Topological characterization of neuronal arbor morphology via sequence representation: II - global alignment
Date
2015-07-04
Authors
Gillette, Todd A.
Hosseini, Parsa
Ascoli, Giorgio A.
Journal Title
Journal ISSN
Volume Title
Publisher
BioMed Central
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.
Description
Keywords
Sequence alignment, Neuronal morphology, Multiple sequence alignment, Tree topology
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.