Robert W. Cottingham

The Fast Changing Landscape of Sequencing Technologies and Their Impact on Microbial Genome Assemblies and Annotation

Background The emergence of next generation sequencing (NGS) has provided the means for rapid and high throughput sequencing and data generation at low cost, while concomitantly creating a new set of challenges. The number of available assembled microbial genomes continues to grow rapidly and their quality reflects the quality of the sequencing technology used, but also of the analysis software employed for assembly and annotation. Methodology/Principal Findings In this work, we have explored the quality of the microbial draft genomes across various sequencing technologies. We have compared the draft and finished assemblies of 133 microbial genomes sequenced at the Department of Energy-Joint Genome Institute and finished at the Los Alamos National Laboratory using a variety of combinations of sequencing technologies, reflecting the transition of the institute from Sanger-based sequencing platforms to NGS platforms. The quality of the public assemblies and of the associated gene annotations was evaluated using various metrics. Results obtained with the different sequencing technologies, as well as their effects on downstream processes, were analyzed. Our results demonstrate that the Illumina HiSeq 2000 sequencing system, the primary sequencing technology currently used for de novo genome sequencing and assembly at JGI, has various advantages in terms of total sequence throughput and cost, but it also introduces challenges for the downstream analyses. In all cases assembly results although on average are of high quality, need to be viewed critically and consider sources of errors in them prior to analysis. Conclusion These data follow the evolution of microbial sequencing and downstream processing at the JGI from draft genome sequences with large gaps corresponding to missing genes of significant biological role to assemblies with multiple small gaps (Illumina) and finally to assemblies that generate almost complete genomes (Illumina+PacBio).

Caldicellulosiruptor Core and Pangenomes Reveal Determinants for Noncellulosomal Thermophilic Deconstruction of Plant Biomass

Extremely thermophilic bacteria of the genus Caldicellulosiruptor utilize carbohydrate components of plant cell walls, including cellulose and hemicellulose, facilitated by a diverse set of glycoside hydrolases (GHs). From a biofuel perspective, this capability is crucial for deconstruction of plant biomass into fermentable sugars. While all species from the genus grow on xylan and acid-pretreated switchgrass, growth on crystalline cellulose is variable. The basis for this variability was examined using microbiological, genomic, and proteomic analyses of eight globally diverse Caldicellulosiruptor species. The open Caldicellulosiruptor pangenome (4,009 open reading frames [ORFs]) encodes 106 GHs, representing 43 GH families, but only 26 GHs from 17 families are included in the core (noncellulosic) genome (1,543 ORFs). Differentiating the strongly cellulolytic Caldicellulosiruptor species from the others is a specific genomic locus that encodes multidomain cellulases from GH families 9 and 48, which are associated with cellulose-binding modules. This locus also encodes a novel adhesin associated with type IV pili, which was identified in the exoproteome bound to crystalline cellulose. Taking into account the core genomes, pangenomes, and individual genomes, the ancestral Caldicellulosiruptor was likely cellulolytic and evolved, in some cases, into species that lost the ability to degrade crystalline cellulose while maintaining the capacity to hydrolyze amorphous cellulose and hemicellulose.

A roadmap for research on crassulacean acid metabolism (CAM) to enhance sustainable food and bioenergy production in a hotter, drier world

Crassulacean acid metabolism (CAM) is a specialized mode of photosynthesis that features nocturnal CO2 uptake, facilitates increased water-use efficiency (WUE), and enables CAM plants to inhabit water-limited environments such as semi-arid deserts or seasonally dry forests. Human population growth and global climate change now present challenges for agricultural production systems to increase food, feed, forage, fiber, and fuel production. One approach to meet these challenges is to increase reliance on CAM crops, such as Agave and Opuntia, for biomass production on semi-arid, abandoned, marginal, or degraded agricultural lands. Major research efforts are now underway to assess the productivity of CAM crop species and to harness the WUE of CAM by engineering this pathway into existing food, feed, and bioenergy crops. An improved understanding of CAM has potential for high returns on research investment. To exploit the potential of CAM crops and CAM bioengineering, it will be necessary to elucidate the evolution, genomic features, and regulatory mechanisms of CAM. Field trials and predictive models will be required to assess the productivity of CAM crops, while new synthetic biology approaches need to be developed for CAM engineering. Infrastructure will be needed for CAM model systems, field trials, mutant collections, and data management.

Scenario driven data modelling: a method for integrating diverse sources of data and data streams

BackgroundBiology is rapidly becoming a data intensive, data-driven science. It is essential that data is represented and connected in ways that best represent its full conceptual content and allows both automated integration and data driven decision-making. Recent advancements in distributed multi-relational directed graphs, implemented in the form of the Semantic Web make it possible to deal with complicated heterogeneous data in new and interesting ways.ResultsThis paper presents a new approach, scenario driven data modelling (SDDM), that integrates multi-relational directed graphs with data streams. SDDM can be applied to virtually any data integration challenge with widely divergent types of data and data streams. In this work, we explored integrating genetics data with reports from traditional media. SDDM was applied to the New Delhi metallo-beta-lactamase gene (NDM-1), an emerging global health threat. The SDDM process constructed a scenario, created a RDF multi-relational directed graph that linked diverse types of data to the Semantic Web, implemented RDF conversion tools (RDFizers) to bring content into the Sematic Web, identified data streams and analytical routines to analyse those streams, and identified user requirements and graph traversals to meet end-user requirements.ConclusionsWe provided an example where SDDM was applied to a complex data integration challenge. The process created a model of the emerging NDM-1 health threat, identified and filled gaps in that model, and constructed reliable software that monitored data streams based on the scenario derived multi-relational directed graph. The SDDM process significantly reduced the software requirements phase by letting the scenario and resulting multi-relational directed graph define what is possible and then set the scope of the user requirements. Approaches like SDDM will be critical to the future of data intensive, data-driven science because they automate the process of converting massive data streams into usable knowledge.

Complete Genome Sequence of the Cellulolytic Thermophile Caldicellulosiruptor obsidiansis OB47T

Caldicellulosiruptor obsidiansis OB47(T) (ATCC BAA-2073, JCM 16842) is an extremely thermophilic, anaerobic bacterium capable of hydrolyzing plant-derived polymers through the expression of multidomain/multifunctional hydrolases. The complete genome sequence reveals a diverse set of carbohydrate-active enzymes and provides further insight into lignocellulosic biomass hydrolysis at high temperatures.

The DOE Systems Biology Knowledgebase (KBase)

The U.S. Department of Energy Systems Biology Knowledgebase (KBase) is an open-source software and data platform designed to meet the grand challenge of systems biology — predicting and designing biological function from the biomolecular (small scale) to the ecological (large scale). KBase is available for anyone to use, and enables researchers to collaboratively generate, test, compare, and share hypotheses about biological functions; perform large-scale analyses on scalable computing infrastructure; and combine experimental evidence and conclusions that lead to accurate models of plant and microbial physiology and community dynamics. The KBase platform has (1) extensible analytical capabilities that currently include genome assembly, annotation, ontology assignment, comparative genomics, transcriptomics, and metabolic modeling; (2) a web-browser-based user interface that supports building, sharing, and publishing reproducible and well-annotated analyses with integrated data; (3) access to extensive computational resources; and (4) a software development kit allowing the community to add functionality to the system.

Complete Genome Sequences of Caldicellulosiruptor sp. Strain Rt8.B8, Caldicellulosiruptor sp. Strain Wai35.B1, and “Thermoanaerobacter cellulolyticus”

ABSTRACT The genus Caldicellulosiruptor contains extremely thermophilic, cellulolytic bacteria capable of lignocellulose deconstruction. Currently, complete genome sequences for eleven Caldicellulosiruptor species are available. Here, we report genome sequences for three additional Caldicellulosiruptor species: Rt8.B8 DSM 8990 (New Zealand), Wai35.B1 DSM 8977 (New Zealand), and “Thermoanaerobacter cellulolyticus” strain NA10 DSM 8991 (Japan).

Complete Genome Sequence of the Hyperthermophilic Sulfate-Reducing Bacterium Thermodesulfobacterium geofontis OPF15T.

ABSTRACT Thermodesulfobacterium geofontis OPF15T (ATCC BAA-2454, JCM 18567) was isolated from Obsidian Pool, Yellowstone National Park, and grows optimally at 83°C. The 1.6-Mb genome sequence was finished at the Joint Genome Institute and has been deposited for future genomic studies pertaining to microbial processes and nutrient cycles in high-temperature environments.

Next generation models for storage and representation of microbial biological annotation

BackgroundTraditional genome annotation systems were developed in a very different computing era, one where the World Wide Web was just emerging. Consequently, these systems are built as centralized black boxes focused on generating high quality annotation submissions to GenBank/EMBL supported by expert manual curation. The exponential growth of sequence data drives a growing need for increasingly higher quality and automatically generated annotation.Typical annotation pipelines utilize traditional database technologies, clustered computing resources, Perl, C, and UNIX file systems to process raw sequence data, identify genes, and predict and categorize gene function. These technologies tightly couple the annotation software system to hardware and third party software (e.g. relational database systems and schemas). This makes annotation systems hard to reproduce, inflexible to modification over time, difficult to assess, difficult to partition across multiple geographic sites, and difficult to understand for those who are not domain experts. These systems are not readily open to scrutiny and therefore not scientifically tractable.The advent of Semantic Web standards such as Resource Description Framework (RDF) and OWL Web Ontology Language (OWL) enables us to construct systems that address these challenges in a new comprehensive way.ResultsHere, we develop a framework for linking traditional data to OWL-based ontologies in genome annotation. We show how data standards can decouple hardware and third party software tools from annotation pipelines, thereby making annotation pipelines easier to reproduce and assess. An illustrative example shows how TURTLE (Terse RDF Triple Language) can be used as a human readable, but also semantically-aware, equivalent to GenBank/EMBL files.ConclusionsThe power of this approach lies in its ability to assemble annotation data from multiple databases across multiple locations into a representation that is understandable to researchers. In this way, all researchers, experimental and computational, will more easily understand the informatics processes constructing genome annotation and ultimately be able to help improve the systems that produce them.

KBase: The United States Department of Energy Systems Biology Knowledgebase

Author(s): Arkin, Adam P; Cottingham, Robert W; Henry, Christopher S; Harris, Nomi L; Stevens, Rick L; Maslov, Sergei; Dehal, Paramvir; Ware, Doreen; Perez, Fernando; Canon, Shane; Sneddon, Michael W; Henderson, Matthew L; Riehl, William J; Murphy-Olson, Dan; Chan, Stephen Y; Kamimura, Roy T; Kumari, Sunita; Drake, Meghan M; Brettin, Thomas S; Glass, Elizabeth M; Chivian, Dylan; Gunter, Dan; Weston, David J; Allen, Benjamin H; Baumohl, Jason; Best, Aaron A; Bowen, Ben; Brenner, Steven E; Bun, Christopher C; Chandonia, John-Marc; Chia, Jer-Ming; Colasanti, Ric; Conrad, Neal; Davis, James J; Davison, Brian H; DeJongh, Matthew; Devoid, Scott; Dietrich, Emily; Dubchak, Inna; Edirisinghe, Janaka N; Fang, Gang; Faria, Jose P; Frybarger, Paul M; Gerlach, Wolfgang; Gerstein, Mark; Greiner, Annette; Gurtowski, James; Haun, Holly L; He, Fei; Jain, Rashmi; Joachimiak, Marcin P; Keegan, Kevin P; Kondo, Shinnosuke; Kumar, Vivek; Land, Miriam L; Meyer, Folker; Mills, Marissa; Novichkov, Pavel S; Oh, Taeyun; Olsen, Gary J; Olson, Robert; Parrello, Bruce; Pasternak, Shiran; Pearson, Erik; Poon, Sarah S; Price, Gavin A; Ramakrishnan, Srividya; Ranjan, Priya; Ronald, Pamela C; Schatz, Michael C; Seaver, Samuel MD; Shukla, Maulik; Sutormin, Roman A; Syed, Mustafa H; Thomason, James; Tintle, Nathan L; Wang, Daifeng; Xia, Fangfang; Yoo, Hyunseung; Yoo, Shinjae; Yu, Dantong