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Engineering and Analyzing Multicellular Systems

Overview of attention for book
Cover of 'Engineering and Analyzing Multicellular Systems'

Table of Contents

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    Book Overview
  2. Altmetric Badge
    Chapter 1 Recent progress in engineering human-associated microbiomes.
  3. Altmetric Badge
    Chapter 2 Constructing synthetic microbial communities to explore the ecology and evolution of symbiosis.
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    Chapter 3 Combining engineering and evolution to create novel metabolic mutualisms between species.
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    Chapter 4 Design, construction, and characterization methodologies for synthetic microbial consortia.
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    Chapter 5 An Observation Method for Autonomous Signaling-Mediated Synthetic Diversification in Escherichia coli.
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    Chapter 6 Integration-Free Reprogramming of Human Somatic Cells to Induced Pluripotent Stem Cells (iPSCs) Without Viral Vectors, Recombinant DNA, and Genetic Modification.
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    Chapter 7 Transformation of Bacillus subtilis.
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    Chapter 8 Culturing anaerobes to use as a model system for studying the evolution of syntrophic mutualism.
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    Chapter 9 Therapeutic microbes for infectious disease.
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    Chapter 10 Quantitative measurement and analysis in a synthetic pattern formation multicellular system.
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    Chapter 11 Transcriptome Analysis of a Microbial Coculture in which the Cell Populations Are Separated by a Membrane.
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    Chapter 12 Identification of Mutations in Laboratory-Evolved Microbes from Next-Generation Sequencing Data Using breseq.
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    Chapter 13 3D-Fluorescence In Situ Hybridization of Intact, Anaerobic Biofilm.
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    Chapter 14 The characterization of living bacterial colonies using nanospray desorption electrospray ionization mass spectrometry.
  16. Altmetric Badge
    Chapter 15 Modeling community population dynamics with the open-source language R.
  17. Altmetric Badge
    Chapter 16 Simulating microbial community patterning using biocellion.
Attention for Chapter 12: Identification of Mutations in Laboratory-Evolved Microbes from Next-Generation Sequencing Data Using breseq.
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (52nd percentile)
  • High Attention Score compared to outputs of the same age and source (85th percentile)

Mentioned by

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4 tweeters

Citations

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17 Dimensions

Readers on

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577 Mendeley
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Chapter title
Identification of Mutations in Laboratory-Evolved Microbes from Next-Generation Sequencing Data Using breseq.
Chapter number 12
Book title
Engineering and Analyzing Multicellular Systems
Published in
Methods in molecular biology, April 2014
DOI 10.1007/978-1-4939-0554-6_12
Pubmed ID
Book ISBNs
978-1-4939-0553-9, 978-1-4939-0554-6
Authors

Daniel E Deatherage, Jeffrey E Barrick, Daniel E. Deatherage, Jeffrey E. Barrick, Deatherage, Daniel E., Barrick, Jeffrey E.

Editors

Lianhong Sun, Wenying Shou

Abstract

Next-generation DNA sequencing (NGS) can be used to reconstruct eco-evolutionary population dynamics and to identify the genetic basis of adaptation in laboratory evolution experiments. Here, we describe how to run the open-source breseq computational pipeline to identify and annotate genetic differences found in whole-genome and whole-population NGS data from haploid microbes where a high-quality reference genome is available. These methods can also be used to analyze mutants isolated in genetic screens and to detect unintended mutations that may occur during strain construction and genome editing.

Twitter Demographics

The data shown below were collected from the profiles of 4 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 577 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 6 1%
Spain 3 <1%
Australia 1 <1%
Brazil 1 <1%
Denmark 1 <1%
Germany 1 <1%
Singapore 1 <1%
China 1 <1%
Unknown 562 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 175 30%
Researcher 94 16%
Student > Bachelor 61 11%
Student > Master 60 10%
Student > Doctoral Student 24 4%
Other 68 12%
Unknown 95 16%
Readers by discipline Count As %
Agricultural and Biological Sciences 200 35%
Biochemistry, Genetics and Molecular Biology 150 26%
Immunology and Microbiology 48 8%
Medicine and Dentistry 10 2%
Computer Science 9 2%
Other 52 9%
Unknown 108 19%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 25 August 2016.
All research outputs
#10,157,697
of 17,361,274 outputs
Outputs from Methods in molecular biology
#2,595
of 9,915 outputs
Outputs of similar age
#91,715
of 196,163 outputs
Outputs of similar age from Methods in molecular biology
#14
of 91 outputs
Altmetric has tracked 17,361,274 research outputs across all sources so far. This one is in the 40th percentile – i.e., 40% of other outputs scored the same or lower than it.
So far Altmetric has tracked 9,915 research outputs from this source. They receive a mean Attention Score of 2.7. This one has gotten more attention than average, scoring higher than 72% 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 196,163 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 52% of its contemporaries.
We're also able to compare this research output to 91 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 85% of its contemporaries.