Computational models for metatranscriptome analysis

元基因组学从根本上改变了微生物世界的探索，因为它允许将自然环境中的有机体作为复杂群落的一部分进行研究。基于环境样品中RNA测序的转录组学已成为阐明微生物群落中基因表达的关键技术。在转录组学中，高通量RNA测序(RNA-Seq)已经成功地应用于基因表达分析，并存在着检测和解释差异表达模式的成熟工作流程。拟议项目的目标是研究如何扩展和修改用于差异表达分析的现有管道，以应用于元翻译数据。目前的元译码组分析方法主要采用比较元基因组学方法，没有为差异表达分析提供完整的工具。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翻译的分析能力和结果的重复性。优化的元转录组学管道的设计和评估将与在元基因组分析方面提供杰出专业知识的G？ottingen基因组实验室(G2L)密切合作进行。特别是，该项目将支持正在进行的与G2L的合作，以分析来自不同土壤群落的全面的元转录组数据。

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.