scholarly journals Ssecrett and NeuroTrace: Interactive Visualization and Analysis Tools for Large-Scale Neuroscience Data Sets

2010 ◽  
Vol 30 (3) ◽  
pp. 58-70 ◽  
Author(s):  
Won-Ki Jeong ◽  
Johanna Beyer ◽  
Markus Hadwiger ◽  
Rusty Blue ◽  
Charles Law ◽  
...  
Keyword(s):  
Large Scale ◽  
Interactive Visualization ◽  
Data Sets ◽  
Analysis Tools

scholarly journals An evaluation of the accuracy and speed of metagenome analysis tools

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Stinus Lindgreen ◽  
Karen L. Adair ◽  
Paul P. Gardner
Keyword(s):  
Aquatic Ecosystems ◽  
Large Scale ◽  
High Throughput Sequencing ◽  
Data Sets ◽  
Metagenome Analysis ◽  
Analysis Tools ◽  
Sequencing Platforms ◽  
Capacity Data ◽  
High Degree ◽  
Realistic Data

Abstract Metagenome studies are becoming increasingly widespread, yielding important insights into microbial communities covering diverse environments from terrestrial and aquatic ecosystems to human skin and gut. With the advent of high-throughput sequencing platforms, the use of large scale shotgun sequencing approaches is now commonplace. However, a thorough independent benchmark comparing state-of-the-art metagenome analysis tools is lacking. Here, we present a benchmark where the most widely used tools are tested on complex, realistic data sets. Our results clearly show that the most widely used tools are not necessarily the most accurate, that the most accurate tool is not necessarily the most time consuming and that there is a high degree of variability between available tools. These findings are important as the conclusions of any metagenomics study are affected by errors in the predicted community composition and functional capacity. Data sets and results are freely available from http://www.ucbioinformatics.org/metabenchmark.html

scholarly journals An evaluation of the accuracy and speed of metagenome analysis tools

2015 ◽  
Author(s):  
Stinus Lindgreen ◽  
Karen L Adair ◽  
Paul Gardner
Keyword(s):  
Aquatic Ecosystems ◽  
Large Scale ◽  
High Throughput Sequencing ◽  
State Of The Art ◽  
Data Sets ◽  
Metagenome Analysis ◽  
Analysis Tools ◽  
Sequencing Platforms ◽  
High Degree ◽  
Realistic Data

Metagenome studies are becoming increasingly widespread, yielding important insights into microbial communities covering diverse environments from terrestrial and aquatic ecosystems to human skin and gut. With the advent of high-throughput sequencing platforms, the use of large scale shotgun sequencing approaches is now commonplace. However, a thorough independent benchmark comparing state-of-the-art metagenome analysis tools is lacking. Here, we present a benchmark where the most widely used tools are tested on complex, realistic data sets. Our results clearly show that the most widely used tools are not necessarily the most accurate, that the most accurate tool is not necessarily the most time consuming, and that there is a high degree of variability between available tools. These findings are important as the conclusions of any metagenomics study are affected by errors in the predicted community composition. Data sets and results are freely available from http://www.ucbioinformatics.org/metabenchmark.html

scholarly journals 1Click1View: Interactive Visualization Methodology for RNAi Cell-Based Microscopic Screening

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Lukasz Zwolinski ◽  
Marta Kozak ◽  
Karol Kozak
Keyword(s):  
Large Scale ◽  
State Of The Art ◽  
Interactive Visualization ◽  
Image Data ◽  
Data Sets ◽  
Visualization Method ◽  
Multiple Cell ◽  
Visualization Tools ◽  
Screening Assays ◽  
Numeric Data

Technological advancements are constantly increasing the size and complexity of data resulting from large-scale RNA interference screens. This fact has led biologists to ask complex questions, which the existing, fully automated analyses are often not adequate to answer. We present a concept of 1Click1View (1C1V) as a methodology for interactive analytic software tools. 1C1V can be applied for two-dimensional visualization of image-based screening data sets from High Content Screening (HCS). Through an easy-to-use interface, one-click, one-view concept, and workflow based architecture, visualization method facilitates the linking of image data with numeric data. Such method utilizes state-of-the-art interactive visualization tools optimized for fast visualization of large scale image data sets. We demonstrate our method on an HCS dataset consisting of multiple cell features from two screening assays.

Grid-Based Visualization and its Medical Applications

2011 ◽  
pp. 25-57 ◽  
Author(s):  
Yingjun Qiu ◽  
Youbing Zhao ◽  
Jiaoying Shi
Keyword(s):  
Grid Computing ◽  
Large Scale ◽  
Interactive Visualization ◽  
Research Direction ◽  
Data Sets ◽  
Computing Technology ◽  
Visualization System ◽  
Huge Data ◽  
Visualization Systems ◽  
Grid Based

Traditional visualization approaches cannot handle new challenges in the visualization field such as visualizing huge data sets, communicating between existing visualization systems and providing interactive visualization services, widely. In this chapter, the authors introduce an emerging research direction in the visualization field, grid-based visualization, which aims to resolves the above problems by utilizing grid computing technology. However, current grid computing technology is almost batch job-oriented and does not support interactive visualization applications natively. In this chapter, the authors implement a grid-based visualization system (GVis) which utilizes large-scale computing resources to achieve large dataset visualization in real time and provides end users with reliable interactive visualization services, widely. In GVis system, current grid computing technology is extended to support interactive visualization applications.

scholarly journals MetroNome — a visual data exploration platform for integrating human genotypic and phenotypic data across diseases

2019 ◽  
Author(s):  
Christian Stolte ◽  
Kevin Shi ◽  
Nina Lapchyk ◽  
Nathaniel Novod ◽  
Avinash Abhyankar ◽  
...  
Keyword(s):  
Interactive Visualization ◽  
Data Exploration ◽  
Data Sets ◽  
Visual Data ◽  
Phenotypic Data ◽  
Web Based ◽  
Multimodal Data ◽  
Analysis Tools ◽  
New Ideas ◽  
Visual Data Exploration

AbstractMetroNome is a web-based visual data exploration platform which integrates de-identified genomic, transcriptomic, and phenotypic data sets. Users can define and compare cohorts constructed from multimodal data and share the data and analyses with outside tools. MetroNome’s interactive visualization and analysis tools allow researchers to quickly form and explore novel hypotheses. The deidentified data is linked back to the source biosample inventories in multiple biobanks, enabling researchers to further investigate new ideas using the most relevant samples.

scholarly journals OmicsTIDE: Interactive Exploration of Trends in Multi-Omics Data

2021 ◽  
Author(s):  
Theresa Harbig ◽  
Julian Fratte ◽  
Michael Krone ◽  
Kay Nieselt
Keyword(s):  
Large Scale ◽  
Interactive Visualization ◽  
Biological Information ◽  
Data Sets ◽  
Omics Data ◽  
Interactive Analysis ◽  
Analysis Strategies ◽  
Final Integration ◽  
The Impact

AbstractMotivationThe increasing amount of data produced by omics technologies has significantly improved the understanding of how biological information is transferred across different omics layers and to which extent it is involved in the manifestation of a given phenotype. Besides data-driven analysis strategies, interactive visualization tools have been developed to make the analysis in the multi-omics field more transparent. However, most state-of-the-art tools do not reconstruct the impact of a given omics layer on the final integration result. In general, the amount of omics data analyses strategies and the fields of applications lack a clearer classification of the different approaches.ResultsWe developed a classification for omics data focusing on different aspects of multi-omics data sets, such as data type and experimental design. Based on this classification we developed the Omics Trend-comparing Interactive Data Explorer (OmicsTIDE), an interactive visualization tool developed to address the limitations of current visualization approaches in the multi-omics field. The tool consists of an automated part that clusters omics data to determine trends and an interactive visualization. The trends are visualized as profile plots and are connected by a Sankey diagram that allows an interactive pairwise trend comparison to discover concordant and discordant trends. Moreover, large-scale omics data sets are broken down into small subsets of concordant and discordant regulatory trends within few analysis steps. We demonstrate the interactive analysis using OmicsTIDE with two case studies focusing on different types of experimental designs.AvailabilityOmicsTIDE is a web tool and available via http://tuevis.informatik.uni-tuebingen.de/[email protected]

scholarly journals Galaxy spin direction distribution in HST and SDSS show similar large-scale asymmetry

2020 ◽  
Vol 37 ◽  
Author(s):  
Lior Shamir
Keyword(s):  
Large Scale ◽  
Spiral Galaxies ◽  
Hubble Space Telescope ◽  
Gravitational Interaction ◽  
Large Data ◽  
Sloan Digital Sky Survey ◽  
Data Sets ◽  
Dipole Axis ◽  
Data Set ◽  
The Asymmetry

Abstract Several recent observations using large data sets of galaxies showed non-random distribution of the spin directions of spiral galaxies, even when the galaxies are too far from each other to have gravitational interaction. Here, a data set of $\sim8.7\cdot10^3$ spiral galaxies imaged by Hubble Space Telescope (HST) is used to test and profile a possible asymmetry between galaxy spin directions. The asymmetry between galaxies with opposite spin directions is compared to the asymmetry of galaxies from the Sloan Digital Sky Survey. The two data sets contain different galaxies at different redshift ranges, and each data set was annotated using a different annotation method. The results show that both data sets show a similar asymmetry in the COSMOS field, which is covered by both telescopes. Fitting the asymmetry of the galaxies to cosine dependence shows a dipole axis with probabilities of $\sim2.8\sigma$ and $\sim7.38\sigma$ in HST and SDSS, respectively. The most likely dipole axis identified in the HST galaxies is at $(\alpha=78^{\rm o},\delta=47^{\rm o})$ and is well within the $1\sigma$ error range compared to the location of the most likely dipole axis in the SDSS galaxies with $z>0.15$ , identified at $(\alpha=71^{\rm o},\delta=61^{\rm o})$ .

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