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Part 1: Technological Aspects of Marine Metagenomics: Sample Collection and preparation methods
Chapter 1: Metagenomic methods: from seawater to the database
Chapter 2: Collection of microbial DNA from marine sediments
Chapter 3: Primer design, evaluation of primer universality and estimation of identification power of amplicon sequences in silico
Chapter 4: High coverage expression profiling (HiCEP) of microbial community genomes in the ocean
Part 2: Technological Aspects of Marine Metagenomics: Metagenome Data Analysis
Chapter 5: Introduction and application of Digital DNA Chip Analysis (DDCA) to metagenomic analysis
Chapter 6: Horizontal gene transfer in marine environment: a technical perspective on metagenomics
Chapter 7: MAPLE enables functional assessment of microbiota in various environments
Part 3: Applications in Ocean and Fisheries Sciences: Diversity and Function of Microbial Community
Chapter 8: Comparison of microscopic and PCR amplicon and shotgun metagenomic approaches applied to marine diatom communities
Chapter 9: Seasonal dynamics of bacterial community composition in coastal seawater at Sendai Bay, Japan
Chapter 10: Shotgun metagenome analyses: seasonality monitoring in Sendai Bay and search for red tide marker sequences
Chapter 11: Distribution and community composition of ammonia-oxidizing archaea and bacteria in coastal sediments in response to sediment material gradients at Sendai Bay, Japan
Chapter 12: Marine metagenomic sequence counts of reads assigned to taxa consistently proportionate to read counts obtained for per g of sea water sample
Chapter 13: New aquaculture technology based on host-symbiotic co-metabolism
Part 4: Applications in Ocean and Fisheries Sciences: Analysis of the Red Tide
Chapter 14: Influences of diurnal sampling bias on fixed-point monitoring of plankton biodiversity determined using a massively parallel sequencing-based technique
Chapter 15: Detection of microorg anisms which show positive or negative correlations with red tide causing alga using a new time-series network model.

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