Motivation Transcriptomics is a common approach to identify changes in gene expression induced by a disease state. Standard transcriptomic analyses consider differentially expressed genes (DEGs) as indicative of disease states so only a few genes would be treated as signals when the effect size is small, such as in brain tissue. For tissue with small effect sizes, if the DEGs do not belong to a pathway known to be involved in the disease, there would be little left in the transcriptome for researchers to follow up with. Results We developed RNA Solutions: Synthesizing Information to Support Transcriptomics (RNASSIST), a new approach to identify hidden signals in transcriptomic data by linking differential expression and co-expression networks using machine learning. We applied our approach to RNA-seq data of post-mortem brains that compared the Alcohol Use Disorder (AUD) group with the control group. Many of the candidate genes are not differentially expressed so would likely be ignored by standard transcriptomic analysis pipelines. Through multiple validation strategies, we concluded that these RNASSIST-identified genes likely play a significant role in AUD. Availability and implementation The RNASSIST algorithm is available at https://github.com/netrias/rnassist and both the software and the data used in RNASSIST are available at https://figshare.com/articles/software/RNAssist_Software_and_Data/16617250. Supplementary information Supplementary data are available at Bioinformatics online.

中文翻译：

---

RNA 解决方案：合成信息以支持转录组学 (RNASSIST)

动机转录组学是识别疾病状态引起的基因表达变化的常用方法。标准转录组分析将差异表达基因 (DEG) 视为疾病状态的指示，因此当效应较小时（例如在脑组织中），只有少数基因会被视为信号。对于效应量较小的组织，如果 DEG 不属于已知与疾病有关的途径，那么转录组中就没有什么可供研究人员跟踪的了。结果我们开发了 RNA 解决方案：合成信息以支持转录组学 (RNASSIST)，这是一种通过使用机器学习连接差异表达和共表达网络来识别转录组数据中隐藏信号的新方法。我们将我们的方法应用于死后大脑的 RNA 测序数据，将酒精使用障碍 (AUD) 组与对照组进行比较。许多候选基因没有差异表达，因此可能会被标准转录组分析流程忽略。通过多种验证策略，我们得出结论，这些 RNASSIST 识别的基因可能在 AUD 中发挥重要作用。可用性和实施​​ RNASSIST 算法可在 https://github.com/netrias/rnassist 上获取，RNASSIST 中使用的软件和数据可在 https://figshare.com/articles/software/RNAssist_Software_and_Data/16617250 上获取。补充信息 补充数据可在生物信息学在线获取。