Directing experimental biology: a case study in mitochondrial biogenesis.
Directing experimental biology: a case study in mitochondrial biogenesis.
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指导实验生物学：线粒体生物发生的案例研究。
DOI：
10.1371/journal.pcbi.1000322
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发表时间：
2009-03
影响因子：
4.3
通讯作者：
Troyanskaya OG
中科院分区：
文献类型：
--
作者：
Hibbs MA;Myers CL;Huttenhower C;Hess DC;Li K;Caudy AA;Troyanskaya OG
计算方法已经承诺将功能基因组学数据集合组织成基因和蛋白质参与生物过程和途径的可测试预测。然而，这样的预测很少得到大规模的实验验证，使许多生物信息学方法在生物界未经证实和未充分利用。此外，在使用计算方法推动现实世界的实验努力时，还不清楚应该考虑哪些生物学问题。为了研究这些问题并建立基因功能计算预测的实用性，我们实验测试了数百种预测，这些预测来自三种互补方法的集合，用于酿酒酵母线粒体组织和生物发生过程。有关线粒体的生物学数据发表在《公共科学图书馆·遗传学》（doi:10.1371/journal.pgen.1000407）的一篇论文中。在这里，我们分析和探索了这项研究的结果，这些结果广泛适用于应用基因功能预测技术的计算学家，包括与代表基因组背景的48个基因的新实验比较。我们的研究得出了几个结论，这些结论在使用计算预测方法进行实验室研究时非常重要。虽然酵母中的大多数基因已经知道参与至少一个生物过程，但我们确认具有已知功能的基因仍然可以作为附加基因功能注释的强有力候选者。我们发现，不同的分析技术和不同的底层数据都可以极大地影响计算方法产生的功能预测类型。这种多样性使得综合技术能够大大拓宽生物学的范围和预测的广度。我们还发现，迭代地执行预测和验证步骤使我们能够更完整地表征感兴趣的生物区域。虽然这项研究集中在酵母的特定功能区域，但许多这些观察结果可能在其他过程和生物体的背景下有用。基因组测序为我们提供了许多生物基因的“部件列表”，但这些基因的许多生物学作用仍然未知。虽然存在大量的功能基因组数据，提供了有关这些基因及其作用的信息，但这些数据转化为具体生物学知识的速度远远落后于数据生成的速度。许多计算方法已经被开发出来，以产生基因功能的准确预测，其目标是弥合这一鸿沟。然而，由于没有基于这些方法的大规模实验努力，它们的有效性和实用性仍未得到证实。我们进行了一项研究，实验评估了三种计算函数预测方法的组合预测，重点关注啤酒酵母中线粒体相关过程作为模型系统。通过使用计算预测来指导我们的实验室研究，我们大大加快了蛋白质被分配到生物过程的速度。此外，我们的研究结果表明，为了获得最佳结果，计算生物学家考虑用于预测功能的方法的潜在数据和算法基础是很重要的。最后，我们证明了通过预测和验证阶段的迭代已经迅速而广泛地扩展了我们对线粒体生物学的知识。
Computational approaches have promised to organize collections of functional genomics data into testable predictions of gene and protein involvement in biological processes and pathways. However, few such predictions have been experimentally validated on a large scale, leaving many bioinformatic methods unproven and underutilized in the biology community. Further, it remains unclear what biological concerns should be taken into account when using computational methods to drive real-world experimental efforts. To investigate these concerns and to establish the utility of computational predictions of gene function, we experimentally tested hundreds of predictions generated from an ensemble of three complementary methods for the process of mitochondrial organization and biogenesis in Saccharomyces cerevisiae. The biological data with respect to the mitochondria are presented in a companion manuscript published in PLoS Genetics (doi:10.1371/journal.pgen.1000407). Here we analyze and explore the results of this study that are broadly applicable for computationalists applying gene function prediction techniques, including a new experimental comparison with 48 genes representing the genomic background. Our study leads to several conclusions that are important to consider when driving laboratory investigations using computational prediction approaches. While most genes in yeast are already known to participate in at least one biological process, we confirm that genes with known functions can still be strong candidates for annotation of additional gene functions. We find that different analysis techniques and different underlying data can both greatly affect the types of functional predictions produced by computational methods. This diversity allows an ensemble of techniques to substantially broaden the biological scope and breadth of predictions. We also find that performing prediction and validation steps iteratively allows us to more completely characterize a biological area of interest. While this study focused on a specific functional area in yeast, many of these observations may be useful in the contexts of other processes and organisms. Genome sequencing has provided us with “parts lists” of genes for many organisms, but many of the biological roles these genes are still unknown. While a great deal of functional genomic data exists, providing information about these genes and their roles, the rate at which these data are leveraged into concrete biological knowledge lags far behind the rate of data generation. Many computational approaches have been developed to generate accurate predictions of gene functions, with the goal of bridging this divide. However, as no large-scale experimental efforts have been based on such approaches, their validity and utility remains unproven. We have performed a study that experimentally evaluates predictions from a combination of three computational function prediction approaches, focusing on mitochondrion-related processes in brewer's yeast as a model system. By using computational predictions to guide our laboratory investigation, we have greatly accelerated the rate at which proteins can be assigned to biological processes. Further, our results demonstrate that in order to achieve the best results, it is important for computational biologists to consider both the underlying data and the algorithmic foundations of the methods used to predict function. Lastly, we demonstrate that iterating through phases of prediction and validation has quickly and extensively expanded our knowledge of mitochondrial biology.
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影响因子：
5.8
作者：
Hibbs, Matthew A.;Hess, David C.;Troyanskaya, Olga G.
通讯作者：
Troyanskaya, Olga G.
DOI：
10.1083/jcb.141.6.1371
发表时间：
1998-06-15
期刊：
The Journal of cell biology
影响因子：
--
作者：
Boldogh I;Vojtov N;Karmon S;Pon LA
通讯作者：
Pon LA
影响因子：
30.8
作者：
Koutnikova, H;Campuzano, V;Koenig, M
通讯作者：
Koenig, M
影响因子：
12.3
作者：
Myers CL;Robson D;Wible A;Hibbs MA;Chiriac C;Theesfeld CL;Dolinski K;Troyanskaya OG
通讯作者：
Troyanskaya OG
影响因子：
56.9
作者：
Babcock, M;deSilva, D;Kaplan, J
通讯作者：
Kaplan, J