ProbAnnoWeb and ProbAnnoPy: Probabilistic annotation and gap-filling of metabolic reconstructions

Brendan King, Terry Farrah, Matthew A. Richards, Michael Mundy, Evangelos Simeonidis, Nathan D. Price

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


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 informationSupplementary dataare available at Bioinformatics online.

Original languageEnglish (US)
Pages (from-to)1594-1596
Number of pages3
Issue number9
StatePublished - May 1 2018
Externally publishedYes

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics


Dive into the research topics of 'ProbAnnoWeb and ProbAnnoPy: Probabilistic annotation and gap-filling of metabolic reconstructions'. Together they form a unique fingerprint.

Cite this