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Bioinformatics

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Attention for Chapter 16: Scaling Up the Phylogenetic Detection of Lateral Gene Transfer Events.
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Chapter title
Scaling Up the Phylogenetic Detection of Lateral Gene Transfer Events.
Chapter number 16
Book title
Bioinformatics
Published in
Methods in molecular biology, January 2017
DOI 10.1007/978-1-4939-6622-6_16
Pubmed ID
Book ISBNs
978-1-4939-6620-2, 978-1-4939-6622-6
Authors

Cheong Xin Chan, Robert G. Beiko, Mark A. Ragan

Editors

Jonathan M. Keith

Abstract

Lateral genetic transfer (LGT) is the process by which genetic material moves between organisms (and viruses) in the biosphere. Among the many approaches developed for the inference of LGT events from DNA sequence data, methods based on the comparison of phylogenetic trees remain the gold standard for many types of problem. Identifying LGT events from sequenced genomes typically involves a series of steps in which homologous sequences are identified and aligned, phylogenetic trees are inferred, and their topologies are compared to identify unexpected or conflicting relationships. These types of approach have been used to elucidate the nature and extent of LGT and its physiological and ecological consequences throughout the Tree of Life. Advances in DNA sequencing technology have led to enormous increases in the number of sequenced genomes, including ultra-deep sampling of specific taxonomic groups and single cell-based sequencing of unculturable "microbial dark matter." Environmental shotgun sequencing enables the study of LGT among organisms that share the same habitat.This abundance of genomic data offers new opportunities for scientific discovery, but poses two key problems. As ever more genomes are generated, the assembly and annotation of each individual genome receives less scrutiny; and with so many genomes available it is tempting to include them all in a single analysis, but thousands of genomes and millions of genes can overwhelm key algorithms in the analysis pipeline. Identifying LGT events of interest therefore depends on choosing the right dataset, and on algorithms that appropriately balance speed and accuracy given the size and composition of the chosen set of genomes.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 17 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 17 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 4 24%
Researcher 3 18%
Student > Master 2 12%
Student > Doctoral Student 1 6%
Professor 1 6%
Other 2 12%
Unknown 4 24%
Readers by discipline Count As %
Agricultural and Biological Sciences 7 41%
Biochemistry, Genetics and Molecular Biology 2 12%
Immunology and Microbiology 2 12%
Engineering 1 6%
Unknown 5 29%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 06 December 2016.
All research outputs
#18,483,671
of 22,903,988 outputs
Outputs from Methods in molecular biology
#7,927
of 13,132 outputs
Outputs of similar age
#310,539
of 420,462 outputs
Outputs of similar age from Methods in molecular biology
#691
of 1,074 outputs
Altmetric has tracked 22,903,988 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,132 research outputs from this source. They receive a mean Attention Score of 3.4. This one is in the 24th percentile – i.e., 24% of its peers scored the same or lower than it.
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