title: Rapid comparison and correlation analysis among massive number of microbial community samples based on MDV data model.
abstract: The research in microbial communities would potentially impact a vast number of applications in "bio"-related disciplines. Large-scale analyses became a clear trend in microbial community studies, thus it is increasingly important to perform efficient and in-depth data mining for insightful biological principles from large number of samples. However, as microbial communities are from different sources and of different structures, comparison and data-mining from large number of samples become quite difficult. In this work, we have proposed a data model to represent large-scale comparison of microbial community samples, namely the "Multi-Dimensional View" data model (the MDV model) that should at least include 3 aspects: samples profile (S), taxa profile (T) and meta-data profile (V). We have also proposed a method for rapid data analysis based on the MDV model and applied it on the case studies with samples from various environmental conditions. Results have shown that though sampling environments usually define key variables, the analysis could detect bio-makers and even subtle variables based on large number of samples, which might be used to discover novel principles that drive the development of communities. The efficiency and effectiveness of data analysis method based on the MDV model have been validated by the results.
authors.affiliation: Shandong Key Laboratory of Energy Genetics, CAS Key Laboratory of Biofuels and BioEnergy Genome Center, Computational Biology Group of Single Cell Center, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences. Qingdao 266101, People's Republic of China.
authors.forename: Xiaoquan
authors.initials: X
authors.lastname: Su
authors.affiliation: 1] Shandong Key Laboratory of Energy Genetics, CAS Key Laboratory of Biofuels and BioEnergy Genome Center, Computational Biology Group of Single Cell Center, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences. Qingdao 266101, People's Republic of China [2] University of Chinese Academy of Sciences, Beijing 100049, China.
authors.forename: Jianqiang
authors.initials: J
authors.lastname: Hu
authors.affiliation: 1] Shandong Key Laboratory of Energy Genetics, CAS Key Laboratory of Biofuels and BioEnergy Genome Center, Computational Biology Group of Single Cell Center, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences. Qingdao 266101, People's Republic of China [2] University of Chinese Academy of Sciences, Beijing 100049, China.
authors.forename: Shi
authors.initials: S
authors.lastname: Huang
authors.affiliation: Shandong Key Laboratory of Energy Genetics, CAS Key Laboratory of Biofuels and BioEnergy Genome Center, Computational Biology Group of Single Cell Center, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences. Qingdao 266101, People's Republic of China.
authors.forename: Kang
authors.initials: K
authors.lastname: Ning
mesh_terms: D000465:Algorithms; D001419:Bacteria / Q000145:classification* / Q000235:genetics*; D019295:Computational Biology / Q000379:methods*; D057225:Data Mining / Q000379:methods*; D005260:Female; D006801:Humans; D008297:Male; D008962:Models, Theoretical; D010802:Phylogeny; D011381:Programming Languages; D012984:Software; D012988:Soil Microbiology; D014871:Water Microbiology