@article {King151258, author = {Brendan King and Terry Farrah and Matthew Richards and Michael Mundy and Evangelos Simeonidis and Nathan D. Price}, title = {ProbAnnoWeb and ProbAnnoPy: probabilistic annotation and gap-filling of metabolic reconstructions}, elocation-id = {151258}, year = {2017}, doi = {10.1101/151258}, publisher = {Cold Spring Harbor Laboratory}, abstract = {Summary Gap-filling is a necessary step to produce quality genome-scale metabolic reconstructions capable of flux-balance simulation. Most available gap-filling tools use an organism-agnostic approach, where reactions are selected from a database to fill gaps without consideration of the target organism. Conversely, our likelihood based gap-filling with probabilistic annotations selects candidate reactions based on a likelihood score derived specifically from the target organism{\textquoteright}s genome. Here, we present two new implementations of probabilistic annotation and likelihood based gap-filling: a web service called ProbAnnoWeb, and a standalone python package called ProbAnnoPy.Availability and Implementation Our tools are available as a web service with no installation needed (ProbAnnoWeb), available at http://probannoweb.systemsbiology.net, and as a local python package implementation (ProbAnnoPy), available for download at http://github.com/PriceLab/probannopy.Contact Evangelos.Simeonidis{at}systemsbiology.org; Nathan.Price{at}systemsbiology.org}, URL = {https://www.biorxiv.org/content/early/2017/06/16/151258}, eprint = {https://www.biorxiv.org/content/early/2017/06/16/151258.full.pdf}, journal = {bioRxiv} }