SWC files (file extension of .swc) are text-based (ASCII text) files that describe three-dimensional neuronal or glial morphology. These digital reconstructions represent morphology as a vectorized tree structure, made of a series of connected nodes. An SWC file contains a series of text-based rows where each neuron node is described by a single row of only seven space-separated values. The format is simple and intuitive; a parser of or writer to the format could be implemented by anyone with knowledge of any programming language.
Links:
Documentation: https://swc-specification.readthedocs.io/en/latest/swc.html
Github: https://github.com/INCF/swc-specification
Similar Standards:
MBF File Format 4.0, Amira, Neurolucida ASCII, SNT
Publications:
Cannon, R.C., Turner, D.A., Pyapali, G.K., Wheal, H. V., 1998. An on-line archive of reconstructed hippocampal neurons. J. Neurosci. Methods 84, 49–54.
Link to the publication
Nanda, S., Chen, H., Das, R., Bhattacharjee, S., Cuntz, H., Torben-Nielsen, B., Peng, H., Cox, D.N., De Schutter, E., Ascoli, G.A., 2018. Design and implementation of multi-signal and time-varying neural reconstructions. Sci. Data 5, 170207.
Link to the publication
Commentaries on endorsed standards
https://www.incf.org/commentaries/swcSupporting software
Supporting Software
- Conversion and standardization tool: https://neuromorpho.org/xyz2swc/ui/
- Compatible FAIR tools: https://neuromorpho.org/tools.jsp
- Comparative morphometric analyses Read the article
- Electrophysiological simulations Read the article
- Large-scale biophysically-detailed modeling Read the article
- Algorithmic generation of virtual neurons Article 1 , Article 2
- SWC reconstructions have also been extended to produce multi-signal neural reconstructions, as well as dynamic time-varying neural reconstructions Read the article.
- While SWC reconstructions were primarily invented to digitally describe neural morphology, this system can also be seamlessly adopted to describe any arbor-like structure such as angiographic data (e.g., https://www.nitrc.org/projects/breva/).