RT Journal Article
SR Electronic
T1 ProbAnnoWeb and ProbAnnoPy: probabilistic annotation and gap-filling of metabolic reconstructions
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 151258
DO 10.1101/151258
A1 Brendan King
A1 Terry Farrah
A1 Matthew Richards
A1 Michael Mundy
A1 Evangelos Simeonidis
A1 Nathan D. Price
YR 2017
UL http://biorxiv.org/content/early/2017/06/16/151258.abstract
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