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. 2023 Apr 3;39(4):btad110.
doi: 10.1093/bioinformatics/btad110.

kb_DRAM: annotation and metabolic profiling of genomes with DRAM in KBase

Affiliations

kb_DRAM: annotation and metabolic profiling of genomes with DRAM in KBase

Michael Shaffer et al. Bioinformatics. .

Erratum in

Abstract

Microbial genome annotation is the process of identifying structural and functional elements in DNA sequences and subsequently attaching biological information to those elements. DRAM is a tool developed to annotate bacterial, archaeal, and viral genomes derived from pure cultures or metagenomes. DRAM goes beyond traditional annotation tools by distilling multiple gene annotations to genome level summaries of functional potential. Despite these benefits, a downside of DRAM is the requirement of large computational resources, which limits its accessibility. Further, it did not integrate with downstream metabolic modeling tools that require genome annotation. To alleviate these constraints, DRAM and the viral counterpart, DRAM-v, are now available and integrated with the freely accessible KBase cyberinfrastructure. With kb_DRAM users can generate DRAM annotations and functional summaries from microbial or viral genomes in a point-and-click interface, as well as generate genome-scale metabolic models from DRAM annotations.

Availability and implementation: For kb_DRAM users, the kb_DRAM apps on KBase can be found in the catalog at https://narrative.kbase.us/#catalog/modules/kb_DRAM. For kb_DRAM users, a tutorial workflow with all documentation is available at https://narrative.kbase.us/narrative/129480. For kb_DRAM developers, software is available at https://github.com/shafferm/kb_DRAM.

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Figures

Figure 1
Figure 1
Annotation and modeling performance on three genomes. (A) Number of modelSEED reactions assigned by each tool on each genome. (B) Heatmap showing number of gap filled reactions required for growth on glucose minimal media per pathway. Only pathways with ≥5 gap fills required by at least one annotation set shown. (C) Number of auxotrophies predicted present based on annotations from each tool for each genome

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