Matthew Davis’s research while affiliated with IBM Research - Thomas J. Watson Research Center and other places

For each unique pair of a complete set of data items, a computing device determines a distance between the data items of the unique pair. The computing device repeats the following until no data items remain in the complete set. For each data item remaining in the complete set, the computing device determines a similarity subset including each other data item that the distance between the data item and the other data item is less than a target difference threshold. The computing device moves a selected data item from a largest similarity subset to a reference database that is a subset of the complete set. The computing device removes each data item from the complete set that the distance between the selected data item and the data item is less than the threshold. A new data item can be classified using the reference database.

In this work, we hypothesized that shifts in the food microbiome can be used as an indicator of unexpected contaminants or environmental changes. To test this hypothesis, we sequenced the total RNA of 31 high protein powder (HPP) samples of poultry meal pet food ingredients. We developed a microbiome analysis pipeline employing a key eukaryotic matrix filtering step that improved microbe detection specificity to >99.96% during in silico validation. The pipeline identified 119 microbial genera per HPP sample on average with 65 genera present in all samples. The most abundant of these were Bacteroides, Clostridium, Lactococcus, Aeromonas , and Citrobacter . We also observed shifts in the microbial community corresponding to ingredient composition differences. When comparing culture-based results for Salmonella with total RNA sequencing, we found that Salmonella growth did not correlate with multiple sequence analyses. We conclude that microbiome sequencing is useful to characterize complex food microbial communities, while additional work is required for predicting specific species’ viability from total RNA sequencing.

In this work, we hypothesized that shifts in the food microbiome can be used as an indicator of unexpected contaminants or environmental changes. To test this hypothesis, we sequenced total RNA of 31 high protein powder (HPP) samples of poultry meal pet food ingredients. We developed a microbiome analysis pipeline employing a key eukaryotic matrix filtering step that improved microbe detection specificity to >99.96% during in silico validation. The pipeline identified 119 microbial genera per HPP sample on average with 65 genera present in all samples. The most abundant of these were Bacteroides , Clostridium , Lactococcus , Aeromonas , and Citrobacter . We also observed shifts in the microbial community corresponding to ingredient composition differences. When comparing culture-based results for Salmonella with total RNA sequencing, we found that Salmonella growth did not correlate with multiple sequence analyses. We conclude that microbiome sequencing is useful to characterize complex food microbial communities, while additional work is required for predicting specific species' viability from total RNA sequencing.

Here we propose that using shotgun sequencing to examine food leads to accurate authentication of ingredients and detection of contaminants. To demonstrate this, we developed a bioinformatic pipeline, FASER (Food Authentication from SEquencing Reads), designed to resolve the relative composition of mixtures of eukaryotic species using RNA or DNA sequencing. Our comprehensive database includes >6000 plants and animals that may be present in food. FASER accurately identified eukaryotic species with 0.4% median absolute difference between observed and expected proportions on sequence data from various sources including sausage meat, plants, and fish. FASER was applied to 31 high protein powder raw factory ingredient total RNA samples. The samples mostly contained the expected source ingredient, chicken, while three samples unexpectedly contained pork and beef. Our results demonstrate that DNA/RNA sequencing of food ingredients, combined with a robust analysis, can be used to find contaminants and authenticate food ingredients in a single assay.

Traditional taxonomy in biology assumes that life is organized in a simple tree. Attempts to classify microorganisms in this way in the genomics era led microbiologists to look for finite sets of 'core' genes that uniquely group taxa as clades in the tree. However, the diversity revealed by large-scale whole genome sequencing is calling into question the long-held model of a hierarchical tree of life, which leads to questioning of the definition of a species. Large-scale studies of microbial genome diversity reveal that the cumulative number of new genes discovered increases with the number of genomes studied as a power law and subsequently leads to the lack of evidence for a unique core genome within closely related organisms. Sampling 'enough' new genomes leads to the discovery of a replacement or alternative to any gene. This power law behaviour points to an underlying self-organizing critical process that may be guided by mutation and niche selection. Microbes in any particular niche exist within a local web of organism interdependence known as the microbiome. The same mechanism that underpins the macro-ecological scaling first observed by MacArthur and Wilson also applies to microbial communities. Recent metagenomic studies of a food microbiome demonstrate the diverse distribution of community members, but also genotypes for a single species within a more complex community. Collectively, these results suggest that traditional taxonomic classification of bacteria could be replaced with a quasispecies model. This model is commonly accepted in virology and better describes the diversity and dynamic exchange of genes that also hold true for bacteria. This model will enable microbiologists to conduct population-scale studies to describe microbial behaviour, as opposed to a single isolate as a representative.

Salmonella is a common food-associated bacterium that has substantial impact on worldwide human health and the global economy. This is the public release of 1,183 Salmonella draft genome sequences as part of the 100K Pathogen Genome Project. These isolates represent global genomic diversity in the Salmonella genus.

The diversity revealed by large scale genomics in microbiology is calling into question long held beliefs about genome stability, evolutionary rate, even the definition of a species. MacArthur and Wilson's theory of insular biogeography provides an explanation for the diversity of macroscopic animal and plant species as a consequence of the associated hierarchical web of species interdependence. We report a large scale study of microbial diversity that reveals that the cumulative number of genes discovered increases with the number of genomes studied as a simple power law. This result is demonstrated for three different genera comparing over 15,000 isolates. We show that this power law is formally related to the MacArthur-Wilson exponent, suggesting the emerging diversity of microbial genotypes arises because the scale independent behavior first reported by MacArthur and Wilson extends down to the scale of microbes and their genes. Assessing the depth of available whole genome sequences implies a dynamically changing core genome, suggesting that traditional taxonomic classifications should be replaced with a quasispecies model that captures the diversity and dynamic exchange of genes. We report Species population "clouds" in a defined microbiome, with scale invariance extending down to the level of single-nucleotide polymorphisms (SNPs).

Foodborne disease is a global public health problem that affects millions of people every year. During a foodborne illness outbreak, rapid identification of contaminated food is vital to minimize illness, loss and impact on society. Public health officials face a significant challenge and long delays in obtaining critical information to help identify a contaminated product using traditional methods such as surveys and questionnaires. We propose a novel approach mapping geo-coded sales data against geo-coded confirmed case reports, which has the potential to reduce the time required for foodborne illness investigation. Using real grocery retail scanner data with spatial information from Germany, we have implemented a likelihood-based framework to study how such spatial data can be used to accelerate the investigation during the early stages of an outbreak. Our analysis shows that after receiving as few as 10 laboratory confirmed case reports it is possible to narrow the investigation to approximately 12 suspect products with the contaminated product included in this subset 90% of the time for approximately 80% of food products studied.

Food safety procedures are critical to reducing pathogen caused food-borne disease (FBD). However there is no way to completely eliminate the risk of consuming contaminated products. When prevention efforts fail, rapid identification of the contaminated product is essential. The medical and economic losses incurred grow with the duration of the outbreak. In this paper we show that before an outbreak occurs, analysis of food sales data, as a proactive intervention, can provide useful product intelligence that we can exploit during an outbreak investigation to accelerate the identification process. Using real grocery retail sales data from Germany, we have implemented a likelihood-based approach to study how such data can be used to accelerate the investigation during the early stages of an outbreak.