About this Research Topic
Given the success of Volume I of this Research Topic, and how rapidly the subject area is evolving, we are pleased to announce the launch of Volume II: Computational Solutions for Microbiome and Metagenomics Sequencing Analyses.
Microorganisms take up more than 17% of the known biomass on Earth and show a tremendous biodiversity. Microorganisms present a huge potential genomic deposit for biosciences, with the human microbiota alone possessing 300 times the number of human genes - this includes genes related to favorite biological functions such as high-efficient enzymes, signal metabolites or nutrition that are important to human health. Exploring these genomic functions is an important scope of microbiome genomics. Due to the difficulties of detecting and culturing microbiota using traditional techniques, the pervasive role of microbiota in ecosystems and human health has long been unrecognized, but have been increasingly identified with the advent of high-throughput sequencing technologies.
Metagenomic (amplicon, whole-genome or transcriptome) sequencing can characterize the genomic composition of entire microbiota communities directly using environmental samples, as such it is now a primary tool for exploring the multifarious composition of microbiota communities, facilitating the discovery of microbial biomarkers and investigating their genomic functions. Developing bioinformatic methods for metagenomic data processing and mining would greatly help with advancing our knowledge on microbiota communities and their interactions with humans and the environment.
Despite a fair number of computational pipelines being established and numerous bioscience breakthroughs in recent years, the tremendous diversity and fast-evolving nature of microorganisms still poses severe challenges to accurate and reliable resolution of metagenomic sequencing data. Imperative demands are persistently raised for bioinformatic solutions that can handle the massive amounts of data generated, increase the precision and efficiency of data processing, and provide a more comprehensive and translational interpretation about the biomedical meaning of the computational results.
The aim of this Research Topic is to address the computational challenges raised from processing large-scale microbial metagenomic sequencing data in single or multiple aspects such as taxonomic binning, functional characterization, biomarker discovery and community dynamic modelling. Areas we aim to cover include (but are not limited to):
• Quality control and assembly of high-throughput (2nd or 3rd generation) metagenomic sequencing data
• Sequence alignment, sequence clustering and phylogenetic analysis of highly-diversified genomic components
• Approaches for defining or identifying microbial taxonomy units from sequencing
• Bioinformatics for novel high-throughput viral or microbial detection techniques
• Functional genomics/transcriptomics for viral and microbial subjects
• Computational methods for modelling the dynamics of metagenomic compositions
• Machine learning methods for microbe biomarker discovery and microbial network analysis
• Metagenome association study for human diseases and environment diversity
• Interactions of genomic variations and metagenome for human healthcare
Microorganisms take up more than 17% of the known biomass on Earth and show a tremendous biodiversity. Microorganisms present a huge potential genomic deposit for biosciences, with the human microbiota alone possessing 300 times the number of human genes - this includes genes related to favorite biological functions such as high-efficient enzymes, signal metabolites or nutrition that are important to human health. Exploring these genomic functions is an important scope of microbiome genomics. Due to the difficulties of detecting and culturing microbiota using traditional techniques, the pervasive role of microbiota in ecosystems and human health has long been unrecognized, but have been increasingly identified with the advent of high-throughput sequencing technologies.
Metagenomic (amplicon, whole-genome or transcriptome) sequencing can characterize the genomic composition of entire microbiota communities directly using environmental samples, as such it is now a primary tool for exploring the multifarious composition of microbiota communities, facilitating the discovery of microbial biomarkers and investigating their genomic functions. Developing bioinformatic methods for metagenomic data processing and mining would greatly help with advancing our knowledge on microbiota communities and their interactions with humans and the environment.
Despite a fair number of computational pipelines being established and numerous bioscience breakthroughs in recent years, the tremendous diversity and fast-evolving nature of microorganisms still poses severe challenges to accurate and reliable resolution of metagenomic sequencing data. Imperative demands are persistently raised for bioinformatic solutions that can handle the massive amounts of data generated, increase the precision and efficiency of data processing, and provide a more comprehensive and translational interpretation about the biomedical meaning of the computational results.
The aim of this Research Topic is to address the computational challenges raised from processing large-scale microbial metagenomic sequencing data in single or multiple aspects such as taxonomic binning, functional characterization, biomarker discovery and community dynamic modelling. Areas we aim to cover include (but are not limited to):
• Quality control and assembly of high-throughput (2nd or 3rd generation) metagenomic sequencing data
• Sequence alignment, sequence clustering and phylogenetic analysis of highly-diversified genomic components
• Approaches for defining or identifying microbial taxonomy units from sequencing
• Bioinformatics for novel high-throughput viral or microbial detection techniques
• Functional genomics/transcriptomics for viral and microbial subjects
• Computational methods for modelling the dynamics of metagenomic compositions
• Machine learning methods for microbe biomarker discovery and microbial network analysis
• Metagenome association study for human diseases and environment diversity
• Interactions of genomic variations and metagenome for human healthcare
Keywords: Metagenomics, Human Microbiome, Environmental Microbiome, Microbial Networks, Microbe Biomarkers
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