scholarly journals BigDataProcessor2: a free and open-source Fiji plugin for inspection and processing of TB sized image data

2021 ◽  
Author(s):  
Christian Tischer ◽  
Ashis Ravindran ◽  
Sabine Reither ◽  
Nicolas Chiaruttini ◽  
Rainer Pepperkok ◽  
...  
Keyword(s):  
Electron Microscopy ◽  
Open Source ◽  
Imaging Techniques ◽  
Image Data ◽  
Light Sheet ◽  
Supplementary Information ◽  
Sensor Technology ◽  
Supplementary Data ◽  
Time Points ◽  
Light Sheet Microscopy

Abstract Summary Modern bioimaging and related areas such as sensor technology have undergone tremendous development over the last few years. As a result, contemporary imaging techniques, particularly electron microscopy (EM) and light sheet microscopy, can frequently generate datasets attaining sizes of several terabytes (TB). As a consequence, even seemingly simple data operations such as cropping, chromatic- and drift-corrections and even visualisation, poses challenges when applied to thousands of time points or tiles. To address this we developed BigDataProcessor2—a Fiji plugin facilitating processing workflows for TB sized image datasets. Availability and implementation BigDataProcessor2 is available as a Fiji plugin via the BigDataProcessor update site. The application is implemented in Java and the code is publicly available on GitHub (https://github.com/bigdataprocessor/bigdataprocessor2). Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals BigDataProcessor2: A free and open-source Fiji plugin for inspection and processing of TB sized image data

2020 ◽  
Author(s):  
Christian Tischer ◽  
Ashis Ravindran ◽  
Sabine Reither ◽  
Rainer Pepperkok ◽  
Nils Norlin
Keyword(s):  
Electron Microscopy ◽  
Open Source ◽  
Imaging Techniques ◽  
Image Data ◽  
Light Sheet ◽  
Sensor Technology ◽  
Time Points ◽  
Light Sheet Microscopy ◽  
Link Type ◽  
Image Datasets

SummaryModern bioimaging and related areas such as sensor technology has seen tremendous development the last years allowing several contemporary imaging techniques, electron microscopy (EM) and light sheet microscopy in particular, to generate datasets frequently reaching the size of several terabytes (TB). As a consequence, even seemingly simple data operations such as cropping, chromatic- and drift-corrections and even visualisation, poses challenges when applied to thousands of time points or tiles. To address this we developed BigDataProcessor2 – a Fiji plugin facilitating processing workflows for TB sized image datasets.Availability and implementationBigDataProcessor2 is available as a Fiji plugin via the BigDataProcessor update site. The application is implemented in Java and the code is publicly available on GitHub (https://github.com/bigdataprocessor/bigdataprocessor2)[email protected], [email protected]

scholarly journals ccNetViz: a WebGL-based JavaScript library for visualization of large networks

2020 ◽  
Vol 36 (16) ◽  
pp. 4527-4529
Author(s):  
Ales Saska ◽  
David Tichy ◽  
Robert Moore ◽  
Achilles Rasquinha ◽  
Caner Akdas ◽  
...  
Keyword(s):  
Systems Biology ◽  
Complex Networks ◽  
Open Source ◽  
High Speed ◽  
A Priori ◽  
Supplementary Information ◽  
Network Visualization ◽  
Supplementary Data ◽  
Web Based ◽  
Flow Of Information

Abstract Summary Visualizing a network provides a concise and practical understanding of the information it represents. Open-source web-based libraries help accelerate the creation of biologically based networks and their use. ccNetViz is an open-source, high speed and lightweight JavaScript library for visualization of large and complex networks. It implements customization and analytical features for easy network interpretation. These features include edge and node animations, which illustrate the flow of information through a network as well as node statistics. Properties can be defined a priori or dynamically imported from models and simulations. ccNetViz is thus a network visualization library particularly suited for systems biology. Availability and implementation The ccNetViz library, demos and documentation are freely available at http://helikarlab.github.io/ccNetViz/. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Tracking cells in epithelial acini by light sheet microscopy reveals proximity effects in breast cancer initiation

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Ashna Alladin ◽  
Lucas Chaible ◽  
Lucia Garcia del Valle ◽  
Reither Sabine ◽  
Monika Loeschinger ◽  
...  
Keyword(s):  
Single Cells ◽  
Image Data ◽  
Integrated Approach ◽  
Light Sheet ◽  
Mammary Epithelial ◽  
Proximity Effects ◽  
Transformed Cells ◽  
Light Sheet Microscopy ◽  
Primary Mouse ◽  
Longitudinal Imaging

Cancer clone evolution takes place within tissue ecosystem habitats. But, how exactly tumors arise from a few malignant cells within an intact epithelium is a central, yet unanswered question. This is mainly due to the inaccessibility of this process to longitudinal imaging together with a lack of systems that model the progression of a fraction of transformed cells within a tissue. Here, we developed a new methodology based on primary mouse mammary epithelial acini, where oncogenes can be switched on in single cells within an otherwise normal epithelial cell layer. We combine this stochastic breast tumor induction model with inverted light-sheet imaging to study single-cell behavior for up to four days and analyze cell fates utilizing a newly developed image-data analysis workflow. The power of this integrated approach is illustrated by us finding that small local clusters of transformed cells form tumors while isolated transformed cells do not.

scholarly journals DrawGlycan-SNFG and gpAnnotate: rendering glycans and annotating glycopeptide mass spectra

2019 ◽  
Author(s):  
Kai Cheng ◽  
Gabrielle Pawlowski ◽  
Xinheng Yu ◽  
Yusen Zhou ◽  
Sriram Neelamegham
Keyword(s):  
Mass Spectrometry ◽  
Open Source ◽  
Mass Spectra ◽  
Supplementary Information ◽  
Supplementary Data ◽  
International Union ◽  
Open Source Program ◽  
Source Program ◽  
Wide Range ◽  
Peptide Modifications

Abstract Summary This manuscript describes an open-source program, DrawGlycan-SNFG (version 2), that accepts IUPAC (International Union of Pure and Applied Chemist)-condensed inputs to render Symbol Nomenclature For Glycans (SNFG) drawings. A wide range of local and global options enable display of various glycan/peptide modifications including bond breakages, adducts, repeat structures, ambiguous identifications etc. These facilities make DrawGlycan-SNFG ideal for integration into various glycoinformatics software, including glycomics and glycoproteomics mass spectrometry (MS) applications. As a demonstration of such usage, we incorporated DrawGlycan-SNFG into gpAnnotate, a standalone application to score and annotate individual MS/MS glycopeptide spectrum in different fragmentation modes. Availability and implementation DrawGlycan-SNFG and gpAnnotate are platform independent. While originally coded using MATLAB, compiled packages are also provided to enable DrawGlycan-SNFG implementation in Python and Java. All programs are available from https://virtualglycome.org/drawglycan; https://virtualglycome.org/gpAnnotate. Contact [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

ClearVolume: open-source live 3D visualization for light-sheet microscopy

2015 ◽  
Vol 12 (6) ◽  
pp. 480-481 ◽  
Author(s):  
Loic A Royer ◽  
Martin Weigert ◽  
Ulrik Günther ◽  
Nicola Maghelli ◽  
Florian Jug ◽  
...  
Keyword(s):  
Open Source ◽  
3D Visualization ◽  
Light Sheet ◽  
Light Sheet Microscopy

scholarly journals SynapseJ: an automated, synapse identification macro for ImageJ

2021 ◽  
Author(s):  
Juan Felipe Moreno Manrique ◽  
Parker R. Voit ◽  
Kathryn E. Windsor ◽  
Aamuktha R. Karla ◽  
Sierra R. Rodriguez ◽  
...  
Keyword(s):  
Electron Microscopy ◽  
Open Source ◽  
Fluorescent Protein ◽  
Imaging Techniques ◽  
Yellow Fluorescent Protein ◽  
Synaptic Proteins ◽  
Scaffolding Protein ◽  
Substantia Nigra Pars Compacta ◽  
Distinct Advantage ◽  
Specific Cell

While electron microscopy represents the gold standard for detection of synapses, a number of limitations prevent its broad applicability. A key method for detecting synapses is immunostaining for markers of pre- and post-synaptic proteins, which can infer a synapse based upon the apposition of the two markers. While immunostaining and imaging techniques have improved to allow for identification of synapses in tissue, analysis and identification of these appositions are not facile, and there has been a lack of tools to accurately identify these appositions. Here, we delineate a macro that uses open-source and freely available ImageJ or FIJI for analysis of multichannel, z-stack confocal images. With use of a high magnification with a high NA objective, we outline two methods to identify puncta in either sparsely or densely labeled images. Puncta from each channel are used to eliminate non-apposed puncta and are subsequently linked with their cognate from the other channel. These methods are applied to analysis of a presynaptic marker, bassoon, with two different postsynaptic markers, gephyrin and N-methyl-d-aspartate (NMDA) receptor subunit 1 (NR1). Using gephyrin as an inhibitory, postsynaptic scaffolding protein, we identify inhibitory synapses in basolateral amygdala, central amygdala, arcuate and the ventromedial hypothalamus. Systematic variation of the settings identify the parameters most critical for this analysis. Identification of specifically overlapping puncta allows for correlation of morphometry data between each channel. Finally, we extend the analysis to only examine puncta overlapping with a cytoplasmic marker of specific cell types, a distinct advantage beyond electron microscopy. Bassoon puncta are restricted to virally transduced, pedunculopontine tegmental neuron (PPN) axons expressing yellow fluorescent protein. NR1 puncta are restricted to tyrosine hydroxylase labeled dopaminergic neurons of the substantia nigra pars compacta (SNc). The macro identifies bassoon-NR1 overlap throughout the image, or those only restricted to the PPN-SNc connections. Thus, we have extended the available analysis tools that can be used to study synapses in situ. Our analysis code is freely available and open-source allowing for further innovation. 

scholarly journals DNA Chisel, a versatile sequence optimizer

2020 ◽  
Vol 36 (16) ◽  
pp. 4508-4509 ◽  
Author(s):  
Valentin Zulkower ◽  
Susan Rosser
Keyword(s):  
Open Source ◽  
Dna Sequence ◽  
Web Application ◽  
Optimization Problems ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Sequence Optimization ◽  
Sequence Design ◽  
Optimization Framework ◽  
Dna Sequence Design

Abstract Motivation Accounting for biological and practical requirements in DNA sequence design often results in challenging optimization problems. Current software solutions are problem-specific and hard to combine. Results DNA Chisel is an easy-to-use, easy-to-extend sequence optimization framework allowing to freely define and combine optimization specifications via Python scripts or Genbank annotations. Availability and implementation The framework is available as a web application (https://cuba.genomefoundry.org/sculpt_a_sequence) or open-source Python library (see at https://github.com/Edinburgh-Genome-Foundry/DNAChisel for code and documentation). Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals GlycanFormatConverter: a conversion tool for translating the complexities of glycans

2018 ◽  
Vol 35 (14) ◽  
pp. 2434-2440 ◽  
Author(s):  
Shinichiro Tsuchiya ◽  
Issaku Yamada ◽  
Kiyoko F Aoki-Kinoshita
Keyword(s):  
Open Source ◽  
Source Code ◽  
Supplementary Information ◽  
Biological Processes ◽  
Supplementary Data ◽  
Unique Representation ◽  
Open Source Tool ◽  
Living Organisms ◽  
Conversion Tool ◽  
Complex Glycan

Abstract Motivation Glycans are biomolecules that take an important role in the biological processes of living organisms. They form diverse, complicated structures such as branched and cyclic forms. Web3 Unique Representation of Carbohydrate Structures (WURCS) was proposed as a new linear notation for uniquely representing glycans during the GlyTouCan project. WURCS defines rules for complex glycan structures that other text formats did not support, and so it is possible to represent a wide variety glycans. However, WURCS uses a complicated nomenclature, so it is not human-readable. Therefore, we aimed to support the interpretation of WURCS by converting WURCS to the most basic and widely used format IUPAC. Results In this study, we developed GlycanFormatConverter and succeeded in converting WURCS to the three kinds of IUPAC formats (IUPAC-Extended, IUPAC-Condensed and IUPAC-Short). Furthermore, we have implemented functionality to import IUPAC-Extended, KEGG Chemical Function (KCF) and LinearCode formats and to export WURCS. We have thoroughly tested our GlycanFormatConverter and were able to show that it was possible to convert all the glycans registered in the GlyTouCan repository, with exceptions owing only to the limitations of the original format. The source code for this conversion tool has been released as an open source tool. Availability and implementation https://github.com/glycoinfo/GlycanFormatConverter.git Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals PolishEM: image enhancement in FIB–SEM

2020 ◽  
Vol 36 (12) ◽  
pp. 3947-3948
Author(s):  
Jose-Jesus Fernandez ◽  
Teobaldo E Torres ◽  
Eva Martin-Solana ◽  
Gerardo F Goya ◽  
Maria-Rosario Fernandez-Fernandez
Keyword(s):  
Electron Microscopy ◽  
Scanning Electron Microscopy ◽  
Focused Ion Beam ◽  
Ion Beam ◽  
Source Code ◽  
Software Tool ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Efficient Processing ◽  
Scanning Electron

Abstract Summary We have developed a software tool to improve the image quality in focused ion beam–scanning electron microscopy (FIB–SEM) stacks: PolishEM. Based on a Gaussian blur model, it automatically estimates and compensates for the blur affecting each individual image. It also includes correction for artifacts commonly arising in FIB–SEM (e.g. curtaining). PolishEM has been optimized for an efficient processing of huge FIB–SEM stacks on standard computers. Availability and implementation PolishEM has been developed in C. GPL source code and binaries for Linux, OSX and Windows are available at http://www.cnb.csic.es/%7ejjfernandez/polishem. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Imaging mitotic processes in three dimensions with lattice light-sheet microscopy

2021 ◽  
Author(s):  
Yuko Mimori-Kiyosue
Keyword(s):  
Dimensional Space ◽  
Three Dimensional ◽  
Image Data ◽  
Light Sheet ◽  
Three Dimensions ◽  
Cell Level ◽  
Spatiotemporal Resolution ◽  
New Era ◽  
Light Sheet Microscopy ◽  
Three Dimensional Space

AbstractThere are few technologies that can capture mitotic processes occurring in three-dimensional space with the desired spatiotemporal resolution. Due to such technical limitations, our understanding of mitosis, which has been studied since the early 1880s, is still incomplete with regard to mitotic processes and their regulatory mechanisms at a molecular level. A recently developed high-resolution type of light-sheet microscopy, lattice light-sheet microscopy (LLSM), has achieved unprecedented spatiotemporal resolution scans of intracellular spaces at the whole-cell level. This technology enables experiments that were not possible before (e.g., tracking of growth of every spindle microtubule end and discrimination of individual chromosomes in living cells), thus providing a new avenue for the analysis of mitotic processes. Herein, principles of LLSM technology are introduced, as well as experimental techniques that became possible with LLSM. In addition, issues remaining to be solved for use of this technology in mitosis research, big image data problems, are presented to help guide mitosis research into a new era.

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