About this Research Topic
Given the the success of the previous Research Topic ‘Computational Methods for Microbiome Analysis’, we are pleased to launch a second volume for further submissions.
Microbes play critical roles in the lives of hosts (plants, animals, humans) and the environment. 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 the various subfields of Animal, Plant and Human health and Microbial Ecology dealing with Computational Methods for Microbiome Analysis.
Any microbiome-relevant method is of interest, including but not limited to methods applied to 16S/18S/ITS amplicon, shotgun metagenomics, metatranscriptomics, metaproteomics, metabolomics, and viromics data.
We welcome submissions of manuscripts describing novel methods for computational microbiome analysis, as well as a limited number of perspective papers and reviews on this general topic. Specific themes include, but are not limited to, machine learning methods, interaction modelling, databases, data mining, taxonomy classification, metagenome-assembled genomes, and phylogeny reconstruction methods specifically tailored to microbiome data.
Keywords: microbial ecology, human health, metagenomics, data mining, machine learning
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.