KU Leuven ESAT/STADIUS, VDA-lab.
IMEC Smart Applications and Innovation Services.
Bioinformatics. 2019 Jun 1;35(12):2159-2161. doi: 10.1093/bioinformatics/bty916.
Inferring a Gene Regulatory Network (GRN) from gene expression data is a computationally expensive task, exacerbated by increasing data sizes due to advances in high-throughput gene profiling technology, such as single-cell RNA-seq. To equip researchers with a toolset to infer GRNs from large expression datasets, we propose GRNBoost2 and the Arboreto framework. GRNBoost2 is an efficient algorithm for regulatory network inference using gradient boosting, based on the GENIE3 architecture. Arboreto is a computational framework that scales up GRN inference algorithms complying with this architecture. Arboreto includes both GRNBoost2 and an improved implementation of GENIE3, as a user-friendly open source Python package.
Arboreto is available under the 3-Clause BSD license at http://arboreto.readthedocs.io.
Supplementary data are available at Bioinformatics online.
从基因表达数据推断基因调控网络 (GRN) 是一项计算成本很高的任务，由于高通量基因分析技术（如单细胞 RNA-seq）的进步，数据量不断增加，使得这一任务更加复杂。为了为研究人员提供从大型表达数据集推断 GRN 的工具集，我们提出了 GRNBoost2 和 Arboreto 框架。GRNBoost2 是一种基于 GENIE3 架构的使用梯度提升进行调控网络推断的高效算法。Arboreto 是一个计算框架，可根据该架构扩展符合要求的 GRN 推断算法。Arboreto 包括 GRNBoost2 和 GENIE3 的改进实现，作为一个用户友好的开源 Python 包。
Arboreto 可在 3 条款 BSD 许可证下在 http://arboreto.readthedocs.io 获得。
补充数据可在 Bioinformatics 在线获得。
