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Updated: Feb 16, 2026
A Web Tool for Generating High Quality Machine-readable Biological Pathways
Published on: February 8, 2017
ProbAnnoWeb and ProbAnnoPy: probabilistic annotation and gap-filling of metabolic reconstructions
Brendan King1, Terry Farrah1, Matthew A Richards1
1Institute for Systems Biology, Seattle, WA 98102, USA.
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) at probannoweb.systemsbiology.net, and as a local python package implementation (ProbAnnoPy) at github.com/PriceLab/probannopy.
Contact:
evangelos.simeonidis@systemsbiology.org or nathan.price@systemsbiology.org.
Supplementary Information:
Supplementary data are available at Bioinformatics online.
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