全球暖化對地球的海洋帶來嚴重威脅，影響海洋生態系統，同時引起對生態
多樣性和可持續性的關切。為了更好地了解海洋生物如何應對這些變化，海洋生物
專家致力於深入研究不同海洋環境中基因變異的差異。現有的基因組學工具在基因
層面提供了細緻的視覺化，然而在族群基因組學的研究中，有著其他不一樣的需求，
例如能夠視覺化完整基因序列並精確突顯基因變異水平。為了滿足這些需求，我們
開發了 VariaVisio，一個基因組視覺化系統，旨在提高生物研究人員進行深入且準確
的族群基因組學研究的能力。VariaVisio 讓專家能夠視覺化完整的基因序列，還能夠
突顯基因變異的程度，並提供有關特定基因內變異位置的深入洞察。

Global warming poses a danger to the oceans of our planet affecting marine ecosystems and
raising concerns about biodiversity and sustainability. Therefore, to better understand how
marine organisms adapt to environmental changes, population genomics has emerged as a
crucial research field, with biology experts focusing on the detailed study of genetic
variations across diverse marine environments. Existing genomic systems offer detailed
visualizations at the genetic level. However, population genomics research presents different
needs, such as visualizing complete gene sequences and accurately highlighting levels of
genetic variation. To meet these needs, we have developed VariaVisio, a genomic
visualization system specifically designed to support population genomics research.
VariaVisio enables biology researchers to visualize complete gene sequences, highlights the
degree of genetic variation and providing in-depth insights into the locations of variations
within specific genes.

論文審定書 ..................................................................................................................... i
致謝 .................................................................................................................... ii
摘要 ................................................................................................................... iii
Abstract ................................................................................................................... iv
Table of Contents .............................................................................................................. v
List of Figures ................................................................................................................. vii
List of Table ................................................................................................................... xi
CHAPTER 1 Introduction .......................................................................................... 1
1.1 Background ......................................................................................................... 1
1.2 Terminology ....................................................................................................... 3
1.3 Research Questions ............................................................................................. 6
CHAPTER 2 Literature Review ................................................................................. 7
2.1 Synteny Visualization ......................................................................................... 7
2.2 Variation Visualization ....................................................................................... 9
2.3 Gene Expression Visualization ......................................................................... 12
CHAPTER 3 System Design ..................................................................................... 15
3.1 User Requirements ........................................................................................... 15
3.2 VariaVisio ......................................................................................................... 19
3.2.1 Compression and Variation .......................................................................... 21
3.2.2 Gene Expression ........................................................................................... 29
3.3 Usage Scenario ................................................................................................. 32
CHAPTER 4 Evaluation ............................................................................................ 35
4.1 Study 1: User Study .......................................................................................... 35
4.1.1 Participants ................................................................................................... 35
4.1.2 Data ............................................................................................................... 35
4.1.3 Tasks ............................................................................................................. 36
4.1.4 Outcome Measures ....................................................................................... 38
4.1.5 Study Procedure ............................................................................................ 40
4.1.6 Results .......................................................................................................... 41
4.2 Study 2: Expert Analysis .................................................................................. 46
4.2.1 Participants ................................................................................................... 46
4.2.2 Data ............................................................................................................... 47
4.2.3 Outcome Measures ....................................................................................... 48
4.2.4 Study Procedure ............................................................................................ 49
4.2.5 Results .......................................................................................................... 51
CHAPTER 5 Discussion and Conclusion ................................................................. 61
References .................................................................................................................. 63
Appendix A: User Study Questions .............................................................................. 73
Appendix B: Expert Analysis Questions ...................................................................... 79

A reference standard for genome biology. (2018). Nature Biotechnology, 36(12), 1121–1121.
https://doi.org/10.1038/nbt.4318
Bangor, A., Kortum, P. T., & Miller, J. T. (2008). An Empirical Evaluation of the System
Usability Scale. International Journal of Human-Computer Interaction, 24(6), 574–594.
https://doi.org/10.1080/10447310802205776
Bartolomeo, S. Di, Zhang, Y., Sheng, F., & Dunne, C. (2021). Sequence Braiding: Visual
Overviews of Temporal Event Sequences and Attributes. IEEE Transactions on
Visualization and Computer Graphics, 27(2), 1353–1363.
https://doi.org/10.1109/TVCG.2020.3030442
Belkin, I. M. (2009). Rapid warming of Large Marine Ecosystems. Progress in
Oceanography, 81(1–4), 207–213. https://doi.org/10.1016/j.pocean.2009.04.011
Bernatchez, L. (2016). On the maintenance of genetic variation and adaptation to
environmental change: considerations from population genomics in fishes. Journal of
Fish Biology, 89(6), 2519–2556. https://doi.org/10.1111/jfb.13145
Bohyoung Kim, Bongshin Lee, Knoblach, S., Hoffman, E., & Jinwook Seo. (2009).
GeneShelf: A Web-based Visual Interface for Large Gene Expression Time-Series Data
Repositories. IEEE Transactions on Visualization and Computer Graphics, 15(6), 905–
912. https://doi.org/10.1109/TVCG.2009.146
Bostock, M., Ogievetsky, V., & Heer, J. (2011). D3 Data-Driven Documents. IEEE
Transactions on Visualization and Computer Graphics, 17(12), 2301–2309.
https://doi.org/10.1109/TVCG.2011.185
Brooke, J. (1996). SUS-A quick and dirty usability scale. Usability Evaluation in Industry,
189(194), 4–7.
Chen, Y., Puri, A., Yuan, L., & Qu, H. (2018). StageMap: Extracting and Summarizing
Progression Stages in Event Sequences. 2018 IEEE International Conference on Big
Data (Big Data), 975–981. https://doi.org/10.1109/BigData.2018.8622571
Clamp, M., Fry, B., Kamal, M., Xie, X., Cuff, J., Lin, M. F., Kellis, M., Lindblad-Toh, K.,
& Lander, E. S. (2007). Distinguishing protein-coding and noncoding genes in the
human genome. Proceedings of the National Academy of Sciences, 104(49), 19428–
19433. https://doi.org/10.1073/pnas.0709013104
Cui, J., Yang, Y., Luo, S., Wang, L., Huang, R., Wen, Q., Han, X., Miao, N., Cheng, J., Liu,
Z., Zhang, C., Feng, C., Zhu, H., Su, J., Wan, X., Hu, F., Niu, Y., Zheng, X., Yang,
Y., … Hu, K. (2020). Whole-genome sequencing provides insights into the genetic
diversity and domestication of bitter gourd (Momordica spp.). Horticulture Research,
7(1), 85. https://doi.org/10.1038/s41438-020-0305-5
Diesh, C., Stevens, G. J., Xie, P., De Jesus Martinez, T., Hershberg, E. A., Leung, A., Guo,
E., Dider, S., Zhang, J., Bridge, C., Hogue, G., Duncan, A., Morgan, M., Flores, T.,
Bimber, B. N., Haw, R., Cain, S., Buels, R. M., Stein, L. D., & Holmes, I. H. (2023).
JBrowse 2: a modular genome browser with views of synteny and structural variation.
Genome Biology, 24(1), 74. https://doi.org/10.1186/s13059-023-02914-z
Douglas, G. M., Maffei, V. J., Zaneveld, J. R., Yurgel, S. N., Brown, J. R., Taylor, C. M.,
Huttenhower, C., & Langille, M. G. I. (2020). PICRUSt2 for prediction of metagenome
functions. Nature Biotechnology, 38(6), 685–688. https://doi.org/10.1038/s41587-020-
0548-6
Du, F., Shneiderman, B., Plaisant, C., Malik, S., & Perer, A. (2017). Coping with Volume
and Variety in Temporal Event Sequences: Strategies for Sharpening Analytic Focus.
IEEE Transactions on Visualization and Computer Graphics, 23(6), 1636–1649.
https://doi.org/10.1109/TVCG.2016.2539960
Emilsson, V., Thorleifsson, G., Zhang, B., Leonardson, A. S., Zink, F., Zhu, J., Carlson, S.,
Helgason, A., Walters, G. B., Gunnarsdottir, S., Mouy, M., Steinthorsdottir, V.,
Eiriksdottir, G. H., Bjornsdottir, G., Reynisdottir, I., Gudbjartsson, D., Helgadottir, A.,
Jonasdottir, A., Jonasdottir, A., … Stefansson, K. (2008). Genetics of gene expression
and its effect on disease. Nature, 452(7186), 423–428.
https://doi.org/10.1038/nature06758
Estaki, M., Jiang, L., Bokulich, N. A., McDonald, D., González, A., Kosciolek, T., Martino,
C., Zhu, Q., Birmingham, A., Vázquez‐Baeza, Y., Dillon, M. R., Bolyen, E., Caporaso,
J. G., & Knight, R. (2020). QIIME 2 Enables Comprehensive End‐to‐End Analysis of
Diverse Microbiome Data and Comparative Studies with Publicly Available Data.
Current Protocols in Bioinformatics, 70(1). https://doi.org/10.1002/cpbi.100
Ferstay, J. A., Nielsen, C. B., & Munzner, T. (2013). Variant View: Visualizing Sequence
Variants in their Gene Context. IEEE Transactions on Visualization and Computer
Graphics, 19(12), 2546–2555. https://doi.org/10.1109/TVCG.2013.214
Gaitatzes, A., Johnson, S. H., Smadbeck, J. B., & Vasmatzis, G. (2018). Genome U-Plot: a
whole genome visualization. Bioinformatics, 34(10), 1629–1634.
https://doi.org/10.1093/bioinformatics/btx829
Guo, S., Jin, Z., Chen, Q., Gotz, D., Zha, H., & Cao, N. (2022). Interpretable Anomaly
Detection in Event Sequences via Sequence Matching and Visual Comparison. IEEE
Transactions on Visualization and Computer Graphics, 28(12), 4531–4545.
https://doi.org/10.1109/TVCG.2021.3093585
Guo, Y., Guo, S., Jin, Z., Kaul, S., Gotz, D., & Cao, N. (2022). Survey on Visual Analysis
of Event Sequence Data. IEEE Transactions on Visualization and Computer Graphics,
28(12), 5091–5112. https://doi.org/10.1109/TVCG.2021.3100413
Hadfield, J., Croucher, N. J., Goater, R. J., Abudahab, K., Aanensen, D. M., & Harris, S. R.
(2018). Phandango: an interactive viewer for bacterial population genomics.
Bioinformatics, 34(2), 292–293. https://doi.org/10.1093/bioinformatics/btx610
Hohenlohe, P. A., Funk, W. C., & Rajora, O. P. (2021a). Population genomics for wildlife
conservation and management. Molecular Ecology, 30(1), 62–82.
https://doi.org/10.1111/mec.15720
Hohenlohe, P. A., Funk, W. C., & Rajora, O. P. (2021b). Population genomics for wildlife
conservation and management. Molecular Ecology, 30(1), 62–82.
https://doi.org/10.1111/mec.15720
James Petersen, Robert E. Gabler, & Dorothy Sack. (2014). Introduction to the Oceans. In
Fundamentals of Physical Geography (2nd Editio). Cengage Learning.
John Houghton. (2015). Global Warming. Reports on Progress in Physics, 68(6).
Jorde, L. B. (2001). Population genomics: a bridge from evolutionary history to genetic
medicine. Human Molecular Genetics, 10(20), 2199–2207.
https://doi.org/10.1093/hmg/10.20.2199
Kanehisa, M. (2000). KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids
Research, 28(1), 27–30. https://doi.org/10.1093/nar/28.1.27
Kent, W. J., Sugnet, C. W., Furey, T. S., Roskin, K. M., Pringle, T. H., Zahler, A. M., &
Haussler, and D. (2002). The Human Genome Browser at UCSC. Genome Research,
12(6), 996–1006. https://doi.org/10.1101/gr.229102
Krzywinski, M., Schein, J., Birol, İ., Connors, J., Gascoyne, R., Horsman, D., Jones, S. J., &
Marra, M. A. (2009). Circos: An information aesthetic for comparative genomics.
Genome Research, 19(9), 1639–1645. https://doi.org/10.1101/gr.092759.109
Li, H., Handsaker, B., Wysoker, A., Fennell, T., Ruan, J., Homer, N., Marth, G., Abecasis,
G., & Durbin, R. (2009). The Sequence Alignment/Map format and SAMtools.
Bioinformatics, 25(16), 2078–2079. https://doi.org/10.1093/bioinformatics/btp352
Li, W., Cowley, A., Uludag, M., Gur, T., McWilliam, H., Squizzato, S., Park, Y. M., Buso,
N., & Lopez, R. (2015). The EMBL-EBI bioinformatics web and programmatic tools
framework. Nucleic Acids Research, 43(W1), W580–W584.
https://doi.org/10.1093/nar/gkv279
Liu, D., Hunt, M., & Tsai, I. J. (2018). Inferring synteny between genome assemblies: a
systematic evaluation. BMC Bioinformatics, 19(1), 26. https://doi.org/10.1186/s12859-
018-2026-4
Lonsdale, J., Thomas, J., Salvatore, M., Phillips, R., Lo, E., Shad, S., Hasz, R., Walters, G.,
Garcia, F., Young, N., Foster, B., Moser, M., Karasik, E., Gillard, B., Ramsey, K.,
Sullivan, S., Bridge, J., Magazine, H., Syron, J., … Moore, H. F. (2013). The Genotype-
Tissue Expression (GTEx) project. Nature Genetics, 45(6), 580–585.
https://doi.org/10.1038/ng.2653
Lou, R. N., Jacobs, A., Wilder, A. P., & Therkildsen, N. O. (2021). A beginner’s guide to
low‐coverage whole genome sequencing for population genomics. Molecular Ecology,
30(23), 5966–5993. https://doi.org/10.1111/mec.16077
Marcus, J. H., & Novembre, J. (2017). Visualizing the geography of genetic variants.
Bioinformatics, 33(4), 594–595. https://doi.org/10.1093/bioinformatics/btw643
Markert, J. A., Champlin, D. M., Gutjahr-Gobell, R., Grear, J. S., Kuhn, A., McGreevy, T.
J., Roth, A., Bagley, M. J., & Nacci, D. E. (2010). Population genetic diversity and
fitness in multiple environments. BMC Evolutionary Biology, 10(1), 205.
https://doi.org/10.1186/1471-2148-10-205
Morgan, T. H. (1917). The Theory of the Gene. The American Naturalist, 51(609), 513–544.
https://doi.org/10.1086/279629
Munsky, B., Neuert, G., & van Oudenaarden, A. (2012). Using Gene Expression Noise to
Understand Gene Regulation. Science, 336(6078), 183–187.
https://doi.org/10.1126/science.1216379
Nattestad, M., Aboukhalil, R., Chin, C.-S., & Schatz, M. C. (2021). Ribbon: intuitive
visualization for complex genomic variation. Bioinformatics, 37(3), 413–415.
https://doi.org/10.1093/bioinformatics/btaa680
Nedoluzhko, A. (2023). Sea of opportunities: marine genomics in an era of global
environmental change. BMC Genomics, 24(1), 286. https://doi.org/10.1186/s12864-
023-09392-4
Nevo, E. (1978). Genetic variation in natural populations: Patterns and theory. Theoretical
Population Biology, 13(1), 121–177. https://doi.org/10.1016/0040-5809(78)90039-4
Nielsen, E. E., Hemmer‐Hansen, J., Larsen, P. F., & Bekkevold, D. (2009). Population
genomics of marine fishes: identifying adaptive variation in space and time. Molecular
Ecology, 18(15), 3128–3150. https://doi.org/10.1111/j.1365-294X.2009.04272.x
Nusrat, S., Harbig, T., & Gehlenborg, N. (2019). Tasks, Techniques, and Tools for Genomic
Data Visualization. Computer Graphics Forum, 38(3), 781–805.
https://doi.org/10.1111/cgf.13727
Ondov, B. D., Bergman, N. H., & Phillippy, A. M. (2011). Interactive metagenomic
visualization in a Web browser. BMC Bioinformatics, 12(1), 385.
https://doi.org/10.1186/1471-2105-12-385
Paganin, M., Tebaldi, T., Lauria, F., & Viero, G. (2023). Visualizing gene expression changes
in time, space, and single cells with expressyouRcell. IScience, 26(6), 106853.
https://doi.org/10.1016/j.isci.2023.106853
Poonperm, R., Takata, H., Hamano, T., Matsuda, A., Uchiyama, S., Hiraoka, Y., & Fukui,
K. (2015). Chromosome Scaffold is a Double-Stranded Assembly of Scaffold Proteins.
Scientific Reports, 5(1), 11916. https://doi.org/10.1038/srep11916
Preston, M. D., Assefa, S. A., Ocholla, H., Sutherland, C. J., Borrmann, S., Nzila, A., Michon,
P., Hien, T. T., Bousema, T., Drakeley, C. J., Zongo, I., Ouédraogo, J.-B., Djimde, A.
A., Doumbo, O. K., Nosten, F., Fairhurst, R. M., Conway, D. J., Roper, C., & Clark, T.
G. (2014). PlasmoView: A Web-based Resource to Visualise Global Plasmodium
falciparum Genomic Variation. The Journal of Infectious Diseases, 209(11), 1808–1815.
https://doi.org/10.1093/infdis/jit812
Reid, P. C., Fischer, A. C., Lewis-Brown, E., Meredith, M. P., Sparrow, M., Andersson, A.
J., Antia, A., Bates, N. R., Bathmann, U., Beaugrand, G., Brix, H., Dye, S., Edwards,
M., Furevik, T., Gangstø, R., Hátún, H., Hopcroft, R. R., Kendall, M., Kasten, S., …
Washington, R. (2009). Chapter 1 Impacts of the Oceans on Climate Change (pp. 1–
150). https://doi.org/10.1016/S0065-2881(09)56001-4
Schubert, I. (2007). Chromosome evolution. Current Opinion in Plant Biology, 10(2), 109–
115. https://doi.org/10.1016/j.pbi.2007.01.001
Sherry, S. T. (2001). dbSNP: the NCBI database of genetic variation. Nucleic Acids Research,
29(1), 308–311. https://doi.org/10.1093/nar/29.1.308
Shin, J., Jeon, J., Jung, D., Kim, K., Kim, Y. J., Jeong, D.-H., & Yoon, J. (2022). PhenGenVar:
A User-Friendly Genetic Variant Detection and Visualization Tool for Precision
Medicine. Journal of Personalized Medicine, 12(6), 959.
https://doi.org/10.3390/jpm12060959
Sinha, A. U., & Meller, J. (2007). Cinteny: flexible analysis and visualization of synteny and
genome rearrangements in multiple organisms. BMC Bioinformatics, 8(1), 82.
https://doi.org/10.1186/1471-2105-8-82
Tamagawa, K., Yoshida, K., Ohrui, S., & Takahashi, Y. (2022). Population transcriptomics
reveals the effect of gene flow on the evolution of range limits. Scientific Reports, 12(1),
1318. https://doi.org/10.1038/s41598-022-05248-1
Thorvaldsdottir, H., Robinson, J. T., & Mesirov, J. P. (2013). Integrative Genomics Viewer
(IGV): high-performance genomics data visualization and exploration. Briefings in
Bioinformatics, 14(2), 178–192. https://doi.org/10.1093/bib/bbs017
Tichkule, S., Myung, Y., Naung, M. T., Ansell, B. R. E., Guy, A. J., Srivastava, N., Mehra,
S., Cacciò, S. M., Mueller, I., Barry, A. E., van Oosterhout, C., Pope, B., Ascher, D. B.,
& Jex, A. R. (2022). VIVID: A Web Application for Variant Interpretation and
Visualization in Multi-dimensional Analyses. Molecular Biology and Evolution, 39(9).
https://doi.org/10.1093/molbev/msac196
Townsend, J. P. (2003). Population Genetic Variation in Genome-Wide Gene Expression.
Molecular Biology and Evolution, 20(6), 955–963.
https://doi.org/10.1093/molbev/msg106
UniProt: a hub for protein information. (2015). Nucleic Acids Research, 43(D1), D204–D212.
https://doi.org/10.1093/nar/gku989
Venter, J. C., Adams, M. D., Myers, E. W., Li, P. W., Mural, R. J., Sutton, G. G., Smith, H.
O., Yandell, M., Evans, C. A., Holt, R. A., Gocayne, J. D., Amanatides, P., Ballew, R.
M., Huson, D. H., Wortman, J. R., Zhang, Q., Kodira, C. D., Zheng, X. H., Chen, L., …
Zhu, X. (2001). The Sequence of the Human Genome. Science, 291(5507), 1304–1351.
https://doi.org/10.1126/science.1058040
Wang, Y., Tang, H., DeBarry, J. D., Tan, X., Li, J., Wang, X., Lee, T. -h., Jin, H., Marler, B.,
Guo, H., Kissinger, J. C., & Paterson, A. H. (2012). MCScanX: a toolkit for detection
and evolutionary analysis of gene synteny and collinearity. Nucleic Acids Research,
40(7), e49–e49. https://doi.org/10.1093/nar/gkr1293
Weigel, D., & Nordborg, M. (2015). Population Genomics for Understanding Adaptation in
Wild Plant Species. Annual Review of Genetics, 49(1), 315–338.
https://doi.org/10.1146/annurev-genet-120213-092110
Yu, B., Doraiswamy, H., Chen, X., Miraldi, E., Arrieta-Ortiz, M. L., Hafemeister, C., Madar,
A., Bonneau, R., & Silva, C. T. (2014). Genotet: An Interactive Web-based Visual
Exploration Framework to Support Validation of Gene Regulatory Networks. IEEE
Transactions on Visualization and Computer Graphics, 20(12), 1903–1912.
https://doi.org/10.1109/TVCG.2014.2346753