MODalyseR——一种用于推断疾病模块枢纽调节因子的新型软件，识别出一个得到独立表达定量性状位点（eQTL）数据支持的假定多发性硬化症调节因子。

MODalyseR-a novel software for inference of disease module hub regulators identified a putative multiple sclerosis regulator supported by independent eQTL data.

作者信息

de Weerd Hendrik A, Åkesson Julia, Guala Dimitri, Gustafsson Mika, Lubovac-Pilav Zelmina

机构信息

School of Bioscience, Systems Biology Research Center, University of Skövde, Skövde 541 45, Sweden.

Department of Physics, Chemistry and Biology, Linköping University, Linköping 581 83, Sweden.

出版信息

Bioinform Adv. 2022 Jan 25;2(1):vbac006. doi: 10.1093/bioadv/vbac006. eCollection 2022.

DOI:10.1093/bioadv/vbac006

PMID:36699378

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9710626/

Abstract

MOTIVATION

Network-based disease modules have proven to be a powerful concept for extracting knowledge about disease mechanisms, predicting for example disease risk factors and side effects of treatments. Plenty of tools exist for the purpose of module inference, but less effort has been put on simultaneously utilizing knowledge about regulatory mechanisms for predicting disease module hub regulators.

RESULTS

We developed MODalyseR, a novel software for identifying disease module regulators and reducing modules to the most disease-associated genes. This pipeline integrates and extends previously published software packages MODifieR and ComHub and hereby provides a user-friendly network medicine framework combining the concepts of disease modules and hub regulators for precise disease gene identification from transcriptomics data. To demonstrate the usability of the tool, we designed a case study for multiple sclerosis that revealed IKZF1 as a promising hub regulator, which was supported by independent ChIP-seq data.

AVAILABILITY AND IMPLEMENTATION

MODalyseR is available as a Docker image at https://hub.docker.com/r/ddeweerd/modalyser with user guide and installation instructions found at https://gustafsson-lab.gitlab.io/MODalyseR/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at online.

摘要

动机

基于网络的疾病模块已被证明是一种强大的概念，可用于提取有关疾病机制的知识，例如预测疾病风险因素和治疗的副作用。存在大量用于模块推断的工具，但在同时利用调控机制知识来预测疾病模块枢纽调节因子方面投入的精力较少。

结果

我们开发了MODalyseR，这是一种用于识别疾病模块调节因子并将模块精简为与疾病关联度最高的基因的新型软件。该流程整合并扩展了先前发布的软件包MODifieR和ComHub，从而提供了一个用户友好的网络医学框架，将疾病模块和枢纽调节因子的概念相结合，以便从转录组学数据中精确识别疾病基因。为了证明该工具的可用性，我们针对多发性硬化症设计了一个案例研究，结果显示IKZF1是一个有前景的枢纽调节因子，独立的染色质免疫沉淀测序（ChIP-seq）数据也支持这一结果。