Xiaoquan Su1, Jian Xu, Kang Ning. 1. Shandong Key Laboratory of Energy Genetics, CAS Key Laboratory of Biofuels and BioEnergy Genome Center, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao 266101, Shandong Province, People's Republic of China.
Abstract
BACKGROUND: It has long been intriguing scientists to effectively compare different microbial communities (also referred as 'metagenomic samples' here) in a large scale: given a set of unknown samples, find similar metagenomic samples from a large repository and examine how similar these samples are. With the current metagenomic samples accumulated, it is possible to build a database of metagenomic samples of interests. Any metagenomic samples could then be searched against this database to find the most similar metagenomic sample(s). However, on one hand, current databases with a large number of metagenomic samples mostly serve as data repositories that offer few functionalities for analysis; and on the other hand, methods to measure the similarity of metagenomic data work well only for small set of samples by pairwise comparison. It is not yet clear, how to efficiently search for metagenomic samples against a large metagenomic database. RESULTS: In this study, we have proposed a novel method, Meta-Storms, that could systematically and efficiently organize and search metagenomic data. It includes the following components: (i) creating a database of metagenomic samples based on their taxonomical annotations, (ii) efficient indexing of samples in the database based on a hierarchical taxonomy indexing strategy, (iii) searching for a metagenomic sample against the database by a fast scoring function based on quantitative phylogeny and (iv) managing database by index export, index import, data insertion, data deletion and database merging. We have collected more than 1300 metagenomic data from the public domain and in-house facilities, and tested the Meta-Storms method on these datasets. Our experimental results show that Meta-Storms is capable of database creation and effective searching for a large number of metagenomic samples, and it could achieve similar accuracies compared with the current popular significance testing-based methods. CONCLUSION: Meta-Storms method would serve as a suitable database management and search system to quickly identify similar metagenomic samples from a large pool of samples. CONTACT: ningkang@qibebt.ac.cn SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
BACKGROUND: It has long been intriguing scientists to effectively compare different microbial communities (also referred as 'metagenomic samples' here) in a large scale: given a set of unknown samples, find similar metagenomic samples from a large repository and examine how similar these samples are. With the current metagenomic samples accumulated, it is possible to build a database of metagenomic samples of interests. Any metagenomic samples could then be searched against this database to find the most similar metagenomic sample(s). However, on one hand, current databases with a large number of metagenomic samples mostly serve as data repositories that offer few functionalities for analysis; and on the other hand, methods to measure the similarity of metagenomic data work well only for small set of samples by pairwise comparison. It is not yet clear, how to efficiently search for metagenomic samples against a large metagenomic database. RESULTS: In this study, we have proposed a novel method, Meta-Storms, that could systematically and efficiently organize and search metagenomic data. It includes the following components: (i) creating a database of metagenomic samples based on their taxonomical annotations, (ii) efficient indexing of samples in the database based on a hierarchical taxonomy indexing strategy, (iii) searching for a metagenomic sample against the database by a fast scoring function based on quantitative phylogeny and (iv) managing database by index export, index import, data insertion, data deletion and database merging. We have collected more than 1300 metagenomic data from the public domain and in-house facilities, and tested the Meta-Storms method on these datasets. Our experimental results show that Meta-Storms is capable of database creation and effective searching for a large number of metagenomic samples, and it could achieve similar accuracies compared with the current popular significance testing-based methods. CONCLUSION: Meta-Storms method would serve as a suitable database management and search system to quickly identify similar metagenomic samples from a large pool of samples. CONTACT: ningkang@qibebt.ac.cn SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.