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

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.

scholarly journals CRAMER: a lightweight, highly customizable web-based genome browser supporting multiple visualization instances

2020 ◽  
Vol 36 (11) ◽  
pp. 3556-3557
Author(s):  
M Anastasiadi ◽  
E Bragin ◽  
P Biojoux ◽  
A Ahamed ◽  
J Burgin ◽  
...  
Keyword(s):  
Web Application ◽  
Variant Calling ◽  
Genomic Data ◽  
Genome Browser ◽  
Supplementary Information ◽  
Web Based ◽  
Server Side ◽  
Genome Browsers ◽  
Set Up ◽  
Effective Visualization

Abstract Summary In recent years, the ability to generate genomic data has increased dramatically along with the demand for easily personalized and customizable genome browsers for effective visualization of diverse types of data. Despite the large number of web-based genome browsers available nowadays, none of the existing tools provides means for creating multiple visualization instances without manual set up on the deployment server side. The Cranfield Genome Browser (CRAMER) is an open-source, lightweight and highly customizable web application for interactive visualization of genomic data. Once deployed, CRAMER supports seamless creation of multiple visualization instances in parallel while allowing users to control and customize multiple tracks. The application is deployed on a Node.js server and is supported by a MongoDB database which stored all customizations made by the users allowing quick navigation between instances. Currently, the browser supports visualizing a large number of file formats for genome annotation, variant calling, reads coverage and gene expression. Additionally, the browser supports direct Javascript coding for personalized tracks, providing a whole new level of customization both functionally and visually. Tracks can be added via direct file upload or processed in real-time via links to files stored remotely on an FTP repository. Furthermore, additional tracks can be added by users via simple drag and drop to an existing visualization instance. Availability and implementation CRAMER is implemented in JavaScript and is publicly available on GitHub on https://github.com/FadyMohareb/cramer. The application is released under an MIT licence and can be deployed on any server running Linux or Mac OS. Contact [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Epidemiological modeling in StochSS Live!

2021 ◽  
Author(s):  
Richard Jiang ◽  
Bruno Jacob ◽  
Matthew Geiger ◽  
Sean Matthew ◽  
Bryan Rumsey ◽  
...  
Keyword(s):  
Stochastic Model ◽  
Epidemiological Model ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Web Based ◽  
Epidemiological Modeling ◽  
Modeling Simulation ◽  
Wide Range ◽  
Biochemical Systems

Abstract Summary We present StochSS Live!, a web-based service for modeling, simulation and analysis of a wide range of mathematical, biological and biochemical systems. Using an epidemiological model of COVID-19, we demonstrate the power of StochSS Live! to enable researchers to quickly develop a deterministic or a discrete stochastic model, infer its parameters and analyze the results. Availability and implementation StochSS Live! is freely available at https://live.stochss.org/ Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals PyRice: a Python package for querying Oryza Sativa databases

2020 ◽  
Author(s):  
Quan Do ◽  
Ho Bich Hai ◽  
Pierre Larmande
Keyword(s):  
Oryza Sativa ◽  
Heterogeneous Data ◽  
Supplementary Information ◽  
Web Based ◽  
Domain Experts ◽  
Link Type ◽  
Heterogeneous Data Sources ◽  
Query System ◽  
Gene Information ◽  
Python Package

AbstractSummaryCurrently, gene information available for Oryza sativa species is located in various online heterogeneous data sources. Moreover, methods of access are also diverse, mostly web-based and sometimes query APIs, which might not always be straightforward for domain experts. The challenge is to collect information quickly from these applications and combine it logically, to facilitate scientific research. We developed a Python package named PyRice, a unified programming API to access all supported databases at the same time with consistent output. PyRice design is modular and implements a smart query system which fits the computing resources to optimize the query speed. As a result, PyRice is easy to use and produces intuitive results.Availability and implementationhttps://github.com/SouthGreenPlatform/PyRiceDocumentationhttps://[email protected] informationMITSupplementary informationSupplementary data are available online.

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

F1000Research ◽  
2014 ◽  
Vol 3 ◽  
pp. 46 ◽  
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

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.

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

F1000Research ◽  
2014 ◽  
Vol 3 ◽  
pp. 45 ◽  
Author(s):  
Alexis Kalderimis ◽  
Radek Stepan ◽  
Julie Sullivan ◽  
Rachel Lyne ◽  
Michael Lyne ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Genomic Data ◽  
Data Warehouses ◽  
Data Types ◽  
Link Type ◽  
Wide Range ◽  
Flexible Queries

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.

scholarly journals blupADC: An R package and shiny toolkit for comprehensive genetic data analysis in animal and plant breeding

2021 ◽  
Author(s):  
Quanshun Mei ◽  
Chuanke Fu ◽  
Jieling Li ◽  
Shuhong Zhao ◽  
Tao Xiang
Keyword(s):  
Genetic Analysis ◽  
Plant Breeding ◽  
Genomic Data ◽  
R Package ◽  
Genotype Imputation ◽  
Supplementary Information ◽  
Composition Analysis ◽  
Relationship Matrix ◽  
Link Type ◽  
Plant Breeding Program

AbstractSummaryGenetic analysis is a systematic and complex procedure in animal and plant breeding. With fast development of high-throughput genotyping techniques and algorithms, animal and plant breeding has entered into a genomic era. However, there is a lack of software, which can be used to process comprehensive genetic analyses, in the routine animal and plant breeding program. To make the whole genetic analysis in animal and plant breeding straightforward, we developed a powerful, robust and fast R package that includes genomic data format conversion, genomic data quality control and genotype imputation, breed composition analysis, pedigree tracing, analysis and visualization, pedigree-based and genomic-based relationship matrix construction, and genomic evaluation. In addition, to simplify the application of this package, we also developed a shiny toolkit for users.Availability and implementationblupADC is developed primarily in R with core functions written in C++. The development version is maintained at https://github.com/TXiang-lab/blupADC.Supplementary informationSupplementary data are available online

scholarly journals sangeranalyseR: simple and interactive analysis of Sanger sequencing data in R

2020 ◽  
Author(s):  
Kuan-Hao Chao ◽  
Kirston Barton ◽  
Sarah Palmer ◽  
Robert Lanfear
Keyword(s):  
Sanger Sequencing ◽  
Reference Sequence ◽  
Supplementary Information ◽  
File Format ◽  
Bioconductor Package ◽  
Sequencing Data ◽  
Interactive Analysis ◽  
Link Type ◽  
Online Documentation ◽  
Wide Range

AbstractSummarysangeranalyseR is an interactive R/Bioconductor package and two associated Shiny applications designed for analysing Sanger sequencing from data from the ABIF file format in R. It allows users to go from loading reads to saving aligned contigs in a few lines of R code. sangeranalyseR provides a wide range of options for a number of commonly-performed actions including read trimming, detecting secondary peaks, viewing chromatograms, and detecting indels using a reference sequence. All parameters can be adjusted interactively either in R or in the associated Shiny applications. sangeranalyseR comes with extensive online documentation, and outputs detailed interactive HTML reports.Availability and implementationsangeranalyseR is implemented in R and released under an MIT license. It is available for all platforms on Bioconductor (https://bioconductor.org/packages/sangeranalyseR) and on Github (https://github.com/roblanf/sangeranalyseR)[email protected] informationDocumentation at https://sangeranalyser.readthedocs.io/.

EBDMSS

2012 ◽  
pp. 265-284
Author(s):  
Fen Wang ◽  
Natalie Lupton ◽  
David Rawlinson ◽  
Xingguo Zhang
Keyword(s):  
Decision Making ◽  
Real Time ◽  
Support System ◽  
Balanced Scorecard ◽  
Business Strategy ◽  
Business Managers ◽  
Web Based ◽  
Intelligent Decision Making ◽  
Intelligent Decision ◽  
Wide Range

This paper describes a Web-based intelligent decision making support system (DMSS) to deliver balanced scorecard (BSC) based modelling and analysis in support of strategic E-business management. This framework supports E-business managers during the strategy making process in a comprehensive, integrated, and continuous manner. The paper demonstrates how practitioners can use this system to deliver a wide range of embodied E-business strategy expertise in support of real-time decision making.

scholarly journals Efficient management and analysis of large-scale genome-wide data with two R packages: bigstatsr and bigsnpr

2017 ◽  
Author(s):  
Florian Privé ◽  
Hugues Aschard ◽  
Michael G.B. Blum
Keyword(s):  
Data Analysis ◽  
Large Scale ◽  
Genomic Data ◽  
Supplementary Information ◽  
Risk Scores ◽  
Analysis Pipeline ◽  
Polygenic Risk ◽  
Link Type ◽  
Genome Wide ◽  
R Packages

AbstractMotivation:Genome-wide datasets produced for association studies have dramatically increased in size over the past few years, with modern datasets commonly including millions of variants measured in dozens of thousands of individuals. This increase in data size is a major challenge severely slowing down genomic analyses. Specialized software for every part of the analysis pipeline have been developed to handle large genomic data. However, combining all these software into a single data analysis pipeline might be technically difficult.Results:Here we present two R packages, bigstatsr and bigsnpr, allowing for management and analysis of large scale genomic data to be performed within a single comprehensive framework. To address large data size, the packages use memory-mapping for accessing data matrices stored on disk instead of in RAM. To perform data pre-processing and data analysis, the packages integrate most of the tools that are commonly used, either through transparent system calls to existing software, or through updated or improved implementation of existing methods. In particular, the packages implement a fast derivation of Principal Component Analysis, functions to remove SNPs in Linkage Disequilibrium, and algorithms to learn Polygenic Risk Scores on millions of SNPs. We illustrate applications of the two R packages by analysing a case-control genomic dataset for the celiac disease, performing an association study and computing Polygenic Risk Scores. Finally, we demonstrate the scalability of the R packages by analyzing a simulated genome-wide dataset including 500,000 individuals and 1 million markers on a single desktop computer.Availability:https://privefl.github.io/bigstatsr/ & https://privefl.github.io/bigsnpr/Contact:[email protected] & [email protected] information:Supplementary data are available at Bioinformatics online.

scholarly journals PyRanges: efficient comparison of genomic intervals in Python

2019 ◽  
Author(s):  
Endre Bakken Stovner ◽  
Pål Sætrom
Keyword(s):  
Supplementary Information ◽  
Supplementary Data ◽  
Genomic Libraries ◽  
Link Type ◽  
Simple Set ◽  
Set Operations ◽  
Wide Range ◽  
Genomic Analyses ◽  
Associated Data ◽  
Memory Efficient

AbstractSummaryComplex genomic analyses often use sequences of simple set operations like intersection, overlap, and nearest on genomic intervals. These operations, coupled with some custom programming, allow a wide range of analyses to be performed. To this end, we have written PyRanges, a data structure for representing and manipulating genomic intervals and their associated data in Python. Run single-threaded on binary set operations, PyRanges is in median 2.3-9.6 times faster than the popular R GenomicRanges library and is equally memory efficient; run multi-threaded on 8 cores, our library is up to 123 times faster. PyRanges is therefore ideally suited both for individual analyses and as a foundation for future genomic libraries in Python.AvailabilityPyRanges is available open-source under the MIT license at https://github.com/biocore-NTNU/pyranges and documentation exists at https://biocore-NTNU.github.io/pyranges/[email protected] informationSupplementary data are available.

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