Method development for cross-study microbiome data mining: Challenges and opportunities

Abstract

During the past decade, tremendous amount of microbiome sequencing data has been generated to study on the dynamic associations between microbial profiles and environments. How to precisely and efficiently decipher large-scale of microbiome data and furtherly take advantages from it has become one of the most essential bottlenecks for microbiome research at present. In this mini-review, we focus on the three key steps of analyzing cross-study microbiome datasets, including microbiome profiling, data integrating and data mining. By introducing the current bioinformatics approaches and discussing their limitations, we prospect the opportunities in development of computational methods for the three steps, and propose the promising solutions to multi-omics data analysis for comprehensive understanding and rapid investigation of microbiome from different angles, which could potentially promote the data-driven research by providing a broader view of the "microbiome data space".

Keywords: Amplicon sequencing; Data mining; Microbiome; Microbiome search; Multi-omics data; Shotgun metagenome.

Fig. 1

Key steps for meta-analysis on cross-study microbiome big-data. (a) Compositional profiling decodes the microbiome taxonomical and functional profiles from sequences. (b) Data integration curates, normalizes and unifies existing datasets. (c) Data mining identifies and classifies the status of a given specimen by learned microbial features from integrated data.