A systematic study of genome context methods: calibration, normalization and combination.
A systematic study of genome context methods: calibration, normalization and combination.
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DOI：
10.1186/1471-2105-11-493
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发表时间：
2010-10-01
影响因子：
3
通讯作者：
Karp PD
中科院分区：
文献类型：
--
作者：
Ferrer L;Dale JM;Karp PD
基因组背景方法在过去十年中被引入，作为自动方法，使用一组参考基因组中这些基因同源物的存在模式和相对位置来预测目标基因组中基因之间的功能相关性。在将这些方法应用于不同的生物信息学任务方面已经做了很多工作，但很少有论文对这些方法及其最佳使用所需的组合进行系统研究。我们对文献中发现的基因组背景方法的四个主要家族进行了全面的研究：系统发育谱、基因融合、基因簇和基因邻居。我们发现，对于大多数生物体，基因邻居方法的灵敏度比系统发育图谱方法高出 40%，在低灵敏度下与基因簇方法具有竞争力。基因融合通常是四种方法中表现最差的。对每种方法的参数空间进行了彻底的探索，并给出了不同目标生物体的结果。我们建议使用标准化程序，就像用于基因组上下文评分的微阵列数据一样。我们表明，使用简单的归一化技术可以获得显着的收益。特别是，系统发育图谱方法的灵敏度在归一化后提高了约 25%，据我们所知，这是文献中表现最好的系统发育图谱系统。最后，我们展示了将各种基因组背景方法组合成一个分数的结果。当使用交叉验证程序来训练组合器时，以原始分数和归一化分数作为输入，决策树组合器相对于基因邻居方法可获得高达 20% 的增益。总体而言，这比该领域的最新技术提高了约 15%：使用 STRING 数据库中使用的程序组合的四种原始基因组背景方法。不幸的是，我们发现，当组合器仅用系统发育上远离目标生物体的生物体进行训练时，这些收益就会消失。我们的实验表明，基因邻居是最好的个体基因组背景方法，并且个体方法组合的收益对用于获得组合器参数的训练数据非常敏感。如果没有足够的训练数据，则使用基因邻居得分本身而不是组合得分可能是最佳选择。
Genome context methods have been introduced in the last decade as automatic methods to predict functional relatedness between genes in a target genome using the patterns of existence and relative locations of the homologs of those genes in a set of reference genomes. Much work has been done in the application of these methods to different bioinformatics tasks, but few papers present a systematic study of the methods and their combination necessary for their optimal use. We present a thorough study of the four main families of genome context methods found in the literature: phylogenetic profile, gene fusion, gene cluster, and gene neighbor. We find that for most organisms the gene neighbor method outperforms the phylogenetic profile method by as much as 40% in sensitivity, being competitive with the gene cluster method at low sensitivities. Gene fusion is generally the worst performing of the four methods. A thorough exploration of the parameter space for each method is performed and results across different target organisms are presented. We propose the use of normalization procedures as those used on microarray data for the genome context scores. We show that substantial gains can be achieved from the use of a simple normalization technique. In particular, the sensitivity of the phylogenetic profile method is improved by around 25% after normalization, resulting, to our knowledge, on the best-performing phylogenetic profile system in the literature. Finally, we show results from combining the various genome context methods into a single score. When using a cross-validation procedure to train the combiners, with both original and normalized scores as input, a decision tree combiner results in gains of up to 20% with respect to the gene neighbor method. Overall, this represents a gain of around 15% over what can be considered the state of the art in this area: the four original genome context methods combined using a procedure like that used in the STRING database. Unfortunately, we find that these gains disappear when the combiner is trained only with organisms that are phylogenetically distant from the target organism. Our experiments indicate that gene neighbor is the best individual genome context method and that gains from the combination of individual methods are very sensitive to the training data used to obtain the combiner's parameters. If adequate training data is not available, using the gene neighbor score by itself instead of a combined score might be the best choice.
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影响因子：
14.9
作者：
Caspi R;Altman T;Dale JM;Dreher K;Fulcher CA;Gilham F;Kaipa P;Karthikeyan AS;Kothari A;Krummenacker M;Latendresse M;Mueller LA;Paley S;Popescu L;Pujar A;Shearer AG;Zhang P;Karp PD
通讯作者：
Karp PD
影响因子：
64.8
作者：
Marcotte, EM;Pellegrini, M;Eisenberg, D
通讯作者：
Eisenberg, D
影响因子：
14.9
作者：
Karp PD;Ouzounis CA;Moore-Kochlacs C;Goldovsky L;Kaipa P;Ahrén D;Tsoka S;Darzentas N;Kunin V;López-Bigas N
通讯作者：
López-Bigas N
影响因子：
12.3
作者：
Bowers PM;Pellegrini M;Thompson MJ;Fierro J;Yeates TO;Eisenberg D
通讯作者：
Eisenberg D
影响因子：
14.9
作者：
Green ML;Karp PD
通讯作者：
Karp PD