowing reseachers to find patterns and identify potential symptom triggers. But as Volchenboum and Kis note, “If we want better devices, we must do a better job of telling manufacturers what kinds of measurements and outputs we need. … Standards and data provenance aren’t sexy, but they are absolutely essential to any compelling future vision of clinical research.”

A story today in Science Life highlights the research on zebrafish antigen processing genes, conducted in part by the CRI Bioinformatics Core (read about our contribution here), that was recently published in the Proceedings of the National Academy of Sciences. Read the article to hear from the study’s lead author Sean McConnell, PhD, of the Department of Pediatrics about why these new discoveries are so exciting and how research on zebrafish may translate to advances in our understanding of the human immune system.

A new publication in the Proceedings of the National Academy of Sciences highlights findings about novel antigen processing and presentation genes in zebrafish. This newly identified genetic diversity, discovered by de novo assembly, represents the most extensive diversity yet seen in the antigen processing genes of any species and fills in previously unrecognized knowledge gaps about the evolution of vertebrate adaptive immunity.

This research was enabled in part by our Bioinformatics Core. CRI Director of Bioinformatics Jorge Andrade, PhD, and Bioinformatician Kyle Hernandez, PhD, are co-authors on the paper. They produced the de novo assembly of the genome of a clonal line of zebrafish, using the CRI’s computing infrastructure for this complex data- and memory-intensive task. They then used many tools to determine which de novo scaffolds aligned near the chromosomal region being studied and aggregated these scaffolds to orient and order them across the region.

Read the full paper here, and see more publications enabled by the CRI here.

A recent publication in the journal Cell details the findings of the first phase of the 1001 Genomes project, in which researchers sequenced and analyzed the genomes of 1,135 strains of Arabidopsis thaliana. This data has led to discoveries about the evolutionary and migratory history of A. thaliana and creates opportunities for new research about how genes and environment interact. Read about how CRI resources contributed to this study.

In a study published in the Proceedings of the National Academy of Sciences, a team of researchers from the University of Chicago and other institutions used genomic analysis to provide new evidence for the prehistoric origins of modern Himalayan populations. Dr. Anna Di Rienzo of the Department of Human Genetics used the CRI’s Tarbell high-performance computing cluster, as well as the Computation Institute’s Beagle supercomputer, to sequence the genomes of eight individuals who lived thousands of years in the past and to analyze them in comparison with those of modern Himalayan dwellers. Read more about the study at Science Node.