TANGO: a generic tool for high-throughput 3D image analysis for studying nuclear organization

By J. Ollion, Julien Cochennec, François Loll, C. Escudé, Thomas Boudier. Bioinform., 2013

788 citations recorded by the source corpus.

Motivation: The cell nucleus is a highly organized cellular organelle that contains the genetic material. The study of nuclear architecture has become an important field of cellular biology. Extracting quantitative data from 3D fluorescence imaging helps understand the functions of different nuclear compartments. However, such approaches are limited by the requirement for processing and analyzing large sets of images. Results: Here, we describe Tools for Analysis of Nuclear Genome Organization (TANGO), an image analysis tool dedicated to the study of nuclear architecture. TANGO is a coherent framework allowing biologists to perform the complete analysis process of 3D fluorescence images by combining two environments: ImageJ (http://imagej.nih.gov/ij/) for image processing and quantitative analysis and R (http://cran.r-project.org) for statistical processing of measurement results. It includes an intuitive user interface providing the means to precisely build a segmentation procedure and set-up analyses, without possessing programming skills. TANGO is a versatile tool able to process large sets of images, allowing quantitative study of nuclear organization. Availability: TANGO is composed of two programs: (i) an ImageJ plug-in and (ii) a package (rtango) for R. They are both free and open source, available (http://biophysique.mnhn.fr/tango) for Linux, Microsoft Windows and Macintosh OSX. Distribution is under the GPL v.2 licence. Contact: thomas.boudier@snv.jussieu.fr Supplementary information: Supplementary data are available at Bioinformatics online.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/4ec4081798926d2ddbda53a46216e202051f4b58

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In Molecular Biology

Citations in the corpus

Generation of a realistic 3D sand assembly using X‐ray micro‐computed tomography and spherical harmonic‐based principal component analysis (2017, methodology): “The built-in 3D segmentation plugin [25] within the image processing package ImageJ [26] was used in this study.”
Machine learning and computer vision approaches for phenotypic profiling (2017, methodology): “Although these methods are mostly applicable to 2D images, new tools are being developed to extract features from 3D images as well (Ollion et al., 2013).”