Serra Angela, Saarimäki Laura Aliisa, Pavel Alisa, Del Giudice Giusy, Fratello Michele, Cattelani Luca, Federico Antonio, Laurino Omar, Marwah Veer Singh, Fortino Vittorio, Scala Giovanni, Sofia Kinaret Pia Anneli, Greco Dario
Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland.
BioMediTech Institute, Tampere University, Tampere University, Tampere, Finland.
Comput Struct Biotechnol J. 2022 Mar 18;20:1413-1426. doi: 10.1016/j.csbj.2022.03.014. eCollection 2022.
The recent advancements in toxicogenomics have led to the availability of large omics data sets, representing the starting point for studying the exposure mechanism of action and identifying candidate biomarkers for toxicity prediction. The current lack of standard methods in data generation and analysis hampers the full exploitation of toxicogenomics-based evidence in regulatory risk assessment. Moreover, the pipelines for the preprocessing and downstream analyses of toxicogenomic data sets can be quite challenging to implement. During the years, we have developed a number of software packages to address specific questions related to multiple steps of toxicogenomics data analysis and modelling. In this review we present the Nextcast software collection and discuss how its individual tools can be combined into efficient pipelines to answer specific biological questions. Nextcast components are of great support to the scientific community for analysing and interpreting large data sets for the toxicity evaluation of compounds in an unbiased, straightforward, and reliable manner. The Nextcast software suite is available at: ( https://github.com/fhaive/nextcast).
毒理基因组学的最新进展带来了大量组学数据集，这是研究暴露作用机制和识别毒性预测候选生物标志物的起点。目前在数据生成和分析方面缺乏标准方法，阻碍了在监管风险评估中充分利用基于毒理基因组学的证据。此外，毒理基因组数据集的预处理和下游分析流程实施起来颇具挑战。多年来，我们开发了多个软件包，以解决与毒理基因组数据分析和建模多个步骤相关的特定问题。在本综述中，我们介绍了Nextcast软件集，并讨论如何将其各个工具组合成高效流程，以回答特定的生物学问题。Nextcast组件对科学界以无偏倚、直接且可靠的方式分析和解释用于化合物毒性评估的大数据集提供了极大支持。Nextcast软件套件可在以下网址获取：(https://github.com/fhaive/nextcast)
