Gene prioritization through genomic data fusion
Gene prioritization through genomic data fusion
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DOI：
10.1038/nbt1203
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发表时间：
2006-05-01
影响因子：
46.9
通讯作者：
Moreau, Y
中科院分区：
文献类型：
--
作者：
Aerts, S;Lambrechts, D;Moreau, Y
识别与健康和疾病有关的基因仍然是一个挑战。我们描述了一种生物信息学方法，以及一种名为Endeavour的免费访问、交互和灵活的软件，根据候选基因与参与这些现象的已知基因的相似性，对潜在的生物过程或疾病的候选基因进行优先排序。与以前的方法不同，我们的方法为多个不同的数据源生成不同的优先级，然后使用顺序统计将这些数据源集成或融合到全局排名中。此外，它还提供了包括其他数据源的灵活性。对我们的方法的验证表明，它能够有效地优先处理疾病数据集中的627个基因和生物路径集中的76个基因，识别16种单基因或多基因疾病的候选基因，并发现髓系分化的调节基因。此外，该方法从2-Mb染色体区域发现了一个与头面部发育有关的新基因，该基因在一些DiGeorge样出生缺陷患者中缺失。本文介绍的方法为基因发现提供了一种可供选择的综合方法。
The identification of genes involved in health and disease remains a challenge. We describe a bioinformatics approach, together with a freely accessible, interactive and flexible software termed Endeavour, to prioritize candidate genes underlying biological processes or diseases, based on their similarity to known genes involved in these phenomena. Unlike previous approaches, ours generates distinct prioritizations for multiple heterogeneous data sources, which are then integrated, or fused, into a global ranking using order statistics. In addition, it offers the flexibility of including additional data sources. Validation of our approach revealed it was able to efficiently prioritize 627 genes in disease data sets and 76 genes in biological pathway sets, identify candidates of 16 mono- or polygenic diseases, and discover regulatory genes of myeloid differentiation. Furthermore, the approach identified a novel gene involved in craniofacial development from a 2-Mb chromosomal region, deleted in some patients with DiGeorge-like birth defects. The approach described here offers an alternative integrative method for gene discovery.