Ross Overbeek

Argonne National Laboratory, Лимонт, Illinois, United States

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Publications (186)699.1 Total impact

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    ABSTRACT: 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.
    Scientific Reports 02/2015; 5:8365. DOI:10.1038/srep08365 · 5.58 Impact Factor

  • F1000 Research 09/2014; DOI:10.12688/f1000research.5140.1
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    ABSTRACT: The increasing number of sequenced plant genomes is placing new demands on the methods applied to analyze, annotate, and model these genomes. Today's annotation pipelines result in inconsistent gene assignments that complicate comparative analyses and prevent efficient construction of metabolic models. To overcome these problems, we have developed the PlantSEED, an integrated, metabolism-centric database to support subsystems-based annotation and metabolic model reconstruction for plant genomes. PlantSEED combines SEED subsystems technology, first developed for microbial genomes, with refined protein families and biochemical data to assign fully consistent functional annotations to orthologous genes, particularly those encoding primary metabolic pathways. Seamless integration with its parent, the prokaryotic SEED database, makes PlantSEED a unique environment for cross-kingdom comparative analysis of plant and bacterial genomes. The consistent annotations imposed by PlantSEED permit rapid reconstruction and modeling of primary metabolism for all plant genomes in the database. This feature opens the unique possibility of model-based assessment of the completeness and accuracy of gene annotation and thus allows computational identification of genes and pathways that are restricted to certain genomes or need better curation. We demonstrate the PlantSEED system by producing consistent annotations for 10 reference genomes. We also produce a functioning metabolic model for each genome, gapfilling to identify missing annotations and proposing gene candidates for missing annotations. Models are built around an extended biomass composition representing the most comprehensive published to date. To our knowledge, our models are the first to be published for seven of the genomes analyzed.
    Proceedings of the National Academy of Sciences 06/2014; 111(26). DOI:10.1073/pnas.1401329111 · 9.67 Impact Factor
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    ABSTRACT: For many scientific applications, it is highly desirable to be able to compare metabolic models of closely related genomes. In this short report, we attempt to raise awareness to the fact that taking annotated genomes from public repositories and using them for metabolic model reconstructions is far from being trivial due to annotation inconsistencies. We are proposing a protocol for comparative analysis of metabolic models on closely related genomes, using fifteen strains of genus Brucella, which contains pathogens of both humans and livestock. This study lead to the identification and subsequent correction of inconsistent annotations in the SEED database, as well as the identification of 31 biochemical reactions that are common to Brucella, which are not originally identified by automated metabolic reconstructions. We are currently implementing this protocol for improving automated annotations within the SEED database and these improvements have been propagated into PATRIC, Model-SEED, KBase and RAST. This method is an enabling step for the future creation of consistent annotation systems and high-quality model reconstructions that will support in predicting accurate phenotypes such as pathogenicity, media requirements or type of respiration. Electronic supplementary material The online version of this article (doi:10.1007/s13205-014-0202-4) contains supplementary material, which is available to authorized users.
    03/2014; 5(1). DOI:10.1007/s13205-014-0202-4
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    ABSTRACT: In 2004, the SEED ( 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 ( 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.
    Nucleic Acids Research 11/2013; 42(Database issue). DOI:10.1093/nar/gkt1226 · 9.11 Impact Factor
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    ABSTRACT: The ERGO TM ( ERGO/) genome analysis and discovery suite is an integration of biological data from genomics, bio-chemistry, high-throughput expression profiling, genetics and peer-reviewed journals to achieve a comprehensive analysis of genes and genomes. Far beyond any conventional systems that facilitate functional assignments, ERGO combines pattern-based analysis with comparative genomics by visua-lizing genes within the context of regulation, expres-sion profiling, phylogenetic clusters, fusion events, networked cellular pathways and chromosomal neighborhoods of other functionally related genes. The result of this multifaceted approach is to provide an extensively curated database of the largest avail-able integration of genomes, with a vast collection of reconstructed cellular pathways spanning all domains of life. Although access to ERGO is provided only under subscription, it is already widely used by the academic community. The current version of the system integrates 500 genomes from all domains of life in various levels of completion, 403 of which are available for subscription.
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    ABSTRACT: The Pathosystems Resource Integration Center (PATRIC) is the all-bacterial Bioinformatics Resource Center (BRC) ( 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.
    Nucleic Acids Research 11/2013; 42(D1). DOI:10.1093/nar/gkt1099 · 9.11 Impact Factor
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    ABSTRACT: Maintaining consistency in genome annotations is important for supporting many computational tasks, particularly metabolic modeling. The SEED project has implemented a process that improves annotation consistencies across microbial genomes for proteins with conserved sequences and genomic context. In this research report, we describe this process and show how this effort has resulted in improvements to microbial genome annotations in the SEED. We also compare SEED annotation consistencies with other commonly used resources such as IMG (the Joint Genome Institute’s Integrated Microbial Genomes system), RefSeq (the National Center for Biotechnology Information’s Reference Sequence Database), Swiss-Prot (the annotated protein sequence database of the Swiss Institute of Bioinformatics, European Molecular Biology Laboratory and the European Bioinformatics Institute) and TrEMBL (Translated European Molecular Biology Laboratory nucleotide sequence data Library). Our analysis indicates that manual and computational efforts are paying off for the databases where consistency is a major goal.
    06/2013; 4(3). DOI:10.1007/s13205-013-0152-2
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    James J Davis · Fangfang Xia · Ross A Overbeek · Gary J Olsen ·
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    ABSTRACT: The tree of life is paramount for achieving an integrated understanding of microbial evolution and the relationships between physiology, genealogy and genomics. It provides the framework for interpreting environmental sequence data, whether applied to microbial ecology or to human health. However, there remain many instances where there is ambiguity in our understanding of the phylogeny of major lineages, and/or confounding nomenclature. Here we apply recent genomic sequence data to examine the evolutionary history of the Mollicutes (phylum Tenericutes) and the Erysipelotrichia (phylum Firmicutes). Consistent with previous analyses, we find evidence of a specific relationship between them in molecular phylogenies and signatures of the 16S rRNA, 23S rRNA, ribosomal proteins and aminoacyl-tRNA synthetase proteins. Furthermore, by mapping functions over the phylogenetic tree we find that the Erysipelotrichia lineages are involved in various stages of genomic reduction, having lost (often repeatedly) a variety of metabolic functions and the ability to form endospores. Although molecular phylogeny has driven numerous taxonomic revisions, we find it puzzling that the most recent taxonomic revision of the Firmicutes and Tenericutes has further separated them into distinct phyla, rather than reflecting their common roots.
    International Journal of Systematic and Evolutionary Microbiology 04/2013; 63(Pt 7). DOI:10.1099/ijs.0.048983-0 · 2.51 Impact Factor
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    ABSTRACT: Over the past decade, genome-scale metabolic models have proven to be a crucial resource for predicting organism phenotypes from genotypes. These models provide a means of rapidly translating detailed knowledge of thousands of enzymatic processes into quantitative predictions of whole-cell behavior. Until recently, the pace of new metabolic model development was eclipsed by the pace at which new genomes were being sequenced. To address this problem, the RAST and the Model SEED framework were developed as a means of automatically producing annotations and draft genome-scale metabolic models. In this chapter, we describe the automated model reconstruction process in detail, starting from a new genome sequence and finishing on a functioning genome-scale metabolic model. We break down the model reconstruction process into eight steps: submitting a genome sequence to RAST, annotating the genome, curating the annotation, submitting the annotation to Model SEED, reconstructing the core model, generating the draft biomass reaction, auto-completing the model, and curating the model. Each of these eight steps is documented in detail.
    Methods in molecular biology (Clifton, N.J.) 02/2013; 985:17-45. DOI:10.1007/978-1-62703-299-5_2 · 1.29 Impact Factor
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    ABSTRACT: Advances in sequencing technology are resulting in the rapid emergence of large numbers of complete genome sequences. High-throughput annotation and metabolic modeling of these genomes is now a reality. The high-throughput reconstruction and analysis of genome-scale transcriptional regulatory networks represent the next frontier in microbial bioinformatics. The fruition of this next frontier will depend on the integration of numerous data sources relating to mechanisms, components and behavior of the transcriptional regulatory machinery, as well as the integration of the regulatory machinery into genome-scale cellular models. Here, we review existing repositories for different types of transcriptional regulatory data, including expression data, transcription factor data and binding site locations and we explore how these data are being used for the reconstruction of new regulatory networks. From template network-based methods to de novo reverse engineering from expression data, we discuss how regulatory networks can be reconstructed and integrated with metabolic models to improve model predictions and performance. We also explore the impact these integrated models can have in simulating phenotypes, optimizing the production of compounds of interest or paving the way to a whole-cell model.
    Briefings in Bioinformatics 02/2013; 15(4). DOI:10.1093/bib/bbs071 · 9.62 Impact Factor
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    ABSTRACT: The remarkable advance in sequencing technology and the rising interest in medical and environmental microbiology, biotechnology, and synthetic biology resulted in a deluge of published microbial genomes. Yet, genome annotation, comparison, and modeling remain a major bottleneck to the translation of sequence information into biological knowledge, hence computational analysis tools are continuously being developed for rapid genome annotation and interpretation. Among the earliest, most comprehensive resources for prokaryotic genome analysis, the SEED project, initiated in 2003 as an integration of genomic data and analysis tools, now contains >5,000 complete genomes, a constantly updated set of curated annotations embodied in a large and growing collection of encoded subsystems, a derived set of protein families, and hundreds of genome-scale metabolic models. Until recently, however, maintaining current copies of the SEED code and data at remote locations has been a pressing issue. To allow high-performance remote access to the SEED database, we developed the SEED Servers ( four network-based servers intended to expose the data in the underlying relational database, support basic annotation services, offer programmatic access to the capabilities of the RAST annotation server, and provide access to a growing collection of metabolic models that support flux balance analysis. The SEED servers offer open access to regularly updated data, the ability to annotate prokaryotic genomes, the ability to create metabolic reconstructions and detailed models of metabolism, and access to hundreds of existing metabolic models. This work offers and supports a framework upon which other groups can build independent research efforts. Large integrations of genomic data represent one of the major intellectual resources driving research in biology, and programmatic access to the SEED data will provide significant utility to a broad collection of potential users.
    PLoS ONE 10/2012; 7(10):e48053. DOI:10.1371/journal.pone.0048053 · 3.23 Impact Factor
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    ABSTRACT: Summary: Annotation of metagenomes involves comparing the individual sequence reads with a database of known sequences and assigning a unique function to each read. This is a time-consuming task that is computationally intensive (though not computationally complex). Here we present a novel approach to annotate metagenomes using unique k-mer oligopeptide sequences from 7 to 12 amino acids long. We demonstrate that k-mer-based annotations are faster and approach the sensitivity and precision of blastx-based annotations without loosing accuracy. A last-common ancestor approach was also developed to describe the members of the community.Availability and implementation: This open-source application was implemented in Perl and can be accessed via a user-friendly website at In addition, code to access the annotation servers is available for download from FIGfams and k-mers are available for download from redwards@mail.sdsu.eduSupplementary information: Supplementary data are available at Bioinformatics online.
    Bioinformatics 10/2012; 28(24). DOI:10.1093/bioinformatics/bts599 · 4.98 Impact Factor
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    ABSTRACT: As a result of next generation sequencing technology, exciting new opportunities are emerging for the application of model-based comparative approaches to improving our understanding of biological systems through the high-throughput analysis of genomic data. Here we present our work in the application of the Model SEED framework ( [1] to construct draft genome-scale metabolic models for approximately 3500 complete genome sequences. In the reconstruction of these models, we develop new optimization-based algorithms for identifying and filling gaps in the metabolic network with increased accuracy over previous methods; we propose a new approach for predicting essential biomass precursors in microbes based on genomic evidence; and we explore how metabolic modeling may be applied to assign confidence to gene annotations and identify incorrect annotations that should be reassigned. In the process of generating and gap-filling our 3500 draft models, we apply comparative genomics approaches to identify candidate genes that may be associated with the gapfilled reactions in each model. This includes the use of chromosomal clustering and functional co-occurrence in addition to BLAST scores to identify new high-confidence annotations that may be experimentally validated. This work has resulted in improved, consistent subsystems-based annotations for all 3500 genomes analyzed. All models and annotations are available for download from the SEED ( and Model SEED sites. We analyze how the properties and behavior of our 3500 draft models are conserved across the diverse set of microbial genomes included in the study. We identify the least and most variable metabolic pathways; we identify significant variability in the predicted essential metabolic genes and the redundancy of essential metabolic functions; and we explore metabolic properties that are most conserved amongst phylogenetically close organisms. This work reveals insights into the diversity of microbial genomes, the completeness of our knowledge of these genomes, and the areas of our knowledge where more gaps presently exist. 1. Henry, C.S., et al., High-throughput generation, optimization, and analysis of genome-scale metabolic models. Nature Biotechnology, 2010. Nbt.1672: p. 1-6.
    2011 AIChE Annual Meeting; 10/2011
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    ABSTRACT: Genome-scale prediction of gene regulation and reconstruction of transcriptional regulatory networks in bacteria is one of the critical tasks of modern genomics. The Shewanella genus is comprised of metabolically versatile gamma-proteobacteria, whose lifestyles and natural environments are substantially different from Escherichia coli and other model bacterial species. The comparative genomics approaches and computational identification of regulatory sites are useful for the in silico reconstruction of transcriptional regulatory networks in bacteria. To explore conservation and variations in the Shewanella transcriptional networks we analyzed the repertoire of transcription factors and performed genomics-based reconstruction and comparative analysis of regulons in 16 Shewanella genomes. The inferred regulatory network includes 82 transcription factors and their DNA binding sites, 8 riboswitches and 6 translational attenuators. Forty five regulons were newly inferred from the genome context analysis, whereas others were propagated from previously characterized regulons in the Enterobacteria and Pseudomonas spp.. Multiple variations in regulatory strategies between the Shewanella spp. and E. coli include regulon contraction and expansion (as in the case of PdhR, HexR, FadR), numerous cases of recruiting non-orthologous regulators to control equivalent pathways (e.g. PsrA for fatty acid degradation) and, conversely, orthologous regulators to control distinct pathways (e.g. TyrR, ArgR, Crp). We tentatively defined the first reference collection of ~100 transcriptional regulons in 16 Shewanella genomes. The resulting regulatory network contains ~600 regulated genes per genome that are mostly involved in metabolism of carbohydrates, amino acids, fatty acids, vitamins, metals, and stress responses. Several reconstructed regulons including NagR for N-acetylglucosamine catabolism were experimentally validated in S. oneidensis MR-1. Analysis of correlations in gene expression patterns helps to interpret the reconstructed regulatory network. The inferred regulatory interactions will provide an additional regulatory constrains for an integrated model of metabolism and regulation in S. oneidensis MR-1.
    BMC Genomics 06/2011; 12 Suppl 1(Suppl 1):S3. DOI:10.1186/1471-2164-12-S1-S3 · 3.99 Impact Factor
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    ABSTRACT: Identifying functions for all gene products in all sequenced organisms is a central challenge of the post-genomic era. However, at least 30-50% of the proteins encoded by any given genome are of unknown or vaguely known function, and a large number are wrongly annotated. Many of these 'unknown' proteins are common to prokaryotes and plants. We set out to predict and experimentally test the functions of such proteins. Our approach to functional prediction integrates comparative genomics based mainly on microbial genomes with functional genomic data from model microorganisms and post-genomic data from plants. This approach bridges the gap between automated homology-based annotations and the classical gene discovery efforts of experimentalists, and is more powerful than purely computational approaches to identifying gene-function associations. Among Arabidopsis genes, we focused on those (2,325 in total) that (i) are unique or belong to families with no more than three members, (ii) occur in prokaryotes, and (iii) have unknown or poorly known functions. Computer-assisted selection of promising targets for deeper analysis was based on homology-independent characteristics associated in the SEED database with the prokaryotic members of each family. In-depth comparative genomic analysis was performed for 360 top candidate families. From this pool, 78 families were connected to general areas of metabolism and, of these families, specific functional predictions were made for 41. Twenty-one predicted functions have been experimentally tested or are currently under investigation by our group in at least one prokaryotic organism (nine of them have been validated, four invalidated, and eight are in progress). Ten additional predictions have been independently validated by other groups. Discovering the function of very widespread but hitherto enigmatic proteins such as the YrdC or YgfZ families illustrates the power of our approach. Our approach correctly predicted functions for 19 uncharacterized protein families from plants and prokaryotes; none of these functions had previously been correctly predicted by computational methods. The resulting annotations could be propagated with confidence to over six thousand homologous proteins encoded in over 900 bacterial, archaeal, and eukaryotic genomes currently available in public databases.
    BMC Genomics 06/2011; 12 Suppl 1(Suppl 1):S2. DOI:10.1186/1471-2164-12-S1-S2 · 3.99 Impact Factor
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    ABSTRACT: The development of next generation sequencing technology is rapidly changing the face of the genome annotation and analysis field. One of the primary uses for genome sequence data is to improve our understanding and prediction of phenotypes for microbes and microbial communities, but the technologies for predicting phenotypes must keep pace with the new sequences emerging. This review presents an integrated view of the methods and technologies used in the inference of phenotypes for microbes and microbial communities based on genomic and metagenomic data. Given the breadth of this topic, we place special focus on the resources available within the SEED Project. We discuss the two steps involved in connecting genotype to phenotype: sequence annotation, and phenotype inference, and we highlight the challenges in each of these steps when dealing with both single genome and metagenome data. This integrated view of the genotype-to-phenotype problem highlights the importance of a controlled ontology in the annotation of genomic data, as this benefits subsequent phenotype inference and metagenome annotation. We also note the importance of expanding the set of reference genomes to improve the annotation of all sequence data, and we highlight metagenome assembly as a potential new source for complete genomes. Finally, we find that phenotype inference, particularly from metabolic models, generates predictions that can be validated and reconciled to improve annotations. This review presents the first look at the challenges and opportunities associated with the inference of phenotype from genotype during the next generation sequencing revolution. This article is part of a Special Issue entitled: Systems Biology of Microorganisms.
    Biochimica et Biophysica Acta 03/2011; 1810(10):967-77. DOI:10.1016/j.bbagen.2011.03.010 · 4.66 Impact Factor

Publication Stats

21k Citations
699.10 Total Impact Points


  • 1982-2015
    • Argonne National Laboratory
      • Division of Mathematics and Computer Science
      Лимонт, Illinois, United States
  • 2012
    • San Diego State University
      San Diego, California, United States
  • 2005-2009
    • University of Chicago
      • Computation Institute
      Chicago, Illinois, United States
  • 2002-2005
    • University of Illinois at Chicago
      Chicago, Illinois, United States
    • The University of Scranton
      Scranton, Pennsylvania, United States
  • 2003
    • Complete Genomics Incorporated
      Mountain View, California, United States
    • Northwestern University
      • Department of Pathology
      Evanston, Illinois, United States
  • 2001
    • Virginia Polytechnic Institute and State University
      • Department of Biochemistry
      Blacksburg, VA, United States
  • 1994-1997
    • University of Illinois, Urbana-Champaign
      • Department of Microbiology
      Urbana, Illinois, United States
    • Indiana University Bloomington
      • Department of Biology
      Bloomington, Indiana, United States
  • 1974-1982
    • Northern Illinois University
      • • Department of Computer Science
      • • Department of Mathematical Sciences
      DeKalb, Illinois, United States