A critical assessment of Mus musculus gene function prediction using integrated genomic evidence.
A critical assessment of Mus musculus gene function prediction using integrated genomic evidence.
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DOI：
10.1186/gb-2008-9-s1-s2
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发表时间：
2008
期刊：
影响因子：
12.3
通讯作者：
Roth FP
中科院分区：
文献类型：
--
作者：
Peña-Castillo L;Tasan M;Myers CL;Lee H;Joshi T;Zhang C;Guan Y;Leone M;Pagnani A;Kim WK;Krumpelman C;Tian W;Obozinski G;Qi Y;Mostafavi S;Lin GN;Berriz GF;Gibbons FD;Lanckriet G;Qiu J;Grant C;Barutcuoglu Z;Hill DP;Warde-Farley D;Grouios C;Ray D;Blake JA;Deng M;Jordan MI;Noble WS;Morris Q;Klein-Seetharaman J;Bar-Joseph Z;Chen T;Sun F;Troyanskaya OG;Marcotte EM;Xu D;Hughes TR;Roth FP
对人类基因组和小鼠基因组进行测序几年后，关于大多数人类和小鼠基因的功能仍有许多待发现。基因功能的计算预测有望帮助将有限的实验资源集中在最可能的假设上。使用多种基因组数据的几种算法已应用于模型生物的该任务。但是，尚未评估哺乳动物中这种方法的性能。
在这项研究中，组装了小鼠功能基因组数据的标准化集合。九个生物信息学团队使用此数据集来独立训练分类器并生成基因本体论（GO）项定义的功能的预测，以21,603个小鼠基因；并且表现最好的提交组合成一组预测。我们确定了当前功能基因组数据集的优势和缺点，并比较了功能预测算法的性能。该分析推断了76％的小鼠基因的功能，其中包括目前未表征的5,000个。以20％的召回率，一组统一的预测平均为41％的精度，26％的GO期限的精度高于90％。
我们对哺乳动物中异质数据源预测基因功能的多样化，独立开发的计算方法进行了系统评估。结果表明，当前的哺乳动物可用数据允许具有广度和准确性的预测。重要的是，许多高度新颖的预测出现了38％的小鼠基因，这些小鼠基因保持未表征。
Several years after sequencing the human genome and the mouse genome, much remains to be discovered about the functions of most human and mouse genes. Computational prediction of gene function promises to help focus limited experimental resources on the most likely hypotheses. Several algorithms using diverse genomic data have been applied to this task in model organisms; however, the performance of such approaches in mammals has not yet been evaluated. In this study, a standardized collection of mouse functional genomic data was assembled; nine bioinformatics teams used this data set to independently train classifiers and generate predictions of function, as defined by Gene Ontology (GO) terms, for 21,603 mouse genes; and the best performing submissions were combined in a single set of predictions. We identified strengths and weaknesses of current functional genomic data sets and compared the performance of function prediction algorithms. This analysis inferred functions for 76% of mouse genes, including 5,000 currently uncharacterized genes. At a recall rate of 20%, a unified set of predictions averaged 41% precision, with 26% of GO terms achieving a precision better than 90%. We performed a systematic evaluation of diverse, independently developed computational approaches for predicting gene function from heterogeneous data sources in mammals. The results show that currently available data for mammals allows predictions with both breadth and accuracy. Importantly, many highly novel predictions emerge for the 38% of mouse genes that remain uncharacterized.
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影响因子：
5.8
作者：
Clare, A;Karwath, A;King, RD
通讯作者：
King, RD
Pfam：氏族、网络工具和服务。
影响因子：
14.9
作者：
Finn, Robert D.;Mistry, Jaina;Schuster-Bockler, Benjamin;Griffiths-Jones, Sam;Hollich, Volker;Lassmann, Timo;Moxon, Simon;Marshall, Mhairi;Khanna, Ajay;Durbin, Richard;Eddy, Sean R.;Sonnhammer, Erik L. L.;Bateman, Alex
通讯作者：
Bateman, Alex
DOI：
10.1073/pnas.0307326101
发表时间：
2004-03-02
影响因子：
11.1
作者：
Karaoz, U;Murali, TM;Kasif, S
通讯作者：
Kasif, S
影响因子：
5.8
作者：
Lanckriet, GRG;De Bie, T;Noble, WS
通讯作者：
Noble, WS
影响因子：
14.9
作者：
Chen, Y;Xu, D
通讯作者：
Xu, D