Skip to main content

CitrusGenome: Applying User Centered Design for Evaluating the Usability of Genomic User Interfaces

  • 243 Accesses

Part of the Communications in Computer and Information Science book series (CCIS,volume 1556)

Abstract

Several tools have been developed to extract knowledge from the vast amount of data in genomics. The success of the knowledge extraction process depends to arge extent on how easy to learn and use are the tools for bioinformaticians. User interface design is neglected frequently in the genomic tool development process. As a result, user interfaces contain usability problems that make knowledge extraction a complex task. User-Centered Design (UCD) is a design approach that can be applied to improve the usability of interfaces. A fundamental principle of UCD is to design the UI based on users’ knowledge, their needs, objectives, and tasks to ensure that the resulting user interface meets the real user needs. We apply the UCD approach to design, evaluate and improve CitrusGenome, a tool that enables bioinformaticians to extract knowledge from genomic data. Following the UCD process, we first conduct user research to define user needs by applying UCD techniques such as interviews and task analysis. Then, we design a user interface that meets those user needs by using GenomIUm. GenomIUm is a systematic method that guides the design process of user interfaces in the genomic domain. We have performed two UCD iterations and, after each iteration, the user interfaces were validated by bioinformaticians. Some usability problems were found in each iteration. Therefore, we refined the user interface by solving the usability problems and incorporating such solutions into the final design. Finally, bioinformaticians using the refined user interface reported a reduction in the complexity of extracting knowledge from genomic data. We conclude that UCD techniques, together with GenomIUm, can be a useful strategy to design user interfaces that are easier to learn and use in the genomic domain.

Keywords

  • Genomics
  • User-centred design
  • User interface
  • GenomIUm

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-030-96648-5_10
  • Chapter length: 28 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   69.99
Price excludes VAT (USA)
  • ISBN: 978-3-030-96648-5
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   89.99
Price excludes VAT (USA)
Fig. 1.

Source: [21].

Fig. 2.

Source: [21].

Fig. 3.

Source: [21].

Fig. 4.

Source: [21].

Fig. 5.

Source: [21].

Fig. 6.
Fig. 7.
Fig. 8.
Fig. 9.
Fig. 10.
Fig. 11.
Fig. 12.
Fig. 13.
Fig. 14.
Fig. 15.
Fig. 16.
Fig. 17.
Fig. 18.
Fig. 19.

Notes

  1. 1.

    https://www.genome.gov/genetics-glossary/Diploid.

  2. 2.

    https://angular.io/.

  3. 3.

    https://github.com/emn178/angular2-chartjs.

  4. 4.

    https://github.com/eweitz/ideogram.

  5. 5.

    https://github.com/ag-grid/ag-grid.

  6. 6.

    A cultivar is defined as a group of selected plants that share common characteristics.

  7. 7.

    https://material.io/components/chips.

  8. 8.

    https://www.nngroup.com/articles/ten-usability-heuristics/.

References

  1. Al-Ageel, N., Al-Wabil, A., Badr, G., AlOmar, N.: Human factors in the design and evaluation of bioinformatics tools. Procedia Manuf. 3, 2003–2010 (2015). https://doi.org/10.1016/j.promfg.2015.07.247. https://www.sciencedirect.com/science/article/pii/S2351978915002486

  2. Bangerth, F.: Abscission and thinning of young fruit and their regulation by plant hormones and bioregulators. Plant Growth Regul. 31(1), 43–59 (2000). https://doi.org/10.1023/A:1006398513703

  3. Bolchini, D., Finkelstein, A., Perrone, V., Nagl, S.: Better bioinformatics through usability analysis. Bioinformatics 25(3), 406–412 (2009). https://doi.org/10.1093/bioinformatics/btn633. http://www.ncbi.nlm.nih.gov/pubmed/19073592. https://academic.oup.com/bioinformatics/article-lookup/doi/10.1093/bioinformatics/btn633

  4. Carpenter, A.E., Kamentsky, L., Eliceiri, K.W.: A call for bioimaging software usability (2012). https://doi.org/10.1038/nmeth.2073

  5. Chilana, P.K., Wobbrock, J.O., Ko, A.J.: Understanding usability practices in complex domains. In: Conference on Human Factors in Computing Systems - Proceedings, vol. 4, pp. 2337–2346. ACM Press, New York (2010). https://doi.org/10.1145/1753326.1753678. http://portal.acm.org/citation.cfm?doid=1753326.1753678

  6. Cingolani, P., et al.: A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of drosophila melanogaster strain w1118; iso-2; iso-3. Fly 6(2), 80–92 (2012)

    Google Scholar 

  7. Clément, L., et al.: A data-supported history of bioinformatics tools. arXiv preprint arXiv:1807.06808 (2018)

  8. Galperin, M.Y.: The molecular biology database collection: 2008 update. Nucleic Acids Res. 36(Suppl. 1), D2 (2008). https://doi.org/10.1093/nar/gkm1037

  9. Goodwin, S., McPherson, J.D., McCombie, W.R.: Coming of age: ten years of next-generation sequencing technologies. Nat. Rev. Genet. 17(6), 333–351 (2016). https://doi.org/10.1038/nrg.2016.49

    CrossRef  Google Scholar 

  10. Iñiguez-Jarrin, C.: GenomIUm: a pattern based method for designing user interfaces for genomic data access. Ph.D. thesis, Universitat Politècnica de València (2019)

    Google Scholar 

  11. Jakob, N.: Severity Ratings for Usability Problems: Article by Jakob Nielsen (1994). https://www.nngroup.com/articles/how-to-rate-the-severity-of-usability-problems/

  12. Jaspers, M.W.: A comparison of usability methods for testing interactive health technologies: methodological aspects and empirical evidence. Int. J. Med. Inform. 78(5), 340–353 (2009). https://doi.org/10.1016/j.ijmedinf.2008.10.002

  13. Javahery, H., Seffah, A.: A model for usability pattern-oriented design. In: Proceedings of the First International Workshop on Task Models and Diagrams for User Interface Design, TAMODIA 2002, pp. 104–110. INFOREC Publishing House Bucharest (2002). http://dl.acm.org/citation.cfm?id=646617.697237

  14. Javahery, H., Seffah, A., Radhakrishnan, T.: Beyond power: making bioinformatics tools user-centered. Commun. ACM 47(11), 58–63 (2004). https://doi.org/10.1145/1029496.1029527. http://doi.acm.org/10.1145/1029496.1029527

  15. Mardis, E.R.: A decade’s perspective on DNA sequencing technology. Nature 470(7333), 198–203 (2011). https://doi.org/10.1038/nature09796

    CrossRef  Google Scholar 

  16. de Matos, P., et al.: The Enzyme portal: a case study in applying user-centred design methods in bioinformatics. BMC Bioinform. 14 (2013). https://doi.org/10.1186/1471-2105-14-103

  17. Paternò, F.: ConcurTaskTrees: an engineered notation for task models. In: The Handbook of Task Analysis for Human-Computer Interaction, pp. 483–503 (2003)

    Google Scholar 

  18. Pavelin, K., Cham, J.A., de Matos, P., Brooksbank, C., Cameron, G., Steinbeck, C.: Bioinformatics meets user-centred design: a perspective. PLoS Comput. Biol. 8(7), e1002554 (2012). https://doi.org/10.1371/journal.pcbi.1002554. http://dx.plos.org/10.1371/journal.pcbi.1002554

  19. Rimmer, J.: Improving software environments through usability and interaction design (2004). https://doi.org/10.1080/01405110310001639050

  20. Rutherford, P., Abell, W., Churcher, C., McKinnon, A., McCallum, J.: Usability of navigation tools for browsing genetic sequences. In: Conferences in Research and Practice in Information Technology Series, vol. 106, pp. 33–41 (2010). http://researcharchive.lincoln.ac.nz/handle/10182/4484

  21. García, A., et al.: Applying user centred design to improve the design of genomic user interfaces. In: Proceedings of the 16th International Conference on Evaluation of Novel Approaches to Software Engineering - ENASE, pp. 25–35. INSTICC, SciTePress (2021). https://doi.org/10.5220/0010187800250035

  22. Stephens, Z.D., et al.: Big data: astronomical or genomical? PLoS Biol. 13(7) (2015). https://doi.org/10.1371/journal.pbio.1002195

  23. Stevens, R., Goble, C., Baker, P., Brass, A.: A classification of tasks in bioinformatics. Bioinformatics 17(2), 180–188 (2001). https://doi.org/10.1093/bioinformatics/17.2.180. http://www.ncbi.nlm.nih.gov/pubmed/11238075

  24. Sutcliffe, A., et al.: User engagement by user-centred design in e-Health. In: Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol. 368, pp. 4209–4224. Royal Society (2010). https://doi.org/10.1098/rsta.2010.0141

  25. Svanæs, D., Das, A., Alsos, O.A.: The contextual nature of usability and its relevance to medical informatics. In: Studies in Health Technology and Informatics, vol. 136, pp. 541–546 (2008). https://pubmed.ncbi.nlm.nih.gov/18487787/

  26. Toxboe, A.: User Interface Design Pattern Library (2007). http://ui-patterns.com/

  27. Tran, D., Dubay, C., Gorman, P., Hersh, W.: Applying task analysis to describe and facilitate bioinformatics tasks. In: Studies in Health Technology and Informatics, vol. 107, pp. 818–822 (2004). https://doi.org/10.3233/978-1-60750-949-3-818. https://www.ncbi.nlm.nih.gov/pubmed/15360926

  28. Valentin, F., Squizzato, S., Goujon, M., McWilliam, H., Paern, J., Lopez, R.: Fast and efficient searching of biological data resources-using EB-eye. Briefings Bioinform. 11(4), 375–384 (2010). https://doi.org/10.1093/bib/bbp065. https://pubmed.ncbi.nlm.nih.gov/20150321/

  29. Wu, G.A., et al.: Sequencing of diverse mandarin, pummelo and orange genomes reveals complex history of admixture during citrus domestication. Nat. Biotechnol. 32(7), 656–662 (2014). https://doi.org/10.1038/nbt.2906

  30. Wu, G.A., et al.: Genomics of the origin and evolution of Citrus. Nature 554(7692), 311–316 (2018). https://doi.org/10.1038/nature25447

Download references

Acknowledgements

The authors would like to thank the members of the PROS Research Center Genome group for fruitful discussions regarding the application of Conceptual Modeling in the medical field. This work has been developed with the financial support of the Spanish State Research Agency and the Generalitat Valenciana under the projects TIN2016-80811-P, PROMETEO/2018/176, and INNEST/2021/57 and co-financed with ERDF. Work at IVIA is funding by the Ministerio de Ciencia, Innovación y Universidades (Spain) trough grant RTI2018-097790-R-100 and by the Instituto Valenciano de Investigaciones Agrarias (Spain), through grants 51915 and 52002.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Alberto García S. .

Editor information

Editors and Affiliations

A Appendix A: Tasks

A Appendix A: Tasks

We have divided the problem in three steps: 1) select the working data, 2) set the filter conditions, and 3) analyze the data. These steps correspond to the three UIs that we have designed and implemented.

Select the Working Data: this step consists of creating the two groups under study, the varieties of the first group (group A) have high levels of PFA, and the varieties of the second group (group B) have low levels of PFA. The first step is divided into two tasks:

  • Task 1: Create group A with 17 varieties of citrus with high levels of PFA.

  • Task 2: Create group B with 12 varieties of citrus with low levels of PFA.

Set the Filter Conditions: this step consists of setting the different criteria used to perform the analysis. Only those variants that fulfill the criteria will be visualized. The second step is divided into 8 tasks:

  • Task 3: Accept Single Nucleotide Polymorphism (SNP) variations.

  • Task 4: Accept variations that appear in every variety of group A and at most in four varieties of group B.

  • Task 5: Accept variations that appear in every variety of group B and at most in four varieties of group A.

  • Task 6: Accept variations with every GQ and DP values.

  • Task 7: Accept variations annotated with high, moderate, or low impacts.

  • Task 8: Accept variations with an allele balance value between 0.45 and 1 in all the varieties.

  • Task 9: Accept variations that are located inside gene sequences.

  • Task 10: Accept variations located between the 28,000,000 and 36,400,000 positions of chromosome 2.

Analyze the Data: this step consists of visualizing the accepted variations and get insight from these data. The third step is divided into X tasks. Each task consists of answering a knowledge question using the data available:

  • Task 11: How many variations have been accepted?

  • Task 12: What group has more variations?

  • Task 13: What variety has more variations?

  • Task 14: Which varieties compose group A and group B?

  • Task 15: How many variations with a 0/1 genotype have been accepted in group A?

  • Task 16: How many variations with a 0/1 genotype have been accepted in group B?

  • Task 17: How many variations with a 1/1 genotype have been accepted in group A?

  • Task 18: How many variations with a 1/1 genotype have been accepted in group B?

  • Task 19: How many high impact annotations have been identified?

  • Task 20: How many cellular component annotations have been identified?

  • Task 21: What is the most common group of enzymes affected by the accepted variations?

  • Task 22: What are the 3 genes with the most variations?

  • Task 23: What are the 3 genes with the most high impact annotations?

  • Task 24: What is the enzyme type with the most variations?

  • Task 25: What is the pathway type with the most variations?

  • Task 26: What are the biological processes affected by variations annotated with a high impact, with a 0/1 genotype in group A, and 1/1 genotype in group B?

  • Task 27: How many variations have low Genotype Quality values in group A?

  • Task 28: How many variations have low Genotype Quality values in group B?

Rights and permissions

Reprints and Permissions

Copyright information

© 2022 Springer Nature Switzerland AG

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

García S., A. et al. (2022). CitrusGenome: Applying User Centered Design for Evaluating the Usability of Genomic User Interfaces. In: Ali, R., Kaindl, H., Maciaszek, L.A. (eds) Evaluation of Novel Approaches to Software Engineering. ENASE 2021. Communications in Computer and Information Science, vol 1556. Springer, Cham. https://doi.org/10.1007/978-3-030-96648-5_10

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-96648-5_10

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-96647-8

  • Online ISBN: 978-3-030-96648-5

  • eBook Packages: Computer ScienceComputer Science (R0)