Biological data visualization

Biology data visualization is a branch of bioinformatics concerned with the application of computer graphics, scientific visualization, and information visualization to different areas of the life sciences. This includes visualization of sequences, genomes, alignments, phylogenies, macromolecular structures, systems biology, microscopy, and magnetic resonance imaging data. Software tools used for visualizing biological data range from simple, standalone programs to complex, integrated systems.

State-of-the-art and perspectives

Today we are experiencing a rapid growth in volume and diversity of biological data, presenting an increasing challenge for biologists. A key step in understanding and learning from these data is visualization. Thus, there has been a corresponding increase in the number and diversity of systems for visualizing biological data.

An emerging trend is the blurring of boundaries between the visualization of 3D structures at atomic resolution, visualization of larger complexes by cryo-electron microscopy, and visualization of the location of proteins and complexes within whole cells and tissues.[1][2]

A second emerging trend is an increase in the availability and importance of time-resolved data from systems biology, electron microscopy[3][4] and cell and tissue imaging. In contrast, visualization of trajectories has long been a prominent part of molecular dynamics.

Finally, as datasets are increasing in size, complexity, and interconnectness, biological visualization systems are improving in usability, data integration and standardization.

List of visualization systems

Many software systems are available for visualization biological data.[5][6][7][8] The links below link lists of such systems, grouped by application areas.


  1. Lucić V, Förster F, Baumeister W (2005). "Structural studies by electron tomography: from cells to molecules". Annual Review of Biochemistry. 74: 833–65. doi:10.1146/annurev.biochem.73.011303.074112. PMID 15952904.
  2. Steven AC, Baumeister W (2008). "The future is hybrid". Journal of Structural Biology. 163 (3): 186–95. doi:10.1016/j.jsb.2008.06.002. PMID 18602011.
  3. Plattner H, Hentschel J (2006). "Sub-Second Cellular Dynamics: Time-Resolved Electron Microscopy and Functional Correlation". A Survey of Cell Biology (Submitted manuscript). International Review of Cytology. 255. pp. 133–76. doi:10.1016/S0074-7696(06)55003-X. ISBN 9780123735997. PMID 17178466.
  4. Frank J, Schlichting I (2004). "Time-resolved imaging of macromolecular processes and interactions". Journal of Structural Biology. 147 (3): 209–10. doi:10.1016/j.jsb.2004.06.003. PMID 15450290.
  5. Pavlopoulos, GA; Malliarakis, D; Papanikolaou, N; Theodosiou, T; Enright, AJ; Iliopoulos, I (2015). "Visualizing genome and systems biology: technologies, tools, implementation techniques and trends, past, present and future". GigaScience. 4: 38. doi:10.1186/s13742-015-0077-2. PMC 4548842. PMID 26309733.
  6. Pavlopoulos, GA; Wegener, AL; Schneider, R (28 November 2008). "A survey of visualization tools for biological network analysis". BioData Mining. 1: 12. doi:10.1186/1756-0381-1-12. PMC 2636684. PMID 19040716.
  7. Pavlopoulos, GA; Soldatos, TG; Barbosa-Silva, A; Schneider, R (22 February 2010). "A reference guide for tree analysis and visualization". BioData Mining. 3 (1): 1. doi:10.1186/1756-0381-3-1. PMC 2844399. PMID 20175922.
  8. Pavlopoulos, Georgios A.; Iacucci, Ernesto; Iliopoulos, Ioannis; Bagos, Pantelis (2013). Interpreting the Omics 'era' Data. Multimedia Services in Intelligent Environments. Smart Innovation, Systems and Technologies. 25. pp. 79–100. doi:10.1007/978-3-319-00375-7_6. ISBN 978-3-319-00374-0.
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