Marine Metagenomics

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 microorganisms which show positive or negative correlations with red tide causing alga using a new time-series network model.