dingo: a Python package for metabolic flux sampling.

IF 2.4 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY

Bioinformatics advances Pub Date : 2024-03-22 eCollection Date: 2024-01-01 DOI:10.1093/bioadv/vbae037

Apostolos Chalkis, Vissarion Fisikopoulos, Elias Tsigaridas, Haris Zafeiropoulos

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引用次数: 0

Abstract

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).

Availability and implementation: 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.

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dingo：用于代谢通量采样的 Python 软件包。

我们介绍的 dingo 是一个 Python 软件包，它基于最先进的随机游走和舍入方法，支持从代谢模型的通量空间进行采样的多种方法。对于均匀采样，dingo 的采样方法能显著提高速度并优于现有软件。具体来说，dingo 可以在多种统计保证下，使用个人电脑在不到一天的时间内对迄今为止最大的代谢模型（Recon3D）的通量空间进行采样；这是其他类似软件无法达到的计算速度。此外，dingo 还支持通量平衡分析和通量变异性分析等常用分析方法以及可视化组件。dingo 支持高维度（数千维）通量采样，为代谢模型工具库做出了贡献：dingo Python 库可在 GitHub https://github.com/GeomScale/dingo 上获取，本文的基础数据可在 https://doi.org/10.5281/zenodo.10423335 上获取。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Bioinformatics advances

CiteScore

1.60

自引率

0.00%

发文量