TY - GEN A1 - Costa, Marta A1 - Ostrovsky, Aaron D. A1 - Manton, James D. A1 - Prohaska, Steffen A1 - Jefferis, Gregory S.X.E. T1 - NBLAST: Rapid, sensitive comparison of neuronal structure and construction of neuron family databases T2 - bioRxiv preprint Y1 - 2015 U6 - https://doi.org/10.1101/006346 ER - TY - JOUR A1 - Costa, Marta A1 - Manton, James D. A1 - Ostrovsky, Aaron D. A1 - Prohaska, Steffen A1 - Jefferis, Gregory S.X.E. T1 - NBLAST: Rapid, Sensitive Comparison of Neuronal Structure and Construction of Neuron Family Databases JF - Neuron N2 - Neural circuit mapping is generating datasets of tens of thousands 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 unreported topographical features. Fully automated clustering organized the validation dataset into 1,052 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. Y1 - 2016 U6 - https://doi.org/10.1016/j.neuron.2016.06.012 VL - 91 IS - 2 SP - 293 EP - 311 ER - TY - GEN A1 - Costa, Marta A1 - Manton, James D. A1 - Ostrovsky, Aaron D. A1 - Prohaska, Steffen A1 - Jefferis, Gregory S.X.E. T1 - NBLAST: Rapid, sensitive comparison of neuronal structure and construction of neuron family databases N2 - 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 unreported 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. T3 - ZIB-Report - 16-34 KW - neuroinformatics KW - NBLAST KW - neuron similarity KW - cell type KW - clustering Y1 - 2016 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-59672 SN - 1438-0064 ER -