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使用DAVID算法对非模式生物虹鳟鱼中自定义注释的基因列表进行聚类。

Use of DAVID algorithms for clustering custom annotated gene lists in a non-model organism, rainbow trout.

作者信息

Ma Hao, Gao Guangtu, Weber Gregory M

机构信息

National Center for Cool and Cold Water Aquaculture, Kearneysville, WV, 25430, USA.

出版信息

BMC Res Notes. 2018 Jan 23;11(1):63. doi: 10.1186/s13104-018-3154-7.

DOI:10.1186/s13104-018-3154-7
PMID:29361978
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5781295/
Abstract

OBJECTIVE

The DAVID gene functional classification tool requires adaptations for use in non-model species and there is little available information to guide selection of a kappa score. Our objective was to develop an R-script that allows custom gene identifiers and novel annotation information to be incorporated into analyses, then use such data to evaluate the number of differentially expressed genes (DEGs) in a comparison based on kappa score selection.

RESULTS

Using an R-script we developed and multiple data sets ranging from 555 to 3340 annotated DEGs from a study in rainbow trout, we found the percentage of DEGs harbored within a module and the number of genes shared among multiple modules decreased with increasing kappa score regardless of the number of DEGs in the comparison. The number of genes in enriched modules peaked at a kappa score of 0.5 for the comparisons with 3340 and 1313 DEGs and 0.3 for 555 DEGs. The number of genes harbored within enriched modules generally decreased with increasing kappa score; however, this was affected by whether the largest modules were significantly enriched. Large non-enriched modules can be reanalyzed using a higher kappa score resulting in some of the genes clustering in smaller enriched modules.

摘要

目的

DAVID基因功能分类工具需要进行调整以用于非模式物种,并且几乎没有可用信息来指导kappa评分的选择。我们的目标是开发一个R脚本,该脚本允许将自定义基因标识符和新的注释信息纳入分析,然后使用这些数据基于kappa评分选择来评估比较中差异表达基因(DEG)的数量。

结果

使用我们开发的R脚本以及来自虹鳟鱼研究的555至3340个注释DEG的多个数据集,我们发现模块内所含DEG的百分比以及多个模块之间共享的基因数量随kappa评分的增加而减少,而与比较中DEG的数量无关。对于有3340个和1313个DEG的比较,富集模块中的基因数量在kappa评分为0.5时达到峰值,对于555个DEG的比较,在kappa评分为0.3时达到峰值。富集模块中所含的基因数量通常随kappa评分的增加而减少;然而,这受到最大模块是否显著富集的影响。大型非富集模块可以使用更高的kappa评分重新分析,从而导致一些基因聚集在较小的富集模块中。

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