scholarly journals pileup.js: a JavaScript library for interactive and in-browser visualization of genomic data

2016 ◽  
Author(s):  
Dan Vanderkam ◽  
B. Arman Aksoy ◽  
Isaac Hodes ◽  
Jaclyn Perrone ◽  
Jeffrey Hammerbacher
Keyword(s):  
Web Applications ◽  
Genomic Data ◽  
Genomic Variants ◽  
Genome Viewer ◽  
Recent Developments

pileup.js is a new browser-based genome viewer. It is designed to facilitate the investigation of evidence for genomic variants within larger web applications. It takes advantage of recent developments in the JavaScript ecosystem to provide a modular, reliable and easily embedded library.

scholarly journals Dynamic Data Visualizations to Enhance Insight and Communication Across the Lifecycle of a Scientific Project

2021 ◽  
Author(s):  
Kristina Wiebels ◽  
David Moreau
Keyword(s):  
Data Visualization ◽  
Web Applications ◽  
Scientific Communication ◽  
R Language ◽  
Challenges And Opportunities ◽  
Recent Developments ◽  
Data Points ◽  
Scientific Project ◽  
Active Exploration ◽  
Data Visualizations

In scientific communication, figures are typically rendered as static displays. This often prevents active exploration of the underlying data, for example to gauge the influence of particular data points or of particular analytic choices. Yet modern data visualization tools, from animated plots to interactive notebooks and reactive web applications, allow psychologists to share and present their findings in dynamic and transparent ways. In this tutorial, we present a number of recent developments to build interactivity and animations into scientific communication and publications, using examples and illustrations in the R language. In particular, we discuss when and how to build dynamic figures, with step-by-step reproducible code that can easily be extended to the reader’s own projects. We illustrate how interactivity and animations can facilitate insight and communication across a project lifecycle—from initial exchanges and discussions within a team to peer-review and final publication—and provide a number of recommendations to use dynamic visualizations effectively. We close with a reflection on how the scientific publishing model is currently evolving, and consider the challenges and opportunities this shift might bring along for data visualization.

scholarly journals A Common Methodological Phylogenomics Framework for intra-patient heteroplasmies to infer SARS-CoV-2 sublineages and tumor clones

2020 ◽  
Author(s):  
Filippo Utro ◽  
Chaya Levovitz ◽  
Kahn Rhrissorrakrai ◽  
Laxmi Parida
Keyword(s):  
Cancer Patient ◽  
Phylogenetic Tree ◽  
Genomic Data ◽  
Methodological Framework ◽  
Computational Framework ◽  
Multiple Tumor ◽  
Genomic Variants ◽  
Parallel Mutation

AbstractWe present a common methodological framework to infer the phylogenomics from genomic data, be it reads of SARS-CoV-2 of multiple COVID-19 patients or bulk DNAseq of the tumor of a cancer patient. The commonality is in the phylogenetic retrodiction based on the genomic reads in both scenarios. While there is evidence of heteroplasmy, i.e., multiple lineages of SARS-CoV-2 in the same COVID-19 patient; to date, there is no evidence of sublineages recombining within the same patient. The heterogeneity in a patient’s tumor is analogous to intra-patient heteroplasmy and the absence of recombination in the cells of tumor is a widely accepted assumption. Just as the different frequencies of the genomic variants in a tumor presupposes the existence of multiple tumor clones and provides a handle to computationally infer them, we postulate that so do the different variant frequencies in the viral reads, offering the means to infer the multiple co-infecting sublineages. We describe the Concerti computational framework for inferring phylogenies in each of the two scenarios. To demonstrate the accuracy of the method, we reproduce some known results in both scenarios. We also make some additional discoveries. We uncovered new potential parallel mutation in the evolution of the SARS-CoV-2 virus. In the context of cancer, we uncovered new clones harboring resistant mutations to therapy from clinically plausible phylogenetic tree in a patient.

scholarly journals GenomeD3Plot: a library for rich, interactive visualizations of genomic data in web applications: Fig. 1.

2015 ◽  
Vol 31 (20) ◽  
pp. 3348-3349 ◽  
Author(s):  
Matthew R. Laird ◽  
Morgan G.I. Langille ◽  
Fiona S.L. Brinkman
Keyword(s):  
Web Applications ◽  
Genomic Data ◽  
Interactive Visualizations

scholarly journals VariantSpark: Cloud-based machine learning for association study of complex phenotype and large-scale genomic data

GigaScience ◽  
2020 ◽  
Vol 9 (8) ◽  
Author(s):  
Arash Bayat ◽  
Piotr Szul ◽  
Aidan R O’Brien ◽  
Robert Dunne ◽  
Brendan Hosking ◽  
...  
Keyword(s):  
Machine Learning ◽  
Association Studies ◽  
Genomic Data ◽  
Genome Wide Association ◽  
Risk Scores ◽  
Genome Wide Association Studies ◽  
Epistatic Interactions ◽  
Genomic Variants ◽  
Complex Phenotypes ◽  
Genome Wide

Abstract Background Many traits and diseases are thought to be driven by >1 gene (polygenic). Polygenic risk scores (PRS) hence expand on genome-wide association studies by taking multiple genes into account when risk models are built. However, PRS only considers the additive effect of individual genes but not epistatic interactions or the combination of individual and interacting drivers. While evidence of epistatic interactions ais found in small datasets, large datasets have not been processed yet owing to the high computational complexity of the search for epistatic interactions. Findings We have developed VariantSpark, a distributed machine learning framework able to perform association analysis for complex phenotypes that are polygenic and potentially involve a large number of epistatic interactions. Efficient multi-layer parallelization allows VariantSpark to scale to the whole genome of population-scale datasets with 100,000,000 genomic variants and 100,000 samples. Conclusions Compared with traditional monogenic genome-wide association studies, VariantSpark better identifies genomic variants associated with complex phenotypes. VariantSpark is 3.6 times faster than ReForeSt and the only method able to scale to ultra-high-dimensional genomic data in a manageable time.

scholarly journals Comparative Phylogenomics, a Stepping Stone for Bird Biodiversity Studies

Diversity ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 115 ◽  
Author(s):  
Josefin Stiller ◽  
Guojie Zhang
Keyword(s):  
Evolutionary History ◽  
Phylogenetic Signal ◽  
Genomic Data ◽  
Genome Project ◽  
Tree Of Life ◽  
Long Read ◽  
Recent Developments ◽  
Genome Assemblies ◽  
Avian Genomes ◽  
Chromosome Level

Birds are a group with immense availability of genomic resources, and hundreds of forthcoming genomes at the doorstep. We review recent developments in whole genome sequencing, phylogenomics, and comparative genomics of birds. Short read based genome assemblies are common, largely due to efforts of the Bird 10K genome project (B10K). Chromosome-level assemblies are expected to increase due to improved long-read sequencing. The available genomic data has enabled the reconstruction of the bird tree of life with increasing confidence and resolution, but challenges remain in the early splits of Neoaves due to their explosive diversification after the Cretaceous-Paleogene (K-Pg) event. Continued genomic sampling of the bird tree of life will not just better reflect their evolutionary history but also shine new light onto the organization of phylogenetic signal and conflict across the genome. The comparatively simple architecture of avian genomes makes them a powerful system to study the molecular foundation of bird specific traits. Birds are on the verge of becoming an extremely resourceful system to study biodiversity from the nucleotide up.

Rough Web Intelligent Techniques for Page Recommendation

2013 ◽  
pp. 170-191
Author(s):  
H. Inbarani ◽  
K. Thangavel
Keyword(s):  
Recommender Systems ◽  
Web Applications ◽  
Predictive Accuracy ◽  
Relevant Information ◽  
User Preference ◽  
Data Sets ◽  
Web Page ◽  
Recent Developments ◽  
Access Patterns ◽  
The Web

Recommender systems represent a prominent class of personalized Web applications, which particularly focus on the user-dependent filtering and selection of relevant information. Recommender Systems have been a subject of extensive research in Artificial Intelligence over the last decade, but with today’s increasing number of e-commerce environments on the Web, the demand for new approaches to intelligent product recommendation is higher than ever. There are more online users, more online channels, more vendors, more products, and, most importantly, increasingly complex products and services. These recent developments in the area of recommender systems generated new demands, in particular with respect to interactivity, adaptivity, and user preference elicitation. These challenges, however, are also in the focus of general Web page recommendation research. The goal of this chapter is to develop robust techniques to model noisy data sets containing an unknown number of overlapping categories and apply them for Web personalization and mining. In this chapter, rough set-based clustering approaches are used to discover Web user access patterns, and these techniques compute a number of clusters automatically from the Web log data using statistical techniques. The suitability of rough clustering approaches for Web page recommendation are measured using predictive accuracy metrics.

Multimedia Distance Learning Solutions for Surgery

2009 ◽  
pp. 390-398
Author(s):  
Jelena Vucetic
Keyword(s):  
Distance Learning ◽  
Knowledge Base ◽  
Web Applications ◽  
Text Documents ◽  
Continued Education ◽  
Technological Advances ◽  
Hands On ◽  
Patient Health ◽  
Recent Developments ◽  
Medical Disciplines

In the last decade, advances in medicine, telemedicine, computer technologies, information systems, Web applications, robotics and telecommunications have enabled creation of new solutions for training and continued education in various medical disciplines. This chapter presents most recent developments and future trends in distance learning for surgeons, focusing on the following goals: a) Building a comprehensive, world-wide, virtual knowledge base for various disciplines of surgery and telesurgery, including text documents, videos, case studies, expert surgeons’ opinions, and relevant references; b) Building a virtual knowledge base for rare medical cases, conditions and recommended procedures; c) Interactive multimedia simulators for hands-on training in all surgical disciplines; d) Building a worldwide surgical community, which will accelerate the accumulation and sharing of the latest surgical breakthroughs and technological advances throughout the world. Above all these goals, the most important goal is to improve patient health and convenience, and reduce risks of mortality and complications.

scholarly journals 3DGV: Immersive Exploration of 3D Genome Structures using Virtual Reality

2019 ◽  
Author(s):  
Éric Zhang ◽  
Chrisostomos Drogaris ◽  
Antoine Gédon ◽  
Aaron Sossin ◽  
Rajae Faraj ◽  
...  
Keyword(s):  
Virtual Reality ◽  
Genome Organization ◽  
Spatial Organization ◽  
Genomic Data ◽  
Regulatory Mechanisms ◽  
3D Genome ◽  
Open Platform ◽  
3D Objects ◽  
Link Type ◽  
Genome Viewer

The analysis of 3D genomic data is expected to revolutionize our understanding of genome organization and regulatory mechanisms. Yet, the complex spatial organization of this information can be difficult to interpret with 2D viewers. Virtual Reality (VR) technologies offer an opportunity to rethink our methods to visualize and navigate 3D objects. In this paper, we introduce the Virtual Reality 3D Genome Viewer (3DGV), an open platform to experiment and develop VR solutions to explore 3D genome structures.Availabilityhttp://3dgv.cs.mcgill.ca/

scholarly journals Machine learning and genomics: precision medicine versus patient privacy

2018 ◽  
Vol 376 (2128) ◽  
pp. 20170350 ◽  
Author(s):  
C.-A. Azencott
Keyword(s):  
Machine Learning ◽  
Precision Medicine ◽  
Large Body ◽  
Genomic Data ◽  
Patient Privacy ◽  
Computational Data ◽  
Genetic Features ◽  
Recent Developments ◽  
Study Participants ◽  
Technical Solutions

Machine learning can have a major societal impact in computational biology applications. In particular, it plays a central role in the development of precision medicine, whereby treatment is tailored to the clinical or genetic features of the patient. However, these advances require collecting and sharing among researchers large amounts of genomic data, which generates much concern about privacy. Researchers, study participants and governing bodies should be aware of the ways in which the privacy of participants might be compromised, as well as of the large body of research on technical solutions to these issues. We review how breaches in patient privacy can occur, present recent developments in computational data protection and discuss how they can be combined with legal and ethical perspectives to provide secure frameworks for genomic data sharing. This article is part of a discussion meeting issue ‘The growing ubiquity of algorithms in society: implications, impacts and innovations’.

Sign in / Sign up

Export Citation Format

Share Document