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      The PATRIC Bioinformatics Resource Center: expanding data and analysis capabilities

      1 , 2 , 2 , 3 , 4 , 5 , 1 , 6 , 2 , 7 , 2 , 8 , 1 , 2 , 3 , 1 , 6 , 9 , 8 , 1 , 3 , 3 , 3 , 1 , 6 , 1 , 2 , 10 , 11 , 12 , 1 , 2 , 1 , 2 , 1 , 1 , 2 , 8 , 1 , 2 , 1 , 8 , 8 , 3 , 1 , 2 , 3 , 1 , 2 , 6 , 13
      Nucleic Acids Research
      Oxford University Press (OUP)

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          Abstract

          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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          The comprehensive antibiotic resistance database.

          The field of antibiotic drug discovery and the monitoring of new antibiotic resistance elements have yet to fully exploit the power of the genome revolution. Despite the fact that the first genomes sequenced of free living organisms were those of bacteria, there have been few specialized bioinformatic tools developed to mine the growing amount of genomic data associated with pathogens. In particular, there are few tools to study the genetics and genomics of antibiotic resistance and how it impacts bacterial populations, ecology, and the clinic. We have initiated development of such tools in the form of the Comprehensive Antibiotic Research Database (CARD; http://arpcard.mcmaster.ca). The CARD integrates disparate molecular and sequence data, provides a unique organizing principle in the form of the Antibiotic Resistance Ontology (ARO), and can quickly identify putative antibiotic resistance genes in new unannotated genome sequences. This unique platform provides an informatic tool that bridges antibiotic resistance concerns in health care, agriculture, and the environment.
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            Rapid and precise alignment of raw reads against redundant databases with KMA

            Background As the cost of sequencing has declined, clinical diagnostics based on next generation sequencing (NGS) have become reality. Diagnostics based on sequencing will require rapid and precise mapping against redundant databases because some of the most important determinants, such as antimicrobial resistance and core genome multilocus sequence typing (MLST) alleles, are highly similar to one another. In order to facilitate this, a novel mapping method, KMA (k-mer alignment), was designed. KMA is able to map raw reads directly against redundant databases, it also scales well for large redundant databases. KMA uses k-mer seeding to speed up mapping and the Needleman-Wunsch algorithm to accurately align extensions from k-mer seeds. Multi-mapping reads are resolved using a novel sorting scheme (ConClave scheme), ensuring an accurate selection of templates. Results The functionality of KMA was compared with SRST2, MGmapper, BWA-MEM, Bowtie2, Minimap2 and Salmon, using both simulated data and a dataset of Escherichia coli mapped against resistance genes and core genome MLST alleles. KMA outperforms current methods with respect to both accuracy and speed, while using a comparable amount of memory. Conclusion With KMA, it was possible map raw reads directly against redundant databases with high accuracy, speed and memory efficiency. Electronic supplementary material The online version of this article (10.1186/s12859-018-2336-6) contains supplementary material, which is available to authorized users.
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              Scaling read aligners to hundreds of threads on general-purpose processors

              Abstract Motivation General-purpose processors can now contain many dozens of processor cores and support hundreds of simultaneous threads of execution. To make best use of these threads, genomics software must contend with new and subtle computer architecture issues. We discuss some of these and propose methods for improving thread scaling in tools that analyze each read independently, such as read aligners. Results We implement these methods in new versions of Bowtie, Bowtie 2 and HISAT. We greatly improve thread scaling in many scenarios, including on the recent Intel Xeon Phi architecture. We also highlight how bottlenecks are exacerbated by variable-record-length file formats like FASTQ and suggest changes that enable superior scaling. Availability and implementation Experiments for this study: https://github.com/BenLangmead/bowtie-scaling . Bowtie http://bowtie-bio.sourceforge.net . Bowtie 2 http://bowtie-bio.sourceforge.net/bowtie2 . HISAT http://www.ccb.jhu.edu/software/hisat Supplementary information Supplementary data are available at Bioinformatics online.
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                Author and article information

                Journal
                Nucleic Acids Research
                Oxford University Press (OUP)
                0305-1048
                1362-4962
                October 31 2019
                October 31 2019
                Affiliations
                [1 ]University of Chicago Consortium for Advanced Science and Engineering, University of Chicago, Chicago, IL 60637, USA
                [2 ]Division of Data Science and Learning, Argonne National Laboratory, Argonne, IL 60439, USA
                [3 ]Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA 22904, USA
                [4 ]Department of Microbiology and Immunology, Faculty of Pharmacy, Cairo University, 11562 Cairo, Egypt
                [5 ]Center for Genome and Microbiome Research, Cairo University, 11562 Cairo, Egypt
                [6 ]Computing Environment and Life Sciences, Argonne National Laboratory, Argonne, IL 60439, USA
                [7 ]Middle Tennessee State University, Murfreesboro, TN 37132, USA
                [8 ]Fellowship for Interpretation of Genomes, Burr Ridge, IL 60527, USA
                [9 ]Virginia Tech, Blacksburg, VA 24061, USA
                [10 ]Transportation Institute, Virginia Tech University, Blacksburg, VA 24061, USA
                [11 ]Department of Microbiology, University of Illinois, Urbana, IL 61801, USA
                [12 ]Carl R. Woese Institute for Genomic Biology, University of Illinois, Urbana, IL 61801, USA
                [13 ]University of Chicago, Department of Computer Science, Chicago, IL 60637, USA
                Article
                10.1093/nar/gkz943
                7145515
                31667520
                fbeb7cd6-ac16-45e8-8d6f-5afe4d3105d6
                © 2019
                History

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