RT Journal Article SR Electronic T1 NBLAST: Rapid, sensitive comparison of neuronal structure and construction of neuron family databases JF bioRxiv FD Cold Spring Harbor Laboratory SP 006346 DO 10.1101/006346 A1 Marta Costa A1 James D. Manton A1 Aaron D. Ostrovsky A1 Steffen Prohaska A1 Gregory S. X. E. Jefferis YR 2016 UL http://biorxiv.org/content/early/2016/02/02/006346.abstract AB 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.