Globus Genomics provides tools and services to help researchers manage and analyze large genomic datasets. It uses Globus data management tools to securely transfer data between institutions. Researchers can then run analysis workflows on cloud compute resources through Galaxy interfaces. This enables researchers to assemble diverse datasets, apply multiple computational models, and publish results for others to discover, validate, and reuse. Examples show researchers using Globus Genomics to process petabytes of sequencing data and perform genome-wide analysis across many institutions. The goal is to accelerate scientific discovery by making it easier for researchers to find "needles in haystacks" through data-intensive computational approaches.
Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)
Finding Needles in Haystacks - Big Data Analysis Using Globus
1. globus.org/genomics
Finding Needles in a Haystack – Big Data
Management and Analysis using Globus
Ravi Madduri
madduri@anl.gov
JSM 2015, Seattle, Washington
2. globus.org/genomics
• Globus Genomics is developed, operated, and supported by
researchers, developers, and bioinformaticians at the
Computation Institute – University of Chicago/Argonne
National Lab
• We are a non-profit organization building solutions for non-
profit researchers
• Our goal is to support the advancement of science by bringing
together our strengths and capabilities to help meet the
unique needs of researchers and research institutions
Who We Are
4. globus.org/genomics
Imagine if a researcher, when
tackling a problem, could easily:
• Assemble, integrate, and interpret all
relevant data within a knowledge network
• Be informed of anomalies, patterns, gaps
• Formulate & apply computational models
• Outsource tasks if local expertise lacking
• Launch automated processes to test
hypotheses, expand knowledge network
• Pay for all this by taking on other tasks
5. globus.org/genomics
We will cover
• Accelerating Scientific Discovery Process
by providing Science as a Service
– Research Data Management
– Analyzing Research Data
• Interactive Analysis
• Large-scale Analysis
– Publishing Results so others can
• Discover
• Validate
• Reproduce/Use
6. globus.org/genomics
90% of cancer patients carry a
mutation that may be
responsive to a known drug
Mark Rubin, Weill Cornell Medical College and NewYork-Presbyterian
Hospital in New York in Nature, April, 2015
7. Trying to find a single causative gene for
diseases with a complex genetic background
is like looking for the proverbial needle in a
haystack
– Nancy Cox
(Vanderbilt)
8. globus.org/genomics
Higgs discovery “only possible because
of the extraordinary achievements of …
grid computing”
Rolf Heuer, CERN DG
10s of PB, 100s of institutions,1000s of
scientists, 100Ks of CPUs, Bs of tasks
9. globus.org/genomics
How do we accelerate discovery
without requiring that every lab acquire
a haystack-sorting machine?
Clayton & Shuttleworth thresher, 1910: Museum Victoria, Australia
10. globus.org/genomics
Managing big data with Globus
PI initiates transfer
request; or requested
automatically by script,
science gateway
1
Globus transfers files
reliably, securely
Light Source
Compute Facility
2
PI selects files to
share, selects
user or group,
and sets access
permissions
Globus controls
access to shared
files on existing
storage; no need
to move files to
cloud storage!
Researcher logs in to
Globus and accesses
shared files; no local
account required;
download via Globus
Researcher
assembles data set;
describes it using
metadata (Dublin
core and domain-
specific)
Curator reviews and
approves; data set
published on campus
or other system
Peers, collaborators
search and discover
datasets; transfer and
share using Globus
4
7
6
3
5
• SaaS Only a web
browser required
• Access using your
campus credentials
• Globus monitors and
informs throughout
6 8
Publication
Repository
Personal Computer
12. globus.org/genomics
Globus Adoption and Usage
• 166,449 active Globus endpoints
• 27,961 users registered
• Biggest transfer: 500.42TB
• Longest running transfer: 182 days.
• Fastest transfer: 58.5Gbps (average)
• 55TB moved per day, on average, since the
service was launched in November 2010
• Average throughput: 637.7Mbps (since
service launch)
13. globus.org/genomics
Analyzing Big Data using Globus
Galaxies
Sequencing
Centers
Sequencing
Centers
Public
Data
Storage
Local Cluster/
CloudSeq
Center
Research Lab
Globus provides for
• High-performance
• Fault-tolerant
• Secure
file transfer between
all data-endpoints
Data management Data analysis
Picard
GATK
Fastq Ref Genome
Alignment
Variant Calling
Galaxy
Data Libraries
Globus Genomics
on Amazon EC2
• Analytical tools are
automatically run
on the scalable
compute
resources when
possible
• Globus integrated
within Galaxy
• Web-based UI
• Drag-Drop
workflow
creations
• Easily modify
workflows with
new tools
Galaxy-based workflow
managementGlobus
Genomics
20. globus.org/genomics
Olopade lab, UChicago
A profile of inherited predisposition to breast
cancer among Nigerian women
Y. Zheng, T. Walsh, F. Yoshimatsu, M. Lee, S. Gulsuner,
S. Casadei, A. Rodriguez, T. Ogundiran, C. Babalola,
O. Ojengbede, D. Sighoko, R. Madduri, M.-C. King, O. Olopade
• 200 targeted exomes
• 200 GB data initially
• 76,920 core hours in 1.25 days
22. globus.org/genomics
14 deleterious SNVs and 11 damaging
Indels (BRCA1: 15, BRCA2: 4, PALB2: 2,
BRIP1: 1, CHEK2: 1, NBN: 1, TP53: 1) were
found in 29 subjects, and they were all
confidently detected among 5 callers.
Identified SNVs and Indels were all
confirmed by Sanger sequencing.
Preliminary Results are very
encouraging
25. globus.org/genomics
Globus Genomics at a
glance
30
institutions, groups
10s
million core hours
labs
2 PBs
raw sequences
analyzed
>1500
analysis tools
1000s
genomes processed
>50
workflows
99%
uptime over the past
two years
1 PB
largest single transfer
to do
5 days
longest running
workflow
100s
different species
1000s
genomes processed
5 days
longest running
workflow
29. globus.org/genomics
Other Examples of
Science as a Service
• PDACS - Portal for data analysis services for
cosmological simulations
• CVRG Galaxy – Large-scale ECG Data
Analysis
• Globus Proteomics
• eMatter – Material Science Simulations
• FACE-IT - Framework to Advance Climate,
Economic, and Impact Investigations with
Information Technology (usefaceit.org)
The basic research process remains essentially unchanged since the emergence of the scientific method in the 17th Century.
Collect data, analyze data, identify patterns within data, seek explanations for those patterns, collect new data to test explanations.
Speed of discovery depends to a significant degree on the time required for this cycle. Here, new technologies are changing the research process rapidly and dramatically.
Data collection time used to dominate research. For example, Janet Rowley took several years to collect data on gross chromosomal abnormalities for a few patients. Today, we can generate genome data at the rate of billions of base pairs per day. So other steps become bottlenecks, like managing and analyzing data—a key issue for Midway.
It is important to realize that the vast majority of research is performed within “small and medium labs.” For example, almost all of the ~1000 faculty in BSD and PSD at UChicago work in their own lab.
Academic research is a cottage industry—albeit one that is increasingly interconnected—and is likely to stay that way.
Given continued exponential growth along so many dimensions …
… process efficiencies must improve at a comparable rate to maintain just constant progress
Highlight CI Connect; coming up in Rob Gardner’s talk
Highlight XSEDE’s planned adoption of user, group and profile management
Our goal is to operationalize key capabilities so researchers can depend on them. Think of Gmail for science..
We built this pipeline to create high quality variants using multiple genotyping algorithms
Applying previous pipeline to targeted exomes in breast cancer. http://abstracts.ashg.org/cgi-bin/2014/ashg14s.pl?author=madduri&sort=ptimes&sbutton=Detail&absno=140122328&sid=84013
Normal/tumor – for 2 subjects (form Geo, south Korean population)
Run workflow on each normal and tumor and publish
Qc, alignment, feature count, alignment qc QC files, alignment file, and count file.
Differential expression
http://gene.gmi.ac.kr/geneList/LUAD_ExpLevels_EGFR.jpg
Picture shows how the gene EGFR expresses in lung and cancer tumor samples we have analyzed. We can do very similar analysis for ADNI, PPMI and other data sources easily.