BioJS InterMine List Analysis: A BioJS component for displaying graphical or statistical analysis of collections of items from InterMine endpoints

Summary: The InterMineTable component is a reusable JavaScript component as part of the BioJS project. It enables users to embed powerful table-based query facilities in their websites with access to genomic data-warehouses such as http://www.flymine.org, which allow users to perform flexible queries over a wide range of integrated data types.Availability: http://github.com/alexkalderimis/im-tables-biojs; http://github.com/biojs/biojs; http://dx.doi.org/10.5281/zenodo.8301.

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BioJS InterMineTable Component: A BioJS component for displaying data from InterMine compatible webservice endpoints

F1000Research ◽

10.12688/f1000research.3-46.v1 ◽

2014 ◽

Vol 3 ◽

pp. 46 ◽

Cited By ~ 1

Author(s):

Alexis Kalderimis ◽

Radek Stepan ◽

Julie Sullivan ◽

Rachel Lyne ◽

Michael Lyne ◽

...

Keyword(s):

Genomic Data ◽

Data Warehouses ◽

Data Types ◽

Link Type ◽

Wide Range ◽

Flexible Queries

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Go Get Data (GGD) is a framework that facilitates reproducible access to genomic data

Nature Communications ◽

10.1038/s41467-021-22381-z ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Michael J. Cormier ◽

Jonathan R. Belyeu ◽

Brent S. Pedersen ◽

Joseph Brown ◽

Johannes Köster ◽

...

Keyword(s):

Data Integration ◽

Genomic Data ◽

Data Types ◽

Multiple Genome ◽

Link Type ◽

Wide Range

AbstractThe rapid increase in the amount of genomic data provides researchers with an opportunity to integrate diverse datasets and annotations when addressing a wide range of biological questions. However, genomic datasets are deposited on different platforms and are stored in numerous formats from multiple genome builds, which complicates the task of collecting, annotating, transforming, and integrating data as needed. Here, we developed Go Get Data (GGD) as a fast, reproducible approach to installing standardized data recipes. GGD is available on Github (https://gogetdata.github.io/), is extendable to other data types, and can streamline the complexities typically associated with data integration, saving researchers time and improving research reproducibility.

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Unsupervised deep learning on biomedical data with BoltzmannMachines.jl

10.1101/578252 ◽

2019 ◽

Cited By ~ 2

Author(s):

Stefan Lenz ◽

Moritz Hess ◽

Harald Binder

Keyword(s):

Input Data ◽

Genomic Data ◽

Small Sample ◽

Pattern Detection ◽

Biomedical Data ◽

Data Types ◽

Boltzmann Machines ◽

Link Type ◽

Unsupervised Deep Learning ◽

Small Sample Sizes

AbstractDeep Boltzmann machines (DBMs) are models for unsupervised learning in the field of artificial intelligence, promising to be useful for dimensionality reduction and pattern detection in clinical and genomic data. Multimodal and partitioned DBMs alleviate the problem of small sample sizes and make it possible to combine different input data types in one DBM model. We present the package “BoltzmannMachines” for the Julia programming language, which makes this model class available for practical use in working with biomedical data.AvailabilityNotebook with example data: http://github.com/stefan-m-lenz/BMs4BInf2019 Julia package: http://github.com/stefan-m-lenz/BoltzmannMachines.jl

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PBrowse: A web-based platform for real-time collaborative exploration of genomic data

10.1101/068049 ◽

2016 ◽

Author(s):

Peter S. Szot ◽

Andrian Yang ◽

Xin Wang ◽

Uwe Röhm ◽

Koon Ho Wong ◽

...

Keyword(s):

Real Time ◽

Genomic Data ◽

File Sharing ◽

Genome Browser ◽

Human Interaction ◽

Supplementary Information ◽

Web Based ◽

Link Type ◽

Multiple Users ◽

Wide Range

ABSTRACTSummaryThe central task of a genome browser is to enable easy visual exploration of large genomic data to gain biological insight. Most existing genome browsers were designed for data exploration by individual users, while a few allow some limited forms of collaboration among multiple users, such as file sharing and wiki-style collaborative editing of gene annotations. Our work’s premise is that allowing sharing of genome browser views instantaneously in real-time enables the exchange of ideas and insight in a collaborative project, thus harnessing the wisdom of the crowd. PBrowse is a parallel-access real-time collaborative web-based genome browser that provides both an integrated, real-time collaborative platform and a comprehensive file sharing system. PBrowse also allows real-time track comment and has integrated group chat to facilitate interactive discussion among multiple users. Through the Distributed Annotation Server protocol, PBrowse can easily access a wide range of publicly available genomic data, such as the ENCODE data sets. We argue that PBrowse, with the re-designed user management, data management and novel collaborative layer based on Biodalliance, represents a paradigm shift from seeing genome browser merely as a tool of data visualisation to a tool that enables real-time human-human interaction and knowledge exchange in a collaborative setting.AvailabilityPBrowse is available at http://pbrowse.victorchang.edu.au, and its source code is available via the open source BSD 3 license at http://github.com/VCCRI/[email protected] InformationSupplementary video demonstrating collaborative feature of pbrowse is available in https://www.youtube.com/watch?v=ROvKXZoXiIc.

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Image Statistics Preserving Encrypt-then-Compress Scheme Dedicated for JPEG Compression Standard

Entropy ◽

10.3390/e23040421 ◽

2021 ◽

Vol 23 (4) ◽

pp. 421

Author(s):

Dariusz Puchala ◽

Kamil Stokfiszewski ◽

Mykhaylo Yatsymirskyy

Keyword(s):

Statistical Analysis ◽

Image Encryption ◽

Experimental Studies ◽

Quality Measures ◽

Input Image ◽

Jpeg Compression ◽

Image Statistics ◽

Compression Stage ◽

Wide Range ◽

The Impact

In this paper, the authors analyze in more details an image encryption scheme, proposed by the authors in their earlier work, which preserves input image statistics and can be used in connection with the JPEG compression standard. The image encryption process takes advantage of fast linear transforms parametrized with private keys and is carried out prior to the compression stage in a way that does not alter those statistical characteristics of the input image that are crucial from the point of view of the subsequent compression. This feature makes the encryption process transparent to the compression stage and enables the JPEG algorithm to maintain its full compression capabilities even though it operates on the encrypted image data. The main advantage of the considered approach is the fact that the JPEG algorithm can be used without any modifications as a part of the encrypt-then-compress image processing framework. The paper includes a detailed mathematical model of the examined scheme allowing for theoretical analysis of the impact of the image encryption step on the effectiveness of the compression process. The combinatorial and statistical analysis of the encryption process is also included and it allows to evaluate its cryptographic strength. In addition, the paper considers several practical use-case scenarios with different characteristics of the compression and encryption stages. The final part of the paper contains the additional results of the experimental studies regarding general effectiveness of the presented scheme. The results show that for a wide range of compression ratios the considered scheme performs comparably to the JPEG algorithm alone, that is, without the encryption stage, in terms of the quality measures of reconstructed images. Moreover, the results of statistical analysis as well as those obtained with generally approved quality measures of image cryptographic systems, prove high strength and efficiency of the scheme’s encryption stage.

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Using Satellite Data for CBRN (Chemical, Biological, Radiological, and Nuclear) Threat Detection, Monitoring, and Modelling

Surveys in Geophysics ◽

10.1007/s10712-021-09637-5 ◽

2021 ◽

Author(s):

Gary Sutlieff ◽

Lucy Berthoud ◽

Mark Stinchcombe

Keyword(s):

Satellite Data ◽

Meteorological Data ◽

Atmospheric Composition ◽

Chemical Detection ◽

List Type ◽

Data Types ◽

Wide Range ◽

Future Technologies ◽

The Impact ◽

Near Future

Abstract CBRN (Chemical, Biological, Radiological, and Nuclear) threats are becoming more prevalent, as more entities gain access to modern weapons and industrial technologies and chemicals. This has produced a need for improvements to modelling, detection, and monitoring of these events. While there are currently no dedicated satellites for CBRN purposes, there are a wide range of possibilities for satellite data to contribute to this field, from atmospheric composition and chemical detection to cloud cover, land mapping, and surface property measurements. This study looks at currently available satellite data, including meteorological data such as wind and cloud profiles, surface properties like temperature and humidity, chemical detection, and sounding. Results of this survey revealed several gaps in the available data, particularly concerning biological and radiological detection. The results also suggest that publicly available satellite data largely does not meet the requirements of spatial resolution, coverage, and latency that CBRN detection requires, outside of providing terrain use and building height data for constructing models. Lastly, the study evaluates upcoming instruments, platforms, and satellite technologies to gauge the impact these developments will have in the near future. Improvements in spatial and temporal resolution as well as latency are already becoming possible, and new instruments will fill in the gaps in detection by imaging a wider range of chemicals and other agents and by collecting new data types. This study shows that with developments coming within the next decade, satellites should begin to provide valuable augmentations to CBRN event detection and monitoring. Article Highlights There is a wide range of existing satellite data in fields that are of interest to CBRN detection and monitoring. The data is mostly of insufficient quality (resolution or latency) for the demanding requirements of CBRN modelling for incident control. Future technologies and platforms will improve resolution and latency, making satellite data more viable in the CBRN management field

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RiboA: a web application to identify ribosome A-site locations in ribosome profiling data

BMC Bioinformatics ◽

10.1186/s12859-021-04068-w ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Danying Shao ◽

Nabeel Ahmed ◽

Nishant Soni ◽

Edward P. O’Brien

Keyword(s):

Integer Programming ◽

Web Application ◽

Stop Codon ◽

Ribosome Profiling ◽

Programming Method ◽

Analysis Tool ◽

Site Location ◽

Link Type ◽

Wide Range ◽

A Site

Abstract Background Translation is a fundamental process in gene expression. Ribosome profiling is a method that enables the study of transcriptome-wide translation. A fundamental, technical challenge in analyzing Ribo-Seq data is identifying the A-site location on ribosome-protected mRNA fragments. Identification of the A-site is essential as it is at this location on the ribosome where a codon is translated into an amino acid. Incorrect assignment of a read to the A-site can lead to lower signal-to-noise ratio and loss of correlations necessary to understand the molecular factors influencing translation. Therefore, an easy-to-use and accurate analysis tool is needed to accurately identify the A-site locations. Results We present RiboA, a web application that identifies the most accurate A-site location on a ribosome-protected mRNA fragment and generates the A-site read density profiles. It uses an Integer Programming method that reflects the biological fact that the A-site of actively translating ribosomes is generally located between the second codon and stop codon of a transcript, and utilizes a wide range of mRNA fragment sizes in and around the coding sequence (CDS). The web application is containerized with Docker, and it can be easily ported across platforms. Conclusions The Integer Programming method that RiboA utilizes is the most accurate in identifying the A-site on Ribo-Seq mRNA fragments compared to other methods. RiboA makes it easier for the community to use this method via a user-friendly and portable web application. In addition, RiboA supports reproducible analyses by tracking all the input datasets and parameters, and it provides enhanced visualization to facilitate scientific exploration. RiboA is available as a web service at https://a-site.vmhost.psu.edu/. The code is publicly available at https://github.com/obrien-lab/aip_web_docker under the MIT license.

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Product formalisms for measures on spaces with binary tree structures: representation, visualization, and multiscale noise

Forum of Mathematics Sigma ◽

10.1017/fms.2020.40 ◽

2020 ◽

Vol 8 ◽

Author(s):

Devasis Bassu ◽

Peter W. Jones ◽

Linda Ness ◽

David Shallcross

Keyword(s):

Data Science ◽

Theoretical Foundation ◽

Continuous Functions ◽

Noise Model ◽

Data Types ◽

Data Set ◽

Tree Structures ◽

Simple Concept ◽

Wide Range ◽

Computer Scientists

Abstract In this paper, we present a theoretical foundation for a representation of a data set as a measure in a very large hierarchically parametrized family of positive measures, whose parameters can be computed explicitly (rather than estimated by optimization), and illustrate its applicability to a wide range of data types. The preprocessing step then consists of representing data sets as simple measures. The theoretical foundation consists of a dyadic product formula representation lemma, and a visualization theorem. We also define an additive multiscale noise model that can be used to sample from dyadic measures and a more general multiplicative multiscale noise model that can be used to perturb continuous functions, Borel measures, and dyadic measures. The first two results are based on theorems in [15, 3, 1]. The representation uses the very simple concept of a dyadic tree and hence is widely applicable, easily understood, and easily computed. Since the data sample is represented as a measure, subsequent analysis can exploit statistical and measure theoretic concepts and theories. Because the representation uses the very simple concept of a dyadic tree defined on the universe of a data set, and the parameters are simply and explicitly computable and easily interpretable and visualizable, we hope that this approach will be broadly useful to mathematicians, statisticians, and computer scientists who are intrigued by or involved in data science, including its mathematical foundations.

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A FIJI Macro for quantifying pattern in extracellular matrix

10.1101/867507 ◽

2019 ◽

Author(s):

Esther Wershof ◽

Danielle Park ◽

David J Barry ◽

Robert P Jenkins ◽

Antonio Rullan ◽

...

Keyword(s):

Extracellular Matrix ◽

Quantitative Analysis ◽

Comprehensive Description ◽

Link Type ◽

Wide Range ◽

Automated Pipeline ◽

Imagej Plugin

AbstractDiverse extracellular matrix patterns are observed in both normal and pathological tissue. However, most current tools for quantitative analysis focus on a single aspect of matrix patterning. Thus, an automated pipeline that simultaneously quantifies a broad range of metrics and enables a comprehensive description of varied matrix patterns is needed. To this end we have developed an ImageJ plugin called TWOMBLI, which stands for The Workflow Of Matrix BioLogy Informatics. TWOMBLI is designed to be quick, versatile and easy-to-use particularly for non-computational scientists. TWOMBLI can be downloaded from https://github.com/wershofe/TWOMBLI together with detailed documentation. Here we present an overview of the pipeline together with examples from a wide range of contexts where matrix patterns are generated.

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LandScape: a web application for interactive genomic summary visualization

10.1101/866087 ◽

2019 ◽

Author(s):

Wenlong Jia ◽

Hechen Li ◽

Shiying Li ◽

Shuaicheng Li

Keyword(s):

Genetic Information ◽

Web Application ◽

Genomic Research ◽

File Format ◽

Data Types ◽

Web Based ◽

Link Type ◽

Level Data ◽

Real Time Visualization ◽

Information Landscape

ABSTRACTSummaryVisualizing integrated-level data from genomic research remains a challenge, as it requires sufficient coding skills and experience. Here, we present LandScapeoviz, a web-based application for interactive and real-time visualization of summarized genetic information. LandScape utilizes a well-designed file format that is capable of handling various data types, and offers a series of built-in functions to customize the appearance, explore results, and export high-quality diagrams that are available for publication.Availability and implementationLandScape is deployed at bio.oviz.org/demo-project/analyses/landscape for online use. Documentation and demo data are freely available on this website and GitHub (github.com/Nobel-Justin/Oviz-Bio-demo)[email protected]

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