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

Abstract

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 informationSupplementary dataare available at Bioinformatics online.

Original languageEnglish (US)
Pages (from-to)1594-1596
Number of pages3
JournalBioinformatics
Volume34
Issue number9
DOIs
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

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