KBase: The United States Department of Energy Systems Biology Knowledgebase

Arkin, Adam P.; Cottingham, Robert W.; Henry, Christopher S.; Harris, Nomi L.; Stevens, Rick; Maslov, Sergei; Dehal, Paramvir; Ware, Doreen; Pérez, Fernando; Canon, Shane; Sneddon, Michael W.; Henderson, Matthew; Riehl, William J.; Murphy-Olson, Dan; Chan, Stephen Y.; Kamimura, Roy T.; Kumari, Sunita; Drake, Meghan M.; Brettin, Thomas; Glass, Elizabeth M.; Chivian, Dylan; Gunter, Dan; Weston, David J.; Allen, Benjamin H.; Baumohl, Jason K.; Best, Aaron A.; Bowen, Ben; Brenner, Steven E.; Bun, Christopher; 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, José 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; Meyer, Folker; Mills, Marissa; Novichkov, Pavel S.; Oh, Taeyun; Olsen, Gary J.; Olson, Robert; Parrello, Bruce; Pasternak, Shiran; Pearson, Erik; Poon, Sarah; Price, Gavin A.; Ramakrishnan, S.; Ranjan, Priya; Ronald, Pamela C.; Schatz, Michael C.; Seaver, Samuel M. D.; Shukla, Maulik; Sutormin, Roman A.; Syed, Mustafa; Thomason, James; Tintle, Nathan L.; Wang, Daifeng; Xia, Fangfang; Yoo, Hyunseung; Yoo, Shinjae; Yu, D.

doi:10.1038/nbt.4163

Cited by 960 publications

(687 citation statements)

References 16 publications

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“…For all RNA-seq experiments, analyses were performed through a combination of KBase [247] and custom jupyter notebook-based methods. Briefly, Illumina reads were trimmed using Trimmomatic [248] v0.…”

Section: Rna-seq Data Analysismentioning

confidence: 99%

High-throughput mapping of the phage resistance landscape inE. coli

Mutalik¹,

Adler²,

Rishi³

et al. 2020

Preprint

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Bacteriophages (phages) are critical players in the dynamics and function of microbial communities and drive processes as diverse as global biogeochemical cycles and human health. Phages tend to be predators finely tuned to attack specific hosts, even down to the strain level, which in turn defend themselves using an array of mechanisms. However, to date, efforts to rapidly and comprehensively identify bacterial host factors important in phage infection and resistance have yet to be fully realized. Here, we globally map the host genetic determinants involved in resistance to 14 phylogenetically diverse double-stranded DNA phages using two model Escherichia coli strains (K-12 and BL21) with known sequence divergence to demonstrate strain-specific differences. Using genome-wide loss-of-function and gain-of-function genetic technologies, we are able to confirm previously described phage receptors as well as uncover a number of previously unknown host factors that confer resistance to one or more of these phages. We uncover differences in resistance factors that strongly align with the susceptibility of K-12 and BL21 to specific phage. We also identify both phage specific mechanisms, such as the unexpected role of cyclic-di-GMP in host sensitivity to phage N4, and more generic defenses, such as the overproduction of colanic acid capsular polysaccharide that defends against a wide array of phages. Our results indicate that host responses to phages can occur via diverse cellular mechanisms. Our systematic and highthroughput genetic workflow to characterize phage-host interaction determinants can be extended to diverse bacteria to generate datasets that allow predictive models of how phagemediated selection will shape bacterial phenotype and evolution. The results of this study and future efforts to map the phage resistance landscape will lead to new insights into the coevolution of hosts and their phage, which can ultimately be used to design better phage therapeutic treatments and tools for precision microbiome engineering.3 Introduction:

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Section: Rna-seq Data Analysismentioning

confidence: 99%

High-throughput mapping of the phage resistance landscape inE. coli

Mutalik¹,

Adler²,

Rishi³

et al. 2020

Preprint

Self Cite

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“…Furthermore, the ability of gapseq to predict bacterial phenotypes was compared to two other commonly used automatic reconstruction methods, namely, CarveMe [50] and ModelSEED [34] (Table 1). ModelSEED is also implemented in the KBASE online software platform [42].…”

Section: Discussionmentioning

confidence: 99%

gapseq: Informed prediction of bacterial metabolic pathways and reconstruction of accurate metabolic models

Zimmermann

Kaleta

Waschina

2020

Preprint

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Microbial metabolic processes greatly impact ecosystem functioning and the physiology of multi-cellular host organisms. The inference of metabolic capabilities and phenotypes from genome sequences with the help of prior biomolecular knowledge stored in online databases remains a major challenge in systems biology. Here, we present gapseq: a novel tool for automated pathway prediction and metabolic network reconstruction from microbial genome sequences. gapseq combines databases of reference protein sequences (UniProt, TCDB), in tandem with pathway and reaction databases (MetaCyc, KEGG, ModelSEED). This enables the statistical prediction of an organism's metabolic capabilities from sequence homology and pathway topology criteria. By incorporating a novel LP-based gap-filling algorithm, gapseq facilitates the construction of genomescale metabolic models that are suitable for metabolic phenotype predictions by using constraint-based flux analysis. We validated gapseq by comparing predictions to experimental data for more than 1, 000 bacterial organisms comprising over 10, 000 phenotypic traits that include enzyme activity, energy sources, fermentation products, and gene essentiality. This large-scale phenotypic trait prediction test showed, that gapseq yields an overall accuracy of 80% and thereby outperforming other commonly used reconstruction tools. Furthermore, we illustrate the application of gapseq-reconstructed models to simulate biochemical interactions between microorganisms in multi-species communities. Altogether, gapseq (https://github.com/jotech/gapseq) is a new method that improves the predictive potential of automated metabolic network reconstructions and further increases their applicability in biotechnological, ecological, and medical research.

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“…For instance, while all test models as well as iJR904/iAF1260 have been prokaryotic systems, there is no reason why this approach would not similarly work in a eukaryotic organism. Further, the Supplemental Files, as well as the mathematical methods, are flexible enough that any system of reaction and metabolite identifiers, such as KEGG (Kanehisa et al, 2017), MetaCyc (Caspi et al, 2014), BIGG (King et al, 2016), K-Base (Arkin et al, 2018), or custom identifiers, may be used for metabolites and/or reactions, making this tool applicable to a wide variety of existing GSM-building methods. This has been demonstrated in that KEGG identifiers were used in the test models, whereas BIGG identifiers were used by the iJR904 and iAF1260 models (Reed et al, 2003) (Feist et al, 2007b).…”

Section: Discussionmentioning

confidence: 99%

“…In concert with advances in optimization solvers and available computational power, these methods, the TFP, CPs, and their modified versions, will provide an alternative holistic method of model curation. At present, those model-building tools with high computational power at their disposal, such as ModelSeed (Overbeek et al, 2005) and K-Base (Arkin et al, 2018), may well be able to implement OptFill and its components for large GSMs to improve their automated curation capabilities.…”

Section: Discussionmentioning

confidence: 99%

A Novel Optimization-Based Tool to Automate Infeasible Cycle-Free Gapfilling of Genome-Scale Metabolic Models

Schroeder

Saha

2019

Preprint

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ARTICLE TYPE: Math | BioIn Brief Stoichiometric models of metabolism are useful in studying metabolic interactions in biological systems, but are labor-intensive to create, particularly when addressing gaps or cycles in metabolic reconstruction process. Introduced here is a novel tool, OptFill, which can be used to address both gaps and cycles in model reconstruction, increasing automation. Highlights• This work presents an alternative to state-of-the-art methods for gapfilling.• Unlike current methods, this method is holistic and infeasible cycle free.• This method is applied to three test and one published model.• This method might also be used to address infeasible cycling. SUMMARYStoichiometric metabolic modeling, particularly Genome-Scale Models (GSMs), is now an indispensable tool for systems biology. The model reconstruction process typically involves collecting information from public databases; however, incomplete systems knowledge leaves gaps in any reconstruction. Current tools for addressing gaps use databases of biochemical functionalities to address gaps on a per-metabolite basis and can provide multiple solutions, but cannot avoid Thermodynamically Infeasible Cycles (TICs), invariably requiring lengthy manual curation. To address these limitations, this work introduces an optimization-based multi-step method named OptFill which performs TIC-avoiding whole-model gapfilling. We applied OptFill to three fictional prokaryotic models of increasing sizes and to a published GSM of Escherichia coli, iJR904. This application resulted in holistic and infeasible cycle free gapfilling solutions. Part of OptFill can, in addition, be adapted to automate inherent TICs identification in any GSM, such as iJR904. Overall, OptFill can address critical issues in automated development of highquality GSMs.Manuscript The use of systems biology in uni-and multi-cellular organisms to engineer or enhance desirable phenotypes and study system-wide metabolic processes in microbes, plants, and animal systems, is well-established and capable of affecting the lives of millions of individuals, such as in the case of artemisinin production in yeast or enhancing the nutritional value of agricultural products (Beyer et al., 2002) (Hall, Brouwer andFitzgerald, 2008). As opposed to traditional qualitative approaches, computational approaches based on stoichiometric Genome-Scale Models (GSMs) of metabolism can be used to predict non-intuitive genetic interventions (Srinivasan, Cluett and Mahadevan,

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KBase: The United States Department of Energy Systems Biology Knowledgebase

Cited by 960 publications

References 16 publications

High-throughput mapping of the phage resistance landscape inE. coli

High-throughput mapping of the phage resistance landscape inE. coli

gapseq: Informed prediction of bacterial metabolic pathways and reconstruction of accurate metabolic models

A Novel Optimization-Based Tool to Automate Infeasible Cycle-Free Gapfilling of Genome-Scale Metabolic Models

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