灵活的调控网络建模改进了转录因子活性估计。

IF 3.5 2区 生物学 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Chen Chen, Megha Padi
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引用次数: 0

摘要

转录调控在决定细胞命运和疾病方面起着至关重要的作用,然而从基因表达数据中推断关键调控因子仍是一项重大挑战。现有的转录因子(TF)活性估算方法通常依赖于静态的TF-基因相互作用数据库,无法适应不同细胞类型和疾病条件下调控机制的变化。在这里,我们提出了一种新算法--利用基因表达和调控数据进行转录推断(TIGER)--它克服了这些局限性,灵活地对激活和抑制事件建模,通过稀疏贝叶斯先验法提高基本边的权重,将无关边缩小为零,并同时估计TF活性水平和底层调控网络的变化。当应用于酵母和癌症TF基因敲除数据集时,TIGER在预测准确性方面优于同类方法。此外,我们将 TIGER 应用于组织和细胞类型特异性 RNA-seq 数据,也证明了它发现调控机制差异的能力。总之,我们的研究结果凸显了在推断转录因子活性时建立特定环境调控模型的实用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Flexible modeling of regulatory networks improves transcription factor activity estimation.

Flexible modeling of regulatory networks improves transcription factor activity estimation.

Transcriptional regulation plays a crucial role in determining cell fate and disease, yet inferring the key regulators from gene expression data remains a significant challenge. Existing methods for estimating transcription factor (TF) activity often rely on static TF-gene interaction databases and cannot adapt to changes in regulatory mechanisms across different cell types and disease conditions. Here, we present a new algorithm - Transcriptional Inference using Gene Expression and Regulatory data (TIGER) - that overcomes these limitations by flexibly modeling activation and inhibition events, up-weighting essential edges, shrinking irrelevant edges towards zero through a sparse Bayesian prior, and simultaneously estimating both TF activity levels and changes in the underlying regulatory network. When applied to yeast and cancer TF knock-out datasets, TIGER outperforms comparable methods in terms of prediction accuracy. Moreover, our application of TIGER to tissue- and cell-type-specific RNA-seq data demonstrates its ability to uncover differences in regulatory mechanisms. Collectively, our findings highlight the utility of modeling context-specific regulation when inferring transcription factor activities.

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来源期刊
NPJ Systems Biology and Applications
NPJ Systems Biology and Applications Mathematics-Applied Mathematics
CiteScore
5.80
自引率
0.00%
发文量
46
审稿时长
8 weeks
期刊介绍: npj Systems Biology and Applications is an online Open Access journal dedicated to publishing the premier research that takes a systems-oriented approach. The journal aims to provide a forum for the presentation of articles that help define this nascent field, as well as those that apply the advances to wider fields. We encourage studies that integrate, or aid the integration of, data, analyses and insight from molecules to organisms and broader systems. Important areas of interest include not only fundamental biological systems and drug discovery, but also applications to health, medical practice and implementation, big data, biotechnology, food science, human behaviour, broader biological systems and industrial applications of systems biology. We encourage all approaches, including network biology, application of control theory to biological systems, computational modelling and analysis, comprehensive and/or high-content measurements, theoretical, analytical and computational studies of system-level properties of biological systems and computational/software/data platforms enabling such studies.
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