Open Peer Review Any reports and responses or comments on the article can be found at the end of the article. METHOD ARTICLE Elucidating genomic gaps using phenotypic profiles [version 1; peer review: 2 approved with reservations] Daniel A. Cuevas , Daniel Garza , Savannah E. Sanchez , Jason Rostron , Chris S. Henry , Veronika Vonstein , Ross A. Overbeek , Anca Segall , Forest Rohwer , Elizabeth A. Dinsdale , Robert A. Edwards 1-5 Computational Science Research Center, San Diego State University, San Diego, CA, 92182, USA Department of Computer Science, San Diego State University, San Diego, CA, 92182, USA Department of Biology, San Diego State University, San Diego, CA, 92182, USA Mathematics and Computer Science Division, Argonne National Laboratory, Argonne, IL, 60439, USA Fellowship for Interpretation of Genomes, Burr Ridge, IL, 60527, USA Environmental Microbiology Laboratory, Evandro Chagas Institute, Ananindeua-PA, Brazil Abstract Advances in genomic sequencing provide the ability to model the metabolism of organisms from their genome annotation. The bioinformatics tools developed to deduce gene function through homology-based methods are dependent on public databases; thus, novel discoveries are not readily extrapolated from current analysis tools with a homology dependence. Multi-phenotype Assay Plates (MAPs) provide a high-throughput method to profile bacterial phenotypes by growing bacteria in various growth conditions, simultaneously. More robust and accurate computational models can be constructed by coupling MAPs with current genomic annotation methods. is an online tool that analyzes PMAnalyzer bacterial growth curves from the MAP system which are then used to optimize metabolic models during growth simulations. Using in silico as a prototype, the Rapid Annotation using Subsystem Citrobacter sedlakii Technology (RAST) tool produced a model consisting of 1,367 enzymatic reactions. After the optimization, 44 reactions were added to, or modified within, the model. The model correctly predicted the outcome on 93% of growth experiments. 1 2,6 3 3 4 5 5 3 3 3 1-5 1 2 3 4 5 6 Reviewer Status Invited Reviewers version 2 published 17 Oct 2016 version 1 published 04 Sep 2014 1 2 report report report , Tel Aviv University, Tel Matthew A. Oberhardt Aviv, Israel 1 , Hope College, Holland, USA Aaron Best 2 04 Sep 2014, :210 ( First published: 3 ) https://doi.org/10.12688/f1000research.5140.1 17 Oct 2016, :210 ( Latest published: 3 ) https://doi.org/10.12688/f1000research.5140.2 v1 Page 1 of 25 F1000Research 2014, 3:210 Last updated: 16 MAY 2019