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

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          Abstract

          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@ 123456snv.jussieu.fr

          Supplementary information: Supplementary data are available at Bioinformatics online.

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          Most cited references3

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          NEMO: a tool for analyzing gene and chromosome territory distributions from 3D-FISH experiments.

          Three-dimensional fluorescence in situ hybridization (3D-FISH) is used to study the organization and the positioning of chromosomes or specific sequences such as genes or RNA in cell nuclei. Many different programs (commercial or free) allow image analysis for 3D-FISH experiments. One of the more efficient open-source programs for automatically processing 3D-FISH microscopy images is Smart 3D-FISH, an ImageJ plug-in designed to automatically analyze distances between genes. One of the drawbacks of Smart 3D-FISH is that it has a rather basic user interface and produces its results in various text and image files thus making the data post-processing step time consuming. We developed a new Smart 3D-FISH graphical user interface, NEMO, which provides all information in the same place so that results can be checked and validated efficiently. NEMO gives users the ability to drive their experiments analysis in either automatic, semi-automatic or manual detection mode. We also tuned Smart 3D-FISH to better analyze chromosome territories. NEMO is a stand-alone Java application available for Windows and Linux platforms. The program is distributed under the creative commons licence and can be freely downloaded from https://www-lgc.toulouse.inra.fr/nemo
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            FISH Finder: a high-throughput tool for analyzing FISH images.

            Fluorescence in situ hybridization (FISH) is used to study the organization and the positioning of specific DNA sequences within the cell nucleus. Analyzing the data from FISH images is a tedious process that invokes an element of subjectivity. Automated FISH image analysis offers savings in time as well as gaining the benefit of objective data analysis. While several FISH image analysis software tools have been developed, they often use a threshold-based segmentation algorithm for nucleus segmentation. As fluorescence signal intensities can vary significantly from experiment to experiment, from cell to cell, and within a cell, threshold-based segmentation is inflexible and often insufficient for automatic image analysis, leading to additional manual segmentation and potential subjective bias. To overcome these problems, we developed a graphical software tool called FISH Finder to automatically analyze FISH images that vary significantly. By posing the nucleus segmentation as a classification problem, compound Bayesian classifier is employed so that contextual information is utilized, resulting in reliable classification and boundary extraction. This makes it possible to analyze FISH images efficiently and objectively without adjustment of input parameters. Additionally, FISH Finder was designed to analyze the distances between differentially stained FISH probes. FISH Finder is a standalone MATLAB application and platform independent software. The program is freely available from: http://code.google.com/p/fishfinder/downloads/list.
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              The dynamic landscape of the cell nucleus.

              While the cell nucleus was described for the first time almost two centuries ago, our modern view of the nuclear architecture is primarily based on studies from the last two decades. This surprising late start coincides with the development of new, powerful strategies to probe for the spatial organization of nuclear activities in both fixed and live cells. As a result, three major principles have emerged: first, the nucleus is not just a bag filled with nucleic acids and proteins. Rather, many distinct functional domains, including the chromosomes, resides within the confines of the nuclear envelope. Second, all these nuclear domains are highly dynamic, with molecules exchanging rapidly between them and the surrounding nucleoplasm. Finally, the motion of molecules within the nucleoplasm appears to be mostly driven by random diffusion. Here, the emerging roles of several subnuclear domains are discussed in the context of the dynamic functions of the cell nucleus.
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                Author and article information

                Journal
                Bioinformatics
                Bioinformatics
                bioinformatics
                bioinfo
                Bioinformatics
                Oxford University Press
                1367-4803
                1367-4811
                15 July 2013
                16 May 2013
                16 May 2013
                : 29
                : 14
                : 1840-1841
                Affiliations
                1CNRS UMR7196, INSERM U565, MNHN, 43 rue Cuvier 75005 Paris, France and 2Université Pierre et Marie Curie, IFR83/FRE3595, 7 quai St Bernard, 75005 Paris, France
                Author notes
                *To whom correspondence should be addressed.

                Associate Editor: Jonathan Wren

                Article
                btt276
                10.1093/bioinformatics/btt276
                3702251
                23681123
                3e383b79-b2c9-4d66-bec4-ab67e9630a84
                © The Author 2013. Published by Oxford University Press.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/3.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com

                History
                : 31 January 2013
                : 8 May 2013
                : 9 May 2013
                Page count
                Pages: 2
                Categories
                Applications Notes
                Bioimage Informatics

                Bioinformatics & Computational biology
                Bioinformatics & Computational biology

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