TITLE:
Current Trend of Metagenomic Data Analytics for Cyanobacteria Blooms
AUTHORS:
JianDong Huang, Huiru Jane Zheng, Haiying Wang
KEYWORDS:
Cyanobacteria Blooms, Harmful algal, Metagenomics, Machine Learning, Environmental Factors, Next Generation Sequencing Techniques (NGS), 16S rRNA, Fresh Water Ecosystem, Lakes
JOURNAL NAME:
Journal of Geoscience and Environment Protection,
Vol.5 No.6,
June
30,
2017
ABSTRACT: Cyanobacterial harmful algal blooms are a major threat to freshwater eco-systems 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.