Computational biology approaches for mapping transcriptional regulatory networks,Computational and Structural Biotechnology Journal

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Computational biology approaches for mapping transcriptional regulatory networks
Computational and Structural Biotechnology Journal ( IF 4.4 ) Pub Date : 2021-08-21 , DOI: 10.1016/j.csbj.2021.08.028
Violaine Saint-André ₁

Affiliation

Transcriptional Regulatory Networks (TRNs) are mainly responsible for the cell-type- or cell-state-specific expression of gene sets from the same DNA sequence. However, so far there are no precise maps of TRNs available for each cell-type or cell-state, and no ideal tool to map those networks clearly and in full from biological samples. In this review, major approaches and tools to map TRNs from high-throughput data are presented, depending on the type of methods or data used to infer them, and their advantages and limitations are discussed. After summarizing the main principles defining the topology and structure–function relationships in TRNs, an overview of the extensive work done to map TRNs from bulk transcriptomic data will be presented by type of methodological approach. Most recent modellings of TRNs using other types of molecular data or integrating different data types, including single-cell RNA-sequencing and chromatin information, will then be discussed, before briefly concluding with improvements expected to come in the field.

中文翻译：

绘制转录调控网络的计算生物学方法

转录调控网络 (TRN) 主要负责来自相同 DNA 序列的基因集的细胞类型或细胞状态特异性表达。然而，到目前为止，还没有针对每种细胞类型或细胞状态的 TRN 精确图谱，也没有理想的工具可以从生物样本中清晰、完整地绘制这些网络。在这篇综述中，根据用于推断 TRN 的方法或数据的类型，介绍了从高通量数据映射 TRN 的主要方法和工具，并讨论了它们的优点和局限性。在总结了定义 TRN 中的拓扑和结构-功能关系的主要原则后，将按方法学方法的类型概述从大量转录组数据中映射 TRN 所做的大量工作。然后将讨论使用其他类型的分子数据或集成不同数据类型（包括单细胞 RNA 测序和染色质信息）的最新 TRN 建模，然后简要总结该领域预计将出现的改进。

更新日期：2021-08-21

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