DNA sequencing productivity increases with ARC-TS services

By | HPC, News, Research, Systems and Services
NovaSeq, the DNA sequencer that is about the size of large laser printer.

The Advanced Genomics Core’s Illumina NovaSeq 6000 sequencing platform. It’s about the size of large laser printer.

On the cutting-edge of research at U-M is the Advanced Genomics Core’s Illumina NovaSeq 6000 sequencing platform. The AGC is one of the first academic core facilities to optimize this exciting and powerful instrument, that is about the size of a large laser printer. 

The Advanced Genomics Core (AGC), part of the Biomedical Research Core Facilities within the Medical School Office of Research, provides high-quality, low-cost next generation sequencing analysis for research clients on a recharge basis. 

One NovaSeq run can generate as much as 4TB of raw data. So how is the AGC able to generate, process, analyze, and transfer so much data for researchers? They have partnered with Advanced Research Computing – Technology Services (ARC-TS) to leverage the speed and power of the Great Lakes High-Performance Computing Cluster

With Great Lakes, AGC can process the data, and then store the output on other ARC-TS services: Turbo Research Storage and Data Den Research Archive, and share with clients using Globus File Transfer. All three services work together. Turbo offers the capacity and speed to match the computational performance of Great Lakes, Data Den provides an archive of raw data in case of catastrophic failure, and Globus has the performance needed for the transfer of big data. 

“Thanks to Great Lakes, we were able to process dozens of large projects simultaneously, instead of being limited to just a couple at a time with our in-house system,” said Olivia Koues, Ph.D., AGC managing director. 

“In calendar year 2020, the AGC delivered nearly a half petabyte of data to our research community. We rely on the speed of Turbo for storage, the robustness of Data Den for archiving, and the ease of Globus for big data file transfers. Working with ARC-TS has enabled incredible research such as making patients resilient to COVID-19. We are proudly working together to help patients.”

“Our services process more than 180,000GB of raw data per year for the AGC. That’s the same as streaming the three original Star Wars movies and the three prequels more than 6,000 times,” said Brock Palen, ARC-TS director. “We enjoy working with AGC to assist them into the next step of their big data journey.”

ARC-TS is a division of Information and Technology Services (ITS). The Advanced Genomics Core (ACG) is part of the Biomedical Research Core Facilities (BRCF) within the Medical School Office of Research.

Prof. Alfred Hero Distinguished Professor Lecture

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This lecture is presented by Alfred O. Hero in honor of being named the John H. Holland Distinguished University Professor of Electrical Engineering and Computer Science

 “Locating the nodes: from sensor arrays to genomic networks”

 Reception following

Abstract

Spatially distributed measurements have been used for hundreds of years to perform geolocation, geodesy and triangulation.  In WW1 acoustic sensor arrays were used to locate the direction of cannon fire based on correlation between sensor readings. Sensors in the Internet-of-Things (IoT) auto-locate their nodes  based on correlation between received pilot signals. In genomics influential nodes are located in transcriptional or lineage networks based on correlation between omic profiles. Whether the node is a target, a sensor, or a nucleotide sequence, the problem of node localization is of central interest in many disciplines of science and technology.  In this talk  I will provide perspectives on the general node localization problem, discuss solutions and algorithms,  and address future opportunities and challenges.

Bio

Alfred O. Hero III is the John H. Holland Distinguished University Professor of Electrical Engineering and Computer Science and the R. Jamison and Betty Williams Professor of Engineering. He is also the Co-Director of the University’s Michigan Institute for Data Science (MIDAS). He is also a professor of Biomedical Engineering and Statistics.

Hero’s recent research interests are in high dimensional spatio-temporal data, multi-modal data integration, statistical signal processing, and machine learning. Of particular interest are applications to social networks, network security and forensics, computer vision, and personalized health.

Hero received a B.S. (summa cum laude) from Boston University (1980) and a Ph.D from Princeton University (1984), both in Electrical Engineering. He joined the faculty of the University of Michigan in 1984. He received the University of Michigan Distinguished Faculty Achievement Award (2011), the Stephen S. Attwood Excellence in Engineering Award (2017), the IEEE Signal Processing Society Meritorious Service Award (1998), the IEEE Third Millenium Medal (2000), and the IEEE Signal Processing Society Technical Achievement Award (2014). In 2015 he received the IEEE Signal Processing Society Award, which is the highest career award bestowed by this Society. Hero was President of the IEEE Signal Processing Society (2006-2008) and was on the Board of Directors of the IEEE (2009-2011) where he served as Director of Division IX (Signals and Applications). He is a Fellow of the Institute of Electrical and Electronics Engineers (IEEE), and is chair of the Committee on Applied and Theoretical Statistics (CATS) of the US National Academies of Science.

2017 Single-Cell Genomic Data Analytics Symposium

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Please join us for the Single-cell Genomic Data Analytics Symposium. The day long symposium will highlight U-M researchers whose work is on the leading edge of innovation and discovery. This symposium is organized by the Michigan Center for Single-Cell Genomic Data Analytics and sponsored by the Michigan Institute for Data Science.

For more information, see the symposium webpage.

To register, please fill out this form.