CINECA, SuperComputing Applications and Innovation Department, Via dei Tizii 6, 00185, Roma, Italy.
DIBAF, University of Tuscia, 01100, Viterbo, Italy.
Sci Data. 2020 Dec 16;7(1):437. doi: 10.1038/s41597-020-00772-z.
Stressful experiences are part of everyday life and animals have evolved physiological and behavioral responses aimed at coping with stress and maintaining homeostasis. However, repeated or intense stress can induce maladaptive reactions leading to behavioral disorders. Adaptations in the brain, mediated by changes in gene expression, have a crucial role in the stress response. Recent years have seen a tremendous increase in studies on the transcriptional effects of stress. The input raw data are freely available from public repositories and represent a wealth of information for further global and integrative retrospective analyses. We downloaded from the Sequence Read Archive 751 samples (SRA-experiments), from 18 independent BioProjects studying the effects of different stressors on the brain transcriptome in mice. We performed a massive bioinformatics re-analysis applying a single, standardized pipeline for computing differential gene expression. This data mining allowed the identification of novel candidate stress-related genes and specific signatures associated with different stress conditions. The large amount of computational results produced was systematized in the interactive "Stress Mice Portal".
压力体验是日常生活的一部分，动物已经进化出生理和行为反应，旨在应对压力和维持体内平衡。然而，反复或强烈的压力会导致适应不良的反应，从而导致行为障碍。由基因表达变化介导的大脑适应在应激反应中起着至关重要的作用。近年来，关于应激的转录效应的研究有了巨大的增长。输入的原始数据可从公共存储库免费获得，为进一步的全球和综合回顾性分析提供了丰富的信息。我们从序列读取档案中下载了 751 个样本（SRA 实验），来自 18 个独立的生物项目，这些项目研究了不同应激源对小鼠大脑转录组的影响。我们应用单一的标准化管道进行了大规模的生物信息学重新分析，以计算差异基因表达。这种数据挖掘允许鉴定与不同应激条件相关的新的候选应激相关基因和特定特征。大量生成的计算结果在交互式“应激小鼠门户”中进行了系统组织。
