TY - JOUR
T1 - Shiny GATOM
T2 - omics-based identification of regulated metabolic modules in atom transition networks
AU - Emelianova, Mariia
AU - Gainullina, Anastasiia
AU - Poperechnyi, Nikolay
AU - Loboda, Alexander
AU - Artyomov, Maxim
AU - Sergushichev, Alexey
N1 - Publisher Copyright:
© 2022 The Author(s). Published by Oxford University Press on behalf of Nucleic Acids Research.
PY - 2022/7/5
Y1 - 2022/7/5
N2 - Multiple high-throughput omics techniques provide different angles on systematically quantifying and studying metabolic regulation of cellular processes. However, an unbiased analysis of such data and, in particular, integration of multiple types of data remains a challenge. Previously, for this purpose we developed GAM web-service for integrative metabolic network analysis. Here we describe an updated pipeline GATOM and the corresponding web-service Shiny GATOM, which takes as input transcriptional and/or metabolomic data and finds a metabolic subnetwork most regulated between the two conditions of interest. GATOM features a new metabolic network topology based on atom transition, which significantly improves interpretability of the analysis results. To address computational challenges arising with the new network topology, we introduce a new variant of the maximum weight connected subgraph problem and provide a corresponding exact solver. To make the used networks up-to-date we upgraded the KEGG-based network construction pipeline and developed one based on the Rhea database, which allows analysis of lipidomics data. Finally, we simplified local installation, providing R package mwcsr for solving relevant graph optimization problems and R package gatom, which implements the GATOM pipeline. The web-service is available at https://ctlab.itmo.ru/shiny/gatom and https://artyomovlab.wustl.edu/shiny/gatom.
AB - Multiple high-throughput omics techniques provide different angles on systematically quantifying and studying metabolic regulation of cellular processes. However, an unbiased analysis of such data and, in particular, integration of multiple types of data remains a challenge. Previously, for this purpose we developed GAM web-service for integrative metabolic network analysis. Here we describe an updated pipeline GATOM and the corresponding web-service Shiny GATOM, which takes as input transcriptional and/or metabolomic data and finds a metabolic subnetwork most regulated between the two conditions of interest. GATOM features a new metabolic network topology based on atom transition, which significantly improves interpretability of the analysis results. To address computational challenges arising with the new network topology, we introduce a new variant of the maximum weight connected subgraph problem and provide a corresponding exact solver. To make the used networks up-to-date we upgraded the KEGG-based network construction pipeline and developed one based on the Rhea database, which allows analysis of lipidomics data. Finally, we simplified local installation, providing R package mwcsr for solving relevant graph optimization problems and R package gatom, which implements the GATOM pipeline. The web-service is available at https://ctlab.itmo.ru/shiny/gatom and https://artyomovlab.wustl.edu/shiny/gatom.
UR - http://www.scopus.com/inward/record.url?scp=85134372588&partnerID=8YFLogxK
U2 - 10.1093/nar/gkac427
DO - 10.1093/nar/gkac427
M3 - Article
C2 - 35639928
AN - SCOPUS:85134372588
SN - 0305-1048
VL - 50
SP - W690-W696
JO - Nucleic acids research
JF - Nucleic acids research
IS - W1
ER -