scholarly journals Mesmerize is a dynamically adaptable user-friendly analysis platform for 2D and 3D calcium imaging data

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Kushal Kolar ◽  
Daniel Dondorp ◽  
Jordi Cornelis Zwiggelaar ◽  
Jørgen Høyer ◽  
Marios Chatzigeorgiou
Keyword(s):  
Calcium Imaging ◽  
Imaging Data ◽  
Cellular Mechanisms ◽  
Analysis Process ◽  
Organization Analysis ◽  
Interactive Visualizations ◽  
Downstream Analysis ◽  
User Friendly ◽  
Analysis Platform ◽  
2D And 3D

AbstractCalcium imaging is an increasingly valuable technique for understanding neural circuits, neuroethology, and cellular mechanisms. The analysis of calcium imaging data presents challenges in image processing, data organization, analysis, and accessibility. Tools have been created to address these problems independently, however a comprehensive user-friendly package does not exist. Here we present Mesmerize, an efficient, expandable and user-friendly analysis platform, which uses a Findable, Accessible, Interoperable and Reproducible (FAIR) system to encapsulate the entire analysis process, from raw data to interactive visualizations for publication. Mesmerize provides a user-friendly graphical interface to state-of-the-art analysis methods for signal extraction & downstream analysis. We demonstrate the broad scientific scope of Mesmerize’s applications by analyzing neuronal datasets from mouse and a volumetric zebrafish dataset. We also applied contemporary time-series analysis techniques to analyze a novel dataset comprising neuronal, epidermal, and migratory mesenchymal cells of the protochordate Ciona intestinalis.

scholarly journals eSPC: an online data-analysis platform for molecular biophysics

2021 ◽  
Vol 77 (10) ◽  
Author(s):  
Osvaldo Burastero ◽  
Stephan Niebling ◽  
Lucas A. Defelipe ◽  
Christian Günther ◽  
Angelica Struve ◽  
...  
Keyword(s):  
Data Analysis ◽  
Dissociation Constants ◽  
Protein Complexes ◽  
Cellular Mechanisms ◽  
Online Data ◽  
Online Tools ◽  
Equilibrium Dissociation Constants ◽  
Equilibrium Dissociation ◽  
User Friendly ◽  
Analysis Platform

All biological processes rely on the formation of protein–ligand, protein–peptide and protein–protein complexes. Studying the affinity, kinetics and thermodynamics of binding between these pairs is critical for understanding basic cellular mechanisms. Many different technologies have been designed for probing interactions between biomolecules, each based on measuring different signals (fluorescence, heat, thermophoresis, scattering and interference, among others). Evaluation of the data from binding experiments and their fitting is an essential step towards the quantification of binding affinities. Here, user-friendly online tools to analyze biophysical data from steady-state fluorescence spectroscopy, microscale thermophoresis and differential scanning fluorimetry experiments are presented. The modules of the data-analysis platform (https://spc.embl-hamburg.de/) contain classical thermodynamic models and clear user guidelines for the determination of equilibrium dissociation constants (K d) and thermal unfolding parameters such as melting temperatures (T m).

scholarly journals Mesmerize: A highly versatile platform for calcium imaging analysis and creation of self-contained FAIR datasets

2019 ◽  
Author(s):  
Kushal Kolar ◽  
Daniel Dondorp ◽  
Marios Chatzigeorgiou
Keyword(s):  
Calcium Imaging ◽  
Signal Extraction ◽  
Graphical Interface ◽  
Imaging Analysis ◽  
C Elegans ◽  
Analysis Process ◽  
Visual Cortex Neurons ◽  
Mouse Visual Cortex ◽  
Analysis Platform ◽  
Scientific Questions

AbstractWe present an efficient and expandable calcium imaging analysis platform that encapsulates the entire analysis process from raw data to interactive e-figures. It provides a graphical interface to the latest analysis methods for pre-processing, and signal extraction. We demonstrate how Mesmerize can be applied to a broad range of scientific questions by using datasets ranging from the mouse visual cortex, neurons, epidermis and TLCs of the protochordate C. intestinalis, and C. elegans.

scholarly journals A deep learning toolbox for noise-optimized, generalized spike inference from calcium imaging data

2020 ◽  
Author(s):  
Peter Rupprecht ◽  
Stefano Carta ◽  
Adrian Hoffmann ◽  
Mayumi Echizen ◽  
Kazuo Kitamura ◽  
...  
Keyword(s):  
Calcium Imaging ◽  
Action Potentials ◽  
Sampling Rate ◽  
Ground Truth ◽  
Cell Types ◽  
Imaging Data ◽  
Calcium Signals ◽  
Ground Truth Data ◽  
Unseen Data ◽  
User Friendly

ABSTRACTCalcium imaging is a key method to record patterns of neuronal activity across populations of identified neurons. Inference of temporal patterns of action potentials (‘spikes’) from calcium signals is, however, challenging and often limited by the scarcity of ground truth data containing simultaneous measurements of action potentials and calcium signals. To overcome this problem, we compiled a large and diverse ground truth database from publicly available and newly performed recordings. This database covers various types of calcium indicators, cell types, and signal-to-noise ratios and comprises a total of >20 hours from 225 neurons. We then developed a novel algorithm for spike inference (CASCADE) that is based on supervised deep networks, takes advantage of the ground truth database, infers absolute spike rates, and outperforms existing model-based algorithms. To optimize performance for unseen imaging data, CASCADE retrains itself by resampling ground truth data to match the respective sampling rate and noise level. As a consequence, no parameters need to be adjusted by the user. To facilitate routine application of CASCADE we developed systematic performance assessments for unseen data, we openly release all resources, and we provide a user-friendly cloud-based implementation.

scholarly journals Minian: An open-source miniscope analysis pipeline

2021 ◽  
Author(s):  
Zhe Dong ◽  
William Mau ◽  
Yu (Susie) Feng ◽  
Zachary T. Pennington ◽  
Lingxuan Chen ◽  
...  
Keyword(s):  
Open Source ◽  
Calcium Imaging ◽  
Single Photon ◽  
Source Analysis ◽  
Imaging Analysis ◽  
Analysis Pipeline ◽  
Interactive Visualizations ◽  
User Friendly ◽  
In Vivo Calcium Imaging

Miniature microscopes have gained considerable traction for in vivo calcium imaging in freely behaving animals. However, extracting calcium signals from raw videos is a computationally complex problem and remains a bottleneck for many researchers utilizing single-photon in vivo calcium imaging. Despite the existence of many powerful analysis packages designed to detect and extract calcium dynamics, most have either key parameters that are hard-coded or insufficient step-by-step guidance and validations to help the users choose the best parameters. This makes it difficult to know whether the output is reliable and meets the assumptions necessary for proper analysis. Moreover, large memory demand is often a constraint for setting up these pipelines since it limits the choice of hardware. Given these difficulties, there is a need for a low memory demand, user-friendly tool offering interactive visualizations of how altering parameters affects data output. Our open-source analysis pipeline, Minian (Miniscope Analysis), facilitates the transparency and accessibility of single-photon calcium imaging analysis, permitting users with little computational experience to extract the location of cells and their corresponding calcium traces and deconvolved neural activities. Minian contains interactive visualization tools for every step of the analysis, as well as detailed documentation and tips on parameter exploration. Furthermore, Minian has relatively small memory demands and can be run on a laptop, making it available to labs that do not have access to specialized computational hardware. Minian has been validated to reliably and robustly extract calcium events across different brain regions and from different cell types. In practice, Minian provides an open-source calcium imaging analysis pipeline with user-friendly interactive visualizations to explore parameters and validate results.

scholarly journals SHAMAN: a user-friendly website for metataxonomic analysis from raw reads to statistical analysis

2020 ◽  
Author(s):  
Stevenn Volant ◽  
Pierre Lechat ◽  
Perrine Woringer ◽  
Laurence Motreff ◽  
Christophe Malabat ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Data Processing ◽  
Web Application ◽  
Graphical Representation ◽  
Statistical Modelling ◽  
Metagenomic Data ◽  
Sequencing Data ◽  
Microbiome Research ◽  
Interactive Visualizations ◽  
User Friendly

Abstract BackgroundComparing the composition of microbial communities among groups of interest (e.g., patients vs healthy individuals) is a central aspect in microbiome research. It typically involves sequencing, data processing, statistical analysis and graphical representation of the detected signatures. Such an analysis is normally obtained by using a set of different applications that require specific expertise for installation, data processing and in some case, programming skills. ResultsHere, we present SHAMAN, an interactive web application we developed in order to facilitate the use of (i) a bioinformatic workflow for metataxonomic analysis, (ii) a reliable statistical modelling and (iii) to provide among the largest panels of interactive visualizations as compared to the other options that are currently available. SHAMAN is specifically designed for non-expert users who may benefit from using an integrated version of the different analytic steps underlying a proper metagenomic analysis. The application is freely accessible at http://shaman.pasteur.fr/, and may also work as a standalone application with a Docker container (aghozlane/shaman), conda and R. The source code is written in R and is available at https://github.com/aghozlane/shaman. Using two datasets (a mock community sequencing and published 16S rRNA metagenomic data), we illustrate the strengths of SHAMAN in quickly performing a complete metataxonomic analysis. ConclusionsWe aim with SHAMAN to provide the scientific community with a platform that simplifies reproducible quantitative analysis of metagenomic data.

scholarly journals Automated cellular structure extraction in biological images with applications to calcium imaging data

2018 ◽  
Author(s):  
Gal Mishne ◽  
Ronald R. Coifman ◽  
Maria Lavzin ◽  
Jackie Schiller
Keyword(s):  
Calcium Imaging ◽  
Image Plane ◽  
Cellular Level ◽  
Full Potential ◽  
Imaging Data ◽  
Image Domain ◽  
Biological Images ◽  
Large Populations ◽  
In Vivo Calcium Imaging

AbstractRecent advances in experimental methods in neuroscience enable measuring in-vivo activity of large populations of neurons at cellular level resolution. To leverage the full potential of these complex datasets and analyze the dynamics of individual neurons, it is essential to extract high-resolution regions of interest, while addressing demixing of overlapping spatial components and denoising of the temporal signal of each neuron. In this paper, we propose a data-driven solution to these challenges, by representing the spatiotemporal volume as a graph in the image plane. Based on the spectral embedding of this graph calculated across trials, we propose a new clustering method, Local Selective Spectral Clustering, capable of handling overlapping clusters and disregarding clutter. We also present a new nonlinear mapping which recovers the structural map of the neurons and dendrites, and global video denoising. We demonstrate our approach on in-vivo calcium imaging of neurons and apical dendrites, automatically extracting complex structures in the image domain, and denoising and demixing their time-traces.

scholarly journals FastqCleaner: an interactive Bioconductor application for quality-control, filtering and trimming of FASTQ files

2018 ◽  
Author(s):  
Leandro Gabriel Roser ◽  
Fernán Agüero ◽  
Daniel Oscar Sánchez
Keyword(s):  
Quality Control ◽  
Data Analysis ◽  
The Novel ◽  
Web Environment ◽  
Ngs Data Analysis ◽  
Novel Concept ◽  
Next Generation Sequencing Ngs ◽  
User Friendly ◽  
Ngs Data ◽  
Analysis Platform

AbstractBackgroundExploration and processing of FASTQ files are the first steps in state-of-the-art data analysis workflows of Next Generation Sequencing (NGS) platforms. The large amount of data generated by these technologies has put a challenge in terms of rapid analysis and visualization of sequencing information. Recent integration of the R data analysis platform with web visual frameworks has stimulated the development of user-friendly, powerful, and dynamic NGS data analysis applications.ResultsThis paper presents FastqCleaner, a Bioconductor visual application for both quality-control (QC) and pre-processing of FASTQ files. The interface shows diagnostic information for the input and output data and allows to select a series of filtering and trimming operations in an interactive framework. FastqCleaner combines the technology of Bioconductor for NGS data analysis with the data visualization advantages of a web environment.ConclusionsFastqCleaner is an user-friendly, offline-capable tool that enables access to advanced Bioconductor infrastructure. The novel concept of a Bioconductor interactive application that can be used without the need for programming skills, makes FastqCleaner a valuable resource for NGS data analysis.

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