scholarly journals LocusExplorer: a user-friendly tool for integrated visualization of human genetic association data and biological annotations

2015 ◽  
Vol 32 (6) ◽  
pp. 949-951 ◽  
Author(s):  
Tokhir Dadaev ◽  
Daniel A. Leongamornlert ◽  
Edward J. Saunders ◽  
Rosalind Eeles ◽  
Zsofia Kote-Jarai
Keyword(s):  
Genetic Association ◽  
Source Code ◽  
Biological Data ◽  
Single Image ◽  
Web Browser ◽  
File Formats ◽  
Exploration Tool ◽  
Association Data ◽  
User Friendly ◽  
Publication Quality

Abstract Summary: In this article, we present LocusExplorer, a data visualization and exploration tool for genetic association data. LocusExplorer is written in R using the Shiny library, providing access to powerful R-based functions through a simple user interface. LocusExplorer allows users to simultaneously display genetic, statistical and biological data for humans in a single image and allows dynamic zooming and customization of the plot features. Publication quality plots may then be produced in a variety of file formats. Availability and implementation: LocusExplorer is open source and runs through R and a web browser. It is available at www.oncogenetics.icr.ac.uk/LocusExplorer/ or can be installed locally and the source code accessed from https://github.com/oncogenetics/LocusExplorer. Contact: [email protected]

scholarly journals DamageProfiler: Fast damage pattern calculation for ancient DNA

2021 ◽  
Author(s):  
Judith Neukamm ◽  
Alexander Peltzer ◽  
Kay Nieselt
Keyword(s):  
Ancient Dna ◽  
Source Code ◽  
Supplementary Information ◽  
Command Line ◽  
Central Importance ◽  
Command Line Interface ◽  
Analysis Pipeline ◽  
File Formats ◽  
Programming Knowledge ◽  
User Friendly

Abstract Motivation In ancient DNA research, the authentication of ancient samples based on specific features remains a crucial step in data analysis. Because of this central importance, researchers lacking deeper programming knowledge should be able to run a basic damage authentication analysis. Such software should be user-friendly and easy to integrate into an analysis pipeline. Results DamageProfiler is a Java based, stand-alone software to determine damage patterns in ancient DNA. The results are provided in various file formats and plots for further processing. DamageProfiler has an intuitive graphical as well as command line interface that allows the tool to be easily embedded into an analysis pipeline. Availability All of the source code is freely available on GitHub (https://github.com/Integrative-Transcriptomics/DamageProfiler). Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Ontoclick: a Chrome web browser extension to facilitate biomedical knowledge curation

2021 ◽  
Author(s):  
Anthony Xu ◽  
Aravind Venkateswaran ◽  
Lianguizi Zhou ◽  
Andreas Zankl
Keyword(s):  
Source Code ◽  
Biomedical Literature ◽  
Biomedical Knowledge ◽  
Web Browser ◽  
Ontology Term ◽  
Browser Extension ◽  
Widespread Adoption ◽  
User Friendly

Knowledge curation from the biomedical literature is very valuable but can be a repetitive and laborious process. The paucity of user-friendly tools is one of the reasons for the lack of widespread adoption of good biomedical knowledge curation practices. Here we present Ontoclick, a Chrome web browser extension that streamlines the process of annotating a text span with a relevant ontology term. We hope this tool will make biocuration more accessible to a wider audience of biomedical researchers. Ontoclick is freely available under the GPL-3.0 license on the Chrome Web Store. Source code and documentation are available at: https://github.com/azankl/Ontoclick Contact: [email protected]

scholarly journals BioInfograph: An Online Tool to Design and Display Multi-Panel Scientific Figure Interactively

2022 ◽  
Vol 12 ◽  
Author(s):  
Kejie Li ◽  
Jessica Hurt ◽  
Christopher D. Whelan ◽  
Ravi Challa ◽  
Dongdong Lin ◽  
...  
Keyword(s):  
High Resolution ◽  
Biological Data ◽  
Web Based ◽  
Online Tool ◽  
Scalable Vector Graphics ◽  
High Resolution Images ◽  
Fit For Purpose ◽  
Programming Skills ◽  
User Friendly ◽  
Publication Quality

Many fit-for-purpose bioinformatics tools generate plots to interpret complex biological data and illustrate findings. However, assembling individual plots in different formats from various sources into one high-resolution figure in the desired layout requires mastery of commercial tools or even programming skills. In addition, it is a time-consuming and sometimes frustrating process even for a computationally savvy scientist who frequently takes a trial-and-error iterative approach to get satisfactory results. To address the challenge, we developed bioInfograph, a web-based tool that allows users to interactively arrange high-resolution images in diversified formats, mainly Scalable Vector Graphics (SVG), to produce one multi-panel publication-quality composite figure in both PDF and HTML formats in a user-friendly manner, requiring no programming skills. It solves stylesheet conflicts of coexisting SVG plots, integrates a rich-text editor, and allows creative design by providing advanced functionalities like image transparency, controlled vertical stacking of plots, versatile image formats, and layout templates. To highlight, the sharable interactive HTML output with zoom-in function is a unique feature not seen in any other similar tools. In the end, we make the online tool publicly available at https://baohongz.github.io/bioInfograph while releasing the source code at https://github.com/baohongz/bioInfograph under MIT open-source license.

scholarly journals “Guilt by association” is not competitive with genetic association for identifying autism risk genes

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Margot Gunning ◽  
Paul Pavlidis
Keyword(s):  
Machine Learning ◽  
Genetic Association ◽  
Gene Networks ◽  
Rare Variants ◽  
Association Studies ◽  
Genetic Disorders ◽  
Autism Spectrum ◽  
Biological Data ◽  
Disease Genes ◽  
Risk Genes

AbstractDiscovering genes involved in complex human genetic disorders is a major challenge. Many have suggested that machine learning (ML) algorithms using gene networks can be used to supplement traditional genetic association-based approaches to predict or prioritize disease genes. However, questions have been raised about the utility of ML methods for this type of task due to biases within the data, and poor real-world performance. Using autism spectrum disorder (ASD) as a test case, we sought to investigate the question: can machine learning aid in the discovery of disease genes? We collected 13 published ASD gene prioritization studies and evaluated their performance using known and novel high-confidence ASD genes. We also investigated their biases towards generic gene annotations, like number of association publications. We found that ML methods which do not incorporate genetics information have limited utility for prioritization of ASD risk genes. These studies perform at a comparable level to generic measures of likelihood for the involvement of genes in any condition, and do not out-perform genetic association studies. Future efforts to discover disease genes should be focused on developing and validating statistical models for genetic association, specifically for association between rare variants and disease, rather than developing complex machine learning methods using complex heterogeneous biological data with unknown reliability.

scholarly journals AtLoc: Attention Guided Camera Localization

2020 ◽  
Vol 34 (06) ◽  
pp. 10393-10401
Author(s):  
Bing Wang ◽  
Changhao Chen ◽  
Chris Xiaoxuan Lu ◽  
Peijun Zhao ◽  
Niki Trigoni ◽  
...  
Keyword(s):  
Deep Learning ◽  
Experimental Evidence ◽  
State Of The Art ◽  
Source Code ◽  
Single Image ◽  
Saliency Maps ◽  
Camera Localization ◽  
Camera Pose ◽  
Dynamic Objects ◽  
Indoor And Outdoor

Deep learning has achieved impressive results in camera localization, but current single-image techniques typically suffer from a lack of robustness, leading to large outliers. To some extent, this has been tackled by sequential (multi-images) or geometry constraint approaches, which can learn to reject dynamic objects and illumination conditions to achieve better performance. In this work, we show that attention can be used to force the network to focus on more geometrically robust objects and features, achieving state-of-the-art performance in common benchmark, even if using only a single image as input. Extensive experimental evidence is provided through public indoor and outdoor datasets. Through visualization of the saliency maps, we demonstrate how the network learns to reject dynamic objects, yielding superior global camera pose regression performance. The source code is avaliable at https://github.com/BingCS/AtLoc.

Transcriptomics Analysis using Galaxy and other Online Servers for Rheumatoid Arthritis

2021 ◽  
Vol 9 (VII) ◽  
pp. 459-466
Author(s):  
Sourabh Parmar
Keyword(s):  
Rheumatoid Arthritis ◽  
Data Mining ◽  
Gene Ontology ◽  
Complex Disease ◽  
Differential Expression Analysis ◽  
Biological Data ◽  
Systemic Autoimmune Disease ◽  
Transcriptomic Data ◽  
Pathway Enrichment ◽  
User Friendly

Researchers use transcriptomics analyses for biological data mining, interpretation, and presentation. Galaxy-based tools are utilized to analyze various complex disease transcriptomic data to understand the pathogenesis of the disease, which are user-friendly. This work provides simple methods for differential expression analysis and analysis of these results in gene ontology and pathway enrichment tools like David, WebGestalt. This method is very effective in better analysis and understanding the transcriptomic data. Transcriptomics analysis has been made on rheumatoid arthritis sra data. Rheumatoid arthritis (RA) is a systemic autoimmune disease. T cells and autoantibodies mediate the pathogenesis. This article discusses the genes which are differentially expressed between the healthy (n=50) and diseased (n=51) and the functions of those genes in the pathogenesis of RA.

GEView (Gene Expression View) Tool for Intuitive and High Accessible Visualization of Expression Data for Non-Programmer Biologists

2020 ◽  
pp. 580-592
Author(s):  
Libi Hertzberg ◽  
Assif Yitzhaky ◽  
Metsada Pasmanik-Chor
Keyword(s):  
Gene Expression ◽  
Quality Control ◽  
User Interface ◽  
High Throughput ◽  
Graphical User Interface ◽  
Differential Expression Analysis ◽  
Biological Data ◽  
Expression Data ◽  
Batch Correction ◽  
User Friendly

This article describes how the last decade has been characterized by the production of huge amounts of different types of biological data. Following that, a flood of bioinformatics tools have been published. However, many of these tools are commercial, or require computational skills. In addition, not all tools provide intuitive and highly accessible visualization of the results. The authors have developed GEView (Gene Expression View), which is a free, user-friendly tool harboring several existing algorithms and statistical methods for the analysis of high-throughput gene, microRNA or protein expression data. It can be used to perform basic analysis such as quality control, outlier detection, batch correction and differential expression analysis, through a single intuitive graphical user interface. GEView is unique in its simplicity and highly accessible visualization it provides. Together with its basic and intuitive functionality it allows Bio-Medical scientists with no computational skills to independently analyze and visualize high-throughput data produced in their own labs.

scholarly journals HD-eXplosion: visualization of hydrogen–deuterium exchange data as chiclet and volcano plots with statistical filtering

2020 ◽  
Author(s):  
Naifu Zhang ◽  
Xiaohe Yu ◽  
Xinchao Zhang ◽  
Sheena D’Arcy
Keyword(s):  
Source Code ◽  
Deuterium Exchange ◽  
Hydrogen Deuterium Exchange ◽  
Web Based ◽  
Data Presentation ◽  
Software Packages ◽  
Volcano Plots ◽  
Hydrogen Deuterium ◽  
Exchange Data ◽  
Publication Quality

Abstract Summary Hydrogen–Deuterium eXchange coupled to mass spectrometry is a powerful tool for the analysis of protein dynamics and interactions. Bottom-up experiments looking at deuterium uptake differences between various conditions are the most common. These produce multi-dimensional data that can be challenging to depict in a single visual format. Each user must also set significance thresholds to define meaningful differences and make these apparent in data presentation. To assist in this process, we have created HD-eXplosion, an open-source, web-based application for the generation of chiclet and volcano plots with statistical filters. HD-eXplosion fills a void in available software packages and produces customizable plots that are publication quality. Availability and implementation The HD-eXplosion application is available at http://hd-explosion.utdallas.edu. The source code can be found at https://github.com/HD-Explosion.

scholarly journals DiscoRhythm: an easy-to-use web application and R package for discovering rhythmicity

2019 ◽  
Author(s):  
Matthew Carlucci ◽  
Algimantas Kriščiūnas ◽  
Haohan Li ◽  
Povilas Gibas ◽  
Karolis Koncevičius ◽  
...  
Keyword(s):  
Web Application ◽  
Statistical Significance ◽  
R Package ◽  
Biological Data ◽  
Supplementary Information ◽  
Statistical Knowledge ◽  
Health And Disease ◽  
Phase Amplitude ◽  
Almost All ◽  
User Friendly

Abstract Motivation Biological rhythmicity is fundamental to almost all organisms on Earth and plays a key role in health and disease. Identification of oscillating signals could lead to novel biological insights, yet its investigation is impeded by the extensive computational and statistical knowledge required to perform such analysis. Results To address this issue, we present DiscoRhythm (Discovering Rhythmicity), a user-friendly application for characterizing rhythmicity in temporal biological data. DiscoRhythm is available as a web application or an R/Bioconductor package for estimating phase, amplitude, and statistical significance using four popular approaches to rhythm detection (Cosinor, JTK Cycle, ARSER, and Lomb-Scargle). We optimized these algorithms for speed, improving their execution times up to 30-fold to enable rapid analysis of -omic-scale datasets in real-time. Informative visualizations, interactive modules for quality control, dimensionality reduction, periodicity profiling, and incorporation of experimental replicates make DiscoRhythm a thorough toolkit for analyzing rhythmicity. Availability and Implementation The DiscoRhythm R package is available on Bioconductor (https://bioconductor.org/packages/DiscoRhythm), with source code available on GitHub (https://github.com/matthewcarlucci/DiscoRhythm) under a GPL-3 license. The web application is securely deployed over HTTPS (https://disco.camh.ca) and is freely available for use worldwide. Local instances of the DiscoRhythm web application can be created using the R package or by deploying the publicly available Docker container (https://hub.docker.com/r/mcarlucci/discorhythm). Supplementary information Supplementary data are available at Bioinformatics online.

Sign in / Sign up

Export Citation Format

Share Document