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Robust Analysis of Time Series in Virome Metagenomics

  • Jose Manuel MartíEmail author
Part of the Methods in Molecular Biology book series (MIMB, volume 1838)

Abstract

Metagenomics is a powerful tool for assessing the functional and taxonomic contents in biological samples as it makes feasible to study, simultaneously, the whole living community related to a host organism or medium: all the microbes, including virus, bacteria, archaea, fungi, and protists. New DNA and RNA sequencing technologies are dramatically decreasing the cost per sequenced base, so metagenomic sequencing is becoming more and more widespread in biomedical and environmental research. This is opening the possibility of complete longitudinal metagenomic studies, which could unravel the dynamics of microbial communities including intra-microbiome and host-microbiome interactions through in-depth analysis of time series. For viruses, this is particularly interesting because it allows broad interaction studies of viruses and hosts in different time scales, as in bacteria–phages coevolution studies.

This chapter presents computational methods for an automatic and robust analysis of metagenomic time series in virome metagenomics (RATSVM). The same theoretical frame and computational protocol is also suitable for longitudinal studies of spatial series to uncover the dynamics of a microbial community with viruses along a selected dimension in the space. In order to conveniently illustrate the procedure, real data from a published virome study is used. The computational protocol presented here requires only basic computational knowledge. Several scripts have been prepared to ease and automate the most complicate steps, they are available in the RATSVM public repository. For some of the methods a mid-range computing server is advisable, and for some others, it is required. A fat-node with large memory and fast I/O would be the best choice for optimum results.

Key words

Robust-analysis Time-series Longitudinal metagenomics RATSVM Virome Host-microbiome coevolution Bacteria-phages coevolution  

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Institute for Integrative Systems Biology (I2SysBio)Parc Científic de la Universitat de ValènciaValenciaSpain

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