nanoTRON: a Picasso module for MLP-based classification of super-resolution data

Alexander Auer; Maximilian T Strauss; Sebastian Strauss; Ralf Jungmann

doi:10.1093/bioinformatics/btaa154

nanoTRON: a Picasso module for MLP-based classification of super-resolution data

Bioinformatics. 2020 Jun 1;36(11):3620-3622. doi: 10.1093/bioinformatics/btaa154.

Authors

Alexander Auer^{1

2}, Maximilian T Strauss², Sebastian Strauss^{1

2}, Ralf Jungmann^{1

2}

Affiliations

¹ Department of Physics and Center for Nanoscience, Ludwig Maximilian University, 80539 Munich, Germany.
² Max Planck Institute of Biochemistry, 82152 Martinsried, Germany.

Abstract

Motivation: Classification of images is an essential task in higher-level analysis of biological data. By bypassing the diffraction limit of light, super-resolution microscopy opened up a new way to look at molecular details using light microscopy, producing large amounts of data with exquisite spatial detail. Statistical exploration of data usually needs initial classification, which is up to now often performed manually.

Results: We introduce nanoTRON, an interactive open-source tool, which allows super-resolution data classification based on image recognition. It extends the software package Picasso with the first deep learning tool with a graphic user interface.

Availability and implementation: nanoTRON is written in Python and freely available under the MIT license as a part of the software collection Picasso on GitHub (http://www.github.com/jungmannlab/picasso). All raw data can be obtained from the authors upon reasonable request.

Contact: jungmann@biochem.mpg.de.

Supplementary information: Supplementary data are available at Bioinformatics online.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Microscopy*
Software*