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Current Trend of Metagenomic Data Analytics for Cyanobacteria Blooms

Huang, Jian Dong, Zheng, Huiru and Wang, Haiying / HY (2017) Current Trend of Metagenomic Data Analytics for Cyanobacteria Blooms. Journal of Geoscience and Environment Protection, 05 (06). pp. 198-213. [Journal article]

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URL: http://dx.doi.org/10.4236/gep.2017.56018

DOI: 10.4236/gep.2017.56018


Cyanobacterial harmful algal blooms are a major threat to freshwater ecosystems globally. To deal with this threat, researches into the cyanobacteria bloom in fresh water lakes and rivers have been carried out all over the world. This review presents an overlook of studies on cyanobacteria blooms. Conventional studies mainly focus on investigating the environmental factors influencing the blooms, with their limitation in lack of viewing the microbial community structures. Metagenomics study provides insight into the internal community structure of the cyanobacteria at the blooming, and there are researchers reported that sequence data was a better predictor than environmental factors. This further manifests the significance of the metagenomic study. However, large number of the latter appears to be confined only to present snapshoot of the microbial community diversity and structure. This type of investigation has been valuable and important, whilst an effort to integrate and coordinate the conventional approaches that largely focus on the environmental factors control, and the Metagenomics approaches that reveals the microbial community structure and diversity, implemented through machine learning techniques, for a holistic and more comprehensive insight into the cause and control of Cyanobacteria blooms, appear to be a trend and challenge of the study of this field.

Item Type:Journal article
Keywords:Cyanobacteria Blooms, Harmful algal, Metagenomics, Machine Learning, Environmental Factors, Next Generation Sequencing Techniques (NGS), 16S rRNA, Fresh Water Ecosystem, Lakes
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Mathematics
Faculty of Life and Health Sciences > School of Geography and Environmental Sciences
Faculty of Life and Health Sciences
Research Institutes and Groups:Computer Science Research Institute > Smart Environments
Computer Science Research Institute
Computer Science Research Institute > Artificial Intelligence and Applications
Environmental Sciences Research Institute > Coastal Systems
ID Code:38389
Deposited By: Dr Huiru Zheng
Deposited On:02 Aug 2017 08:56
Last Modified:02 Aug 2017 08:56

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