Grammar-Based Interactive Genome Visualization

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http://urn.fi/URN:NBN:fi:hulib-202006243441
Titel: Grammar-Based Interactive Genome Visualization
Författare: Lavikka, Kari
Medarbetare: Helsingin yliopisto, Matemaattis-luonnontieteellinen tiedekunta
University of Helsinki, Faculty of Science
Helsingfors universitet, Matematisk-naturvetenskapliga fakulteten
Utgivare: Helsingin yliopisto
Datum: 2020
Språk: eng
Permanenta länken (URI): http://urn.fi/URN:NBN:fi:hulib-202006243441
http://hdl.handle.net/10138/316957
Nivå: pro gradu-avhandlingar
Utbildningsprogram: Datatieteen maisteriohjelma
Master's Programme in Data Science
Magisterprogrammet i data science
Studieinriktning: ei opintosuuntaa
no specialization
ingen studieinriktning
Ämne: none
Abstrakt: Visualization is an indispensable method in the exploration of genomic data. However, the current state of the art in genome browsers – a class of interactive visualization tools – limit the exploration by coupling the visual representations with specific file formats. Because the tools do not support the exploration of the visualization design space, they are difficult to adapt to atypical data. Moreover, although the tools provide interactivity, the implementations are often rudimentary, encumbering the exploration of the data. This thesis introduces GenomeSpy, an interactive genome visualization tool that improves upon the current state of the art by providing better support for exploration. The tool uses a visualization grammar that allows for implementing novel visualization designs, which can display the underlying data more effectively. Moreover, the tool implements GPU-accelerated interactions that better support navigation in the genomic space. For instance, smoothly animated transitions between loci or sample sets improve the perception of causality and help the users stay in the flow of exploration. The expressivity of the visualization grammar and the benefit of fluid interactions are validated with two case studies. The case studies demonstrate visualization of high-grade serous ovarian cancer data at different analysis phases. First, GenomeSpy is being used to create a tool for scrutinizing raw copy-number variation data along with segmentation results. Second, the segmentations along with point mutations are used in a GenomeSpy-based multi-sample visualization that allows for exploring and comparing both multiple data dimensions and samples at the same time. Although the focus has been on cancer research, the tool could be applied to other domains as well.
Subject: genomic data visualization
grammar of graphics
fluid interactions


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