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Computational models for metatranscriptome analysis

宏转录组分析的计算模型

基本信息

批准号：
215674903
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：
2012
资助国家：
德国
起止时间：
2011-12-31 至 2015-12-31
项目状态：
已结题

来源：
https://gepris.dfg.de/gepris/projekt/215674903?language=en
关键词：
Computational models metatranscriptome analysis

项目摘要

Metagenomics has fundamentally changed the exploration of the microbial world because it allows to study organisms in their natural environment as part of complex communities. Metatranscriptomics based on sequencing of RNA in environmental samples has become a key technology to elucidate gene expression in microbial communities. In transcriptomics, high-throughput sequencing of RNA (RNA-Seq) has successfully been applied to gene expression analysis and well-established workflows exist for detection and interpretation of differential expression patterns. The goal of the proposed project is to examine how the existing pipelines for differential expression analysis can be extended and modified for an application to metatranscriptomic data. Current approaches to metatranscriptome analysis have mainly been adopted from comparative metagenomics, which does not provide integrated tools for differential expression analysis. The project will investigate how the analytical power of metatranscriptomics and the reproducibility of results can be improved by the incorporation of specific models from machine learning and RNA-Seq analysis. The design and evaluation of an optimized metatranscriptomics pipeline will be performed in close collaboration with the G¨ottingen Genomics Laboratory (G2L), which provides an outstanding expertise in metagenome analysis. In particular, the project will support an ongoing cooperation with the G2L for analysis of comprehensive metatranscriptome data from different soil communities.

宏基因组学从根本上改变了微生物世界的探索，因为它允许研究自然环境中的生物体作为复杂群落的一部分。基于环境样本中RNA测序的宏转录组学已成为阐明微生物群落基因表达的关键技术。在转录组学中，RNA 高通量测序 (RNA-Seq) 已成功应用于基因表达分析，并且存在用于检测和解释差异表达模式的完善工作流程。拟议项目的目标是检查如何扩展和修改现有的差异表达分析流程以应用于宏转录组数据。目前的宏转录组分析方法主要采用比较宏基因组学，并没有提供差异表达分析的集成工具。该项目将研究如何通过结合机器学习和 RNA-Seq 分析的特定模型来提高元转录组学的分析能力和结果的可重复性。优化宏转录组学流程的设计和评估将与哥廷根基因组实验室 (G2L) 密切合作，该实验室在宏基因组分析方面提供出色的专业知识。特别是，该项目将支持与 G2L 的持续合作，分析来自不同土壤群落的综合元转录组数据。