The Pathosystems Resource Integration Center (PATRIC) is the all-bacterial Bioinformatics Resource Center (BRC) (http://www.patricbrc.org). A joint effort by two of the original National Institute of Allergy and Infectious Diseases-funded BRCs, PATRIC provides researchers with an online resource that stores and integrates a variety of data types [e.g. genomics, transcriptomics, protein–protein interactions (PPIs), three-dimensional protein structures and sequence typing data] and associated metadata. Datatypes are summarized for individual genomes and across taxonomic levels. All genomes in PATRIC, currently more than 10 000, are consistently annotated using RAST, the Rapid Annotations using Subsystems Technology. Summaries of different data types are also provided for individual genes, where comparisons of different annotations are available, and also include available transcriptomic data. PATRIC provides a variety of ways for researchers to find data of interest and a private workspace where they can store both genomic and gene associations, and their own private data. Both private and public data can be analyzed together using a suite of tools to perform comparative genomic or transcriptomic analysis. PATRIC also includes integrated information related to disease and PPIs. All the data and integrated analysis and visualization tools are freely available. This manuscript describes updates to the PATRIC since its initial report in the 2007 NAR Database Issue.

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.

5Funded by the National Institute of Allergy and Infectious Diseases, the Pathosystems Resource Integration Center (PATRIC) is a genomics-centric relational database and bioinformatics resource designed to assist scientists in infectious-disease research. Specifically, PATRIC provides scientists with (i) a comprehensive bacterial genomics database, (ii) a plethora of associated data relevant to genomic analysis, and (iii) an extensive suite of computational tools and platforms for bioinformatics analysis. While the primary aim of PATRIC is to advance the knowledge underlying the biology of human pathogens, all publicly available genome-scale data for bacteria are compiled and continually updated, thereby enabling comparative analyses to reveal the basis for differences between infectious free-living and commensal species. Herein we summarize the major features available at PATRIC, dividing the resources into two major categories: (i) organisms, genomes, and comparative genomics and (ii) recurrent integration of community-derived associated data. Additionally, we present two experimental designs typical of bacterial genomics research and report on the execution of both projects using only PATRIC data and tools. These applications encompass a broad range of the data and analysis tools available, illustrating practical uses of PATRIC for the biologist. Finally, a summary of PATRIC's outreach activities, collaborative endeavors, and future research directions is provided.

The facultative intracellular bacterial pathogen Brucella infects a wide range of warm-blooded land and marine vertebrates and causes brucellosis. Currently, there are nine recognized Brucella species based on host preferences and phenotypic differences. The availability of 10 different genomes consisting of two chromosomes and representing six of the species allowed for a detailed comparison among themselves and relatives in the order Rhizobiales. Phylogenomic analysis of ortholog families shows limited divergence but distinct radiations, producing four clades as follows: Brucella abortus-Brucella melitensis, Brucella suis-Brucella canis, Brucella ovis, and Brucella ceti. In addition, Brucella phylogeny does not appear to reflect the phylogeny of Brucella species' preferred hosts. About 4.6% of protein-coding genes seem to be pseudogenes, which is a relatively large fraction. Only B. suis 1330 appears to have an intact ␤-ketoadipate pathway, responsible for utilization of plant-derived compounds. In contrast, this pathway in the other species is highly pseudogenized and consistent with the "domino theory" of gene death. There are distinct shared anomalous regions (SARs) found in both chromosomes as the result of horizontal gene transfer unique to Brucella and not shared with its closest relative Ochrobactrum, a soil bacterium, suggesting their acquisition occurred in spite of a predominantly intracellular lifestyle. In particular, SAR 2-5 appears to have been acquired by Brucella after it became intracellular. The SARs contain many genes, including those involved in O-polysaccharide synthesis and type IV secretion, which if mutated or absent significantly affect the ability of Brucella to survive intracellularly in the infected host.Brucellosis is a disease caused by bacteria of the genus Brucella. This disease is zoonotic and endemic in many areas throughout the world, causing chronic infections with common outcomes being abortion and sterility in infected animals. In humans, it is a severe acute febrile disease, producing focal lesions in bones, joints, the genitourinary tract, and other organs. Complications may include arthritis, sacroiliitis, spondylitis, and central nervous system effects. Brucella can cause abortions in women (as can other bacteria), mostly in the first and second trimesters of pregnancy (21, 27), and men can exhibit epididymo-orchitis (37).Currently, there are nine recognized species of Brucella, based on host preferences and phenotypic differences. Six classically recognized species are Brucella abortus (cattle), Brucella canis (dogs), Brucella melitensis (sheep and goats), Brucella neotomae (desert wood rats), Brucella ovis (sheep), and Brucella suis (pigs, reindeer, and hares). These six species have been subdivided into 18 biovars based on a panel of culture and biochemical characteristics (41). Recently, three additional species have been identified, namely Brucella microti from voles (49), "Brucella pinnipediae" from pinnipeds, and Brucella ceti from cetaceans (20).The genome from...

The PathoSystems Resource Integration Center (PATRIC) is one of eight Bioinformatics Resource Centers (BRCs) funded by the National Institute of Allergy and Infection Diseases (NIAID) to create a data and analysis resource for selected NIAID priority pathogens, specifically proteobacteria of the genera Brucella, Rickettsia and Coxiella, and corona-, calici- and lyssaviruses and viruses associated with hepatitis A and E. The goal of the project is to provide a comprehensive bioinformatics resource for these pathogens, including consistently annotated genome, proteome and metabolic pathway data to facilitate research into counter-measures, including drugs, vaccines and diagnostics. The project's curation strategy has three prongs: ‘breadth first’ beginning with whole-genome and proteome curation using standardized protocols, a ‘targeted’ approach addressing the specific needs of researchers and an integrative strategy to leverage high-throughput experimental data (e.g. microarrays, proteomics) and literature. The PATRIC infrastructure consists of a relational database, analytical pipelines and a website which supports browsing, querying, data visualization and the ability to download raw and curated data in standard formats. At present, the site warehouses complete sequences for 17 bacterial and 332 viral genomes. The PATRIC website () will continually grow with the addition of data, analysis and functionality over the course of the project.

The Pathosystems Resource Integration Center (PATRIC, www.patricbrc.org) is designed to provide researchers with the tools and services that they need to perform genomic and other 'omic' data analyses. In response to mounting concern over antimicrobial resistance (AMR), the PATRIC team has been developing new tools that help researchers understand AMR and its genetic determinants. To support comparative analyses, we have added AMR phenotype data to over 15 000 genomes in the PATRIC database, often assembling genomes from reads in public archives and collecting their associated AMR panel data from the literature to augment the collection. We have also been using this collection of AMR metadata to build machine learning-based classifiers that can predict the AMR phenotypes and the genomic regions associated with resistance for genomes being submitted to the annotation service. Likewise, we have undertaken a large AMR protein annotation effort by manually curating data from the literature and public repositories. This collection of 7370 AMR reference proteins, which contains many protein annotations (functional roles) that are unique to PATRIC and RAST, has been manually curated so that it projects stably across genomes. The collection currently projects to 1 610 744 proteins in the PATRIC database. Finally, the PATRIC Web site has been expanded to enable AMR-based custom page views so that researchers can easily explore AMR data and design experiments based on whole genomes or individual genes.

Syndecan-2, a transmembrane heparan sulfate proteoglycan, is a critical mediator in the tumorigenesis of colon carcinoma cells. We explored the function of syndecan-2 in melanoma, one of the most invasive types of cancers, and found that the expression of this protein was elevated in tissue samples from both nevus and malignant human melanomas but not in melanocytes of the normal human skin tissues. Similarly, elevated syndecan-2 expression was observed in various melanoma cell lines. Overexpression of syndecan-2 enhanced migration and invasion of melanoma cells, whereas the opposite was observed when syndecan-2 levels were knocked down using small inhibitory RNAs. Syndecan-2 expression was enhanced by fibroblast growth factor-2, which is known to stimulate melanoma cell migration; however, ␣-melanocyte-stimulating hormone decreased syndecan-2 expression and melanoma cell migration and invasion in a melanin synthesis-independent manner. Furthermore, syndecan-2 overexpression rescued the migration defects induced by ␣-melanocyte-stimulating hormone treatment. Together, these data strongly suggest that syndecan-2 plays a crucial role in the migratory potential of melanoma cells.

Transfer learning, which transfers patterns learned on a source dataset to a related target dataset for constructing prediction models, has been shown effective in many applications. In this paper, we investigate whether transfer learning can be used to improve the performance of anti-cancer drug response prediction models. Previous transfer learning studies for drug response prediction focused on building models to predict the response of tumor cells to a specific drug treatment. We target the more challenging task of building general prediction models that can make predictions for both new tumor cells and new drugs. Uniquely, we investigate the power of transfer learning for three drug response prediction applications including drug repurposing, precision oncology, and new drug development, through different data partition schemes in cross-validation. We extend the classic transfer learning framework through ensemble and demonstrate its general utility with three representative prediction algorithms including a gradient boosting model and two deep neural networks. The ensemble transfer learning framework is tested on benchmark in vitro drug screening datasets. The results demonstrate that our framework broadly improves the prediction performance in all three drug response prediction applications with all three prediction algorithms.