Eric K. Nordberg

PATRIC, the bacterial bioinformatics database and analysis resource

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.

PATRIC: the Comprehensive Bacterial Bioinformatics Resource with a Focus on Human Pathogenic Species

ABSTRACT Funded 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 PATRICs outreach activities, collaborative endeavors, and future research directions is provided.

Rickettsia Phylogenomics: Unwinding the Intricacies of Obligate Intracellular Life

Background Completed genome sequences are rapidly increasing for Rickettsia, obligate intracellular α-proteobacteria responsible for various human diseases, including epidemic typhus and Rocky Mountain spotted fever. In light of phylogeny, the establishment of orthologous groups (OGs) of open reading frames (ORFs) will distinguish the core rickettsial genes and other group specific genes (class 1 OGs or C1OGs) from those distributed indiscriminately throughout the rickettsial tree (class 2 OG or C2OGs). Methodology/Principal Findings We present 1823 representative (no gene duplications) and 259 non-representative (at least one gene duplication) rickettsial OGs. While the highly reductive (∼1.2 MB) Rickettsia genomes range in predicted ORFs from 872 to 1512, a core of 752 OGs was identified, depicting the essential Rickettsia genes. Unsurprisingly, this core lacks many metabolic genes, reflecting the dependence on host resources for growth and survival. Additionally, we bolster our recent reclassification of Rickettsia by identifying OGs that define the AG (ancestral group), TG (typhus group), TRG (transitional group), and SFG (spotted fever group) rickettsiae. OGs for insect-associated species, tick-associated species and species that harbor plasmids were also predicted. Through superimposition of all OGs over robust phylogeny estimation, we discern between C1OGs and C2OGs, the latter depicting genes either decaying from the conserved C1OGs or acquired laterally. Finally, scrutiny of non-representative OGs revealed high levels of split genes versus gene duplications, with both phenomena confounding gene orthology assignment. Interestingly, non-representative OGs, as well as OGs comprised of several gene families typically involved in microbial pathogenicity and/or the acquisition of virulence factors, fall predominantly within C2OG distributions. Conclusion/Significance Collectively, we determined the relative conservation and distribution of 14354 predicted ORFs from 10 rickettsial genomes across robust phylogeny estimation. The data, available at PATRIC (PathoSystems Resource Integration Center), provide novel information for unwinding the intricacies associated with Rickettsia pathogenesis, expanding the range of potential diagnostic, vaccine and therapeutic targets.

Improvements to PATRIC, the all-bacterial Bioinformatics Database and Analysis Resource Center

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 PATRICs 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.

YODA: selecting signature oligonucleotides

MOTIVATION Selecting oligonucleotide probes for use in microarray design, and other applications requiring signature sequences, involves identifying sequences which will bind strongly to their intended target, while binding only weakly (or preferably, not at all) to non-target sequences which may be present in the hybridization reaction. While many tools to assist in selection of such sequences exist, all the ones we examined lack important oligo design and software features. RESULTS YODA is an application for assisting biological researchers in selecting signature sequences. It incorporates a custom sequence similarity search to find potential cross-hybridizing non-target sequences. For this task, most oligo design tools rely on BLAST, which is ill suited for it due to an unacceptable risk of false negatives. YODA supports multiple probe design goals including single-genome, multiple-genome, pathogen-host and species/strain-identification. A graphical interface is provided as well as a command-line interface, both of which support many user-controlled parameters. YODA is easy to install and use and runs on Windows, Mac OS X and Linux platforms. AVAILABILITY Freely available (LGLP) along with source code and additional documentation at http://pathport.vbi.vt.edu/YODA CONTACT: enordber@vbi.vt.edu.

A Rickettsia Genome Overrun by Mobile Genetic Elements Provides Insight into the Acquisition of Genes Characteristic of an Obligate Intracellular Lifestyle

We present the draft genome for the Rickettsia endosymbiont of Ixodes scapularis (REIS), a symbiont of the deer tick vector of Lyme disease in North America. Among Rickettsia species (Alphaproteobacteria: Rickettsiales), REIS has the largest genome sequenced to date (>2 Mb) and contains 2,309 genes across the chromosome and four plasmids (pREIS1 to pREIS4). The most remarkable finding within the REIS genome is the extraordinary proliferation of mobile genetic elements (MGEs), which contributes to a limited synteny with other Rickettsia genomes. In particular, an integrative conjugative element named RAGE (for Rickettsiales amplified genetic element), previously identified in scrub typhus rickettsiae (Orientia tsutsugamushi) genomes, is present on both the REIS chromosome and plasmids. Unlike the pseudogene-laden RAGEs of O. tsutsugamushi, REIS encodes nine conserved RAGEs that include F-like type IV secretion systems similar to that of the tra genes encoded in the Rickettsia bellii and R. massiliae genomes. An unparalleled abundance of encoded transposases (>650) relative to genome size, together with the RAGEs and other MGEs, comprise ~35% of the total genome, making REIS one of the most plastic and repetitive bacterial genomes sequenced to date. We present evidence that conserved rickettsial genes associated with an intracellular lifestyle were acquired via MGEs, especially the RAGE, through a continuum of genomic invasions. Robust phylogeny estimation suggests REIS is ancestral to the virulent spotted fever group of rickettsiae. As REIS is not known to invade vertebrate cells and has no known pathogenic effects on I. scapularis, its genome sequence provides insight on the origin of mechanisms of rickettsial pathogenicity.

Bacterial DNA Sifted from the Trichoplax adhaerens (Animalia: Placozoa) Genome Project Reveals a Putative Rickettsial Endosymbiont

Eukaryotic genome sequencing projects often yield bacterial DNA sequences, data typically considered as microbial contamination. However, these sequences may also indicate either symbiont genes or lateral gene transfer (LGT) to host genomes. These bacterial sequences can provide clues about eukaryote–microbe interactions. Here, we used the genome of the primitive animal Trichoplax adhaerens (Metazoa: Placozoa), which is known to harbor an uncharacterized Gram-negative endosymbiont, to search for the presence of bacterial DNA sequences. Bioinformatic and phylogenomic analyses of extracted data from the genome assembly (181 bacterial coding sequences [CDS]) and trace read archive (16S rDNA) revealed a dominant proteobacterial profile strongly skewed to Rickettsiales (Alphaproteobacteria) genomes. By way of phylogenetic analysis of 16S rDNA and 113 proteins conserved across proteobacterial genomes, as well as identification of 27 rickettsial signature genes, we propose a Rickettsiales endosymbiont of T. adhaerens (RETA). The majority (93%) of the identified bacterial CDS belongs to small scaffolds containing prokaryotic-like genes; however, 12 CDS were identified on large scaffolds comprised of eukaryotic-like genes, suggesting that T. adhaerens might have recently acquired bacterial genes. These putative LGTs may coincide with the placozoan’s aquatic niche and symbiosis with RETA. This work underscores the rich, and relatively untapped, resource of eukaryotic genome projects for harboring data pertinent to host–microbial interactions. The nature of unknown (or poorly characterized) bacterial species may only emerge via analysis of host genome sequencing projects, particularly if these species are resistant to cell culturing, as are many obligate intracellular microbes. Our work provides methodological insight for such an approach.

RNA-Rocket: an RNA-Seq analysis resource for infectious disease research

Motivation: RNA-Seq is a method for profiling transcription using high-throughput sequencing and is an important component of many research projects that wish to study transcript isoforms, condition specific expression and transcriptional structure. The methods, tools and technologies used to perform RNA-Seq analysis continue to change, creating a bioinformatics challenge for researchers who wish to exploit these data. Resources that bring together genomic data, analysis tools, educational material and computational infrastructure can minimize the overhead required of life science researchers. Results: RNA-Rocket is a free service that provides access to RNA-Seq and ChIP-Seq analysis tools for studying infectious diseases. The site makes available thousands of pre-indexed genomes, their annotations and the ability to stream results to the bioinformatics resources VectorBase, EuPathDB and PATRIC. The site also provides a combination of experimental data and metadata, examples of pre-computed analysis, step-by-step guides and a user interface designed to enable both novice and experienced users of RNA-Seq data. Availability and implementation: RNA-Rocket is available at rnaseq.pathogenportal.org. Source code for this project can be found at github.com/cidvbi/PathogenPortal. Contact: anwarren@vt.edu Supplementary information: Supplementary materials are available at Bioinformatics online.

Energy Demand Model for Residential Sector: A First Principles Approach

According to the U.S. Energy Information Administration (EIA), the residential sector accounts for one-third of the countrys energy consumption. This number is steadily increasing, posing a challenge to energy regulators as well as suppliers. To manage the growing demand for energy, there is a need for energy system optimization, especially on the demand side. This paper uses a first principles approach to build a high-resolution energy demand model, which can be used as a test bed by academicians as well as policy makers for performing such optimizations. This framework generates activity-based, building-level, time-dependent demand profiles. The model associates appliance usage with each household activity and calculates energy consumption based on the appliance energy rating, the duration of the energy consuming activity, and the type of activity performed by each household member. It also accounts for shared activities among household members to avoid double counting. Additionally, passive energy consumptions such as space heating/cooling, lighting, etc., are measured. Finally, validation of the results obtained by this model against real-world data for Virginia is carried out. The results indicate that the modeling framework is robust and can be extended to other parts of the U.S. and beyond.

PATRIC as a unique resource for studying antimicrobial resistance

Abstract 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.