期刊：
2014 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE (BIBM),2014年:11-16 ISSN：2156-1125
通讯作者：
Wang, Yan
作者机构：
[Wang, Yan] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Peoples R China.;[He, Tingting; Shen, Xianjun; Hu, Xiaohua; Yuan, Jie] Cent China Normal Univ, Sch Comp Sci, Wuhan 430079, Peoples R China.;[Jiang, Xingpeng; Hu, Xiaohua] Drexel Univ, Coll Comp & Informat, Philadelphia, PA 19104 USA.
通讯机构：
[Wang, Yan] C;Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Peoples R China.
会议名称：
IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM)
会议时间：
NOV 02-22, 2014
会议地点：
Univ Ulster, Belfast, NORTH IRELAND
会议主办单位：
Univ Ulster
会议论文集名称：
IEEE International Conference on Bioinformatics and Biomedicine-BIBM
关键词：
Microbiome;Time series analysis;Vector autoregression model;Microbial interactions;Grouping effect
作者机构：
[Jiang XingPeng] Drexel Univ, Coll Comp & Informat, Philadelphia, PA 19104 USA.;[Jiang XingPeng; Hu XiaoHua] Cent China Normal Univ, Sch Comp, Wuhan 430079, Peoples R China.
通讯机构：
[Hu XiaoHua] C;Cent China Normal Univ, Sch Comp, Wuhan 430079, Peoples R China.
关键词：
species interaction;metagenome;diffusion process;biological network;modularity
摘要：
With the rapid accumulation of high-throughput metagenomic sequencing data, it is possible to infer microbial species relations in a microbial community systematically. In recent years, some approaches have been proposed for identifying microbial interaction network. These methods often focus on one dataset without considering the advantage of data integration. In this study, we propose to use a similarity network fusion (SNF) method to infer microbial relations. The SNF efficiently integrates the similarities of species derived from different datasets by a cross-network diffusion process. We also introduce consensus k-nearest neighborhood (Ck-NN) method instead of k-NN in the original SNF (we call the approach CSNF). The final network represents the augmented species relationships with aggregated evidence from various datasets, taking advantage of complementarity in the data. We apply the method on genus profiles derived from three microbiome datasets and we find that CSNF can discover the modular structure of microbial interaction network which cannot be identified by analyzing a single dataset.
摘要：
There is a growing recognition that the human microbiome - microbes living in intimate association with us - forms a vital part of our biology, and plays an important role in both health and sickness. A huge amount of data are being generated about these communities, much through metagenomics methods, which sequence DNA without directly identifying which organisms they come from. These data pose a tremendous opportunity for understanding, and a tremendous computational and theoretical challenge. In this paper, we compare several linear and nonlinear methods to explore human microbiome. There is a growing recognition that the human microbiome - microbes living in intimate association with us - forms a vital part of our biology, and plays an important role in both health and sickness. A huge amount of data are being generated about these communities, much through metagenomics methods, which sequence DNA without directly identifying which organisms they come from. These data pose a tremendous opportunity for understanding, and a tremendous computational and theoretical challenge. In this paper, we compare several linear and nonlinear methods to explore human microbiome.
作者机构：
College of Information Science and Technology, Drexel University, Philadelphia, PA, United States;Central China Normal University, Wuhan, Hubei, China
通讯机构：
College of Information Science and Technology, Drexel University, United States
会议名称：
IEEE International Conference on Bioinformatics and Biomedicine
会议时间：
2012-10-04
会议地点：
Philadelphia, PA(US)
会议论文集名称：
2012 IEEE international conference on bioinformatics and biomedicine
摘要：
Using metagenomics to detect the global structure of microbial community remains a significant challenge. The structure of a microbial community and its functions are complicated not only because of the complex interactions among microbes but also their complicate interacting with confounding environmental factors. Recently dimension reduction methods such as Principle component analysis, Non-negative matrix factorization and Canonical correlation analysis have been employed extensively to investigate the complex structure embedded in metagenomic profiles which summarize the abundance of functional or taxonomic categorizations in metagenomic studies. However, metagenomic profiles are not necessary to meet the "Assumption of Linearity" behind these methods. Therefore it is worth to investigate how nonlinear methods can be utilized in metagenomic studies. In this paper, a nonlinear manifold learning method- Isomap is used to visualize and analyze large-scale metagenomic profiles. Isomap was applied on a large-scale Pfam profile which are derived from 45 metagenomes in Global Ocean Sampling expedition. In our result, a novel nonlinear structure of protein families is identified and the relationships among the identified nonlinear components and environmental factors of global ocean are explored. The results indicate the strength of nonlinear methods in learning the complex microbial structure. With the coming of the huge number of new sequenced metagenomes, nonlinear methods like Isomap could be necessary complementary tools to current widely used methods.
