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The Human Virome

Overview of attention for book
Cover of 'The Human Virome'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 Flow Cytometry and Direct Sequencing of Viruses
  3. Altmetric Badge
    Chapter 2 Tissue-Based Universal Virus Detection (TUViD-VM) Protocol for Viral Metagenomics
  4. Altmetric Badge
    Chapter 3 Protocol for Generating Infectious RNA Viromes from Complex Biological Samples
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    Chapter 4 Phage on Tap: A Quick and Efficient Protocol for the Preparation of Bacteriophage Laboratory Stocks
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    Chapter 5 Extraction and Purification of Viruses from Fecal Samples for Metagenome and Morphology Analyses
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    Chapter 6 Virome Sequencing of Stool Samples
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    Chapter 7 NetoVIR: Modular Approach to Customize Sample Preparation Procedures for Viral Metagenomics
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    Chapter 8 Viral Genome Isolation from Human Faeces for Succession Assessment of the Human Gut Virome
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    Chapter 9 Introduction to Techniques and Methodologies for Characterizing the Human Respiratory Virome
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    Chapter 10 Targeted Sequencing of Respiratory Viruses in Clinical Specimens for Pathogen Identification and Genome-Wide Analysis
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    Chapter 11 Methods for Enrichment and Sequencing of Oral Viral Assemblages: Saliva, Oral Mucosa, and Dental Plaque Viromes
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    Chapter 12 A Method for Isolation of the Virome from Plasma Samples
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    Chapter 13 Viral Concentration and Amplification from Human Serum Samples Prior to Application of Next-Generation Sequencing Analysis
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    Chapter 14 Identification and Quantification of DNA Viral Populations in Human Urine Using Next-Generation Sequencing Approaches
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    Chapter 15 Diversity Analysis in Viral Metagenomes
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    Chapter 16 Construction of a Comprehensive Database from the Existing Viral Sequences Available from the International Nucleotide Sequence Database Collaboration
  18. Altmetric Badge
    Chapter 17 Robust Analysis of Time Series in Virome Metagenomics
  19. Altmetric Badge
    Chapter 18 Bioinformatics Assembling and Assessment of Novel Coxsackievirus B1 Genome
Attention for Chapter 17: Robust Analysis of Time Series in Virome Metagenomics
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (83rd percentile)
  • High Attention Score compared to outputs of the same age and source (98th percentile)

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8 X users

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Chapter title
Robust Analysis of Time Series in Virome Metagenomics
Chapter number 17
Book title
The Human Virome
Published in
Methods in molecular biology, August 2018
DOI 10.1007/978-1-4939-8682-8_17
Pubmed ID
Book ISBNs
978-1-4939-8681-1, 978-1-4939-8682-8
Authors

Jose Manuel Martí, Martí, Jose Manuel

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.

X Demographics

X Demographics

The data shown below were collected from the profiles of 8 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 22 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 22 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 6 27%
Student > Master 4 18%
Student > Ph. D. Student 3 14%
Student > Doctoral Student 1 5%
Professor 1 5%
Other 1 5%
Unknown 6 27%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 5 23%
Agricultural and Biological Sciences 3 14%
Immunology and Microbiology 2 9%
Environmental Science 1 5%
Computer Science 1 5%
Other 3 14%
Unknown 7 32%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 12. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 24 April 2019.
All research outputs
#2,773,122
of 24,093,053 outputs
Outputs from Methods in molecular biology
#512
of 13,601 outputs
Outputs of similar age
#55,440
of 337,113 outputs
Outputs of similar age from Methods in molecular biology
#6
of 250 outputs
Altmetric has tracked 24,093,053 research outputs across all sources so far. Compared to these this one has done well and is in the 88th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 13,601 research outputs from this source. They receive a mean Attention Score of 3.5. This one has done particularly well, scoring higher than 96% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 337,113 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 83% of its contemporaries.
We're also able to compare this research output to 250 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 98% of its contemporaries.