Tag Archives: next generation sequencing

Minimizing Hazards and Fraud in Milk, IBM Research Partners with Cornell University

By Food Safety Tech Staff
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Americans consume an estimated 600 pounds of milk and milk-based products annually, according to the USDA. In an effort to minimize the hazards in the milk supply and prevent food fraud, IBM Research and Cornell University are joining forces. Combining next-generation sequencing with bioinformatics, the research project will collect genetic data from the microbiome of raw milk samples in a real-world situation at the Cornell University dairy plant and farm in Ithaca, New York.

Specifically, IBM and Cornell will sequence and analyze the DNA and RNA of food microbiomes, which will serve as a raw milk baseline, to develop tools that monitor raw milk and detect abnormalities that could indicate safety hazards and potential fraud. The data collected may also be used to expand existing bioinformatics analytical tools used by the Consortium for Sequencing the Food Supply Chain, a project that was launched by IBM Research and Mars, Inc. at the beginning of 2015.

“As nature’s most perfect food, milk is an excellent model for studying the genetics of food. As a leader in genomics research, the Department of Food Science expects this research collaboration with IBM will lead to exciting opportunities to apply findings to multiple food products in locations worldwide.” – Martin Wiedmann, Gellert Family Professor in Food Safety, Cornell University.

“Characterizing what is ‘normal’ for a food ingredient can better allow the observation of when something goes awry,” said Geraud Dubois, director of the Consortium for Sequencing the Food Supply Chain, IBM Research – Almaden, in a press release. “Detecting unknown anomalies is a challenge in food safety and serious repercussions may arise due to contaminants that may never have been seen in the food supply chain before.”

Cornell University is the first academic institution to join the Consortium for Sequencing the Food Supply Chain.

Sequencing pattern, pathogens

Build Stronger Food Safety Programs With Next-Generation Sequencing

By Akhila Vasan, Mahni Ghorashi
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Sequencing pattern, pathogens

According to a survey by retail consulting firm Daymon Worldwide, 50% of today’s consumers are more concerned about food safety and quality than they were five years ago. Their concerns are not unfounded. Recalls are on the rise, and consumer health is put at risk by undetected cases of food adulteration and contamination.

While consumers are concerned about the quality of the food they eat, buy and sell, the brands responsible for making and selling these products also face serious consequences if their food safety programs don’t safeguard against devastating recalls.

A key cause of recalls, food fraud, or the deliberate and intentional substitution, addition, tampering or misrepresentation of food, food ingredients or food packaging, continues to be an issue for the food safety industry. According to PricewaterhouseCoopers, food fraud is estimated to be a $10–15 billion a year problem.

Some of the more notorious examples include wood shavings in Parmesan cheese, the 2013 horsemeat scandal in the United Kingdom, and Oceana’s landmark 2013 study, which revealed that a whopping 33% of seafood sold in the United States is mislabeled. While international organizations like Interpol have stepped up to tackle food fraud, which is exacerbated by the complexity of globalization, academics estimate that 4% of all food is adulterated in some way.

High-profile outbreaks due to undetected pathogens are also a serious risk for consumers and the food industry alike. The United States’ economy alone loses about $55 billion each year due to food illnesses. The World Health Organization estimates that nearly 1 in 10 people become ill every year from eating contaminated food. In 2016 alone, several high-profile outbreaks rocked the industry, harming consumers and brands alike. From the E. coli O26 outbreak at Chipotle to Salmonella in live poultry to Hepatitis A in raw scallops to the Listeria monocytogenes outbreak at Blue Bell ice cream, the food industry has dealt with many challenges on this front.

What’s Being Done?

Both food fraud and undetected contamination can cause massive, expensive and damaging recalls for brands. Each recall can cost a brand about $10 million in direct costs, and that doesn’t include the cost of brand damage and lost sales.

Frustratingly, more recalls due to food fraud and contamination are happening at a time when regulation and policy is stronger than ever. As the global food system evolves, regulatory agencies around the world are fine-tuning or overhauling their food safety systems, taking a more preventive approach.

At the core of these changes is HACCP, the long implemented and well-understood method of evaluating and controlling food safety hazards. In the United States, while HACCP is still used in some sectors, the move to FSMA is apparent in others. In many ways, 2017 is dubbed the year of FSMA compliance.

There is also the Global Food Safety Initiative (GFSI), a private industry conformance standard for certification, which was established proactively by industry to improve food safety throughout the supply chain. It is important to note that all regulatory drivers, be they public or private, work together to ensure the common goal of delivering safe food for consumers. However, more is needed to ensure that nothing slips through the food safety programs.

Now, bolstered by regulatory efforts, advancements in technology make it easier than ever to update food safety programs to better safeguard against food safety risks and recalls and to explore what’s next in food.

Powering the Food Safety Programs of Tomorrow

Today, food safety programs are being bolstered by new technologies as well, including genomic sequencing techniques like NGS. NGS, which stands for next-generation sequencing, is an automated DNA sequencing technology that generates and analyzes millions of sequences per run, allowing researchers to sequence, re-sequence and compare data at a rate previously not possible.

The traditional methods of polymerase chain reaction (PCR) are quickly being replaced by faster and more accurate solutions. The benefit of NGS over PCR is that PCR is targeted, meaning you have to know what you’re looking for. It is also conducted one target at a time, meaning that each target you wish to test requires a separate run. This is costly and does not scale.

Next-generation sequencing, by contrast, is universal. A single test exposes all potential threats, both expected and unexpected. From bacteria and fungi to the precise composition of ingredients in a given sample, a single NGS test guarantees that hazards cannot slip through your supply chain.  In the not-too-distant future, the cost and speed of NGS will meet and then quickly surpass legacy technologies; you can expect the technology to be adopted with increasing speed the moment it becomes price-competitive with PCR.

Applications of NGS

Even today’s NGS technologies are deployment-ready for applications including food safety and supplier verification. With the bottom line protected, food brands are also able to leverage NGS to build the food chain of tomorrow, and focus funding and resources on research and development.

Safety Testing. Advances in NGS allow retailers and manufacturers to securely identify specific pathogens down to the strain level, test environmental samples, verify authenticity and ultimately reduce the risk of outbreaks or counterfeit incidents.

Compared to legacy PCR methods, brands leveraging NGS are able to test for multiple pathogens with a single test, at a lower cost and higher accuracy. This universality is key to protecting brands against all pathogens, not just the ones for which they know to look.

Supplier Verification. NGS technologies can be used to combat economically motivated food fraud and mislabeling, and verify supplier claims. Undeclared allergens are the number one reason for recalls.

As a result of FSMA, the FDA now requires food facilities to implement preventative controls to avoid food fraud, which today occurs in up to 10% of all food types. Traditional PCR-based tests cannot distinguish between closely related species and have high false-positive rates. NGS offers high-resolution, scalable testing so that you can verify suppliers and authenticate product claims, mitigating risk at every level.

R&D. NGS-based metagenomics analysis can be used in R&D and new product development to build the next-generation of health foods and nutritional products, as well as to perform competitive benchmarking and formulation consistency monitoring.

As the consumer takes more and more control over what goes into their food, brands have the opportunity to differentiate not only on transparency, but on personalization, novel approaches and better consistency.

A Brighter Future for Food Safety

With advances in genomic techniques and analysis, we are now better than ever equipped to safeguard against food safety risks, protect brands from having to issue costly recalls, and even explore the next frontier for food. As the technology gets better, faster and cheaper, we are going to experience a tectonic shift in the way we manage our food safety programs and supply chains at large.

We will be discussing this topic, “Building Stronger Food Safety Programs through Next-Generation Sequencing”, during a live conversation on June 7, 2017 at 2:00 pm ET. Microbiologists, testing personnel, food industry management, and anyone interested in how to leverage these new technologies to fortify their food safety programs will learn how NGS is going to transform the future of food safety.

DNA sequencing

Whole Sample Next-Generation DNA Sequencing Method: An Alternative to DNA Barcoding

By Casey Schlenker, Jenna Brooks, Kent Oostra, Ryan McLaughlin
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DNA sequencing

This article discusses a non-targeted method for whole sample next generation DNA sequencing (NGS) that does not rely on DNA barcoding. DNA barcoding requires amplification of a specific gene region, which introduces bias. Our non-targeted method removes this bias by eliminating the amplification step. The applications of this method are broad and we have begun optimizing workflows for numerous materials, both processed and unprocessed. Some of the materials we have been able to successfully identify at the species level are fish tissue, fish meal, unrefined fish oil, unrefined plant-based oils (nuts, seeds, and fruits), specific components of cooked and processed products such as cookies and powders, and processed meats. Non-targeted NGS is also a very powerful tool to comprehensively identify constituents of microbial communities in probiotics and fermented products like kombucha. Additionally, this non-targeted technique is applicable to detection and identification of microbial contamination at various levels of manufacturing including equipment surfaces, processing water and assaying intermediate processing steps. In this communication we briefly review a current issue in the botanicals industry, discuss the methods that have been used in the past to tackle that problem, and present preliminary results from a pilot study we performed to determine the utility of non-targeted NGS in high-throughput identification of botanical raw materials.

The value of the global herbal dietary supplement (botanical) market was estimated to be greater than $90 billion in 2016, with a projected compound annual growth rate of 5-6%. Currently, regulators and manufacturers in this rapidly expanding market seek to confirm the veracity of label claims, investigate fraud, identify adulterants and ensure product quality.1 These products are often dried and ground, making visual identification difficult, time consuming and sometimes impossible.2 It is critical to this market that botanical identification be high-throughput, accurate and cost effective. Historically, various chromatography techniques have been used to meet this need, but those techniques rely on identification of molecules that can vary significantly due to storage conditions, which has led to the use of DNA barcoding as an analytical technique. However, DNA barcoding is not without significant challenges.1

For quite some time, scientists have had the ability to identify biological samples by sequencing their DNA.3 Currently DNA sequencing-based identification methods rely heavily on a technique called DNA barcoding, which functions analogously to the barcodes found on products in a grocery store. DNA barcoding amplifies a distinct small gene region that serves as a unique identifier and “scans” it by DNA sequencing.4 The advantages of this amplification are high sensitivity and simplification of data analysis. However, this amplification is not completely reliable and in practice can create biases and false positives.5 There is also the possibility that the amplification may fail, causing false negatives.6 When using DNA barcoding to identify botanical raw materials, numerous labs have observed notably high levels of apparent contamination.7 While it is certainly likely that some or even many botanical raw material samples contain contamination, it is also possible that the amplification-based method of DNA barcoding is itself contributing to the levels of contamination that are being observed.

We have partnered with Practical Informatics and Pacific Northwest Genomics to develop comprehensive whole sample DNA screening methods that don’t rely on amplification. To achieve this we are utilizing a non-targeted metagenomics workflow. Non-targeted metagenomic analysis is a powerful tool for examining the entire genetic content of a sample, instead of just one particular gene region (if a gene is a word or phrase, then a genome is the entire book, and the metagenome is the library). Unlike DNA barcoding, which requires PCR amplification, non-targeted metagenomic analysis requires no prior knowledge of a sample’s source and does not introduce the biases that plague PCR initiated methods. All of the DNA extracted from a sample is analyzed without targeting any particular gene region, relying instead on complex data analysis to identify the constituents (Figure 1). This is accomplished with the use of advanced molecular biology techniques and sophisticated computational methods, combined with a highly-curated database of species-identifying DNA sequences. Our research and development team has completed several experiments demonstrating the utility of a non-targeted DNA sequencing method.

DNA sequencing
Figure 1. The traditional targeted method, or DNA barcoding uses a PCR amplification step prior to sequencing. Non-target whole sample sequencing skips the amplification step and all present DNA is sequenced and used in analysis.

Our research endeavors to solve the issues of DNA sequence analysis that originate with the PCR step by simply eliminating amplification from our process entirely. PCR amplification as a prelude to DNA sequencing traces to traditional technologies that were lower throughput and required large amounts of material. Current generation high-throughput DNA sequencing technologies do not require large amounts of starting material, and therefore amplification can be avoided. Many DNA barcoding methods require universal primers, which, during PCR, can amplify some products but not others, leading to false negatives. A solution to that issue is to use specific primers, however this is also inherently problematic as a certain foreknowledge of the sample identity is required. What is the advantage to our non-targeted sequencing method? There is no need to direct the analysis to any particular identification before sequencing, decreasing the introduction of bias and false negatives. As an added bonus, we don’t need to know what the sample is prior to analysis—we can tell you what it is rather than you telling us.

Continue to page 2 below.

Pursuit of Clarity for WGS in Food Production Environments

By Joseph Heinzelmann
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Anyone who has attended a food safety conference in the last few years has experienced some type of whole genome sequencing (WGS) presentation. WGS is the next big thing for food safety. The technology has been adopted by regulatory agencies, academics, and some food companies. A lot has been said, but there are still some questions regarding the implementation and ramifications of WGS in the food processing environment.

There are a few key acronyms to understand the aspects of genomics in food safety (See Table I below).

PFGE Pulse Field Gel Electrophoresis Technique using restriction enzymes and DNA fragment separation via an electronic field for creation of a bacterial isolate DNA fingerprint; PFGE is being replaced by WGS at CDC and other public health laboratories
WGS Whole Genome Sequencing The general term used for sequencing—a misnomer—the entirety of the genome is not used, and depends on the analytical methodology implemented
NGS Next Generation Sequencing NGS is the next set of technology to do WGS and other genomic applications
SNP Single Nucleotide Polymorphisms A variation in a single nucleotide that occurs in specific position of an organism’s genome; Used in WGS as a methodology for determining genetic sameness between organisms
MLST Multilocus sequence typing A methodology for determining genetic sameness between organisms; Compares internal fragment DNA sequences from multiple housekeeping genes
16S 16s RNA sequencing A highly conserved region of the bacterial genome used for species and strain identification

Joseph Heinzelmann will be presenting: Listeria Testing Platforms: Old School Technology vs New Innovative Technology during the 2016 Food Safety Consortium | LEARN MOREIn 1996, the CDC established the PulseNet program for investigating potential foodborne illness outbreaks.  PulseNet has relied on using bacterial DNA fingerprints generated via PFGE as comparisons for mapping potential sources and spread of the outbreaks.  Due to a number of advantages over PFGE, WGS is quickly becoming the preferred method for organism identification and comparison. Moving to WGS has two critical improvements over PFGE: accuracy and relatedness interpretation. Like PFGE there are nuances when defining the difference between two very closely related organisms. However, instead of defining restriction enzymes and comparing the number of bands, the language changes to either single nucleotide polymorphisms (SNP) or the number of alleles. The other important aspect WGS improves is the ability to determine and interpret the relatedness of organisms more broadly. The frequent Listeria outbreaks and incidence from 1983-2015 provide an insight to what the future might hold with WGS implementation.1 The incidence report shows the increased ability to quickly and more accurately define relatedness between clinical cases creates a link of potential cases much faster.

WGS also provides key practical changes for outbreaks and recalls in the food industry. Sequencing provides a much faster response time and therefore means the outbreaks of foodborne illness decrease, as does the number of cases in each outbreak. As the resolution of the outbreaks increases, the number of outbreaks identified increases. The actual number of outbreaks has likely not increased, but the reported number of outbreaks will increase due increased resolution of the analytical method.

Figure 1: (Permission for use of slide from Patricia M. Griffin, M.D. – Center for Disease Control and Prevention)

WGS continues to establish itself as the go-to technology for the food safety agencies. For example, the USDA food safety inspection service recently published the FY2017–2021 goals. The first bullet point under modernizing inspection systems, policies and the use of scientific approaches is the implementation of in-field screening and whole genome sequencing for outbreak expediency.

Agencies and Adoption

The success of FDA and CDC Listeria project provides a foundation for implementation of WGS for outbreak investigations. The three agencies adopting WGS for outbreak investigations and as replacement for PulseNet are the CDC, FDA and USDA. However, there are still questions on the part of the FDA for when WGS is utilized, including under what circumstances and instances the data will be used.

In recent public forums, the FDA has acknowledged that there are situations when a recall would be a potential solution based on WGS results in the absence of any clinical cases.2 One critical question that still exists in spite of the public presentations and published articles is a clear definitions of when WGS surveillance data will be used for recall purposes, and what type of supporting documentation a facility would need to provide to prove that it had adequate controls in place.

A key element is the definition between agencies for sameness or genetic distance. The FDA and FSIS are using a SNP approach. A sequence is generated from a bacterial isolate, then compared with a known clinical case, or a suspected strain, and the number of different SNPs determines if the strains are identical. The CDC is using the Multilocus sequence typing (MLST) approach.

Simple sequence comparisons are unfortunately not alone sufficient for sameness determination, as various metabolic, taxa specific and environmental parameters must also be considered.  Stressful environments and growth rates have significant impact on how quickly SNPs can occur. The three primary pathogens being examined by WGS have very different genetic makeups. Listeria monocytogenes has a relatively conserved genomic taxa, typically associated with cooler environments, and is gram positive. Listeria monocytogenes has a doubling time of 45–60 minutes under enrichment conditions.3 These are contrasted with E. coli O157:H7, a gram negative bacteria, associated with higher growth rates and higher horizontal gene transfer mechanisms. For example, in an examination of E. coli O104, and in research conducted by the University in Madurai, it showed 38 horizontal gene elements.4

These two contrasting examples demonstrate the complexity of the genetic distance question. It demonstrates a need for specific definitions for sameness within a microbiological taxa, and with potential qualifiers based on the environment and potential genetic event triggers. The definitions around SNPs and alleles that define how closely related a Listeria monocytogenes in a cold facility should be vastly different from an E. coli from a warm environment, under more suitable growth conditions. Another element of interest, but largely unexplored is convergent evolution. In a given environment, with similar conditions, what is the probability of two different organisms converging on a nearly identical genome, and how long would it take?


As previously stated, the three agencies have chosen different approaches for the analytical methodology: MLST for CDC and SNP of the FDA and USDA. For clarity, both analytical approaches have demonstrated superiority over the incumbent PFGE mythology. MLST does rely on an existing database for allele comparison. A SNP based approach is supported by a database, but is often used in defining genetic distance specifically between two isolates. Both approaches can help build phylogenetic trees.

There are tradeoffs with both approaches. There is a higher requirement for processing and bioinformatics capabilities when using a SNP based approach. However, the resolution between organisms and large groups of organisms is meaningful using SNP comparison. The key take away is MLST uses a gene-to-gene comparison, and the SNP approach is gene agnostic. As mentioned in Table 1, both approaches do not use every A, T, C, and G in the analytical comparisons. Whole genome sequencing in this context is a misnomer, because not every gene is used in either analysis.

Commercial Applications

Utilizing WGS for companies as a preventive measure is still being developed. GenomeTrakr has been established as the data repository for sequenced isolates from the FDA, USDA, CDC and public health labs. The data is housed at the National Center for Biotechnology Information (NCBI).  The database contains more than 71,000 isolates and has been used in surveillance and outbreak investigations. There is a current gap between on premise bioinformatics and using GenomeTrakr.

The FDA has stated there are examples where isolates found in a processing facility would help support a recall in the absence of epidemiological evidence, and companies are waiting on clarification before adopting GenomeTrakr as a routine analysis tool. However, services like NeoSeek, a genomic test service by Neogen Corp. are an alternative to public gene databases like GenomeTrakr. In addition to trouble shooting events with WGS, NeoSeek provides services such as spoilage microorganism ID and source tracking, pathogen point source tracking. Using next generation sequencing, a private database, and applications such as 16s metagenomic analysis, phylogenetic tree generation, and identification programs with NeoSeek, companies can answer critical food safety and food quality questions.


  1. Carleton, H.A. and Gerner-Smidt, P. (2016). Whole-Genome Sequencing Is Taking over Foodborne Disease Surveillance. Microbe. Retrieved from https://www.cdc.gov/pulsenet/pdf/wgs-in-public-health-carleton-microbe-2016.pdf.
  2. Institute for Food Safety and Health. IFSH Whole Genome Sequencing for Food Safety Symposium. September 28­–30, 2016. Retrieved from https://www.ifsh.iit.edu/sites/ifsh/files/departments/ifsh/pdfs/wgs_symposium_agenda_071416.pdf.
  3. Jones, G.S. and D’Orazio, S.E.F. (2013). Listeria monocytogenes: Cultivation and Laboratory Maintenance. Curr Proto Microbiol. Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3920655/.
  4. Inderscience Publishers. “Horizontal gene transfer in E. coli.” ScienceDaily, 19 May 2015.
  5. Gerner-Smidt, P. (2016). Public Health Food Safety Applications for Whole Genome Sequencing. 4th Asia-Pacific International Food Safety Conference. Retrieved from http://ilsisea-region.org/wp-content/uploads/sites/21/2016/10/Session-2_2-Peter-Gerner-Smidt.pdf.
Gregory Siragusa, Eurofins
Food Genomics

Introducing a New Column: Food Genomics

By Gregory Siragusa, Douglas Marshall, Ph.D.
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Gregory Siragusa, Eurofins

DNA sequencing can be used to determine the names, types, and proportions of microorganisms, the component species in a food sample, and track foodborne disease agents.  Here we introduce a column exploring aspects and applications of these new techniques, known collectively as food genomics. Each month we will provide take-home knowledge in which every food safety scientist should be familiar.

Gregory Siragusa, Eurofins
Gregory Siragusa will be presenting Microbiome Applications in Controlling Food Spoilage and Safety  during the 2016 Food Safety Consortium

We live in an exciting time of great change in all of biological and food sciences. In fact, it is not an overstatement to claim that a large portion of the fields of food science, biology, agriculture and medicine will be reformed in what has been called the post-genomics era or simply the genomics era. Food science and food microbiology are major players in this pack and moving in the fast track of these changes. This game-changing technology is fueled by the convergence of two rapidly evolving fields: DNA sequencing and the analysis of that sequencing data (i.e., bioinformatics).

The common jargon uses the acronym NGS for Next Generation Sequencing. NGS refers to the most updated automated DNA sequencing technology available. In several ways, sequencing can be considered a commodity service; hence its price has dropped and its availability is now widespread. What does this mean? A useful analogy is the following: Think of trying to publish a book you wrote. Would you go out, buy a printing press, paper, ink, binding machinery, and produce thousands of copies of your book, or, would you go to a professional printer and get them to print and manufacture copies?  For most, the simplicity and experience of the professional print master trumps the do-it-yourself route.  Once sequence data is obtained, what is next in the process of using that data? Analysis of sequence data is a specialized field called bioinformatics and has its own  expert practitioners. It is a field of study that is a hybrid combination of mathematics, statistics, computer science, and biology. Bioinformatics analyzes the very large datasets produced by NGS and will be increasingly dependent on the internet cloud for its utility to be fully realized.

How will food genomics impact food safety and quality? How will it help in identifying the sources of outbreaks in a fraction of the time it once took? What will this mean for zero-tolerance, for pathogen control, and for responsibilities and liabilities of food producers and processors?  There is a growing body of examples and literature that begins to apply genomics and microbiomics to the quality of food and sources of its microbial populations.5-7

Over the course of this column, we will be exploring several examples to alert the reader to the myriad of uses of genomics for solving food production issues.

Genomics (NGS and Bioinformatics) are the basis of the US-FDA GenomeTrakr program.1  Genomics offers an alternative means to serotype Salmonella isolates using DNA sequencing.2 There are several examples of using sequencing of solving the epidemiological source of foodborne microbial outbreaks by comparing the entire bacterial genomes of clinical and food isolates.3,4

One powerful application of genomics is to conduct the census of microbial communities to identify the microbial members and their relative proportions, an outcome called a microbiome, all from a single tube! The technique itself is termed microbiomics. Just think, we can now identify all bacteria in a complex mixture without isolating what will grow, as well as the many microorganisms we have not yet learned to culture or require unusual temperatures, nutrients, and atmospheres! Can you feel the excitement? Hopefully with knowledge of the power of food genomics you will begin to see the true utility of this technology and begin to appreciate its awesome power. Most importantly, you will begin to see how food genomics is a useful tool for the food science professional.

The microbiome field is changing as of this writing and moving toward using a technique known as whole shotgun metagenome (WSM) analysis in which all of the DNA in a sample is sequenced and not just bacterial, fungal, or specific genes; i.e., a metagenome approach vs. a targeted approach to determining the microbiome of a sample.8,9  The whole genome shotgun approach is also a powerful tool not only for creating food microbiomes, but can help in the identification of the plant and animal species used as ingredients in foods. WSM requires relatively advanced and sophisticated bioinformatics tools and at the same time sequencing chemistry is advancing, so is bioinformatics. For example, there is an online tool suite known as NEPHELE, which offers publically available online programs, software, and data handling capacity for sequence analysis.10,11

So here we are with some brand new shiny tools in the kit. Now the question is, how can the food safety professional begin to use these tools? More to the point is to understand when food genomic data is called for. The first step is to grasp some of the terminology and basic processes. Table 1 lists a few starter terms to become familiar with as well as some web resources that might be helpful to you in understanding these immensely powerful tools.12,13

Table 1. Starter Terms in Food Genomics
Annotated Whole
Bacterial Genome
High-quality, low-error, gap-free DNA sequence of an entire genome of an organism, in this case, an isolated bacterium, indicating genes and their locations. This can be considered a complete road map of an organism’s genetic makeup as expressed in the nucleotides Adenine, Thymine, Cytosine, and Guanine (ATCG’s). Can be referred to as WGS or Whole Genome Sequencing.
Bioinformatics The science of managing and analyzing biological data using advanced computing techniques. Especially important in analyzing genomic research data.
Metagenomes or Whole Shotgun Sequencing Sequences of Genetic material recovered directly from food, animal, plant, or environmental samples with no foreknowledge of the source of living materials therein. For instance, the metagenome of a yogurt sample will harbor DNA sequences characteristic of starter culture bacteria and bovine DNA (assuming it is bovine milk yogurt).  This is another approach to obtaining a microbiome.6
Microbiome A community of microorganisms that inhabit a particular environment or sample. For example, a plant microbiome includes all the microorganisms that colonize a plant’s surfaces and internal passages. This can be a Targeted (Amplicon Sequencing Based) or a Metagenome (Whole Shotgun Metagenome based) microbiome.6
Microbiomics The process of determining a microbiome.
Microbiota The ecological community of commensal, symbiotic, and pathogenic microorganisms that literally share a space or are within a sample. Formerly the term ‘microflora’ was used, but this term is waning in usage.14
NGS (Next Generation Sequencing) High throughput automated sequencing of nucleic acids DNA or RNA.

Finally, in the reference section we have tried to provide you with some useful online reference sources. The U.S. Department of Energy has perhaps the most intuitive, user-friendly and informative sites we have encountered as of late (“Genome Glossary,” 2016). The same source also published a talking glossary (“Talking Glossary of Genetic Terms,” 2016).  The reader should be advised that genomic terminology and nomenclature is still not fully mature. In fact, the number of vague meanings, cross references, and acronyms can sometimes be frustrating; but fear not, as one reads and discusses the terms, they will become clearer. As a start we recommend downloading a helpful reference that follows.15 There are many other sites you will locate by performing a single web-search. If you would like to share your favorite genomics sites, please drop a line to either author and we will try to compile them into a single electronic document.

We hope this first column will find you coming back for more as we explore this burgeoning field and learn how it is being linked to food safety. Look for future articles on specific food applications, methods, and hot topics in food genomics.  Goodbye for now.


  1. Allard, M. W., Strain, E., Melka, D., Bunning, K., Musser, S. M., Brown, E. W., & Timme, R. (2016). Practical Value of Food Pathogen Traceability through Building a Whole-Genome Sequencing Network and Database. Journal of Clinical Microbiology, 54(8), 1975–1983. https://doi.org/10.1128/JCM.00081-16
  2. Zhang, S., Yin, Y., Jones, M. B., Zhang, Z., Deatherage Kaiser, B. L., Dinsmore, B. A., … Deng, X. (2015). Salmonella serotype determination utilizing high-throughput genome sequencing data. Journal of Clinical Microbiology, 53(5), 1685–1692. https://doi.org/10.1128/JCM.00323-15
  3. Burall, L. S., Grim, C. J., Mammel, M. K., & Datta, A. R. (2016). Whole Genome Sequence Analysis Using JSpecies Tool Establishes Clonal Relationships between Listeria monocytogenes Strains from Epidemiologically Unrelated Listeriosis Outbreaks. PloS One, 11(3), e0150797. https://doi.org/10.1371/journal.pone.0150797
  4. Chen, Y., Burall, L. S., Luo, Y., Timme, R., Melka, D., Muruvanda, T., Brown, E. W. (2016). Isolation, enumeration and whole genome sequencing of Listeria monocytogenes in stone fruits linked to a multistate outbreak. Applied and Environmental Microbiology. https://doi.org/10.1128/AEM.01486-16
  5. Bokulich, N. A., Lewis, Z. T., Boundy-Mills, K., & Mills, D. A. (2016). A new perspective on microbial landscapes within food production. Current Opinion in Biotechnology, 37, 182–189. https://doi.org/10.1016/j.copbio.2015.12.008
  6. Bokulich, N. A., & Mills, D. A. (2012). Next-generation approaches to the microbial ecology of food fermentations. BMB Reports, 45(7), 377–389.
  7. Zarraonaindia, I., Owens, S. M., Weisenhorn, P., West, K., Hampton-Marcell, J., Lax, S., … Gilbert, J. A. (2015). The soil microbiome influences grapevine-associated microbiota. mBio, 6(2). https://doi.org/10.1128/mBio.02527-14
  8. Microbial Foods – The Science Of Fermented Foods. (n.d.). Retrieved November 21, 2016, from http://microbialfoods.org/
  9. Ranjan, R., Rani, A., Metwally, A., McGee, H. S., & Perkins, D. L. (2016). Analysis of the microbiome: Advantages of whole genome shotgun versus 16S amplicon sequencing. Biochemical and Biophysical Research Communications, 469(4), 967–977. https://doi.org/10.1016/j.bbrc.2015.12.083
  10. Colosimo, M. E., Peterson, M. W., Mardis, S., & Hirschman, L. (2011). Nephele: genotyping via complete composition vectors and MapReduce. Source Code for Biology and Medicine, 6, 13. https://doi.org/10.1186/1751-0473-6-13
  11. Weber, N. (n.d.). Cloud Computing for Scientific Research The NIH Nephele Project for Microbiome Analysis. Accessed November 21, 2016. Retrieved from https://www.google.com/url?q=http://casc.org/meetings/14sep/CASC-NIH-Microbiome-Cloud-Project-20140917.pdf&sa=U&ved=0ahUKEwj2mvTt1LrQAhXMC8AKHUiCBLsQFggHMAE&client=internal-uds-cse&usg=AFQjCNGt_lx2zw4qLNcHuYwZvSg10ivp5Aabout:blank
  12. Genome Glossary. (n.d.). Accessed November 21, 2016. Retrieved from http://doegenomestolife.org/glossary/index.shtml
  13. Talking Glossary of Genetic Terms. (n.d.). Accessed November 21, 2016. Retrieved from https://www.genome.gov/glossary/
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New Whole Genome Sequencing Test Monitors Threat of Pathogens

By Maria Fontanazza

Today food companies will have access to a new whole genome sequencing (WGS) test that could help them prevent dangerous pathogens from getting into their products. Released by Clear Labs, the test takes a detailed approach to identifying pathogen strains in samples, providing information about their geography and from which food groups they originate.

In an exclusive interview with Food Safety Tech, Mahni Ghorashi, co-founder of Clear Labs, explains how he expects the company’s new test, which has a five- to seven-day turnaround time, will offer companies with a more accurate yet less expensive alternative to protecting consumers by actively monitoring their supply chain for emerging pathogens.

Food Safety Tech: What differentiates this WGS test from current available solutions?

Mahni Ghorashi: No one has been able to provide the food industry with modern whole genome sequencing techniques for food safety. What we’re releasing is a quantum leap in terms of what’s been available on the market today. Whole genome sequencing has been largely siloed to regulatory bodies like FDA and CDC to trace outbreaks and inform investigations—the technologies and techniques that they’re using are actually fairly old; they’re some of the original WGS techniques that emerged on the next-gen sequencing platform. We’ve taken the most advanced techniques on the NGS platform for human disease exploration and personalized medicine and adapted them for food industry.

What gives our WGS test a competitive advantage over legacy-based methods is two fold:

1. Clear Labs has a 2-million+ entry-curated database of genomic information and sequences for the accurate ID of food ingredients (pathogenic organisms and microbiomes). Its accuracy and the confidence level that comes behind our matching is a huge step above anything that’s available in the public domain today.

2. Being able to place pathogenic strain information in the context of overall food ingredients and samples. The whole genome sequencing test we developed has been specifically catered for the food industry, and for food samples in particular, [versus] FDA’s GenomeTrakr, CDC’s PulseNet, or other food safety labs that are offering full genomic sequencing of pathogen strains—they’re using some of the earliest methods to do this. On the NGS platform, we’re able to put those strains in the context of food ingredients and suppliers: Specifically, [matching] bacterial strains with food ingredients [and] suppliers.

Clear Labs, whole genome sequencing

FST: Does this test target specific foods?

Ghorashi: Our platform particularly shines in complex foods. The value of next-gen sequencing and DNA barcoding over PCR-based technologies, which is the gold standard in food safety, is its stability to break down complex food ingredients into all of their known parts, and to look in a universal and unbiased way into food samples. It’s untargeted, so you don’t have know what it is that you’re looking for—and that’s the real power.

FST: What impact do you anticipate for this test, especially in the context of FSMA?

Ghorashi: Our customers are using [the test] for monitoring ingredient supplies and the effectiveness of preventive and sanitary controls [and] to match specific pathogen strains to specific food ingredients. They are using it for proactive testing for FSMA compliance—there’s a lot of movement in this direction and hefty budgets are being allocated to put new preventive controls in place in response to FSMA; whole-genome sequencing will play a big role, and we anticipate large-scale partnerships with agencies and private industry on that front. And the most obvious use case is that it’s being used for techniques to mitigate or reduce the risk of product recall and outbreak.

We’ve been able to significantly reduce the price point on whole-genome sequencing, and all of our tests across the board, because we’re intimately familiar with how the inner workings of these platforms and how to best optimize them for scale and cost efficiency. We think the test will be more accurate and leaps and bounds ahead of what’s available, as well as cost competitive. We’re excited about the work we’re doing and its impact on food safety. I don’t think the food industry—retailers and manufacturers—have ever had access to these kind of tools and they’re being made available just in time for FSMA, as the industry moves towards a more proactive approach to food safety and [takes] preventive measures in their supply chains.  Hopefully we’ll soon be living in a world where outbreaks, illness and the financial toll are a thing of a past.

Clear Labs also just released a microbiome test that helps companies associate microbiomes with specific food ingredients.

Mahni Ghorashi: The microbiome test we’ve developed is able to sequence samples from the human gut and from food, and look at how the microorganisms are interacting. Our customers for this test have been large brands that have advanced R&D departments and academic research centers that are looking for how diet research and the microbiome interact together and how new product development can help us move toward personalized diets when it comes to prebiotic and probiotic diets.”

The impact of the microbiome and the correlations between bacteria of the human gut and the bacteria in the food we eat. The prevailing thesis at the moment is that the microbiome has a significant impact on our health when it comes to disease risk and diet, inflammation and mood disorders. We’re seeing very forward thinking brands like Nestle, ConAgra and Mars putting a lot of attention on the impact of the microbiome when it comes the development of new products, [such as] prebiotics and probiotics, or even specific food products as it pertains to the microbiome. We believe that this intersection— nutrigenomics and the personalized diet—is going to be a massive market, and we’re at the early stages of that.