Mining phenotypes for gene function prediction.
Mining phenotypes for gene function prediction.
复制标题
挖掘表型以进行基因功能预测。
DOI：
10.1186/1471-2105-9-136
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发表时间：
2008-03-03
影响因子：
3
通讯作者：
Leser U
中科院分区：
文献类型：
--
作者：
Groth P;Weiss B;Pohlenz HD;Leser U
生物体的健康和疾病反映在它们的表型中。通常，只有在明确定义疾病的表型后，才能发现疾病的遗传成分。在过去的几年中，已经开发了许多以高通量方式系统地产生表型的技术，例如RNA干扰或基因敲除，并用于破译基因的功能。然而，已经有相对较少的努力，利用表型数据以外的单一基因型-表型关系。我们目前的研究结果，我们使用了大量的表型数据-在文本形式-预测基因注释。为此，我们使用文本聚类的基因组的表型描述的基础上。我们发现，这些集群与基因组中的生物一致性的几个指标，如基因本体论（GO）和蛋白质-蛋白质相互作用的功能注释相关。我们利用这些集群来预测基因功能，将注释从注释良好的基因转移到同一集群中的其他特征较少的基因。对于通过应用客观标准选择的组的子集，我们可以从生物过程子本体预测GO术语注释，其精确度高达72.6%，召回率为16.7%，通过交叉验证进行评估。我们手动验证了其中的一些集群，发现它们表现出高度的生物一致性，例如，一组包含所有可用的触角果蝇气味受体，尽管不一致的GO注释。表型明显反映遗传活性的内在本质强调了它们在推断新基因功能方面的有用性。因此，大规模系统分析这些数据为推断基因的功能注释提供了许多可能性。我们表明，文本聚类可以在这个过程中发挥重要作用。
Health and disease of organisms are reflected in their phenotypes. Often, a genetic component to a disease is discovered only after clearly defining its phenotype. In the past years, many technologies to systematically generate phenotypes in a high-throughput manner, such as RNA interference or gene knock-out, have been developed and used to decipher functions for genes. However, there have been relatively few efforts to make use of phenotype data beyond the single genotype-phenotype relationships. We present results on a study where we use a large set of phenotype data – in textual form – to predict gene annotation. To this end, we use text clustering to group genes based on their phenotype descriptions. We show that these clusters correlate well with several indicators for biological coherence in gene groups, such as functional annotations from the Gene Ontology (GO) and protein-protein interactions. We exploit these clusters for predicting gene function by carrying over annotations from well-annotated genes to other, less-characterized genes in the same cluster. For a subset of groups selected by applying objective criteria, we can predict GO-term annotations from the biological process sub-ontology with up to 72.6% precision and 16.7% recall, as evaluated by cross-validation. We manually verified some of these clusters and found them to exhibit high biological coherence, e.g. a group containing all available antennal Drosophila odorant receptors despite inconsistent GO-annotations. The intrinsic nature of phenotypes to visibly reflect genetic activity underlines their usefulness in inferring new gene functions. Thus, systematically analyzing these data on a large scale offers many possibilities for inferring functional annotation of genes. We show that text clustering can play an important role in this process.
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来自果蝇的非常规的肌球蛋白重链基因。
影响因子：
7.8
作者：
KELLERMAN, KA;MILLER, KG
通讯作者：
MILLER, KG
影响因子：
5.8
作者：
Gaulton, Kyle J.;Mohlke, Karen L.;Vision, Todd J.
通讯作者：
Vision, Todd J.
影响因子：
16.2
作者：
Dobritsa, AA;van der Goes van Naters, W;Carlson, JR
通讯作者：
Carlson, JR
影响因子：
46.9
作者：
Butte, AJ;Kohane, IS
通讯作者：
Kohane, IS
DOI：
10.1083/jcb.129.6.1575
发表时间：
1995-06
期刊：
The Journal of cell biology
影响因子：
--
作者：
Mermall V;Miller KG
通讯作者：
Miller KG