Citations 2
O3‐03‐01: MECHANISTIC AND DIRECTIONAL TRANSCRIPTIONAL REGULATORY NETWORKS IN ALZHEIMER'S DISEASE
Funk, Cory C. , Richards, Matthew A., Shannon, Paul , Robinson, Max , Allen, Mariet , Carrasquillo, Minerva M. , Chakrabarty, Paramita , Jung, Segun, Ertekin‐Taner, Nilüfer , Golde, Todd E. , Ament, Seth A. , Donovan-Maiye, Rory , Rappaport, Noa , Carrasquillo, Minerva M. , McFarland, Karen N. , Rodriguez, Alex C. , Hood, Leroy , Foster, Ian T , Madduri, Ravi , Price, Nathan D.
Alzheimer's & Dementia | October 22, 2018 | Volume: 14 | Issue: 7S_Part_19 | Pages: No page available
ProbAnnoWeb and ProbAnnoPy: probabilistic annotation and gap-filling of metabolic reconstructions
King, Brendan, Farrah, Terry, Richards, Matthew A., Mundy, Michael , Simeonidis, Evangelos , Price, Nathan D. , Price, Nathan D
Bioinformatics | December 15, 2017 | Volume: 34 | Issue: 9 | Pages: 1594-1596
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. Supplementary information Supplementary data are available at Bioinformatics online.