PT  - JOURNAL ARTICLE
AU  - Brendan King
AU  - Terry Farrah
AU  - Matthew Richards
AU  - Michael Mundy
AU  - Evangelos Simeonidis
AU  - Nathan D. Price
TI  - ProbAnnoWeb and ProbAnnoPy: probabilistic annotation and gap-filling of metabolic reconstructions
AID  - 10.1101/151258
DP  - 2017 Jan 01
TA  - bioRxiv
PG  - 151258
4099  - http://biorxiv.org/content/early/2017/06/16/151258.short
4100  - http://biorxiv.org/content/early/2017/06/16/151258.full
AB  - 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’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