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首页> 外文期刊>Cell cycle >Match/X, A gene expression pattern recognition algorithm used to identify genes which may be related to CDC2 function and cell cycle regulation.
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Match/X, A gene expression pattern recognition algorithm used to identify genes which may be related to CDC2 function and cell cycle regulation.

机译：Match / X，一种基因表达模式识别算法，用于识别可能与CDC2功能和细胞周期调控有关的基因。

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摘要

Large-scale microarray gene expression studies can provide insight into complex genetic networks and biological pathways. A comprehensive gene expression database was constructed using Affymetrix GeneChip microarrays and RNA isolated from more than 6,400 distinct normal and diseased human tissues. These individual patient samples were grouped into over 700 sample sets based on common tissue and disease morphologies, and each set contained averaged expression data for over 45,000 gene probe sets representing more than 33,000 known human genes. Sample sets were compared to each other in more than 750 normal vs. disease pairwise comparisons. Relative up or downregulation patterns of genes across these pairwise comparisons provided unique expression fingerprints that could be compared and matched to a gene of interest using the Match/X trade mark algorithm. This algorithm uses the kappa statistic to compute correlations between genes and calculate a distance score between a gene of interest and all other genes in the database. Using cdc2 as a query gene, we identified several hundred genes that had similar expression patterns and highly correlated distance scores. Most of these genes were known components of the cell cycle involved in G2/M progression, spindle function or chromosome arrangement. Some of the identified genes had unknown biological functions but may be related to cdc2 mediated mechanism based on their closely correlated distance scores. This algorithm may provide novel insights into unknown gene function based on correlation to expression profiles of known genes and can identify elements of cellular pathways and gene interactions in a high throughput fashion.
机译：大规模微阵列基因表达研究可以提供对复杂遗传网络和生物学途径的见解。使用Affymetrix GeneChip微阵列和从6400多种不同的正常和患病的人体组织中分离的RNA构建了一个全面的基因表达数据库。根据常见的组织和疾病形态，将这些患者样本分为700多个样本集，每个样本集包含代表33,000多种已知人类基因的45,000多种基因探针集的平均表达数据。在超过750个正常对疾病对的比较中，将样本集相互比较。这些成对比较中基因的相对上调或下调模式提供了独特的表达指纹，可以使用Match / X商标算法将其与感兴趣的基因进行比较和匹配。该算法使用kappa统计量计算基因之间的相关性，并计算目标基因与数据库中所有其他基因之间的距离得分。使用cdc2作为查询基因，我们鉴定了数百个具有相似表达模式和高度相关距离得分的基因。这些基因大多数是参与G2 / M进展，纺锤体功能或染色体排列的细胞周期的已知成分。一些已鉴定的基因具有未知的生物学功能，但基于它们紧密相关的距离得分，可能与cdc2介导的机制有关。该算法可以基于与已知基因的表达谱的相关性，提供未知基因功能的新颖见解，并且可以以高通量的方式鉴定细胞途径的元素和基因相互作用。

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