The creation of genome scale metabolic models lags far behind our ability to sequence genomes and thus has become a bottle neck in deriving value from genomic information. This bottleneck can be relieved by automated annotation and model production pipelines. Model [NB1] organisms, including E. coli and yeast, have strong and diverse communities devoted to continued improvement of genome annotation and model development. Certainly, the work done on these model organisms benefits the larger community of researchers studying genomes because many annotations are based on homology to these model systems, but photosynthetic species like algae or cyanobacteria have unique traits and metabolic functions which are not usually captured using by comparing DNA sequences to heterotrophic bacteria. Automated model annotation algorithms exist for photosynthetic organisms, but these pipeline focus on higher plants and do not perform well when used for algae or cyanobacteria. Instead, these photosynthetic microorganisms currently require extensive manual curation of their unique features, which is an arduous process that requires significant time and effort. In order to reduce repetitive curation tasks and make better use of existing genome scale models, we have developed an automated pipeline focused specifically on rapid annotation of photosynthetic microorganisms. Our method leverages previous genomic annotations and metabolic models, reducing replication of manual tasks, by standardizing reaction names and metabolic abbreviations. Because our pipeline merges together disparate models, it serves as a unification point across the field. The output of our algorithm is a genome-scale metabolic model which can be used for constraints based metabolic modeling. This talk will focus on the algorithm used to construct the models, and provide comparisons between our methods and others used in the field.

RAPS: Rapid annotation of photosynthetic systems

2019 Spring National Meeting

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.