MAVEN: compound mechanism of action analysis and visualisation using transcriptomics and compound structure data in R/Shiny.
MAVEN: compound mechanism of action analysis and visualisation using transcriptomics and compound structure data in R/Shiny.
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DOI：
10.1186/s12859-023-05416-8
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发表时间：
2023-09-15
影响因子：
3
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中科院分区：
文献类型：
--
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了解化合物的作用机制（MoA）通常是药物发现的一个具有挑战性但同样重要的方面，可以帮助提高其有效性和安全性。帮助阐明MoA的计算方法通常要么旨在预测直接的药物靶点，要么试图理解被调节的下游途径或信号蛋白。这种方法通常需要丰富的编码经验，并且结果通常为进一步的计算处理而优化，这使得湿实验室科学家难以执行、解释和从中得出假设。为了解决这个问题，我们在这项工作中提出了MAVEN（作用可视化和浓缩机制），这是一个R/Shiny应用程序，它允许基于gui的基于化学结构的药物靶标预测，结合基于因果蛋白-蛋白质相互作用和转录组摄动特征的因果推理。该应用程序计算输入化合物的作用机制的系统级视图。这被可视化为通过推断的信号蛋白将预测或已知的靶标连接到调节的转录因子的子网络。该工具包括对MSigDB基因集集合的选择，以在结果网络上进行途径富集，并且还允许研究人员上传自定义基因集。因此，对于没有广泛生物信息学或化学信息学知识的研究人员来说，MAVEN是一个用户友好，灵活的工具，可以产生可解释的化合物作用机制假设。MAVEN是一个完全开源的工具，可在https://github.com/laylagerami/MAVEN上获得，并提供安装在Docker或Singularity容器中的选项。完整的文档，包括关于示例数据的教程，可在https://laylagerami.github.io/MAVEN上获得。
Understanding the Mechanism of Action (MoA) of a compound is an often challenging but equally crucial aspect of drug discovery that can help improve both its efficacy and safety. Computational methods to aid MoA elucidation usually either aim to predict direct drug targets, or attempt to understand modulated downstream pathways or signalling proteins. Such methods usually require extensive coding experience and results are often optimised for further computational processing, making them difficult for wet-lab scientists to perform, interpret and draw hypotheses from. To address this issue, we in this work present MAVEN (Mechanism of Action Visualisation and Enrichment), an R/Shiny app which allows for GUI-based prediction of drug targets based on chemical structure, combined with causal reasoning based on causal protein–protein interactions and transcriptomic perturbation signatures. The app computes a systems-level view of the mechanism of action of the input compound. This is visualised as a sub-network linking predicted or known targets to modulated transcription factors via inferred signalling proteins. The tool includes a selection of MSigDB gene set collections to perform pathway enrichment on the resulting network, and also allows for custom gene sets to be uploaded by the researcher. MAVEN is hence a user-friendly, flexible tool for researchers without extensive bioinformatics or cheminformatics knowledge to generate interpretable hypotheses of compound Mechanism of Action. MAVEN is available as a fully open-source tool at https://github.com/laylagerami/MAVEN with options to install in a Docker or Singularity container. Full documentation, including a tutorial on example data, is available at https://laylagerami.github.io/MAVEN.
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影响因子：
8.6
作者：
Aniceto N;Freitas AA;Bender A;Ghafourian T
通讯作者：
Ghafourian T
影响因子：
14.9
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Csabai L;Fazekas D;Kadlecsik T;Szalay-Bekő M;Bohár B;Madgwick M;Módos D;Ölbei M;Gul L;Sudhakar P;Kubisch J;Oyeyemi OJ;Liska O;Ari E;Hotzi B;Billes VA;Molnár E;Földvári-Nagy L;Csályi K;Demeter A;Pápai N;Koltai M;Varga M;Lenti K;Farkas IJ;Türei D;Csermely P;Vellai T;Korcsmáros T
通讯作者：
Korcsmáros T
影响因子：
14.9
作者：
Kim S;Thiessen PA;Bolton EE;Chen J;Fu G;Gindulyte A;Han L;He J;He S;Shoemaker BA;Wang J;Yu B;Zhang J;Bryant SH
通讯作者：
Bryant SH
影响因子：
6
作者：
Carracedo-Reboredo P;Liñares-Blanco J;Rodríguez-Fernández N;Cedrón F;Novoa FJ;Carballal A;Maojo V;Pazos A;Fernandez-Lozano C
通讯作者：
Fernandez-Lozano C
影响因子：
4.3
作者：
Canfield, Kaleigh;Li, Jiaqi;Kurokawa, Manabu
通讯作者：
Kurokawa, Manabu