Browsing by Author "Davis, James J."

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    Antimicrobial Resistance Prediction in PATRIC and RAST
    Davis, James J.; Boisvert, Sebastien; Brettin, Thomas; Kenyon, Ronald W.; Mao, Chunhong; Olson, Robert D.; Overbeek, Ross; Santerre, John; Shukla, Maulik; Wattam, Alice R.; Will, Rebecca; Xia, Fangfang; Stevens, Rick L. (Springer Nature, 2016-06-14)
    The emergence and spread of antimicrobial resistance (AMR) mechanisms in bacterial pathogens, coupled with the dwindling number of effective antibiotics, has created a global health crisis. Being able to identify the genetic mechanisms of AMR and predict the resistance phenotypes of bacterial pathogens prior to culturing could inform clinical decision-making and improve reaction time. At PATRIC (http://patricbrc.org/), we have been collecting bacterial genomes with AMR metadata for several years. In order to advance phenotype prediction and the identification of genomic regions relating to AMR, we have updated the PATRIC FTP server to enable access to genomes that are binned by their AMR phenotypes, as well as metadata including minimum inhibitory concentrations. Using this infrastructure, we custom built AdaBoost (adaptive boosting) machine learning classifiers for identifying carbapenem resistance in Acinetobacter baumannii, methicillin resistance in Staphylococcus aureus, and beta-lactam and co-trimoxazole resistance in Streptococcus pneumoniae with accuracies ranging from 88-99%. We also did this for isoniazid, kanamycin, ofloxacin, rifampicin, and streptomycin resistance in Mycobacterium tuberculosis, achieving accuracies ranging from 71-88%. This set of classifiers has been used to provide an initial framework for species-specific AMR phenotype and genomic feature prediction in the RAST and PATRIC annotation services.
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    Improvements to PATRIC, the all-bacterial Bioinformatics Database and Analysis Resource Center
    Wattam, Alice R.; Davis, James J.; Assaf, Rida; Boisvert, Sebastien; Brettin, Thomas; Bun, Christopher; Conrad, Neal; Dietrich, Emily M.; Disz, Terry L.; Gabbard, Joseph L.; Gerdes, Svetlana; Henry, Christopher S.; Kenyon, Ronald W.; Machi, Dustin; Mao, Chunhong; Nordberg, Eric K.; Olsen, Gary J.; Murphy-Olson, Daniel E.; Olson, Robert D.; Overbeek, Ross; Parrello, Bruce; Pusch, Gordon D.; Shukla, Maulik; Vonstein, Veronika; Warren, Andrew S.; Xia, Fangfang; Yoo, Hyunseung; Stevens, Rick L. (2017-01-04)
    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 integration' to any of PATRIC's public data. The number of genomes available for comparison in PATRIC has expanded to over 80 000, with a special emphasis on genomes with antimicrobial resistance data. PATRIC uses this data to improve both subsystem annotation and k-mer classification, and tags new genomes as having signatures that indicate susceptibility or resistance to specific antibiotics.
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    The PATRIC Bioinformatics Resource Center: expanding data and analysis capabilities
    Davis, James J.; Wattam, Alice R.; Aziz, Ramy K.; Brettin, Thomas; Butler, Ralph; Butler, Rory M.; Chlenski, Philippe; Conrad, Neal; Dickerman, Allan W.; Dietrich, Emily M.; Gabbard, Joseph L.; Gerdes, Svetlana; Guard, Andrew; Kenyon, Ronald W.; Machi, Dustin; Mao, Chunhong; Murphy-Olson, Daniel E.; Nguyen, Marcus; Nordberg, Eric K.; Olsen, Gary J.; Olson, Robert D.; Overbeek, Jamie C.; Overbeek, Ross; Parrello, Bruce; Pusch, Gordon D.; Shukla, Maulik; Thomas, Chris; VanOeffelen, Margo; Vonstein, Veronika; Warren, Andrew S.; Xia, Fangfang; Xie, Dawen; Yoo, Hyunseung; Stevens, Rick L. (2020-01-08)
    The PathoSystems Resource Integration Center (PATRIC) is the bacterial Bioinformatics Resource Center funded by the National Institute of Allergy and Infectious Diseases (https://www.patricbrc.org). PATRIC supports bioinformatic analyses of all bacteria with a special emphasis on pathogens, offering a rich comparative analysis environment that provides users with access to over 250 000 uniformly annotated and publicly available genomes with curated metadata. PATRIC offers web-based visualization and comparative analysis tools, a private workspace in which users can analyze their own data in the context of the public collections, services that streamline complex bioinformatic workflows and command-line tools for bulk data analysis. Over the past several years, as genomic and other omics-related experiments have become more cost-effective and widespread, we have observed considerable growth in the usage of and demand for easy-to-use, publicly available bioinformatic tools and services. Here we report the recent updates to the PATRIC resource, including new web-based comparative analysis tools, eight new services and the release of a command-line interface to access, query and analyze data.
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    PATtyFams: Protein Families for the Microbial Genomes in the PATRIC Database
    Davis, James J.; Gerdes, Svetlana; Olsen, Gary J.; Olson, Robert; Pusch, Gordon D.; Shukla, Maulik; Vonstein, Veronika; Wattam, Alice R.; Yoo, Hyunseung (Frontiers, 2016-02-08)
    The ability to build accurate protein families is a fundamental operation in bioinformatics that influences comparative analyses, genome annotation, and metabolic modeling. For several years we have been maintaining protein families for all microbial genomes in the PATRIC database (Pathosystems Resource Integration Center, patricbrc.org) in order to drive many of the comparative analysis tools that are available through the PATRIC website. However, due to the burgeoning number of genomes, traditional approaches for generating protein families are becoming prohibitive. In this report, we describe a new approach for generating protein families, which we call PATtyFams. This method uses the k-mer-based function assignments available through RAST (Rapid Annotation using Subsystem Technology) to rapidly guide family formation, and then differentiates the function-based groups into families using a Markov Cluster algorithm (MCL). This new approach for generating protein families is rapid, scalable and has properties that are consistent with alignment-based methods.
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    RASTtk: A modular and extensible implementation of the RAST algorithm for building custom annotation pipelines and annotating batches of genomes
    Brettin, Thomas; Davis, James J.; Disz, Terry; Edwards, Robert A.; Gerdes, Svetlana; Olsen, Gary J.; Olson, Robert; Overbeek, Ross; Parrello, Bruce; Pusch, Gordon D.; Shukla, Maulik; Thomason, James A., III; Stevens, Rick L.; Vonstein, Veronika; Wattam, Alice R.; Xia, Fangfang (Springer Nature, 2015-02-10)
    The RAST (Rapid Annotation using Subsystem Technology) annotation engine was built in 2008 to annotate bacterial and archaeal genomes. It works by offering a standard software pipeline for identifying genomic features (i.e., protein-encoding genes and RNA) and annotating their functions. Recently, in order to make RAST a more useful research tool and to keep pace with advancements in bioinformatics, it has become desirable to build a version of RAST that is both customizable and extensible. In this paper, we describe the RAST tool kit (RASTtk), a modular version of RAST that enables researchers to build custom annotation pipelines. RASTtk offers a choice of software for identifying and annotating genomic features as well as the ability to add custom features to an annotation job. RASTtk also accommodates the batch submission of genomes and the ability to customize annotation protocols for batch submissions. This is the first major software restructuring of RAST since its inception.
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    The SEED and the Rapid Annotation of microbial genomes using Subsystems Technology (RAST)
    Overbeek, Ross; Olson, Robert; Pusch, Gordon D.; Olsen, Gary J.; Davis, James J.; Disz, Terry; Edwards, Robert A.; Gerdes, Svetlana; Parrello, Bruce; Shukla, Maulik; Vonstein, Veronika; Wattam, Alice R.; Xia, Fangfang; Stevens, Rick L. (2014-01)
    In 2004, the SEED (http://pubseed.theseed.org/) was created to provide consistent and accurate genome annotations across thousands of genomes and as a platform for discovering and developing de novo annotations. The SEED is a constantly updated integration of genomic data with a genome database, web front end, API and server scripts. It is used by many scientists for predicting gene functions and discovering new pathways. In addition to being a powerful database for bioinformatics research, the SEED also houses subsystems (collections of functionally related protein families) and their derived FIGfams (protein families), which represent the core of the RAST annotation engine (http://rast.nmpdr.org/). When a new genome is submitted to RAST, genes are called and their annotations are made by comparison to the FIGfam collection. If the genome is made public, it is then housed within the SEED and its proteins populate the FIGfam collection. This annotation cycle has proven to be a robust and scalable solution to the problem of annotating the exponentially increasing number of genomes. To date, >12 000 users worldwide have annotated >60 000 distinct genomes using RAST. Here we describe the interconnectedness of the SEED database and RAST, the RAST annotation pipeline and updates to both resources.