Hagenauer Megan H, Sannah Yusra, Hebda-Bauer Elaine K, Rhoads Cosette, O'Connor Angela M, Flandreau Elizabeth, Watson Stanley J, Akil Huda
Michigan Neuroscience Institute, University of Michigan, Ann Arbor, MI 48109, USA.
National Institutes of Health, Bethesda, MD 20892, USA.
MethodsX. 2024 Jun 24;13:102788. doi: 10.1016/j.mex.2024.102788. eCollection 2024 Dec.
Transcriptional profiling has become a common tool for investigating the nervous system. During analysis, differential expression results are often compared to functional ontology databases, which contain curated gene sets representing well-studied pathways. This dependence can cause neuroscience studies to be interpreted in terms of functional pathways documented in better studied tissues (e.g. liver) and topics (e.g. cancer), and systematically emphasizes well-studied genes, leaving other findings in the obscurity of the brain "ignorome". To address this issue, we compiled a curated database of 918 gene sets related to nervous system function, tissue, and cell types ("Brain.GMT") that can be used within common analysis pipelines () to interpret results from three species (rat, mouse, human). Brain.GMT includes brain-related gene sets curated from the Molecular Signatures Database (MSigDB) and extracted from public databases (GeneWeaver, Gemma, DropViz, BrainInABlender, HippoSeq) and published studies containing differential expression results. Although Brain.GMT is still undergoing development and currently only represents a fraction of available brain gene sets, "brain ignorome" genes are already better represented than in traditional Gene Ontology databases. Moreover, Brain.GMT substantially improves the quantity and quality of gene sets identified as enriched with differential expression in neuroscience studies, enhancing interpretation. •We compiled a curated database of 918 gene sets related to nervous system function, tissue, and cell types ("Brain.GMT").•Brain.GMT can be used within common analysis pipelines () to interpret neuroscience transcriptional profiling results from three species (rat, mouse, human).•Although Brain.GMT is still undergoing development, it substantially improved the interpretation of differential expression results within our initial use cases.
转录谱分析已成为研究神经系统的常用工具。在分析过程中，差异表达结果通常会与功能本体数据库进行比较，这些数据库包含经过整理的基因集，代表了研究充分的通路。这种依赖可能导致神经科学研究依据在研究更充分的组织（如肝脏）和主题（如癌症）中记录的功能通路来进行解释，并系统性地强调研究充分的基因，而将其他发现置于大脑“未知基因组”的模糊状态。为了解决这个问题，我们编制了一个包含918个与神经系统功能、组织和细胞类型相关的基因集的数据库（“Brain.GMT”），该数据库可用于常见的分析流程（）中，以解释来自三个物种（大鼠、小鼠、人类）的结果。Brain.GMT包括从分子特征数据库（MSigDB）中整理并从公共数据库（GeneWeaver、Gemma、DropViz、BrainInABlender、HippoSeq）以及包含差异表达结果的已发表研究中提取的与大脑相关的基因集。尽管Brain.GMT仍在开发中，目前仅代表可用大脑基因集的一部分，但“大脑未知基因组”中的基因已比传统基因本体数据库中有更好的体现。此外，Brain.GMT显著提高了在神经科学研究中被鉴定为差异表达富集的基因集的数量和质量，增强了结果的解释力。
•我们编制了一个包含918个与神经系统功能、组织和细胞类型相关的基因集的数据库（“Brain.GMT”）。
•Brain.GMT可用于常见的分析流程（）中，以解释来自三个物种（大鼠、小鼠、人类）的神经科学转录谱分析结果。
•尽管Brain.GMT仍在开发中，但在我们最初的应用案例中，它显著改善了差异表达结果的解释。
