PDF(Pubmed)
Abstract：
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 (GSEA, limma, edgeR) 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 (GSEA, limma, edgeR) 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.
摘要：
转录谱分析已成为调查神经系统的常用工具。在分析过程中,差异表达结果通常与功能本体数据库进行比较，其中包含代表经过充分研究的途径的精选基因集。这种依赖性可以导致神经科学研究被解释为在更好的研究组织中记录的功能途径（例如，肝脏)和主题(例如，cancer),系统地强调研究良好的基因，将其他发现留在大脑的默默无闻“无知”中。为了解决这个问题，我们编制了一个918个与神经系统功能相关的基因集的数据库，组织,和细胞类型(“大脑。GMT\“)，可在通用分析管道(GSEA，limma,edgeR)来解释来自三个物种(大鼠，鼠标,human).大脑。GMT包括从分子签名数据库（MSigDB）中筛选并从公共数据库中提取的大脑相关基因集（GeneWeaver，Gemma,DropViz,BrainInABlender,HippoSeq）和已发表的包含差异表达结果的研究。虽然大脑。GMT仍在发展中，目前仅代表一小部分可用的大脑基因集，“大脑无知”基因已经比传统的基因本体论数据库更好地表示。此外，大脑。GMT大大提高了在神经科学研究中鉴定为富含差异表达的基因集的数量和质量，加强解释。•我们编制了一个与神经系统功能相关的918个基因集的数据库，组织,和细胞类型(“大脑。GMT\“)。•Brain.GMT可用于通用分析管道(GSEA，limma,edgeR)来解释来自三个物种(大鼠，鼠标,human).•虽然大脑。GMT仍在发展中，它大大改善了我们最初用例中差异表达结果的解释。
