Genomic data, effective communication through visualization

Authors

DOI:

https://doi.org/10.14232/abs.2024.2.111-114

Keywords:

AI tools, data visualization, DataViz, genetics, genomics

Abstract

Genomic data are inherently multidimensional and complex, therefore, presenting researchers with significant challenges in analysis and interpretation. Data visualization of genomic datasets can unravel the complexity and provide meaningful insights for effective communication. Here, we discuss that, in data-driven genomic studies, effective storytelling of formulated hypotheses can be significantly enhanced by using suitable data visualization tools. Further, with the ongoing advancement of technology, we argue that, the integration of these tools with artificial intelligence or machine learning concepts could potentially revolutionize the visualization trends within the field of genomic research.

Downloads

Download data is not yet available.

References

Abbasi S, Masoumi S (2020) Next-generation sequencing (NGS). Inter J Adv Sci Tech 29(3):6364-6377.

The 1000 Genomes Project Consortium (2015) A global reference for human genetic variation. Nature 526:68-74 .

Brehmer M, Munzner T (2013) A multi-level typology of abstract visualization tasks. IEEE Trans Visual Comp Graph 19(12):2376-2385.

Cheng J, Novati G, Pan J, Bycroft C, Akvilė Žemgulytė A, et al. (2023) Accurate proteome-wide missense vari-ant effect prediction with AlphaMissense. Science 381(6664):eadg7492.

Cumsille A, Durán RE, Rodríguez-Delherbe A, Saona-Urmeneta V, Cámara B, et al. (2023) GenoVi, an open-source automated circular genome visualizer for bacteria and archaea. PLoS Comput Biol 19(4):e1010998.

Durant E, Rouard M, Ganko EW, Muller C, Cleary AM, et al. (2022b) Ten simple rules for developing visualization tools in genomics. PLOS Comput Biol 18(11):e1010622.

Genereux DP, Serres A, Armstrong J, et al. (2020) A comparative genomics multitool for scientific discovery and conservation. Nature 587(7833):240-245.

Goel M, Schneeberger K (2022) plotsr: visualizing structural similarities and rearrangements between multiple genomes. Bioinformatics 38(10):2922-2926.

Goodstadt M, Marti-Renom MA (2017) Challenges for visualizing three-dimensional data in genomic browsers. FEBS Letters 591(17):2505-2519.

Guo K, Wu M, Soo Z, Yang Y, Zhang Y, Zhang Q, Lin H, Grosser M, Venter D, Zhang G, Lu J (2023) Artificial intelligence-driven biomedical genomics. Knowledge-Based Sys 279:110937.

Jumper J, Evans R, Pritzel A, Green T, Figurnov M, et al. (2021) Highly accurate protein structure prediction with AlphaFold. Nature 596(7873):583-589.

Krzywinski M, Schein J, Birol I, Connors J, Gascoyne R, Horsman D, Jones SJ, Marra MA (2009) Circos: an in-formation aesthetic for comparative genomics. Genome Res 19 (9):1639-16 45.

Langer CCH, Mitter M, Stocsits RR, Gerlich DW (2023) HiCognition: a visual exploration and hypothesis testing tool for 3D genomics. Genome Biol 24(1):158.

Li F, Hu H, Xiao Z, Wang J, Liu J et al. (2023) Visualization and review of reads alignment on the graphical pan-genome with VAG. Preprint BioRxiv:2023.01.20.524849.

Li W, Zhang S, Liu CC, Zhou XJ (2012) Identifying multi-layer gene regulatory modules from multi-dimensional genomic data. Bioinformatics 28(19):2458-2466.

Liu Q, Chen J, Wang Y, Li S, Jia C, Song J, Li F (2021) DeepTorrent: a deep learning-based approach for predicting DNA N4-methylcytosine sites. Brief Bioinform 22(3):bbaa124.

L’Yi S, Wang Q, Lekschas F, Gehlenborg N (2022) Gosling: a grammar-based toolkit for scalable and interactive genomics data visualization. IEEE Trans Vis Comput Gr aph 28 (1):140-150 .

Malinda RR (2023) Biological data studies, scale-up the potential with machine learning. Euro J Hum Genet 31(6):619-620.

Meyer M, Sedlmair M, Munzner T (2012) The four-level nested model revisited: blocks and guidelines. ACM Inter Conf Proc Series (11):1-6.

Munzner T (2014) Visualization Analysis and Design. CRC Press, New York.Nielsen CB, Cantor M, Dubchak I, Gordon D, Wang T (2010) Visualizing genomes: techniques and challenges. Nat Methods 7(3):S5-S15.

Nurk S, Koren S, Rhie A, Rautiainen M, Bzikadze AV, et al. (2022) The complete sequence of a human genome. Science 376(6588):44-53.

Nusrat S, Harbig T, Gehlenborg N (2019) Tasks, techniques, and tools for genomic data visualization. Comput Graph Forum 38(3):781-805.

O’Donoghue SI (2021) Grand challenges in bioinformatics data visualization. Front Bioinform 1:669186.

Parsons P (2022) Understanding data visualization design practice. IEEE Trans Vis Comp Graph 28(1):665-675.

Pearce TM, Nikiforova MN, Roy S (2019) Interactive browser-based genomics data visualization tools for translational and clinical laboratory applications. J Mol Diagn 21(6):985-993.

Qu Z, Lau CW, Nguyen QV, Zhou Y, Catchpoole DR (2019) Visual analytics of genomic and cancer data: A Systematic Review. Cancer Inform (18):1176935119835546.

Rhie A, Nurk S, Cechova M et al. (2023) The complete sequence of a human Y chromosome. Nature 621(7978):344-354.

Singh R, Lanchantin J, Robins G, Qi Y (2016) DeepChrome: deep-learning for predicting gene expression from his-tone modifications. Bioinformatics 32(17):i639-i648.

Tanjo T, Kawai Y, Tokunaga K, Ogasawara O, Nagasaki M (2021) Practical guide for managing large-scale human genome data in research. J Hum Genet 66(1):39-52.

Tumescheit C, Firth AE, Brown K (2022) CIAlign: a highly customisable command line tool to clean, interpret and visualise multiple sequence alignments. PeerJ 10:e12983.

Uffelmann E, Huang QQ, Munung NS, de Vries J, Okada Y, Martin AR, Martin HC, Lappalainen T, Posthuma D (2021) Genome-wide association studies. Nat Rev Methods Primers 1:59.

Wang X, Wu Z, Huang W, Wei Y, Huang Z, Xu M, Chen W (2023) VIS+AI: integrating visualization with artificial intelligence for efficient data analysis. Front Comput S c i 17:17670 9.

Wang Y, Song F, Zhang B, Zhang L, Xu J, et al. (2018) The 3D Genome Browser: a web-based browser for visualizing 3D genome organization and long-range chromatin interactions. Genome Biol 19(1):151.

Wojcik GL, Graff M, Nishimura KK, Tao R, Haessler J, et al. (2019) Genetic analyses of diverse populations improves discovery for complex traits. Nature 570(7762):514:518.

Wong B (2012) Visualizing biological data. Nat Methods 9 :1131.

Wong KC (2019) Big data challenges in genome informatics. Biophys Rev 11(1):51-54.

Xu W, Zhong Q, Lin D, Zuo Y, Dai J, Li G, Cao G (2021) CoolBox: a flexible toolkit for visual analysis of genomics data. BMC Bioinform 22(1):489.

Yokoyama TT, Kasahara M (2020) Visualization tools for human structural variations identified by whole-genome sequencing. J Hum Genet 65(1):49-60.

Yuan Y, Shi Y, Li C, Kim J, Cai W, Han Z, Feng DD (2016) Deepgene: An advanced cancer type classifier based on deep learning and somatic point mutations. BMC Bioinform 17(Suppl 1):476.

Zhou L, Feng T, Xu S, Gao F, Lam TT et al. (2022) ggmsa: A visual exploration tool for multiple sequence alignment and associated data. Brief Bioinform 23(4)bbac222.

Downloads

Published

2025-09-29

How to Cite

Malinda, R. R. and Mishra, D. (2025) “Genomic data, effective communication through visualization”, Acta Biologica Szegediensis, 68(2), pp. 111–114. doi: 10.14232/abs.2024.2.111-114.

Issue

Section

Articles