Strategies for the identification, the assembly and the classification of integrated biological systems in completely sequenced genomes

Yves Quentin , Julie Chabalier , Gwennaele Fichant
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引用次数: 11

Abstract

The proteins involved in a single biological process may form a stable supra-molecular assembly or be transiently in interaction. Although, the first annotation steps of a complete genome may allow the identification of the different partners, their assembly in a functional system, referred to as an integrated system, is a domain where methodological effort has to be done. Indeed, the knowledge required to assemble partners of such systems should be explicitly included in annotation software. The availability of a complete genome, and therefore of all the proteins encoded by that genome, motivated the development of automated approaches through the coordinated combination of different bio-informatic methods allowing the identification of the different partners, their assembly and the classification of the reconstructed systems in functional categories. In this data flux, the identification of the sequence partners represents the principal bottleneck. Here, we describe and compare the results obtained with different classes of methods (blastp2, psi-blast, mast and Meta-meme) applied to the identification in complete genomes of a given family of integrated systems: the ABC transporters. psi-blast appears to significantly outperform motif-based methods, and the results are discussed according to the nature of the proteins and the structure of the sub-families.

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在完全测序的基因组中鉴定、组装和分类整合生物系统的策略
参与单一生物过程的蛋白质可能形成稳定的超分子组合,也可能是短暂的相互作用。虽然,完整基因组的第一个注释步骤可能允许识别不同的伙伴,但它们在一个功能系统中的组装(称为集成系统)是一个必须完成方法学工作的领域。实际上,组装这种系统的伙伴所需的知识应该明确地包含在注释软件中。完整基因组的可用性,以及由该基因组编码的所有蛋白质的可用性,推动了自动化方法的发展,通过协调不同生物信息学方法的组合,允许识别不同的伙伴,它们的组装和在功能类别中对重建系统进行分类。在这种数据流中,序列伙伴的识别是主要的瓶颈。在这里,我们描述并比较了不同类别的方法(blastp2、psi-blast、mast和Meta-meme)在完整基因组中鉴定给定的综合系统家族(ABC转运蛋白)所获得的结果。Psi-blast似乎明显优于基于基序的方法,并根据蛋白质的性质和亚家族的结构对结果进行了讨论。
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Instructions to authors Author Index Keyword Index Volume contents New molecular surface-based 3D-QSAR method using Kohonen neural network and 3-way PLS
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