Univ Lyon, INSA-Lyon, INRAE, BF2i, UMR0203, F-69621 Villeurbanne, France.
Univ Lyon, INRAE, INSA-Lyon, BF2i, UMR0203, F-69621 Villeurbanne, France.
Genes (Basel). 2023 Jan 20;14(2):269. doi: 10.3390/genes14020269.
Inferring gene regulatory networks (GRN) from high-throughput gene expression data is a challenging task for which different strategies have been developed. Nevertheless, no ever-winning method exists, and each method has its advantages, intrinsic biases, and application domains. Thus, in order to analyze a dataset, users should be able to test different techniques and choose the most appropriate one. This step can be particularly difficult and time consuming, since most methods' implementations are made available independently, possibly in different programming languages. The implementation of an open-source library containing different inference methods within a common framework is expected to be a valuable toolkit for the systems biology community. : In this work, we introduce GReNaDIne (Gene Regulatory Network Data-driven Inference), a Python package that implements 18 machine learning data-driven gene regulatory network inference methods. It also includes eight generalist preprocessing techniques, suitable for both RNA-seq and microarray dataset analysis, as well as four normalization techniques dedicated to RNA-seq. In addition, this package implements the possibility to combine the results of different inference tools to form robust and efficient ensembles. This package has been successfully assessed under the DREAM5 challenge benchmark dataset. The open-source GReNaDIne Python package is made freely available in a dedicated GitLab repository, as well as in the official third-party software repository PyPI Python Package Index. The latest documentation on the GReNaDIne library is also available at Read the Docs, an open-source software documentation hosting platform. : The GReNaDIne tool represents a technological contribution to the field of systems biology. This package can be used to infer gene regulatory networks from high-throughput gene expression data using different algorithms within the same framework. In order to analyze their datasets, users can apply a battery of preprocessing and postprocessing tools and choose the most adapted inference method from the GReNaDIne library and even combine the output of different methods to obtain more robust results. The results format provided by GReNaDIne is compatible with well-known complementary refinement tools such as PYSCENIC.
从高通量基因表达数据推断基因调控网络（GRN）是一项具有挑战性的任务，为此已经开发了不同的策略。然而，没有一种方法是万能的，每种方法都有其优点、内在偏见和应用领域。因此，为了分析数据集，用户应该能够测试不同的技术并选择最合适的技术。这一步可能特别困难和耗时，因为大多数方法的实现都是独立提供的，可能使用不同的编程语言。在一个通用框架内包含不同推断方法的开源库的实现有望成为系统生物学界的一个有价值的工具包。 在这项工作中，我们介绍了 GReNaDIne（基于基因调控网络数据的推断），这是一个 Python 包，实现了 18 种机器学习数据驱动的基因调控网络推断方法。它还包括 8 种通用预处理技术，适用于 RNA-seq 和微阵列数据集分析，以及 4 种专门用于 RNA-seq 的归一化技术。此外，这个包还实现了组合不同推断工具的结果以形成稳健有效的集成的可能性。这个包已经在 DREAM5 挑战基准数据集下成功评估。开源的 GReNaDIne Python 包在一个专门的 GitLab 存储库中以及在官方的第三方软件存储库 PyPI Python 包索引中免费提供。GReNaDIne 库的最新文档也可在 Read the Docs 上获得，这是一个开源软件文档托管平台。 GReNaDIne 工具是系统生物学领域的一项技术贡献。这个包可以用于使用同一框架中的不同算法从高通量基因表达数据中推断基因调控网络。为了分析他们的数据集，用户可以应用一系列预处理和后处理工具，并从 GReNaDIne 库中选择最适合的推断方法，甚至可以结合不同方法的输出结果以获得更稳健的结果。GReNaDIne 提供的结果格式与知名的补充精炼工具（如 PYSCENIC）兼容。
