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Compositional descriptors for large scale comparative metagenome analysis

用于大规模比较宏基因组分析的组成描述符

基本信息

批准号：
178869699
负责人：
Dr. Peter Meinicke
金额：
--
依托单位：
Abteilung für Bioinformatik
依托单位国家：
德国
项目类别：
Research Grants
财政年份：
2010
资助国家：
德国
起止时间：
2009-12-31 至 2013-12-31
项目状态：
已结题

来源：
https://gepris.dfg.de/gepris/projekt/178869699?language=en
关键词：
Compositional descriptors large scale comparative

项目摘要

Metagenomics together with metatranscriptomics provides an essential tool for the investigation of the phylogenetic distribution, the functional potential and the metabolic activity of microbial communities under natural conditions. Currently, rapid progress in sequencing technologies dramatically improves the basis for metagenomic studies. The growing amount and the heterogeneity of sequence material, however, implies a big challenge for bioinformatics. The goal of this project is to realize a bioinformatics framework for comparative metagenome and metatranscriptome analysis which is based on statistical descriptors derived from the protein domain distribution of a sample. These descriptors will be designed to provide a high degree of invariance across different sequencing platforms and a highly informative view on the phylogenetic and functional composition of microbial communities. A probabilistic mixture modelling approach will be utilized to achieve cross-platform comparability of taxonomic and functional descriptors. With the inclusion of an ultra-fast method for identification of protein domains, developed by the applicant, the approach will readily be applicable to metagenomic projects of any size, and the results will even be reproducible by small labs and institutions which do not possess extensive computational facilities. For evaluation of the approach strong collaboration partners from the field of marine microbiology will be involved to test the methodology under real world conditions providing valuable feedback for refinement of the methods.

宏基因组学和宏转录组学为研究自然条件下微生物群落的系统发育分布、功能潜力和代谢活动提供了重要的工具。目前，测序技术的快速进步极大地改善了宏基因组研究的基础。然而，序列材料的数量不断增加且异质性给生物信息学带来了巨大的挑战。该项目的目标是实现用于比较宏基因组和宏转录组分析的生物信息学框架，该框架基于从样本的蛋白质域分布衍生的统计描述符。这些描述符的设计目的是在不同测序平台之间提供高度的不变性，并提供有关微生物群落的系统发育和功能组成的信息丰富的视图。将利用概率混合建模方法来实现分类和功能描述符的跨平台可比性。通过包含由申请人开发的用于识别蛋白质结构域的超快速方法，该方法将很容易适用于任何规模的宏基因组项目，并且结果甚至可以由不具备广泛计算设施的小型实验室和机构重现。为了评估该方法，来自海洋微生物学领域的强大合作伙伴将参与在现实条件下测试该方法，为方法的完善提供宝贵的反馈。