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NBLAST: Rapid, sensitive comparison of neuronal structure and construction of neuron family databases

View ORCID ProfileMarta Costa, View ORCID ProfileJames D. Manton, Aaron D. Ostrovsky, Steffen Prohaska, View ORCID ProfileGregory S. X. E. Jefferis
doi: https://doi.org/10.1101/006346
Marta Costa
1Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, CB2 0QH, UK
2Department of Genetics, University of Cambridge, Cambridge, CB2 3EH, UK
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James D. Manton
1Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, CB2 0QH, UK
3Current address: Department of Chemical Engineering & Biotechnology, University of Cambridge, Cambridge, CB2 3RA, UK
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Aaron D. Ostrovsky
1Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, CB2 0QH, UK
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Steffen Prohaska
1Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, CB2 0QH, UK
4Zuse Institute Berlin (ZIB), 14195 Berlin-Dahlem, Germany
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Gregory S. X. E. Jefferis
1Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, CB2 0QH, UK
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Abstract

Neural circuit mapping is generating datasets of 10,000s of labeled neurons. New computational tools are needed to search and organize these data. We present NBLAST, a sensitive and rapid algorithm, for measuring pairwise neuronal similarity. NBLAST considers both position and local geometry, decomposing neurons into short segments; matched segments are scored using a probabilistic scoring matrix defined by statistics of matches and non-matches.

We validated NBLAST on a published dataset of 16,129 single Drosophila neurons. NBLAST can distinguish neuronal types down to the finest level (single identified neurons) without a priori information. Cluster analysis of extensively studied neuronal classes identified new types and un-reported topographical features. Fully automated clustering organized the validation dataset into 1052 clusters, many of which map onto previously described neuronal types. NBLAST supports additional query types including searching neurons against transgene expression patterns. Finally we show that NBLAST is effective with data from other invertebrates and zebrafish.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted February 02, 2016.
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NBLAST: Rapid, sensitive comparison of neuronal structure and construction of neuron family databases
Marta Costa, James D. Manton, Aaron D. Ostrovsky, Steffen Prohaska, Gregory S. X. E. Jefferis
bioRxiv 006346; doi: https://doi.org/10.1101/006346
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NBLAST: Rapid, sensitive comparison of neuronal structure and construction of neuron family databases
Marta Costa, James D. Manton, Aaron D. Ostrovsky, Steffen Prohaska, Gregory S. X. E. Jefferis
bioRxiv 006346; doi: https://doi.org/10.1101/006346

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