A computational approach to candidate gene prioritization for X-linked mental retardation using annotation-based binary filtering and motif-based linear discriminatory analysis.
A computational approach to candidate gene prioritization for X-linked mental retardation using annotation-based binary filtering and motif-based linear discriminatory analysis.
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DOI：
10.1186/1745-6150-6-30
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发表时间：
2011-06-13
期刊：
影响因子：
5.5
通讯作者：
Ramsay M
中科院分区：
文献类型：
--
作者：
Lombard Z;Park C;Makova KD;Ramsay M
最近开发了几种计算候选基因选择和优先排序方法。这些计算机选择和优先排序技术通常基于两个中心方法——检查与已知疾病基因的相似性和/或评估基因的功能注释。这些方法中的每一种都有其自己的注意事项。在这里，我们采用先前描述的主要基于基因注释的候选基因优先排序方法，并结合基于相关序列基序或签名评估的技术，试图改进基因优先排序方法。我们将这种方法应用于 X 连锁智力低下 (XLMR)，这是一组异质性疾病，其一些潜在的遗传学已知。基于基因注释的二元过滤方法产生了推定的 XLMR 候选基因的排名列表，这些基因很可能与精神发育迟滞的发展相关。同时，采用基于线性判别分析 (LDA) 的基序寻找方法来识别可以区分 XLMR 和非 XLMR 基因的短序列模式。正确分类率很高 (>80%)，这表明这些基序的识别有效捕获了与 XLMR 与非 XLMR 基因相关的基因组信号。为基于基序的 LDA 开发的计算工具已集成到免费提供的基因组分析门户 Galaxy (http://main.g2.bx.psu.edu/) 中。九个基因（APLN、ZC4H2、MAGED4、MAGED4B、RAP2C、FAM156A、FAM156B、TBL1X 和 UXT）被强调为排名较高的 XLMR 方法。基因注释信息和面向序列基序的计算候选基因预测方法的结合突出了生成合理候选基因列表的额外好处，正如 XLMR 所证明的那样。审稿人：本文由 Barbara Bardoni 博士（由 Juergen Brosius 教授提名）审阅； Neil Smalheiser 教授和 Dustin Holloway 博士（由 Charles DeLisi 教授提名）。
Several computational candidate gene selection and prioritization methods have recently been developed. These in silico selection and prioritization techniques are usually based on two central approaches - the examination of similarities to known disease genes and/or the evaluation of functional annotation of genes. Each of these approaches has its own caveats. Here we employ a previously described method of candidate gene prioritization based mainly on gene annotation, in accompaniment with a technique based on the evaluation of pertinent sequence motifs or signatures, in an attempt to refine the gene prioritization approach. We apply this approach to X-linked mental retardation (XLMR), a group of heterogeneous disorders for which some of the underlying genetics is known. The gene annotation-based binary filtering method yielded a ranked list of putative XLMR candidate genes with good plausibility of being associated with the development of mental retardation. In parallel, a motif finding approach based on linear discriminatory analysis (LDA) was employed to identify short sequence patterns that may discriminate XLMR from non-XLMR genes. High rates (>80%) of correct classification was achieved, suggesting that the identification of these motifs effectively captures genomic signals associated with XLMR vs. non-XLMR genes. The computational tools developed for the motif-based LDA is integrated into the freely available genomic analysis portal Galaxy (http://main.g2.bx.psu.edu/). Nine genes (APLN, ZC4H2, MAGED4, MAGED4B, RAP2C, FAM156A, FAM156B, TBL1X, and UXT) were highlighted as highly-ranked XLMR methods. The combination of gene annotation information and sequence motif-orientated computational candidate gene prediction methods highlight an added benefit in generating a list of plausible candidate genes, as has been demonstrated for XLMR. Reviewers: This article was reviewed by Dr Barbara Bardoni (nominated by Prof Juergen Brosius); Prof Neil Smalheiser and Dr Dustin Holloway (nominated by Prof Charles DeLisi).
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影响因子：
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作者：
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通讯作者：
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Galaxy：一种基于Web的基因组分析工具。
影响因子：
--
作者：
Blankenberg, Daniel;Von Kuster, Gregory;Coraor, Nathaniel;Ananda, Guruprasad;Lazarus, Ross;Mangan, Mary;Nekrutenko, Anton;Taylor, James
通讯作者：
Taylor, James