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Topological characterization of neuronal arbor morphology via sequence representation: II - global alignment

Overview of attention for article published in BMC Bioinformatics, July 2015
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3 tweeters

Citations

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26 Dimensions

Readers on

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29 Mendeley
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Title
Topological characterization of neuronal arbor morphology via sequence representation: II - global alignment
Published in
BMC Bioinformatics, July 2015
DOI 10.1186/s12859-015-0605-1
Pubmed ID
Authors

Todd A Gillette, Parsa Hosseini, Giorgio A Ascoli

Abstract

The increasing abundance of neuromorphological data provides both the opportunity and the challenge to compare massive numbers of neurons from a wide diversity of sources efficiently and effectively. We implemented a modified global alignment algorithm representing axonal and dendritic bifurcations as strings of characters. Sequence alignment quantifies neuronal similarity by identifying branch-level correspondences between trees. The space generated from pairwise similarities is capable of classifying neuronal arbor types as well as, or better than, traditional topological metrics. Unsupervised cluster analysis produces groups that significantly correspond with known cell classes for axons, dendrites, and pyramidal apical dendrites. Furthermore, the distinguishing consensus topology generated by multiple sequence alignment of a group of neurons reveals their shared branching blueprint. Interestingly, the axons of dendritic-targeting interneurons in the rodent cortex associates with pyramidal axons but apart from the (more topologically symmetric) axons of perisomatic-targeting interneurons. Global pairwise and multiple sequence alignment of neurite topologies enables detailed comparison of neurites and identification of conserved topological features in alignment-defined clusters. The methods presented also provide a framework for incorporation of additional branch-level morphological features. Moreover, comparison of multiple alignment with motif analysis shows that the two techniques provide complementary information respectively revealing global and local features.

Twitter Demographics

The data shown below were collected from the profiles of 3 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 29 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 3%
Unknown 28 97%

Demographic breakdown

Readers by professional status Count As %
Researcher 9 31%
Student > Ph. D. Student 7 24%
Student > Bachelor 3 10%
Student > Master 3 10%
Professor 2 7%
Other 4 14%
Unknown 1 3%
Readers by discipline Count As %
Agricultural and Biological Sciences 9 31%
Neuroscience 8 28%
Computer Science 5 17%
Physics and Astronomy 1 3%
Mathematics 1 3%
Other 2 7%
Unknown 3 10%

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 09 February 2016.
All research outputs
#3,599,183
of 7,156,405 outputs
Outputs from BMC Bioinformatics
#2,276
of 3,295 outputs
Outputs of similar age
#118,767
of 224,394 outputs
Outputs of similar age from BMC Bioinformatics
#83
of 104 outputs
Altmetric has tracked 7,156,405 research outputs across all sources so far. This one is in the 28th percentile – i.e., 28% of other outputs scored the same or lower than it.
So far Altmetric has tracked 3,295 research outputs from this source. They receive a mean Attention Score of 5.0. This one is in the 20th percentile – i.e., 20% of its peers scored the same or lower than it.
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 224,394 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 37th percentile – i.e., 37% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 104 others from the same source and published within six weeks on either side of this one. This one is in the 17th percentile – i.e., 17% of its contemporaries scored the same or lower than it.