评估毒理学剂量反应 RNA-seq 中的重复数量和测序深度

IF 3.1 Q2 TOXICOLOGY
A. Rasim Barutcu
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引用次数: 0

摘要

测序深度和生物复制是毒物基因组学和风险评估中关键的实验设计考虑因素。然而,它们对差异基因表达分析的相对影响仍不清楚。我们利用 A549 细胞中的 8 剂量化学试剂(Prochloraz)扰动 RNA-seq 数据集,系统地对测序深度(5%-100%)和重复序列(2-4)进行了子采样,以评估它们对差异表达基因数量的影响。虽然剂量是主要的变异驱动因素,但在优化检测能力方面,重复比深度的影响更大。在仅有 2 个重复的情况下,超过 80% 的 2000 个差异基因为特定深度所独有,表明变异性很高。将重复次数增加到 4 次大大提高了可重复性,在大多数深度上一致鉴定出 550 多个基因,占差异基因总数的 30%。更高的重复次数也提高了基准剂量途径的重叠率和基准剂量估算中值的精确度。不过,即使在较低的重复率下,与 DNA 复制、细胞周期和分裂相关的关键基因本体通路也能被持续捕获。因此,复制增强了可信度,但并没有从根本上扩展生物学发现。我们的研究为毒物基因组学实验设计划定了测序深度与复制之间的关键权衡。虽然额外的重复次数从根本上提高了可重复性,但深度带来的收益却呈现递减趋势。在不牺牲核心基因表达模式检测的前提下,优先考虑生物复制而非深度,为增强解释提供了一种经济有效的方法。总之,这项研究为毒物基因组学实验设计提供了重要启示。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Evaluation of Replicate Number and Sequencing Depth in Toxicology Dose-Response RNA-seq

Sequencing depth and biological replication represent key experimental design considerations in toxicogenomics and risk assessment. However, their relative impacts on differential gene expression analysis remain unclear. Using an 8-dose chemical (Prochloraz) perturbation RNA-seq dataset in A549 cells, we systematically subsampled sequencing depth (5–100 %) and replicates (2–4) to evaluate effects on number of differentially expressed genes. While dose was the primary variance driver, replication had a greater influence than depth for optimizing detection power. With only 2 replicates, over 80% of the ∼2000 differential genes were unique to specific depths, indicating high variability. Increasing to 4 replicates substantially improved reproducibility, with over 550 genes consistently identified across most depths, representing 30% of the total differential genes. Higher replicates also increased the rate of overlap of benchmark dose pathways and precision of median benchmark dose estimates. However, key gene ontology pathways related to DNA replication, cell cycle, and division were consistently captured even at lower replicates. Thus, replication enhanced confidence but did not fundamentally expand biological findings. Our study delineates key trade-offs between sequencing depth and replication for toxicogenomic experimental design. While additional replicates fundamentally improve reproducibility, gains from depth exhibit diminishing returns. Prioritizing biological replication over depth provides a cost-effective approach to enhance interpretation without sacrificing detection of core gene expression patterns. Altogether, this study provides important insights into the experimental design of toxicogenomics experiments.

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来源期刊
Computational Toxicology
Computational Toxicology Computer Science-Computer Science Applications
CiteScore
5.50
自引率
0.00%
发文量
53
审稿时长
56 days
期刊介绍: Computational Toxicology is an international journal publishing computational approaches that assist in the toxicological evaluation of new and existing chemical substances assisting in their safety assessment. -All effects relating to human health and environmental toxicity and fate -Prediction of toxicity, metabolism, fate and physico-chemical properties -The development of models from read-across, (Q)SARs, PBPK, QIVIVE, Multi-Scale Models -Big Data in toxicology: integration, management, analysis -Implementation of models through AOPs, IATA, TTC -Regulatory acceptance of models: evaluation, verification and validation -From metals, to small organic molecules to nanoparticles -Pharmaceuticals, pesticides, foods, cosmetics, fine chemicals -Bringing together the views of industry, regulators, academia, NGOs
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