Massive gene expression analyses are widely used to find differentially expressed genes under specific conditions. The results of these experiments are often available in public databases that are undergoing a growth similar to that of molecular sequence databases in the past. This now allows novel secondary computational tools to emerge that use such information to gain new knowledge. If several genes have a similar expression profile across heterogeneous transcriptomics experiments, they could be functionally related. These associations are usually useful for the annotation of uncharacterized genes. In addition, the search for genes with opposite expression profiles is useful for finding negative regulators and proposing inhibitory compounds in drug repurposing projects. Here we present a new web application, Automatic and Serial Analysis of CO-expression (ASACO), which has the potential to discover positive and negative correlator genes to a given query gene, based on thousands of public transcriptomics experiments. In addition, examples of use are presented, comparing with previous contrasted knowledge. The results obtained propose ASACO as a useful tool to improve knowledge about genes associated with human diseases and noncoding genes. ASACO is available at http://www.bioinfocabd.upo.es/asaco/.

中文翻译：

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ASACO：共表达的自动连续分析，以发现可用于药物再利用的基因修饰剂

大规模基因表达分析被广泛用于寻找特定条件下差异表达的基因。这些实验的结果通常可以在公共数据库中找到，这些数据库正在经历类似于过去分子序列数据库的增长。现在，新型辅助计算工具的出现可以利用这些信息来获取新知识。如果多个基因在异质转录组学实验中具有相似的表达谱，则它们可能在功能上相关。这些关联通常可用于注释未表征的基因。此外，寻找具有相反表达谱的基因对于寻找负调节因子并在药物再利用项目中提出抑制化合物很有用。在这里，我们提出了一个新的网络应用程序，共表达自动串行分析（ASACO），它有可能根据数千个公共转录组学实验发现给定查询基因的正相关基因和负相关基因。此外，还提供了使用示例，并与以前的对比知识进行了比较。获得的结果表明 ASACO 是一种有用的工具，可以提高有关人类疾病相关基因和非编码基因的知识。ASACO 的网址为：http://www.bioinfocabd.upo.es/asaco/。