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

Please always quote using this URN: urn:nbn:de:0297-zib-59672
  • 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.

Download full text files

Export metadata

  • Export Bibtex
Metadaten
Author:Marta Costa, James D. Manton, Aaron D. Ostrovsky, Steffen Prohaska, Gregory S. X. E. Jefferis
Document Type:ZIB-Report
Tag:NBLAST; cell type; clustering; neuroinformatics; neuron similarity
MSC-Classification:68-XX COMPUTER SCIENCE (For papers involving machine computations and programs in a specific mathematical area, see Section -04 in that area)
92-XX BIOLOGY AND OTHER NATURAL SCIENCES
CCS-Classification:J. Computer Applications
PACS-Classification:80.00.00 INTERDISCIPLINARY PHYSICS AND RELATED AREAS OF SCIENCE AND TECHNOLOGY
Date of first Publication:2016/06/15
Series (Serial Number):ZIB-Report (16-34)
ISSN:1438-0064
Published in:also appeared as bioRxiv preprint http://dx.doi.org/10.1101/006346 and in revised form in Neuron
DOI:http://dx.doi.org/10.1016/j.neuron.2016.06.012

$Rev: 13581 $