Chalkis Apostolos, Fisikopoulos Vissarion, Tsigaridas Elias, Zafeiropoulos Haris
GeomScale.org.
Department of Informatics & Telecommunications, National and Kapodistrian University of Athens, Panepistimioupolis, Ilisia,16122 Athens, Greece.
Bioinform Adv. 2024 Mar 22;4(1):vbae037. doi: 10.1093/bioadv/vbae037. eCollection 2024.
We present dingo, a Python package that supports a variety of methods to sample from the flux space of metabolic models, based on state-of-the-art random walks and rounding methods. For uniform sampling, dingo's sampling methods provide significant speed-ups and outperform existing software. Indicatively, dingo can sample from the flux space of the largest metabolic model up to now (Recon3D) in less than a day using a personal computer, under several statistical guarantees; this computation is out of reach for other similar software. In addition, dingo supports common analysis methods, such as flux balance analysis and flux variability analysis, and visualization components. dingo contributes to the arsenal of tools in metabolic modelling by enabling flux sampling in high dimensions (in the order of thousands).
The dingo Python library is available in GitHub at https://github.com/GeomScale/dingo and the data underlying this article are available in https://doi.org/10.5281/zenodo.10423335.
我们展示了Dingo,一个基于最新随机游走和舍入方法,支持从代谢模型通量空间进行多种采样方法的Python包。对于均匀采样,Dingo的采样方法提供了显著的加速,并优于现有软件。具体而言,Dingo可以在个人电脑上,在不到一天的时间内,在多种统计保证下,从目前最大的代谢模型(Recon3D)的通量空间进行采样;这种计算对于其他类似软件来说是无法实现的。此外,Dingo支持通量平衡分析和通量变异性分析等常见分析方法以及可视化组件。Dingo通过实现高维(数千量级)的通量采样,为代谢建模工具库做出了贡献。
Dingo Python库可在GitHub上的https://github.com/GeomScale/dingo获取,本文的基础数据可在https://doi.org/10.5281/zenodo.10423335获取。