ProbAnnoWeb and ProbAnnoPy: probabilistic annotation and gap-filling of metabolic reconstructions.
Document Type
Article
Publication Date
5-1-2018
Publication Title
Bioinformatics (Oxford, England)
Keywords
Genome; Likelihood Functions; Software
Abstract
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.
Department
Institute for Systems Biology
Recommended Citation
King, Brendan; Farrah, Terry; Richards, Matthew A; Mundy, Michael; Simeonidis, Evangelos; and Price, Nathan D, "ProbAnnoWeb and ProbAnnoPy: probabilistic annotation and gap-filling of metabolic reconstructions." (2018). Articles, Abstracts, and Reports. 1393.
https://digitalcommons.providence.org/publications/1393
