scholarly journals CellTool: an open source software combining bio-image analysis and mathematical modeling

2018 ◽  
Author(s):  
Georgi Danovski ◽  
Teodora Dyankova ◽  
Stoyno Stoynov
Keyword(s):  
Mathematical Modeling ◽  
Image Analysis ◽  
Open Source ◽  
Open Source Software ◽  
Time Lapse ◽  
Supplementary Information ◽  
Data Sets ◽  
Link Type ◽  
Sample Data ◽  
Supplementary Material

AbstractSummaryWe present CellTool, a stand-alone open source software with a Graphical User Interface for image analysis, optimized for measurement of time-lapse microscopy images. It combines data management, image processing, mathematical modeling and graphical presentation of data in a single package. Multiple image filters, segmentation and particle tracking algorithms, combined with direct visualization of the obtained results make CellTool an ideal application for rapid execution of complex tasks. In addition, the software allows for the fitting of the obtained results to predefined or custom mathematical models. Importantly, CellTool provides a platform for easy implementation of custom image analysis packages written on a variety of programing languages.Availability and ImplementationCellTool is a free software available for MS Windows OS under the terms of the GNU General Public License. Executables and source files, supplementary information and sample data sets are freely available for download at URL: https://dnarepair.bas.bg/software/CellTool/[email protected]; [email protected];Supplementary informationSupplementary data are available at URL: https://dnarepair.bas.bg/software/CellTool/Program/CellTool_UserGuide.pdf

scholarly journals PathScore: a web tool for identifying altered pathways in cancer data

2016 ◽  
Author(s):  
Stephen G. Gaffney ◽  
Jeffrey P. Townsend
Keyword(s):  
Web Application ◽  
Somatic Mutations ◽  
Supplementary Information ◽  
Web Tool ◽  
Cancer Data ◽  
Link Type ◽  
Novel Approach ◽  
Supplementary Material ◽  
User Friendly ◽  
Pathway Effect

ABSTRACTSummaryPathScore quantifies the level of enrichment of somatic mutations within curated pathways, applying a novel approach that identifies pathways enriched across patients. The application provides several user-friendly, interactive graphic interfaces for data exploration, including tools for comparing pathway effect sizes, significance, gene-set overlap and enrichment differences between projects.Availability and ImplementationWeb application available at pathscore.publichealth.yale.edu. Site implemented in Python and MySQL, with all major browsers supported. Source code available at github.com/sggaffney/pathscore with a GPLv3 [email protected] InformationAdditional documentation can be found at http://pathscore.publichealth.yale.edu/faq.

scholarly journals Quickomics: exploring omics data in an intuitive, interactive and informative manner

2021 ◽  
Author(s):  
Benbo Gao ◽  
Jing Zhu ◽  
Soumya Negi ◽  
Xinmin Zhang ◽  
Stefka Gyoneva ◽  
...  
Keyword(s):  
Modular Design ◽  
Functional Module ◽  
Supplementary Information ◽  
Data Sets ◽  
Omics Data ◽  
Proteomics Data ◽  
Primary Analysis ◽  
Link Type ◽  
R Shiny ◽  
Advanced Analysis

AbstractSummaryWe developed Quickomics, a feature-rich R Shiny-powered tool to enable biologists to fully explore complex omics data and perform advanced analysis in an easy-to-use interactive interface. It covers a broad range of secondary and tertiary analytical tasks after primary analysis of omics data is completed. Each functional module is equipped with customized configurations and generates both interactive and publication-ready high-resolution plots to uncover biological insights from data. The modular design makes the tool extensible with ease.AvailabilityResearchers can experience the functionalities with their own data or demo RNA-Seq and proteomics data sets by using the app hosted at http://quickomics.bxgenomics.com and following the tutorial, https://bit.ly/3rXIyhL. The source code under GPLv3 license is provided at https://github.com/interactivereport/[email protected], [email protected] informationSupplementary materials are available at https://bit.ly/37HP17g.

Palaeolatitudinal distribution of the Ediacaran macrobiota

2021 ◽  
pp. jgs2021-030
Author(s):  
Catherine E. Boddy ◽  
Emily G. Mitchell ◽  
Andrew Merdith ◽  
Alexander G. Liu
Keyword(s):  
Taxonomic Composition ◽  
Supplementary Information ◽  
Cambrian Explosion ◽  
Content Type ◽  
Link Type ◽  
Environmental Perturbations ◽  
Significant Difference ◽  
Evolutionary Trajectories ◽  
Cambrian Radiation ◽  
Supplementary Material

Macrofossils of the late Ediacaran Period (c. 579–539 Ma) document diverse, complex multicellular eukaryotes, including early animals, prior to the Cambrian radiation of metazoan phyla. To investigate the relationships between environmental perturbations, biotic responses and early metazoan evolutionary trajectories, it is vital to distinguish between evolutionary and ecological controls on the global distribution of Ediacaran macrofossils. The contributions of temporal, palaeoenvironmental and lithological factors in shaping the observed variations in assemblage taxonomic composition between Ediacaran macrofossil sites are widely discussed, but the role of palaeogeography remains ambiguous. Here we investigate the influence of palaeolatitude on the spatial distribution of Ediacaran macrobiota through the late Ediacaran Period using two leading palaeogeographical reconstructions. We find that overall generic diversity was distributed across all palaeolatitudes. Among specific groups, the distributions of candidate ‘Bilateral’ and Frondomorph taxa exhibit weakly statistically significant and statistically significant differences between low and high palaeolatitudes within our favoured palaeogeographical reconstruction, respectively, whereas Algal, Tubular, Soft-bodied and Biomineralizing taxa show no significant difference. The recognition of statistically significant palaeolatitudinal differences in the distribution of certain morphogroups highlights the importance of considering palaeolatitudinal influences when interrogating trends in Ediacaran taxon distributions.Supplementary material: Supplementary information, data and code are available at https://doi.org/10.6084/m9.figshare.c.5488945Thematic collection: This article is part of the Advances in the Cambrian Explosion collection available at: https://www.lyellcollection.org/cc/advances-cambrian-explosion

CellTracker (not only) for dummies

2015 ◽  
Vol 32 (6) ◽  
pp. 955-957 ◽  
Author(s):  
Filippo Piccinini ◽  
Alexa Kiss ◽  
Peter Horvath
Keyword(s):  
Graphical User Interface ◽  
Open Source Software ◽  
Phase Contrast ◽  
Cell Tracking ◽  
Source Code ◽  
Software Tool ◽  
Time Lapse ◽  
Supplementary Information ◽  
Differential Interference Contrast ◽  
User Friendly

Abstract Motivation: Time-lapse experiments play a key role in studying the dynamic behavior of cells. Single-cell tracking is one of the fundamental tools for such analyses. The vast majority of the recently introduced cell tracking methods are limited to fluorescently labeled cells. An equally important limitation is that most software cannot be effectively used by biologists without reasonable expertise in image processing. Here we present CellTracker, a user-friendly open-source software tool for tracking cells imaged with various imaging modalities, including fluorescent, phase contrast and differential interference contrast (DIC) techniques. Availability and implementation: CellTracker is written in MATLAB (The MathWorks, Inc., USA). It works with Windows, Macintosh and UNIX-based systems. Source code and graphical user interface (GUI) are freely available at: http://celltracker.website/. Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.

scholarly journals wft4galaxy: A Workflow Tester for Galaxy

2017 ◽  
Author(s):  
Marco Enrico Piras ◽  
Luca Pireddu ◽  
Gianluigi Zanetti
Keyword(s):  
Complex Analysis ◽  
Computer Programs ◽  
Supplementary Information ◽  
Automated Testing ◽  
Continuous Integration ◽  
Link Type ◽  
Scientific Analysis ◽  
The Galaxy ◽  
Supplementary Material ◽  
High Level

ABSTRACTMotivationWorkflow managers for scientific analysis provide a high-level programming platform facilitating standardization, automation, collaboration and access to sophisticated computing resources. The Galaxy workflow manager provides a prime example of this type of platform. As compositions of simpler tools, workflows effectively comprise specialized computer programs implementing often very complex analysis procedures. To date, no simple way exists to automatically test Galaxy workflows and ensure their correctness has appeared in the literature.ResultsWith wft4galaxy we offer a tool to bring automated testing to Galaxy workflows, making it feasible to bring continuous integration to their development and ensuring that defects are detected promptly. wft4galaxy can be easily installed as a regular Python program or launched directly as a Docker container – the latter reducing installation effort to a minimum.Availabilitywft4galaxy is available online at https://github.com/phnmnl/wft4galaxy under the Academic Free License v3.0.Supplementary informationSupplementary information is available at http://wft4galaxy.readthedocs.io.

scholarly journals HARVESTING, INTEGRATING AND DISTRIBUTING LARGE OPEN GEOSPATIAL DATASETS USING FREE AND OPEN-SOURCE SOFTWARE

2016 ◽  
Vol XLI-B7 ◽  
pp. 939-940
Author(s):  
Ricardo Oliveira ◽  
Rafael Moreno
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
Spatial Information ◽  
Open Data ◽  
Federal State ◽  
Data Sets ◽  
Data Set ◽  
Geospatial Datasets ◽  
State And Local ◽  
The City

Federal, State and Local government agencies in the USA are investing heavily on the dissemination of Open Data sets produced by each of them. The main driver behind this thrust is to increase agencies’ transparency and accountability, as well as to improve citizens’ awareness. However, not all Open Data sets are easy to access and integrate with other Open Data sets available even from the same agency. The City and County of Denver Open Data Portal distributes several types of geospatial datasets, one of them is the city parcels information containing 224,256 records. Although this data layer contains many pieces of information it is incomplete for some custom purposes. Open-Source Software were used to first collect data from diverse City of Denver Open Data sets, then upload them to a repository in the Cloud where they were processed using a PostgreSQL installation on the Cloud and Python scripts. Our method was able to extract non-spatial information from a ‘not-ready-to-download’ source that could then be combined with the initial data set to enhance its potential use.

scholarly journals HARVESTING, INTEGRATING AND DISTRIBUTING LARGE OPEN GEOSPATIAL DATASETS USING FREE AND OPEN-SOURCE SOFTWARE

2016 ◽  
Vol XLI-B7 ◽  
pp. 939-940 ◽  
Author(s):  
Ricardo Oliveira ◽  
Rafael Moreno
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
Spatial Information ◽  
Open Data ◽  
Federal State ◽  
Data Sets ◽  
Data Set ◽  
Geospatial Datasets ◽  
State And Local ◽  
The City

Federal, State and Local government agencies in the USA are investing heavily on the dissemination of Open Data sets produced by each of them. The main driver behind this thrust is to increase agencies’ transparency and accountability, as well as to improve citizens’ awareness. However, not all Open Data sets are easy to access and integrate with other Open Data sets available even from the same agency. The City and County of Denver Open Data Portal distributes several types of geospatial datasets, one of them is the city parcels information containing 224,256 records. Although this data layer contains many pieces of information it is incomplete for some custom purposes. Open-Source Software were used to first collect data from diverse City of Denver Open Data sets, then upload them to a repository in the Cloud where they were processed using a PostgreSQL installation on the Cloud and Python scripts. Our method was able to extract non-spatial information from a ‘not-ready-to-download’ source that could then be combined with the initial data set to enhance its potential use.

scholarly journals RANSAC FOR OUTLIER DETECTION

2012 ◽  
Vol 31 (3) ◽  
pp. 83-87 ◽  
Author(s):  
Birutė Ruzgienė ◽  
Wolfgang Förstner
Keyword(s):  
Image Processing ◽  
Image Analysis ◽  
Image Matching ◽  
Data Sets ◽  
Digital Photogrammetry ◽  
Huge Data ◽  
Straight Line ◽  
Sample Data ◽  
Random Sample Consensus ◽  
Classical Image

Up-to-date digital photogrammetry involves operations on huge data sets, and with classical image processing procedures it might be time consuming to find out the best solution. One of the key tasks is to detect outliers in given data, eg for curve fitting or image matching. The problem is hard as the number of outliers is usually large, possibly larger than 50%, thus powerful estimation techniques are needed. We demonstrate one of these techniques, namely Random Sample Consensus (RANSAC), for fitting a model to sample data, especially for fitting a straight line through a set of given points. Experiments with up to 80% outliers prove the efficiency of RANSAC. The results are representative for image analysis in digital photogrammetry

Sign in / Sign up

Export Citation Format

Share Document