Comparative Analysis and Data Provenance for 1,113 Bacterial Genome Assemblies

Yarmosh, David; Lopera, Juan; Puthuveetil, Nikhita; Combs, Patrick Ford; Reese, Amy L; Tabron, Corina; Pierola, Amanda E; Duncan, James M.; Greenfield, Samuel R; Marlow, Robert; King, Stephen M.; Riojas, Marco A.; Bagnoli, John; Benton, Briana; Jacobs, Jonathan L.

doi:10.1128/msphere.00077-22

Cited by 8 publications

(10 citation statements)

References 49 publications

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“…The treatments containing only U- 12 C Glucose or U- 12 C Glycerol yielded no previously undescribed metabolic phenotypes. We therefore focused our analysis on growth media containing both carbon sources: U- 12 C Glucose and U- 12 C Glycerol, U- 12 C Glucose and U- 13 C Glycerol, or U- 13 C Glucose and U- compositions, enabling the determination of the isotope distribution within the cell during growth.…”

Section: Microbial Growth and Sample Preparation Thementioning

confidence: 87%

“…Although this strategy works well in species that are closely related to these model organisms, it is less effective when applied to species that are highly divergent from the original model . Mismatches due to poor homology result in missing enzymes in the metabolic network, which, without further data refinement, can be misinterpreted as metabolic deficiencies. , This is a critical problem for understanding the evolution of microbes and for making inferences about the metabolic architecture of nonmodel organisms.…”

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confidence: 99%

“… 10 Mismatches due to poor homology result in missing enzymes in the metabolic network, which, without further data refinement, can be misinterpreted as metabolic deficiencies. 11 , 12 This is a critical problem for understanding the evolution of microbes and for making inferences about the metabolic architecture of nonmodel organisms.…”

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confidence: 99%

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MINNO: An Open Source Software for Refining Metabolic Networks and Investigating Complex Network Activity Using Empirical Metabolomics Data

Mandwal,

Bishop,

Castellanos

et al. 2024

Anal. Chem.

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Metabolomics is a powerful tool for uncovering biochemical diversity in a wide range of organisms. Metabolic network modeling is commonly used to frame metabolomics data in the context of a broader biological system. However, network modeling of poorly characterized nonmodel organisms remains challenging due to gene homology mismatches which lead to network architecture errors. To address this, we developed the Metabolic Interactive Nodular Network for Omics (MINNO), a web-based mapping tool that uses empirical metabolomics data to refine metabolic networks. MINNO allows users to create, modify, and interact with metabolic pathway visualizations for thousands of organisms, in both individual and multispecies contexts. Herein, we illustrate the use of MINNO in elucidating the metabolic networks of understudied species, such as those of the Borrelia genus, which cause Lyme and relapsing fever diseases. Using a hybrid genomics-metabolomics modeling approach, we constructed species-specific metabolic networks for threeBorrelia species. Using these empirically refined networks, we were able to metabolically differentiate these species via their nucleotide metabolism, which cannot be predicted from genomic networks. Additionally, using MINNO, we identified 18 missing reactions from the KEGG database, of which nine were supported by the primary literature. These examples illustrate the use of metabolomics for the empirical refining of genetically constructed networks and show how MINNO can be used to study nonmodel organisms.

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Section: Microbial Growth and Sample Preparation Thementioning

confidence: 87%

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confidence: 99%

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MINNO: An Open Source Software for Refining Metabolic Networks and Investigating Complex Network Activity Using Empirical Metabolomics Data

Mandwal,

Bishop,

Castellanos

et al. 2024

Anal. Chem.

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“…Validation of the fungal GAMBIT database using the ATCC Mycology Collection genomes was performed using the Gambit_ Query workflow developed by Theiagen Genomics on Terra. 1 All available fungal genomes were downloaded from the ATCC genome 1 https://github.com/theiagen/public_health_bioinformatics/blob/ PHB-v0.1.0-theiaeuk-manuscript/workflows/standalone_modules/wf_gambit_ query.wdl portal on 2023-03-08 (46)(47)(48). ATCC genomes downloaded from the ATCC genome portal were used exclusively for testing and were not included in the GAMBIT fungal database.…”

Section: Atcc Mycology Genomesmentioning

confidence: 99%

TheiaEuk: a species-agnostic bioinformatics workflow for fungal genomic characterization

Ambrosio,

Scribner,

Wright

et al. 2023

Front. Public Health

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IntroductionThe clinical incidence of antimicrobial-resistant fungal infections has dramatically increased in recent years. Certain fungal pathogens colonize various body cavities, leading to life-threatening bloodstream infections. However, the identification and characterization of fungal isolates in laboratories remain a significant diagnostic challenge in medicine and public health. Whole-genome sequencing provides an unbiased and uniform identification pipeline for fungal pathogens but most bioinformatic analysis pipelines focus on prokaryotic species. To this end, TheiaEuk_Illumina_PE_PHB (TheiaEuk) was designed to focus on genomic analysis specialized to fungal pathogens.MethodsTheiaEuk was designed using containerized components and written in the workflow description language (WDL) to facilitate deployment on the cloud-based open bioinformatics platform Terra. This species-agnostic workflow enables the analysis of fungal genomes without requiring coding, thereby reducing the entry barrier for laboratory scientists. To demonstrate the usefulness of this pipeline, an ongoing outbreak of C. auris in southern Nevada was investigated. We performed whole-genome sequence analysis of 752 new C. auris isolates from this outbreak. Furthermore, TheiaEuk was utilized to observe the accumulation of mutations in the FKS1 gene over the course of the outbreak, highlighting the utility of TheiaEuk as a monitor of emerging public health threats when combined with whole-genome sequencing surveillance of fungal pathogens.ResultsA primary result of this work is a curated fungal database containing 5,667 unique genomes representing 245 species. TheiaEuk also incorporates taxon-specific submodules for specific species, including clade-typing for Candida auris (C. auris). In addition, for several fungal species, it performs dynamic reference genome selection and variant calling, reporting mutations found in genes currently associated with antifungal resistance (FKS1, ERG11, FUR1). Using genome assemblies from the ATCC Mycology collection, the taxonomic identification module used by TheiaEuk correctly assigned genomes to the species level in 126/135 (93.3%) instances and to the genus level in 131/135 (97%) of instances, and provided zero false calls. Application of TheiaEuk to actual specimens obtained in the course of work at a local public health laboratory resulted in 13/15 (86.7%) correct calls at the species level, with 2/15 called at the genus level. It made zero incorrect calls. TheiaEuk accurately assessed clade type of Candida auris in 297/302 (98.3%) of instances.DiscussionTheiaEuk demonstrated effectiveness in identifying fungal species from whole genome sequence. It further showed accuracy in both clade-typing of C. auris and in the identification of mutations known to associate with drug resistance in that organism.

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“…ATCC has thus far produced over 4,000 high-quality or closed reference genomes for microbes within the ATCC collection, all under an ISO 9000 controlled quality assurance framework. This presents some dilemmas, however, as the NCBI Assembly database includes (for example) genome references for bacterial strains that have serious gaps in metadata or include substantial errors in their genome assembly when compared to the ATCC Genome Portal reference (17). Reducing discrepancies between genome references for the “same” organism can be aided by improving our ability to include crucial metadata about the origins of and means by which each genome reference is created in-line with the sequence data itself.…”

Section: Introductionmentioning

confidence: 99%

FAIR Header Reference genome: A TRUSTworthy standard

Wright,

Wilkinson,

Mungall

et al. 2023

Preprint

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The lack of interoperable data standards among reference genome data-sharing platforms inhibits cross-platform analysis while increasing the risk of data provenance loss. Here, we describe the FAIR-bioHeaders Reference genome (FHR), a metadata standard guided by the principles of Findability, Accessibility, Interoperability, and Reuse (FAIR) in addition to the principles of Transparency, Responsibility, User focus, Sustainability, and Technology (TRUST). The objective of FHR is to provide an extensive set of data serialisation methods and minimum data field requirements while still maintaining extensibility, flexibility, and expressivity in an increasingly decentralised genomic data ecosystem. The effort needed to implement FHR is low; FHR's design philosophy ensures easy implementation while retaining the benefits gained from recording both machine and human-readable provenance.

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Comparative Analysis and Data Provenance for 1,113 Bacterial Genome Assemblies

Cited by 8 publications

References 49 publications

MINNO: An Open Source Software for Refining Metabolic Networks and Investigating Complex Network Activity Using Empirical Metabolomics Data

MINNO: An Open Source Software for Refining Metabolic Networks and Investigating Complex Network Activity Using Empirical Metabolomics Data

TheiaEuk: a species-agnostic bioinformatics workflow for fungal genomic characterization

FAIR Header Reference genome: A TRUSTworthy standard

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