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Evolutionary Genomics

Overview of attention for book
Cover of 'Evolutionary Genomics'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 Tangled Trees: The Challenge of Inferring Species Trees from Coalescent and Noncoalescent Genes
  3. Altmetric Badge
    Chapter 2 Modeling Gene Family Evolution and Reconciling Phylogenetic Discord
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    Chapter 3 Genome-wide comparative analysis of phylogenetic trees: the prokaryotic forest of life.
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    Chapter 4 Philosophy and Evolution: Minding the Gap Between Evolutionary Patterns and Tree-Like Patterns
  6. Altmetric Badge
    Chapter 5 Selection on the Protein-Coding Genome
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    Chapter 6 Methods to Detect Selection on Noncoding DNA
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    Chapter 7 The Origin and Evolution of New Genes
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    Chapter 8 Evolution of protein domain architectures.
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    Chapter 9 Estimating recombination rates from genetic variation in humans.
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    Chapter 10 Evolution of Viral Genomes: Interplay Between Selection, Recombination, and Other Forces
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    Chapter 11 Association Mapping and Disease: Evolutionary Perspectives
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    Chapter 12 Ancestral Population Genomics
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    Chapter 13 Nonredundant Representation of Ancestral Recombinations Graphs
  15. Altmetric Badge
    Chapter 14 Using Genomic Tools to Study Regulatory Evolution
  16. Altmetric Badge
    Chapter 15 Characterization and Evolutionary Analysis of Protein–Protein Interaction Networks
  17. Altmetric Badge
    Chapter 16 Statistical methods in metabolomics.
  18. Altmetric Badge
    Chapter 17 Introduction to the Analysis of Environmental Sequences: Metagenomics with MEGAN
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    Chapter 18 Analyzing epigenome data in context of genome evolution and human diseases.
  20. Altmetric Badge
    Chapter 19 Genetical Genomics for Evolutionary Studies
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    Chapter 20 Genomics Data Resources: Frameworks and Standards
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    Chapter 21 Sharing programming resources between bio* projects through remote procedure call and native call stack strategies.
  23. Altmetric Badge
    Chapter 22 Scalable computing for evolutionary genomics.
Attention for Chapter 22: Scalable computing for evolutionary genomics.
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (76th percentile)
  • High Attention Score compared to outputs of the same age and source (81st percentile)

Mentioned by

blogs
1 blog

Citations

dimensions_citation
9 Dimensions

Readers on

mendeley
29 Mendeley
citeulike
1 CiteULike
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Chapter title
Scalable computing for evolutionary genomics.
Chapter number 22
Book title
Evolutionary Genomics
Published in
Methods in molecular biology, March 2012
DOI 10.1007/978-1-61779-585-5_22
Pubmed ID
Book ISBNs
978-1-61779-584-8, 978-1-61779-585-5
Authors

Pjotr Prins, Dominique Belhachemi, Steffen Möller, Geert Smant, Prins, Pjotr, Belhachemi, Dominique, Möller, Steffen, Smant, Geert

Abstract

Genomic data analysis in evolutionary biology is becoming so computationally intensive that analysis of multiple hypotheses and scenarios takes too long on a single desktop computer. In this chapter, we discuss techniques for scaling computations through parallelization of calculations, after giving a quick overview of advanced programming techniques. Unfortunately, parallel programming is difficult and requires special software design. The alternative, especially attractive for legacy software, is to introduce poor man's parallelization by running whole programs in parallel as separate processes, using job schedulers. Such pipelines are often deployed on bioinformatics computer clusters. Recent advances in PC virtualization have made it possible to run a full computer operating system, with all of its installed software, on top of another operating system, inside a "box," or virtual machine (VM). Such a VM can flexibly be deployed on multiple computers, in a local network, e.g., on existing desktop PCs, and even in the Cloud, to create a "virtual" computer cluster. Many bioinformatics applications in evolutionary biology can be run in parallel, running processes in one or more VMs. Here, we show how a ready-made bioinformatics VM image, named BioNode, effectively creates a computing cluster, and pipeline, in a few steps. This allows researchers to scale-up computations from their desktop, using available hardware, anytime it is required. BioNode is based on Debian Linux and can run on networked PCs and in the Cloud. Over 200 bioinformatics and statistical software packages, of interest to evolutionary biology, are included, such as PAML, Muscle, MAFFT, MrBayes, and BLAST. Most of these software packages are maintained through the Debian Med project. In addition, BioNode contains convenient configuration scripts for parallelizing bioinformatics software. Where Debian Med encourages packaging free and open source bioinformatics software through one central project, BioNode encourages creating free and open source VM images, for multiple targets, through one central project. BioNode can be deployed on Windows, OSX, Linux, and in the Cloud. Next to the downloadable BioNode images, we provide tutorials online, which empower bioinformaticians to install and run BioNode in different environments, as well as information for future initiatives, on creating and building such images.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 29 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 3%
Germany 1 3%
Brazil 1 3%
Unknown 26 90%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 10 34%
Researcher 9 31%
Professor > Associate Professor 2 7%
Student > Master 2 7%
Lecturer 1 3%
Other 3 10%
Unknown 2 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 12 41%
Computer Science 8 28%
Biochemistry, Genetics and Molecular Biology 2 7%
Environmental Science 2 7%
Engineering 2 7%
Other 0 0%
Unknown 3 10%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 6. 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 20 September 2012.
All research outputs
#5,566,299
of 22,678,224 outputs
Outputs from Methods in molecular biology
#1,529
of 13,038 outputs
Outputs of similar age
#36,786
of 156,550 outputs
Outputs of similar age from Methods in molecular biology
#12
of 66 outputs
Altmetric has tracked 22,678,224 research outputs across all sources so far. Compared to these this one has done well and is in the 75th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 13,038 research outputs from this source. They receive a mean Attention Score of 3.3. This one has done well, scoring higher than 88% 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 156,550 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 76% of its contemporaries.
We're also able to compare this research output to 66 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 81% of its contemporaries.