ProbAnnoWeb and ProbAnnoPy: probabilistic annotation and gap-filling of metabolic reconstructions.

Document Type


Publication Date


Publication Title

Bioinformatics (Oxford, England)


Genome; Likelihood Functions; Software


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, and as a local python package implementation (ProbAnnoPy) at

Contact: or

Supplementary information: Supplementary data are available at Bioinformatics online.


Institute for Systems Biology