Optimizing gene set annotations combining GO structure and gene expression data
Optimizing gene set annotations combining GO structure and gene expression data
复制标题
结合GO结构和基因表达数据优化基因集注释
DOI：
10.1186/s12918-018-0659-6
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发表时间：
2018-12-31
影响因子：
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通讯作者：
Wang，Yadong
中科院分区：
文献类型：
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作者：
Wang，Dong;Li，Jie;Wang，Yadong
背景随着基因组数据的快速积累，注释和解释这些数据已成为一个挑战问题。作为代表，基因集富集分析已被广泛用于解释生物实验生成的大型分子数据集。基因集富集分析的结果在很大程度上依赖于基因集注释的质量和完整性。尽管开发了多种注释基因集的方法，但仍然缺乏高质量的注释方法。在这里，我们提出了一种通过结合 GO 结构和基因表达数据来提高注释准确性的新方法。结果我们提出了一种优化基因集注释以获得更准确注释结果的新方法。所提出的方法使用 GO 结构信息和通过多个 bootstrap 重采样数据集上的一系列簇大小计算出的概率基因集簇来过滤不一致的注释。该方法用于分析 p53 细胞系、结肠癌和乳腺癌基因表达数据。实验结果表明，该方法可以过滤掉大量与实验数据无关的注释，提高基因集富集能力，减少注释的不一致。结论提出了一种新的基因集注释优化方法，以提高基因注释的质量。实验结果表明，该方法有效提高了基于GO结构和基因表达数据的基因集注释质量。
BackgroundWith the rapid accumulation of genomic data, it has become a challenge issue to annotate and interpret these data. As a representative, Gene set enrichment analysis has been widely used to interpret large molecular datasets generated by biological experiments. The result of gene set enrichment analysis heavily relies on the quality and integrity of gene set annotations. Although several methods were developed to annotate gene sets, there is still a lack of high quality annotation methods. Here, we propose a novel method to improve the annotation accuracy through combining the GO structure and gene expression data.ResultsWe propose a novel approach for optimizing gene set annotations to get more accurate annotation results. The proposed method filters the inconsistent annotations using GO structure information and probabilistic gene set clusters calculated by a range of cluster sizes over multiple bootstrap resampled datasets. The proposed method is employed to analyze p53 cell lines, colon cancer and breast cancer gene expression data. The experimental results show that the proposed method can filter a number of annotations unrelated to experimental data and increase gene set enrichment power and decrease the inconsistent of annotations.ConclusionsA novel gene set annotation optimization approach is proposed to improve the quality of gene annotations. Experimental results indicate that the proposed method effectively improves gene set annotation quality based on the GO structure and gene expression data.