Microbiome Search Engine 2: a Platform for Taxonomic and Functional Search of Global Microbiomes on the Whole-Microbiome Level

Abstract

Metagenomic data sets from diverse environments have been growing rapidly. To ensure accessibility and reusability, tools that quickly and informatively correlate new microbiomes with existing ones are in demand. Here, we introduce Microbiome Search Engine 2 (MSE 2), a microbiome database platform for searching query microbiomes in the global metagenome data space based on the taxonomic or functional similarity of a whole microbiome to those in the database. MSE 2 consists of (i) a well-organized and regularly updated microbiome database that currently contains over 250,000 metagenomic shotgun and 16S rRNA gene amplicon samples associated with unified metadata collected from 798 studies, (ii) an enhanced search engine that enables real-time and fast (<0.5 s per query) searches against the entire database for best-matched microbiomes using overall taxonomic or functional profiles, and (iii) a Web-based graphical user interface for user-friendly searching, data browsing, and tutoring. MSE 2 is freely accessible via http://mse.ac.cn. For standalone searches of customized microbiome databases, the kernel of the MSE 2 search engine is provided at GitHub (https://github.com/qibebt-bioinfo/meta-storms). IMPORTANCE A search-based strategy is useful for large-scale mining of microbiome data sets, such as a bird’s-eye view of the microbiome data space and disease diagnosis via microbiome big data. Here, we introduce Microbiome Search Engine 2 (MSE 2), a microbiome database platform for searching query microbiomes against the existing microbiome data sets on the basis of their similarity in taxonomic structure or functional profile. Key improvements include database extension, data compatibility, a search engine kernel, and a user interface. The new ability to search the microbiome space via functional similarity greatly expands the scope of search-based mining of the microbiome big data.

A Scale-Free, Fully Connected Global Transition Network Underlies Known Microbiome Diversity

“…Theoretically, higher similarity between two communities indicates higher probability for such microbiome transition, since fewer compositional exchanges are needed; however, it is not clear what level of similarity may indicate such microbial transition with reasonable confidence. Based on a pairwise full permutation of similarity calculation among all microbiomes from the Microbiome Search Engine (MSE) database (MSE is a microbiome database platform for searching query microbiomes against the global metagenome data space based on the whole-community-level similarity [ 11 ]; it contains 177,022 samples in total) (refer to the “Microbiome sample collection” section for details) using the Meta-Storms algorithm ( 12 , 13 ) ( Table 1 ), we consider that “direct transition” possibly exists between sample pairs with significant similarities that cause permutation P values of <0.01 ( equation 1 ; Fig. 1 ).…”

“…Here, we propose a microbiome transition model and a network-based analysis framework to describe and simulate the variation and dispersal of the global microbial beta-diversity across multiple habitats. Benefitting from the extremely high search speed of the Microbiome Search Engine ( 11 ), we introduced a global microbiome network with 177,022 microbiome samples that contains 11.3 billion sequences. By traversing such a network, we showed the microbiome structures are connected world-wide by significant similarity that follows the “small world” principle.…”

Elucidating the Beta-Diversity of the Microbiome: from Global Alignment to Local Alignment

“…One key demand and bottleneck has been relating newly sampled microbiomes to existing data. Thus, we developed a Microbiome Search Engine (MSE) for rapid search of query microbiomes against a database of microbiomes, on the whole-community level ( 10 ). Basically, with a given query community, MSE compares it against a data repository and returns top hits with highest Meta-Storms similarity in real time (e.g., <0.5 s per query in 1 million samples).…”

“…An exhaustive screening that compares the target fractions to all samples is a straightforward way, but it is time-consuming when the database is huge. Currently, there are two types of indexing strategies available for accelerating the microbiome search, (i) a static partitions index that groups database into subcategories sorted by structural features, e.g., Microbiome Search Engine v1.0 ( 5 ) or Meta-Prism ( 23 ); (ii) a dynamic index based on the dimension reduction of microbial profiles employed by Microbiome Search Engine 2 ( 10 ). Both of the approaches depend on the preprocessing of the entire collection of reference samples in the database construction step in order to rapidly fetch the candidate hits in the subsequent query step.…”

The predictive power of saliva electrolytes exceeds that of saliva microbiomes in diagnosing early childhood caries

“…The sequencing data in this study have been submitted to the Microbiome Search Engine (MSE) [45] and NCBI Sequence Read Archive, and can be accessed through the project ID P_SCC0005 (http:// mse.single-cell.cn/index.php/mse/get_by_project/P_ SCC0005) and BioProject numbers PRJNA717886.…”