RNA测序(RNA-seq)技术导致了神经科学研究的激增,使用动物模型来探索大脑功能和行为背后的复杂分子机制。包括物质使用障碍(SUDs)。然而,啮齿动物研究的结果往往无法转化为临床治疗。这里,我们开发了一种新的管道,用于通过翻译潜力从临床前研究中缩小候选基因,并证明了其在啮齿动物自我管理的两个RNA-seq研究中的实用性。这个管道使用基因在脑组织中的进化保守和优先表达来优先考虑候选基因,增加RNA-seq在模型生物中的翻译效用。最初,我们使用未校正的p值演示了我们的优先级排序管道的实用性。然而,在校正多重测试后,我们在两个数据集中均未发现差异表达基因(DEGs)(FDR<0.05或<0.1).这可能是由于啮齿动物行为研究中常见的低统计能力,因此,我们还说明了我们的管道在第三个数据集上的使用,其中DEG校正了多次测试(FDR<0.05)。我们还提倡改进RNA-seq数据收集,统计检验,和元数据报告,这将增强该领域识别可靠候选基因的能力,并提高生物信息学在啮齿动物研究中的翻译价值。
RNA-sequencing (RNA-seq) technology has led to a surge of neuroscience research using animal models to probe the complex molecular mechanisms underlying brain function and behavior, including substance use disorders. However, findings from rodent studies often fail to be translated into clinical treatments. Here, we developed a novel pipeline for narrowing candidate genes from preclinical studies by translational potential and demonstrated its utility in 2 RNA-seq studies of rodent self-administration. This pipeline uses evolutionary conservation and preferential expression of genes across brain tissues to prioritize candidate genes, increasing the translational utility of RNA-seq in model organisms. Initially, we demonstrate the utility of our prioritization pipeline using an uncorrected P-value. However, we found no differentially expressed genes in either dataset after correcting for multiple testing with false discovery rate (FDR < 0.05 or <0.1). This is likely due to low statistical power that is common across rodent behavioral studies, and, therefore, we additionally illustrate the use of our pipeline on a third dataset with differentially expressed genes corrected for multiple testing (FDR < 0.05). We also advocate for improved RNA-seq data collection, statistical testing, and metadata reporting that will bolster the field\'s ability to identify reliable candidate genes and improve the translational value of bioinformatics in rodent research.