Microbes play critical roles in the lives of hosts (plants, animals, humans) and in the environment. At the same time, gathering microbiome sequence data has become easier and cheaper than ever before, leading to an exponential growth in the amount of such data available for analysis. With this explosion has come a pressing need for sophisticated computational tools that can help make sense of these data, supported by an active international research community. Current challenges, such as the complexity of microbiome-host-environment interactions and the large sizes of datasets, make for a fascinating research field where inspired scientists drive ongoing innovations.
The goal of this Research Topic is to generate a collection of high-quality papers describing the state of the art in computational methods for microbiome analysis. Any microbiome-relevant method is of interest, including but not limited to 16S/18S/ITS amplicon, shotgun metagenomics, metatranscriptomics, metaproteomics, metabolomics, and viromics data.
We welcome submissions describing novel methods for computational microbiome analysis in the form of Original Research and Methods papers. A limited number of Brief Research Reports, Reviews and Perspectives will also be considered. Specific themes include, but are not limited to, machine learning methods, databases, data mining, taxonomy classification, genome recovery, and phylogeny reconstruction methods specifically tailored to microbiome data.
Microbes play critical roles in the lives of hosts (plants, animals, humans) and in the environment. At the same time, gathering microbiome sequence data has become easier and cheaper than ever before, leading to an exponential growth in the amount of such data available for analysis. With this explosion has come a pressing need for sophisticated computational tools that can help make sense of these data, supported by an active international research community. Current challenges, such as the complexity of microbiome-host-environment interactions and the large sizes of datasets, make for a fascinating research field where inspired scientists drive ongoing innovations.
The goal of this Research Topic is to generate a collection of high-quality papers describing the state of the art in computational methods for microbiome analysis. Any microbiome-relevant method is of interest, including but not limited to 16S/18S/ITS amplicon, shotgun metagenomics, metatranscriptomics, metaproteomics, metabolomics, and viromics data.
We welcome submissions describing novel methods for computational microbiome analysis in the form of Original Research and Methods papers. A limited number of Brief Research Reports, Reviews and Perspectives will also be considered. Specific themes include, but are not limited to, machine learning methods, databases, data mining, taxonomy classification, genome recovery, and phylogeny reconstruction methods specifically tailored to microbiome data.