Publication detail

Using visual analytics to make sense of genomics data

VARGA, M. JOLLEY, K. SCHWARZEROVÁ, J. ČEJKOVÁ, D. MAIDEN, M.

Original Title

Using visual analytics to make sense of genomics data

Type

abstract

Language

English

Original Abstract

Genomics is having a transformational impact on medicine. The availability of whole genome sequence data offers new opportunities for analysing and understanding pathogens, the detection, surveillance, tracking and monitoring of infectious diseases, development, and assessment of vaccines etc. However, genomic data in its raw form is difficult to understand, explore and analyse. The application of visualization and visual analytics together with human factor methods provide an effective means for exploiting these complex data. These techniques can transform such inherently non-visual data into visual forms that enable users readily to gain insight into, and understanding of, information contained within the data. Data can be visualized in pictorial or graphical forms such as charts, networks, graphs, lists and maps so as to elicit or communicate particular aspects of the data, e.g. trends, patterns, correlations, (in)dependencies, anomalies etc. The choice and design of the visualization depends on the purposes. Individual charts showing different aspects of the data can be integrated together to create dashboards that enable exploration of different aspects of the data concurrently. The challenge is to organize and display information in an intuitive manner such that users can understand and explore the data, thus gain insight into the situation, detect the expected, and discover new knowledge. The primary objective is to communicate clearly and succinctly what the data say, to help show patterns / trends / anomalies that would otherwise be missed. It is also crucial to ensure data integrity is maintained.

Keywords

Genomics, Visualization, Infectious Diseases, Data Integrity

Authors

VARGA, M.; JOLLEY, K.; SCHWARZEROVÁ, J.; ČEJKOVÁ, D.; MAIDEN, M.

Released

19. 1. 2024

Publisher

Oxford hub

Location

Oxford UK

Pages count

1

URL

https://virtual.oxfordabstracts.com/event/4373/poster-gallery/grid

BibTex

@misc{BUT193451,
  author="Margaret {Varga} and Keith A {Jolley} and Jana {Schwarzerová} and Darina {Čejková} and Martin CJ {Maiden}",
  title="Using visual analytics to make sense of genomics data",
  year="2024",
  pages="1",
  publisher="Oxford hub",
  address="Oxford UK",
  url="https://virtual.oxfordabstracts.com/event/4373/poster-gallery/grid",
  note="abstract"
}