Prediction of gene expression in embryonic structures of Drosophila melanogaster.
Prediction of gene expression in embryonic structures of Drosophila melanogaster.
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果蝇果蝇胚胎结构中基因表达的预测。
DOI：
10.1371/journal.pcbi.0030144
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发表时间：
2007-07
影响因子：
4.3
通讯作者：
Brazma, Alvis
中科院分区：
文献类型：
--
作者：
Samsonova, Anastasia A.;Niranjan, Mahesan;Russell, Steven;Brazma, Alvis
了解基因组如何在空间和时间上协调调节，以产生细胞类型的多样性，从而使复杂的后生动物成为现代生物学的一个主要挑战。高通量方法的使用，如大规模原位杂交和通过DNA微阵列进行全基因组表达谱分析，正开始为了解发育的复杂性提供见解。然而，在许多生物体中，全面的原位定位数据的收集和注释是一项困难且耗时的任务。在这里，我们提出了一个广泛适用的计算方法，整合发展的时间过程的微阵列数据与注释原位杂交研究，有利于从头预测的组织特异性表达的基因，没有在体内基因表达定位数据。使用分类的方法，训练数据从微阵列和原位杂交研究的基因表达在果蝇胚胎发育过程中，我们提出了一套预测组织特异性表达的果蝇基因，还没有系统地表征原位杂交实验。我们的预测的可靠性证实了文献来源的注释在FlyBase中，基因本体生物过程注释的过度表达，并在选定的一组，从文献中详细的基因特异性研究。我们的新的独立于生物体的方法将在丰富复杂的多细胞生物体中的基因功能和表达的注释方面具有相当大的实用性。破译控制发育的复杂转录调控网络的任务是分子生物学当前的主要挑战之一。如果没有基因表达时空动态的详细知识，这个问题即使不是不可能也很难解决。因此，为了理解发展，我们需要识别和功能特性的所有球员在监管网络。从全转录组微阵列实验中获得的基因表达动态数据，结合基因子集的原位杂交mRNA定位模式，可以提供一种预测那些原位数据尚未生成的基因的基因表达定位的途径，以及为未表征的基因提供功能信息。在这里，我们报告的第一个方法预测基因表达的定位在果蝇胚胎发生的微阵列数据的发展。汇集苍蝇基因组中的基因子集与原位数据形成功能单位，在空间和时间上定位于相关的发育过程，有利于分类问题的陈述，我们用机器学习方法解决这个问题。我们的方法在没有昂贵和耗时的实验分析的情况下促进了对基因生物学功能的更丰富的注释。
Understanding how sets of genes are coordinately regulated in space and time to generate the diversity of cell types that characterise complex metazoans is a major challenge in modern biology. The use of high-throughput approaches, such as large-scale in situ hybridisation and genome-wide expression profiling via DNA microarrays, is beginning to provide insights into the complexities of development. However, in many organisms the collection and annotation of comprehensive in situ localisation data is a difficult and time-consuming task. Here, we present a widely applicable computational approach, integrating developmental time-course microarray data with annotated in situ hybridisation studies, that facilitates the de novo prediction of tissue-specific expression for genes that have no in vivo gene expression localisation data available. Using a classification approach, trained with data from microarray and in situ hybridisation studies of gene expression during Drosophila embryonic development, we made a set of predictions on the tissue-specific expression of Drosophila genes that have not been systematically characterised by in situ hybridisation experiments. The reliability of our predictions is confirmed by literature-derived annotations in FlyBase, by overrepresentation of Gene Ontology biological process annotations, and, in a selected set, by detailed gene-specific studies from the literature. Our novel organism-independent method will be of considerable utility in enriching the annotation of gene function and expression in complex multicellular organisms. The task of deciphering the complex transcriptional regulatory networks controlling development is one of the major current challenges for molecular biology. The problem is difficult, if not impossible, to solve without a detailed knowledge of the spatiotemporal dynamics of gene expression. Thus, to understand development, we need to identify and functionally characterize all players in regulatory networks. Data on gene expression dynamics obtained from whole transcriptome microarray experiments, combined with in situ hybridization mRNA localisation patterns for a subset of genes, may provide a route for predicting the localisation of gene expression for those genes for which in situ data has not been generated, as well as suggesting functional information for uncharacterised genes. Here, we report the development of one of the first methods for predicting the localisation of gene expression during Drosophila embryogenesis from microarray data. Pooling the subset of genes in the fly genome with in situ data to form functional units, localised in space and time for relevant developmental processes, facilitates the statement of a classification problem, which we address with machine-learning methods. Our approach promotes a richer annotation of biological function for genes in the absence of costly and time-consuming experimental analysis.
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DOI：
10.1073/pnas.97.1.262
发表时间：
2000-01-04
影响因子：
11.1
作者：
Brown, MPS;Grundy, WN;Haussler, D
通讯作者：
Haussler, D
影响因子：
12.3
作者：
Gentleman RC;Carey VJ;Bates DM;Bolstad B;Dettling M;Dudoit S;Ellis B;Gautier L;Ge Y;Gentry J;Hornik K;Hothorn T;Huber W;Iacus S;Irizarry R;Leisch F;Li C;Maechler M;Rossini AJ;Sawitzki G;Smith C;Smyth G;Tierney L;Yang JY;Zhang J
通讯作者：
Zhang J
影响因子：
56.9
作者：
Golub, TR;Slonim, DK;Lander, ES
通讯作者：
Lander, ES
影响因子：
7.7
作者：
Adams, NM;Hand, DJ
通讯作者：
Hand, DJ
影响因子：
64.5
作者：
KOLODKIN, AL;MATTHES, DJ;GOODMAN, CS
通讯作者：
GOODMAN, CS