1.
“GenomeTrakr: Whole-Genome
Sequencing for Food Safety and A New
Way Forward in the Microbiological Testing
& Traceability for Foodborne Pathogens”Eric W. Brown, Ph.D.
Director
Division of Microbiology
Center for Food Safety & Applied Nutrition
U.S. Food & Drug Administration
College Park, Maryland 20740

2.
2
“Whole Genome Sequencing Is The
Biggest Thing To
Happen To Food Microbiology Since
Pasteur Showed Us How To Culture
Pathogens…” ~Dr. Jorgen Schlundt
Exec Director and Founder
The Global Microbial Identifier

3.
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Representative* Timeline for Foodborne Illness
Investigation Using Whole Genome Sequencing
Contaminated
food enters
commerce
FDA, CDC, FSIS, and States use WGS in
real-time and in parallel on clinical, food,
and environmental samples
Source of contamination
identified early through WGS
combined database queries
Averted Illnesses
NumberofCases
Days
*Data is for illustrational purposes and does not represent an actual outbreak

4.
Comparison of Nut Butter Outbreaks*
• Salmonella Tennessee, Con Agra, Peter Pan Peanut Butter,
– 2006/2007: 715 cases, 129 hospitalizations, no deaths
• Salmonella Typhimurium, multiple peanut products, Peanut Corporation of
America,
– 2008/2009: 714 cases, 166 hospitalizations, may have contributed to 9
deaths
Post GenomeTrakr Network –Whole Genome Sequencing
• Salmonella Braenderup, nSpired Natural Foods, multiple almond and peanut
butters, 2014: 6 cases, 1 hospitalization, no deaths
* Source: CDC’s Foodborne Outbreak Online Database (FOOD Tool)

5.
GenomeTrakr Network
• Genometrakr was established to accelerate the source tracking and
tracing of foodborne outbreaks through the use of next generation
whole genome sequencing (wgs)
• It is a network of State and Federal laboratories with whole genome
sequencing capability, established by FDA in 2012
• The network provides high resolution genomic sequences of food
pathogens, ex. Salmonella, Listeria, STEC’s, others
• Partnership with NCBI for all storage and sharing of sequence and
metadata in public domain
• Partnered with CDC in 2013 to study all clinical and environmental
isolates of Listeria monocytogenes
• Today the network consists of labs at FDA, CDC, FSIS, 14 state
labs and 9 international labs.
http://www.fda.gov/Food/FoodScienceResearch/WholeGenomeSequencingProgramWGS/default.htm

6.
WGS is BIG data!
 The GenomeTrakr database is about 17 terabytes large currently.
1 terabyte = 17,000 hours of normal human speech.
 Thus, the GenomeTrakr database is equivalent in size to about 289
thousand hours of words spoken or about 32 years worth of continuous
speech.
OR;
 One terabyte = 2000 file cabinets worth of papers.
 You would need 34,000 four drawer standard file cabinets for
GenomeTrakr data if it were printed.
*The hubble telescope has collected 45 terabytes worth of data since its
launch in the early 90s. GenomeTrakr has been live since 2012 and is more
than a third of the way there in data storage.

7.
GenomeTrakr Strategy
• Develop a distributed sequencing based network, rather
than a centralized model
• Public access to data
• Focus on collaborative efforts
• Provide sequence and minimal metadata in a publicly
accessible database
– Partner with NCBI for storage and serving data
– Cost prohibitive for FDA to establish its own high capacity data site
– Industry (food, pharma, and methods development), academia,
hospitals, clinical public health laboratories, and other government
agencies have access to data for their individual needs
FDA GenomeTrakr website
http://www.fda.gov/Food/FoodScienceResearch/WholeGenomeSequencingProgramWGS/ucm363134.htm

8.
NumberofSequences
(asofthelastdayofthequarter)
Total Number of Sequences in the GenomeTrakr Database
2013 2014 2015
Average Number of Sequences
Added Per Month in 2013 = 169
Average Number of Sequences
Added Per Month in 2014 = 1,076
First sequences
uploaded in Feb 2013
Public Health England
uploads more than 8,000
Salmonella sequences
Average Number of Sequences
Added Per Month in 2015 = 2,362
2016

9.
How do we use the GenomeTrakr information?

10.
Environmental sampling

11.
Post inspection

12.
Interpretation
SNP Distance
How close are the isolates? No single threshold for all
species/types: rough, conservative guides
1. Inclusion: <=20 SNPs match, virtually identical
2. Inconclusive: 20-100 SNPs
3. Exclusion: > 100 SNPs exclude
Bootstrapping
Do the isolates form a unique cluster w/ >= 95% support?
Is the cluster distinct from other isolates in the tree?

13.
Data Analysis
SNPs wgMLST
Unit of Measure
Single Nucleotide Substitutions (other
types of mutations are excluded)
Allelle - variant of a gene. Variation
could arise form a number of sources,
including SNPs, insertions, deletions,
etc.
Requirements
Complete or high-quality reference
genome for mapping
Database of named alleles, must be
actively maintained
Pros
Extremely High Resolution, Methods
have been published and validated
Relatively Fast, not directly dependent
upon reference genome
Cons
Requires reference genome,
computationally intense, requires local
bioinformatics expertise
Allele database must be centralized,
cannot compute novel wgMLST types
locally. wgMLST schemas not publicly
available at this time.

14.
CFSAN SNP Pipeline
Documentation: http://snp-pipeline.rtfd.org
Source Code: https://github.com/CFSAN-
Biostatistics/snp-pipeline
PyPI Distribution: https://pypi.python.org/pypi/snp-
pipeline
Pettengill JB, Luo Y, Davis S, Chen Y, Gonzalez-
Escalona N, Ottesen A, Rand H, Allard MW,
Strain E. (2014) An evaluation of alternative
methods for constructing phylogenies from whole
genome sequence data: a case study with
Salmonella. PeerJ 2:e620
http://dx.doi.org/10.7717/peerj.620
Intended for use by bioinformaticists (Linux)

15.
Data Submission
 Do not need to be associated with GenomeTrakr
 Public Health England
 Establish Bioproject
 Upload data and metadata
 Link to surveillance pipeline – kmer tree

16.
Lessons Learned
• WGS provides accurate, informative information in every case we have applied it, and
the distributed model for a WGS network has proved the most effective means of
acquiring sequence data.
• WGS can be used to mitigate trace backs and delimit the scope of food contamination
events unlike ever before – not just a regulatory tool - numerous offshoot applications
exist (i.e., supply chain management, quality assurance, process evaluation, etc.)
• The development of international open source databases is critical due to the global
nature of the food supply.
• Genome sequences are agnostic, portable, and instantly cross-compatible. One
technology approach irrelevant of organism.
• WGS, unlike PFGE, is more than simply a “Molecular Epi-Machine”. It provides
information on AMR, Virulence, serotype, and other critical factors in one assay,
including historical reference to pathogen emergence. Significant lab cost savings
with one approach!
• The need for increased number of well characterized environmental (food, water,
facility, etc.) sequences may outweigh need for extensive clinical isolates

17.
Next-Generation sequencing faces several large challenges
as it deploys to a global public health tool:
How much
metadata?
Will all share data?
Administration, coordination, and oversight?
Who pays?
Who owns the IP?
Quality concerns and curation?
2014 2015
2017
2020
2016
W
G
S

18.
Looking ahead
 Capacity building – hardware, software and people (bioinformatics)
 Slow transition from PFGE to WGS
 Different authorities and distinct mandates with some overlap
 Bioinformatics - training
 IFSH workshops/CDC/IFSTL-JIFSAN-UMD
 “Hands on”
 Sample submission by industry
 Understanding the supply chain
 Facility and transportation sanitation – resident pathogens
 Prevention
 Spoilage organisms
 One microbiology workflow for bacterial pathogens – FDA FOODS
PROGRAM

19.
Multiple Tests for Strain Characterization

20.
Species Resistance
Virulence Subtype
Serotype Adaptations
ONE MICROBIOLOGICAL WORKFLOW: ONE MICROBIOLOGICAL TOOL BOX
All AT YOUR FINGERTIPS
IN THE NOT SO DISTANT FUTURE….. APPs ON YOUR SMARTPHONE

21.
Acknowledgements
• FDA
• Center for Food Safety and Applied Nutrition
• Center for Veterinary Medicine
• Office of Regulatory Affairs
• Office of Food & Veterinary Medicine
• National Institutes of Health
• National Center for Biotechnology Information
• State Health and University Labs
• Alaska
• Arizona
• California
• Florida
• Hawaii
• Maryland
• Minnesota
• New Mexico
• New York
• South Dakota
• Texas
• Virginia
• Washington
• USDA/FSIS
• HQ and The Eastern Laboratory
• CDC
• Enteric Diseases Laboratory
• INEI-ANLIS “Carolos Malbran Institute,”
Argentina
• Centre for Food Safety, University College
Dublin, Ireland
• Food Environmental Research Agency,
UK
• Public Health England, UK
• WHO
• Illumina
• Pac Bio
• CLC Bio
• MANY other independent collaborators