Cappellato Marco, Baruzzo Giacomo, Patuzzi Ilaria, Di Camillo Barbara
1 Department of Information Engineering, University of Padova, Padova, Italy; 2Research & Development, Eubiome Srl, Padova, Italy.
Curr Genomics. 2021 Dec 16;22(4):267-290. doi: 10.2174/1389202921999200905133146.
In the current research landscape, microbiota composition studies are of extreme interest, since it has been widely shown that resident microorganisms affect and shape the ecological niche they inhabit. This complex micro-world is characterized by different types of interactions. Understanding these relationships provides a useful tool for decoding the causes and effects of communities' organizations. Next-Generation Sequencing technologies allow to reconstruct the internal composition of the whole microbial community present in a sample. Sequencing data can then be investigated through statistical and computational method coming from network theory to infer the network of interactions among microbial species. Since there are several network inference approaches in the literature, in this paper we tried to shed light on their main characteristics and challenges, providing a useful tool not only to those interested in using the methods, but also to those who want to develop new ones. In addition, we focused on the frameworks used to produce synthetic data, starting from the simulation of network structures up to their integration with abundance models, with the aim of clarifying the key points of the entire generative process.
在当前的研究领域中，微生物群组成研究备受关注，因为大量研究表明，常驻微生物会影响并塑造它们所栖息的生态位。这个复杂的微观世界具有不同类型的相互作用。理解这些关系为解读群落组织的因果关系提供了有用的工具。新一代测序技术能够重建样本中整个微生物群落的内部组成。然后，可以通过源于网络理论的统计和计算方法对测序数据进行研究，以推断微生物物种之间的相互作用网络。由于文献中有多种网络推断方法，本文试图阐明它们的主要特点和挑战，不仅为有兴趣使用这些方法的人，也为想要开发新方法的人提供有用的工具。此外，我们重点关注了用于生成合成数据的框架，从网络结构的模拟到与丰度模型的整合，旨在阐明整个生成过程的关键点。
