A Bayesian method for identifying missing enzymes in predicted metabolic pathway databases.
A Bayesian method for identifying missing enzymes in predicted metabolic pathway databases.
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DOI：
10.1186/1471-2105-5-76
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发表时间：
2004-06-09
影响因子：
3
通讯作者：
Karp PD
中科院分区：
文献类型：
--
作者：
Green ML;Karp PD
PathoLogic 程序通过使用基因组注释来预测生物体中存在的一组代谢途径来构建途径/基因组数据库。 PathoLogic 根据生物体基因组中注释的酶确定构成这些途径的反应集。大多数注释工作未能将功能分配给 40-60% 的序列。此外，大量序列可能具有非特异性注释（例如硫解酶家族蛋白）。当基因组似乎缺乏催化途径反应所需的酶时，就会出现途径空洞。如果在注释过程中未为蛋白质分配特定功能，则该蛋白质催化的任何反应都将在路径/基因组数据库中显示为缺失的酶或路径孔。我们开发了一种方法，可以有效地将同源性和基于通路的证据结合起来，以识别填补通路/基因组数据库中通路漏洞的候选者。我们的程序不仅识别通路孔的潜在候选序列，而且结合来自多个异质源的数据来评估候选序列具有所需功能的可能性。我们的算法模拟手动序列注释过程，不仅考虑同源搜索的证据，还考虑基因组背景（即基因是操纵子的一部分吗？）和功能背景（例如，基因组附近是否存在功能相关的基因？）的证据，以确定候选者具有所需功能的后验信念。该方法可以应用于整个代谢途径网络，并且通常适用于任何途径数据库。该程序使用一组编码其他基因组中所需活性的序列来识别感兴趣的基因组中的候选蛋白质，然后使用简单的贝叶斯分类器评估每个候选蛋白质，以确定候选蛋白质具有所需功能的概率。在使用计算预测途径数据库中的已知反应进行交叉验证期间，我们在概率阈值 0.9 下实现了 71% 的精度。将我们的方法应用于来自三个通路/基因组数据库的 333 条通路中的 513 个通路孔后，我们将完整通路的数量增加了 42%。我们对 46% 的孔进行了假定的分配，包括对 17 个先前未知功能的序列进行注释。我们的通路孔填充器不仅可用于提高通路/基因组数据库对实验和计算研究人员的实用性，还可用于改进蛋白质功能的预测。
The PathoLogic program constructs Pathway/Genome databases by using a genome's annotation to predict the set of metabolic pathways present in an organism. PathoLogic determines the set of reactions composing those pathways from the enzymes annotated in the organism's genome. Most annotation efforts fail to assign function to 40–60% of sequences. In addition, large numbers of sequences may have non-specific annotations (e.g., thiolase family protein). Pathway holes occur when a genome appears to lack the enzymes needed to catalyze reactions in a pathway. If a protein has not been assigned a specific function during the annotation process, any reaction catalyzed by that protein will appear as a missing enzyme or pathway hole in a Pathway/Genome database. We have developed a method that efficiently combines homology and pathway-based evidence to identify candidates for filling pathway holes in Pathway/Genome databases. Our program not only identifies potential candidate sequences for pathway holes, but combines data from multiple, heterogeneous sources to assess the likelihood that a candidate has the required function. Our algorithm emulates the manual sequence annotation process, considering not only evidence from homology searches, but also considering evidence from genomic context (i.e., is the gene part of an operon?) and functional context (e.g., are there functionally-related genes nearby in the genome?) to determine the posterior belief that a candidate has the required function. The method can be applied across an entire metabolic pathway network and is generally applicable to any pathway database. The program uses a set of sequences encoding the required activity in other genomes to identify candidate proteins in the genome of interest, and then evaluates each candidate by using a simple Bayes classifier to determine the probability that the candidate has the desired function. We achieved 71% precision at a probability threshold of 0.9 during cross-validation using known reactions in computationally-predicted pathway databases. After applying our method to 513 pathway holes in 333 pathways from three Pathway/Genome databases, we increased the number of complete pathways by 42%. We made putative assignments to 46% of the holes, including annotation of 17 sequences of previously unknown function. Our pathway hole filler can be used not only to increase the utility of Pathway/Genome databases to both experimental and computational researchers, but also to improve predictions of protein function.
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影响因子：
14.9
作者：
Tatusov, RL;Natale, DA;Koonin, EV
通讯作者：
Koonin, EV
影响因子：
5.6
作者：
KROGH, A;BROWN, M;HAUSSLER, D
通讯作者：
HAUSSLER, D
DOI：
10.1073/pnas.96.8.4285
发表时间：
1999-04-13
影响因子：
11.1
作者：
Pellegrini, M;Marcotte, EM;Yeates, TO
通讯作者：
Yeates, TO
影响因子：
5.8
作者：
Karplus, K;Barrett, C;Hughey, R
通讯作者：
Hughey, R
影响因子：
12.3
作者：
Reed JL;Vo TD;Schilling CH;Palsson BO
通讯作者：
Palsson BO