Impact Factor 4.076

The 3rd most cited journal in Microbiology

Methods ARTICLE Provisionally accepted The full-text will be published soon. Notify me

Front. Microbiol. | doi: 10.3389/fmicb.2018.00872

Identifying Group-Specific Sequences for Microbial Communities using Long k-mer Sequence Signatures

 Ying Wang1*,  Lei Fu1,  Jie Ren2,  Zhaoxia Yu3, Ting Chen2, 4 and Fengzhu Sun2, 5*
  • 1Dept. of Automation, Xiamen University, China
  • 2University of Southern California, United States
  • 3University of California, Irvine, United States
  • 4Tsinghua University, China
  • 5Fudan University, China

Comparing metagenomic samples is crucial for understanding microbial communities. For different groups of microbial communities, such as human gut metagenomics samples from patients with a certain disease and healthy controls, identifying group-specific sequences offers essential information for potential biomarker discovery. A sequence that is present, or rich, in one group, but absent, or scarce, in another group is considered “group-specific” in our study. Our main purpose is to discover group-specific sequence regions between control and case groups as disease-associated markers. We developed a long k-mer (k >= 30 bps) based computational pipeline to detect group-specific sequences at strain resolution free from reference sequences, sequence alignments and metagenome-wide de novo assembly. We called our method MetaGO: Group-specific Oligonucleotide analysis for Metagenomic samples. An open-source pipeline on Apache Spark was developed with parallel computing.
We applied MetaGO to one simulated and three real metagenomic datasets to evaluate the discriminative capability of identified group-specific markers. In the simulated dataset, 98.91% of group-specific logical 40-mers covered 98.89% disease-specific regions from the disease-associated strain. In addition, 98.83% of group-specific numerical 40-mers covered 99.01% and 97.30% of differential-abundant genome and regions between two groups, respectively. For a large-scale metagenomic Liver Cirrhosis (LC)-associated dataset. We identified 37,647 group-specific 40-mer features. Any one of the features can predict disease status of the training samples with average of sensitivity and specificity higher than 0.8. The random forests classification using the top 10 group-specific features yielded a higher AUC (from ~0.8 to ~0.9) than that of previous studies. All group-specific 40-mers were present in LC patients, but not healthy controls. All the assembled 11 LC-specific sequences can be mapped to two strains of Veillonella parvula: UTDB1-3 and DSM2008. The experiments on the other two real datasets, Inflammatory Bowel Disease-associated and Type 2 Diabetes in Women-associated, consistently demonstrated that MetaGO achieved better prediction accuracy with fewer features compared with previous studies.
The experiments showed that MetaGO is a powerful tool for identifying group-specific k-mers, and these features accurately predict disease status using a single k-mer or only a few k-mers, which would be clinically applicable for disease prediction.

Keywords: Long k-mer, Classification, Metagenomics, microbial community, Disease Prediction, group-specific sequence

Received: 15 Nov 2017; Accepted: 16 Apr 2018.

Edited by:

Jessica Galloway-Pena, University of Texas MD Anderson Cancer Center, United States

Reviewed by:

Wenxuan Zhong, University of Georgia, United States
Jonathan Badger, National Cancer Institute (NCI), United States  

Copyright: © 2018 Wang, Fu, Ren, Yu, Chen and Sun. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence:
PhD. Ying Wang, Xiamen University, Dept. of Automation, Xiamen, China, wangying@xmu.edu.cn
Prof. Fengzhu Sun, University of Southern California, Los Angeles, United States, fsun@dornsife.usc.edu