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

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dc.contributor.author Gillette, Todd A.
dc.contributor.author Hosseini, Parsa
dc.contributor.author Ascoli, Giorgio A.
dc.date.accessioned 2015-09-23T15:40:41Z
dc.date.available 2015-09-23T15:40:41Z
dc.date.issued 2015-07-04
dc.identifier.citation Gillette, Todd, Parsa Hosseini, and Giorgio Ascoli. “Topological Characterization of Neuronal Arbor Morphology via Sequence Representation: II - Global Alignment.” BMC Bioinformatics 16, no. 1 (2015): 209. en_US
dc.identifier.uri https://hdl.handle.net/1920/9903
dc.description.abstract Background 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. Results 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. Conclusions 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.
dc.description.sponsorship This work was supported by NIH grant R01 NS39600. Publication of this article was funded in part by the George Mason University Libraries Open Access Publishing Fund. en_US
dc.language.iso en_US en_US
dc.publisher BioMed Central en_US
dc.rights Attribution 3.0 United States *
dc.rights.uri http://creativecommons.org/licenses/by/3.0/us/ *
dc.subject sequence alignment en_US
dc.subject neuronal morphology en_US
dc.subject multiple sequence alignment en_US
dc.subject tree topology en_US
dc.title Topological characterization of neuronal arbor morphology via sequence representation: II - global alignment en_US
dc.type Article en_US
dc.identifier.doi http://dx.doi.org/10.1186/s12859-015-0605-1


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