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GenoExp: a web tool for predicting gene expression levels from single nucleotide polymorphisms

机译:GenoExp:一种可从单核苷酸多态性预测基因表达水平的网络工具

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Understanding the effect of single nucleotide polymorphisms (SNPs) on the expression level of genes is an important goal. We recently published a study in which we devised a multi-SNP predictive model for gene expression in Lymphoblastoid cell lines (LCL), and showed that it can robustly predict the expression of a small number of genes in test individuals. Here, we validate the generality of our models by predicting expression profiles for genes in LCL in an independent study, and extend the pool of predictable genes for which we are able to explain more than 25% of their expression variability to 232 genes across 14 different cell types. As the number of people who obtained their SNP profiles through companies such as 23andMe is rising rapidly, we developed GenoExp, a web-based tool in which users can upload their individual SNP data and obtain predicted expression levels for the set of predictable genes across the 14 different cell types. Our tool thus allows users with biological knowledge to study the possible effects that their set of SNPs might have on these genes and predict their cell-specific expression levels relative to the population average.
机译:了解单核苷酸多态性(SNP)对基因表达水平的影响是一个重要的目标。我们最近发表了一项研究,其中我们设计了一种多SNP预测模型用于淋巴母细胞样细胞系(LCL)中的基因表达,并表明它可以稳健地预测测试个体中少数基因的表达。在这里,我们通过在一项独立研究中预测LCL中基因的表达谱来验证模型的通用性,并将可预测的基因库扩展到能够解释其14%不同的232个基因的超过25%的表达变异性单元格类型。随着通过23andMe等公司获得SNP资料的人数迅速增加,我们开发了GenoExp,这是一个基于网络的工具,用户可以在其中上传自己的SNP数据,并获得整个基因组中可预测基因的预测表达水平。 14种不同的细胞类型。因此,我们的工具允许具有生物学知识的用户研究其SNP集可能对这些基因的可能影响,并预测其相对于总体平均值的细胞特异性表达水平。

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