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SUMO-Binding Entities SUBEs as Tools for the Enrichment, Isolation, Identification, and Characterization of the SUMO Proteome in Liver Cancer

SUMO-Binding Entities SUBEs as Tools for the Enrichment, Isolation, Identification, and Characterization of the SUMO Proteome in Liver Cancer

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Bioinformatic Tools for Exploring the SUMO Gene Network: An Update.

Pedro Humberto Castro^1,2, Miguel Ângelo Santos^3,4,5, Alexandre Papadopoulos Magalhães^3,4,6

¹CIBIO, Centro de Investigação em Biodiversidade e Recursos Genéticos, InBIO Laboratório Associado, Campus de Vairão, Universidade do Porto, Vairão, Portugal. pedro1berto@cibio.up.pt.

Methods in Molecular Biology (Clifton, N.J.)

|November 22, 2022

Summary

Plant sumoylation research has advanced with over a thousand small ubiquitin-related modifier (SUMO) targets identified. This chapter updates the Arabidopsis thaliana SUMO gene network (SGN) to v4, providing tools for data analysis and in silico hypothesis generation.

Keywords:

Arabidopsis Bioinformatics Data mining Functional categorization Gene expression Gene network Posttranslational modification Small ubiquitin-related modifier (SUMO)

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Published on: November 2, 2017

Area of Science:

Molecular Biology
Genomics
Proteomics

Background:

Plant sumoylation research has significantly advanced due to high-throughput proteomic strategies.
Over one thousand small ubiquitin-related modifier (SUMO) targets have been identified in plants.
Previous iterations of the SUMO gene network (SGN) have been established.

Purpose of the Study:

To update the Arabidopsis thaliana SUMO gene network (SGN) to its fourth version (v4).
To provide a tutorial for interpreting and managing sumoylation-associated datasets.
To detail bioinformatic tools for in silico hypothesis generation in plant sumoylation research.

Main Methods:

Curating a comprehensive assembly of Arabidopsis thaliana genes functionally linked to sumoylation.
Updating the existing SUMO gene network (SGN) to v4.
Describing bioinformatic tools for sumoylation site prediction, comparative genomics, and gene network analysis.

Main Results:

The SUMO gene network (SGN) has been updated to v4.
A tutorial is provided for handling sumoylation datasets.
Bioinformatic tools for in silico analysis are detailed.

Conclusions:

The SGN v4 provides an updated resource for plant sumoylation research.
The provided tutorial and bioinformatic tools facilitate data interpretation and hypothesis generation.
This work supports further investigation into the roles of sumoylation in plants.