- Naperville, Illinois, United States
Rick Stevens
University of Chicago, Department of Computer Science, Faculty Member
Pneumonic tularemia is a highly debilitating and potentially fatal disease caused by inhalation of Francisella tularensis. Most of our current understanding of its pathogenesis is based on the highly virulent F. tularensis subsp.... more
Pneumonic tularemia is a highly debilitating and potentially fatal disease caused by inhalation of Francisella tularensis. Most of our current understanding of its pathogenesis is based on the highly virulent F. tularensis subsp. tularensis strain SCHU S4. However, multiple sources of SCHU S4 have been maintained and propagated independently over the years, potentially generating genetic variants with altered virulence. In this study, the virulence of four SCHU S4 stocks (NR-10492, NR-28534, NR-643 from BEI Resources and FTS-635 from Battelle Memorial Institute) along with another virulent subsp. tularensis strain, MA00-2987, were assessed in parallel. In the Fischer 344 rat model of pneumonic tularemia, NR-643 and FTS-635 were found to be highly attenuated compared to NR-10492, NR-28534, and MA00-2987. In the NZW rabbit model of pneumonic tularemia, NR-643 caused morbidity but not mortality even at a dose equivalent to 500x the LD50 for NR-10492. Genetic analyses revealed that NR-1...
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Nontyphoidal species are the leading bacterial cause of food-borne disease in the United States. ….
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Antimicrobial resistant infections are a serious public health threat worldwide. Whole genome sequencing approaches to rapidly identify pathogens and predict antibiotic resistance phenotypes are becoming more feasible and may offer a way... more
Antimicrobial resistant infections are a serious public health threat worldwide. Whole genome sequencing approaches to rapidly identify pathogens and predict antibiotic resistance phenotypes are becoming more feasible and may offer a way to reduce clinical test turnaround times compared to conventional culture-based methods, and in turn, improve patient outcomes. In this study, we use whole genome sequence data from 1668 clinical isolates of Klebsiella pneumoniae to develop a XGBoost-based machine learning model that accurately predicts minimum inhibitory concentrations (MICs) for 20 antibiotics. The overall accuracy of the model, within ±1 two-fold dilution factor, is 92%. Individual accuracies are ≥90% for 15/20 antibiotics. We show that the MICs predicted by the model correlate with known antimicrobial resistance genes. Importantly, the genome-wide approach described in this study offers a way to predict MICs for isolates without knowledge of the underlying gene content. This stu...
Research Interests: Machine Learning, Biology, Medicine, Antibiotic Resistance, Humans, and 11 moreComputer Simulation, Genome, Antibiotics, Whole Genome Sequencing, Anti-Bacterial Agents, Theoretical Models, Microbial Sensitivity Tests, In Silico, Klebsiella pneumoniae, Turnaround Time, and Minimum Inhibitory Concentration
Understanding gene function and regulation is essential for the interpretation, prediction, and ultimate design of cell responses to changes in the environment. An important step toward meeting the challenge of understanding gene function... more
Understanding gene function and regulation is essential for the interpretation, prediction, and ultimate design of cell responses to changes in the environment. An important step toward meeting the challenge of understanding gene function and regulation is the identification of sets of genes that are always co-expressed. These gene sets, Atomic Regulons (ARs), represent fundamental units of function within a cell and could be used to associate genes of unknown function with cellular processes and to enable rational genetic engineering of cellular systems. Here, we describe an approach for inferring ARs that leverages large-scale expression data sets, gene context, and functional relationships among genes. We computed ARs for Escherichia coli based on 907 gene expression experiments and compared our results with gene clusters produced by two prevalent data-driven methods: Hierarchical clustering and k-means clustering. We compared ARs and purely data-driven gene clusters to the curat...
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Automatically generated bacterial metabolic models, and even some curated models, lack accuracy in predicting energy yields due to poor representation of key pathways in energy biosynthesis and the electron transport chain (ETC). Further... more
Automatically generated bacterial metabolic models, and even some curated models, lack accuracy in predicting energy yields due to poor representation of key pathways in energy biosynthesis and the electron transport chain (ETC). Further compounding the problem, complex interlinking pathways in genome-scale metabolic models, and the need for extensive gapfilling to support complex biomass reactions, often results in predicting unrealistic yields or unrealistic physiological flux profiles. To overcome this challenge, we developed methods and tools ( http://coremodels.mcs.anl.gov ) to build high quality core metabolic models (CMM) representing accurate energy biosynthesis based on a well studied, phylogenetically diverse set of model organisms. We compare these models to explore the variability of core pathways across all microbial life, and by analyzing the ability of our core models to synthesize ATP and essential biomass precursors, we evaluate the extent to which the core metaboli...
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Research Interests: Evolutionary Biology, Genetics, Developmental Biology, Genomics, Biotechnology, and 15 moreBiology, Databases, Genetic Diversity, Biological Sciences, DNA, Archaea, Bacteria, Biochemistry and Molecular Biology of Lipids, Genome, Human Genome, Immunology and Microbiology, Bacterial, Agricultural and Biological Sciences, Genetic, and Medical and Health Sciences
Research Interests: Engineering, Genetics, Computer Science, Chemistry, Genomics, and 15 moreComputational Biology, Biology, Metabolomics, Medicine, Multidisciplinary, Annotation, Biological Sciences, Models, Escherichia coli, Genome, Information Storage and Retrieval, Metabolic, High performance, Bottleneck, and Internet
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“ Candidatus Portiera aleyrodidarum” is the obligate primary endosymbiotic bacterium of whiteflies, including the sweet potato whitefly Bemisia tabaci , and provides essential nutrients to its host. Here we report two complete genome... more
“ Candidatus Portiera aleyrodidarum” is the obligate primary endosymbiotic bacterium of whiteflies, including the sweet potato whitefly Bemisia tabaci , and provides essential nutrients to its host. Here we report two complete genome sequences of this bacterium from the B and Q biotypes of B. tabaci .
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Research Interests: Microbiology, Synthetic Biology, Computational Biology, Molecular Evolution, Biotechnology, and 15 moreComparative Genomics, Biology, Industrial Biotechnology, Medicine, Environmental Biotechnology, Genetic Engineering, Escherichia coli, Medical Biotechnology, Genome, Organism, Mycoplasma Genitalium, Bacillus subtilis, Blueprint, Comparative Genomic Hybridization, and Model organism
Description/Abstract In this commentary, we advocate building a richer set of descriptions about our invaluable and exponentially growing collection of genomes and metagenomic datasets through the construction of consensus-driven data... more
Description/Abstract In this commentary, we advocate building a richer set of descriptions about our invaluable and exponentially growing collection of genomes and metagenomic datasets through the construction of consensus-driven data capture and exchange mechanisms. Standardization activities must proceed within the auspices of open-access and international working bodies, and to tackle the issues surrounding the development of better descriptions of genomic investigations we have formed the Genomic Standards ...
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The Pathosystems Resource Integration Center (PATRIC) is the bacterial Bioinformatics Resource Center (https://www.patricbrc.org). Recent changes to PATRIC include a redesign of the web interface and some new services that provide users... more
The Pathosystems Resource Integration Center (PATRIC) is the bacterial Bioinformatics Resource Center (https://www.patricbrc.org). Recent changes to PATRIC include a redesign of the web interface and some new services that provide users with a platform that takes them from raw reads to an integrated analysis experience. The redesigned interface allows researchers direct access to tools and data, and the emphasis has changed to user-created genome-groups, with detailed summaries and views of the data that researchers have selected. Perhaps the biggest change has been the enhanced capability for researchers to analyze their private data and compare it to the available public data. Researchers can assemble their raw sequence reads and annotate the contigs using RASTtk. PATRIC also provides services for RNA-Seq, variation, model reconstruction and differential expression analysis, all delivered through an updated private workspace. Private data can be compared by 'virtual integratio...
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Research Interests: Mathematics, Computer Science, Human Computer Interaction, Information Technology, Design, and 15 moreHuman Factors, Computer Networks, Nanotechnology, Computing, CAD, Computers, Distributed Processing, Interactions, Complex network, Microcomputers, Heterogeneous Computing, Group, Futures Contract, Informing Science, and Display Devices
Large-area multi-projector displays show significant spatial variation in color, both within a single projector's field of view and across different projectors. Recent research in this area has shown that the color variation is... more
Large-area multi-projector displays show significant spatial variation in color, both within a single projector's field of view and across different projectors. Recent research in this area has shown that the color variation is primarily due to luminance variation. Luminance varies within a single projector's field of view, across different brands of projectors and with the variation in projector parameters. Luminance
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High throughput sequencing has accelerated the determination of genome sequences for thousands of human infectious disease pathogens and dozens of their vectors. The scale and scope of these data are enabling genotype-phenotype... more
High throughput sequencing has accelerated the determination of genome sequences for thousands of human infectious disease pathogens and dozens of their vectors. The scale and scope of these data are enabling genotype-phenotype association studies to identify genetic determinants of pathogen virulence and drug/insecticide resistance, and phylogenetic studies to track the origin and spread of disease outbreaks. To maximize the utility of genomic sequences for these purposes, it is essential that metadata about the pathogen/vector isolate characteristics be collected and made available in organized, clear, and consistent formats. Here we report the development of the GSCID/BRC Project and Sample Application Standard, developed by representatives of the Genome Sequencing Centers for Infectious Diseases (GSCIDs), the Bioinformatics Resource Centers (BRCs) for Infectious Diseases, and the U.S. National Institute of Allergy and Infectious Diseases (NIAID), part of the National Institutes ...
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Research Interests: Computer Science, Biology, Genome Size, Metagenomics, Medicine, and 15 moreBiological Sciences, Software Design, Sequence alignment, Mathematical Sciences, Ecosystems, Genome Analysis, Genome, Meta Analysis, Environmental Conditions, Relative Abundance, Community Composition, Viral Genomics, Microbial community, Software Package, and Genomic databases
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Research Interests: Chemistry, Developmental Biology, Genomics, Computational Biology, Vocabulary, and 15 moreBiology, Medicine, Annotation, Biological Sciences, Software, Environmental Sciences, Ribosomal proteins, Genome, Nucleic Acids, Genome Annotation, Very high throughput, Bacterial, Internet, Genomics Methods, and Leucine
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Background The number of prokaryotic genome sequences becoming available is growing steadily and is growing faster than our ability to accurately annotate them. Description We describe a fully automated service for annotating bacterial... more
Background The number of prokaryotic genome sequences becoming available is growing steadily and is growing faster than our ability to accurately annotate them. Description We describe a fully automated service for annotating bacterial and archaeal genomes. The service identifies protein-encoding, rRNA and tRNA genes, assigns functions to the genes, predicts which subsystems are represented in the genome, uses this information to reconstruct the metabolic network and makes the output easily downloadable for the user. In addition, the annotated genome can be browsed in an environment that supports comparative analysis with the annotated genomes maintained in the SEED environment. The service normally makes the annotated genome available within 12–24 hours of submission, but ultimately the quality of such a service will be judged in terms of accuracy, consistency, and completeness of the produced annotations. We summarize our attempts to address these issues and discuss plans for incr...
Research Interests: Computational Biology, Biology, Medicine, Gene Prediction, Biological Sciences, and 11 morePhylogeny, Comparative Analysis, Proteins, Computer User Interface Design, Time Factors, Reproducibility of Results, Genome sequence, Sensitivity and Specificity, Database, BMC, and Medical and Health Sciences
Background The SEED integrates many publicly available genome sequences into a single resource. The database contains accurate and up-to-date annotations based on the subsystems concept that leverages clustering between genomes and other... more
Background The SEED integrates many publicly available genome sequences into a single resource. The database contains accurate and up-to-date annotations based on the subsystems concept that leverages clustering between genomes and other clues to accurately and efficiently annotate microbial genomes. The backend is used as the foundation for many genome annotation tools, such as the Rapid Annotation using Subsystems Technology (RAST) server for whole genome annotation, the metagenomics RAST server for random community genome annotations, and the annotation clearinghouse for exchanging annotations from different resources. In addition to a web user interface, the SEED also provides Web services based API for programmatic access to the data in the SEED, allowing the development of third-party tools and mash-ups. Results The currently exposed Web services encompass over forty different methods for accessing data related to microbial genome annotations. The Web services provide comprehe...