Machine learning methods for genome reconstruction in metagenomics

元基因组学已成为分析微生物群落的标准方法。环境或临床样本的测序导致大量短序列片段的收集，从而能够对物种组成和代谢潜力进行全面分析。测序技术的进步大大增加了测序深度，因此即使对于最复杂的群落，也有可能组装微生物基因组的长连续区域(重叠群)。最近的研究表明，通过将这些重叠群分组到基因组特定的存储箱中，重建几乎完整的基因组是可能的。元基因组组库是一个具有计算挑战性的问题，尽管目前用于基因组恢复的组库工具都基于相似的聚类技术，但结果的质量取决于用户规范，并且在不同的社区和工具之间存在很大差异。为了充分挖掘元基因组组库用于基因组重建的潜力，我们的目标是开发一个机器学习框架，使结果的准确性和重复性能够自动优化。为了实现这一目标，我们将把最先进的机器学习模型与计数数据的统计模型和基于模拟的总体模型质量控制相结合。在与核桃溪联合基因组研究所的合作下，我们将开发一种新的方法，根据蛋白质结构域内容和与已知基因组的比对来评估恢复的基因组的完整性和污染。与只对最终垃圾箱进行质量检查的常见工作流程不同，我们将把质量控制作为基于机器学习的垃圾箱优化过程的一个组成部分。在与哥廷根基因组和应用微生物学系的持续合作中，我们还将调查来自第三代测序技术的读数有多长可以用来测量和提高入库准确性。与我们来自森林植物和树木生理学系的Göttingen合作伙伴一起，我们的目标是将装箱方法扩展到真菌群落，这将为生态学研究打开新的可能性。

Metagenomics has become a standard approach for the analysis of microbial communities. The sequencing of environmental or clinical samples results in large collections of short sequence fragments that enable a comprehensive analysis of the species composition and the metabolic potential. The progress in sequencing technologies has given rise to a significantly increased sequencing depth so that it has become possible to assemble long contiguous regions (contigs) of microbial genomes even for the most complex communities. Recent studies have shown that it is possible to reconstruct nearly complete genomes by grouping these contigs into genome-specific bins. Metagenome binning is a computationally challenging problem, and although current binning tools for genome recovery are all based on similar clustering techniques, the quality of results depends on user specifications and largely varies across different communities and tools. To fully exploit the potential of metagenome binning for genome reconstruction, we aim to develop a machine learning framework that will enable an automatic optimization of the accuracy and the reproducibility of results. To achieve this goal we will integrate state-of-the-art machine learning models together with statistical models for count data and a simulation-based control of the overall model quality. In a collaboration with the Joint Genome Institute in Walnut Creek we will develop a new approach to assess the completeness and contamination of recovered genomes based on the protein domain content and on alignments with known genomes. In contrast to the common workflow of metagenome binning which applies quality checks just to the final bins we will include quality control as an integral part of the machine learning-based bin optimization process. In an ongoing collaboration with the Department of Genomic and Applied Microbiology in Göttingen we will also investigate how long reads from third generation sequencing technologies can be utilized to measure and improve binning accuracy. Together with our Göttingen collaboration partner from the Department of Forest Botany and Tree Physiology we aim to extend the binning approach to fungal communities which will open up new possibilities for research in ecology.