BACKGROUND High-throughput transcriptomics measures mRNA levels for thousands of genes in a biological sample. Most gene expression studies aim to identify genes that are differentially expressed between different biological conditions, such as between healthy and diseased states. However, these data can also be used to identify genes that are co-expressed within a biological condition. Gene co-expression is used in a guilt-by-association approach to prioritize candidate genes that could be involved in disease, and to gain insights into the functions of genes, protein relations, and signaling pathways. Most existing gene co-expression databases are generic, amalgamating data for a given organism regardless of tissue-type. METHODS To study muscle-specific gene co-expression in both normal and pathological states, publicly available gene expression data were acquired for 2376 mouse and 2228 human striated muscle samples, and separated into 142 categories based on species (human or mouse), tissue origin, age, gender, anatomic part, and experimental condition. Co-expression values were calculated for each category to create the MyoMiner database. RESULTS Within each category, users can select a gene of interest, and the MyoMiner web interface will return all correlated genes. For each co-expressed gene pair, adjusted p-value and confidence intervals are provided as measures of expression correlation strength. A standardized expression-level scatterplot is available for every gene pair r-value. MyoMiner has two extra functions: (a) a network interface for creating a 2-shell correlation network, based either on the most highly correlated genes or from a list of genes provided by the user with the option to include linked genes from the database and (b) a comparison tool from which the users can test whether any two correlation coefficients from different conditions are significantly different. CONCLUSIONS These co-expression analyses will help investigators to delineate the tissue-, cell-, and pathology-specific elements of muscle protein interactions, cell signaling and gene regulation. Changes in co-expression between pathologic and healthy tissue may suggest new disease mechanisms and help define novel therapeutic targets. Thus, MyoMiner is a powerful muscle-specific database for the discovery of genes that are associated with related functions based on their co-expression. MyoMiner is freely available at https://www.sys-myo.com/myominer.

中文翻译：

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MyoMiner：探索正常和病理肌肉中的基因共表达。


背景技术高通量转录组学测量生物样品中数千个基因的mRNA水平。大多数基因表达研究旨在识别在不同生物条件下（例如健康状态和患病状态之间）差异表达的基因。然而，这些数据也可用于识别在生物条件下共表达的基因。基因共表达用于按关联有罪的方法，以优先考虑可能与疾病有关的候选基因，并深入了解基因的功能、蛋白质关系和信号通路。大多数现有的基因共表达数据库都是给定生物体的通用合并数据，无论组织类型如何。方法 为了研究正常和病理状态下肌肉特异性基因的共表达，获取了 2376 份小鼠和 2228 份人类横纹肌样本的公开基因表达数据，并根据物种（人类或小鼠）、组织来源分为 142 类、年龄、性别、解剖部位和实验条件。计算每个类别的共表达值以创建 MyoMiner 数据库。结果 在每个类别中，用户可以选择感兴趣的基因，MyoMiner 网络界面将返回所有相关基因。对于每个共表达基因对，提供调整后的 p 值和置信区间作为表达相关强度的度量。每个基因对的 r 值都可以使用标准化的表达水平散点图。 MyoMiner 有两个额外的功能：（a）一个网络接口，用于创建 2-shell 相关网络，基于相关性最高的基因或用户提供的基因列表，可以选择包含数据库中的链接基因； (b) 比较工具，用户可以通过该工具测试不同条件下任意两个相关系数是否显着不同。结论这些共表达分析将帮助研究人员描绘肌肉蛋白相互作用、细胞信号传导和基因调控的组织、细胞和病理特异性元件。病理组织和健康组织之间共表达的变化可能提示新的疾病机制并有助于确定新的治疗靶点。因此，MyoMiner 是一个强大的肌肉特异性数据库，用于根据其共表达来发现与相关功能相关的基因。 MyoMiner 可在 https://www.sys-myo.com/myominer 上免费获取。