Bioimage informatics

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The Keck Bioimaging Lab and other labs like it depend on bioimage informatics tools ever day.

Bioimage informatics is a multidisciplinary sub-field of bioinformatics and computational biology that involves the development and use of computational techniques to analyze bioimages, especially cellular and molecular images, on a large scale fashion, with the goal of mining useful knowledge out of complicated and heterogeneous images and related metadata.[1]

The field of bioimage informatics is somewhat related to medical imaging informatics, in so much as some of the advances in that field have found their way to the technology of analyzing bioimages. However, "it is very challenging to directly apply existing medical image analysis methods to ... bioimage informatics problems."[2] Some of the challenges bioimages pose to researchers include:

  • special techniques are required to analyze images on the cellular and molecular scales.
  • the file size of some bioimages can be hundreds of megabytes or reach even several gigabytes.
  • many days would be required to manually analyze the thousands of images generated from today's optical microscopy techniques and labeling methods.

These challenges require automatic high-throughput analysis techniques, novel algorithms, and advanced systems to deal with the tasks of processing, storing, visualizing, and mining bioimages.[2]

Application

Imaging informatics can help tackle problems and tasks such as the following[1][2]:

  • the measurement of the effects of pharmaceuticals on cells and their structure
  • the localization of cellular proteomes
  • the analysis of cellular phenotypes
  • the tracking of cellular activity and other dynamic processes
  • the mapping of gene expression in embryos
  • the creation of an "atlas" for modelling an organism
  • the three-dimensional reconstruction of neuronal structures of body and brain
  • the prediction of sequence motifs that may have regulatory functions in an organism

Informatics

Bioimage informatics features five "top-level stages" in its use of computational techniques: image acquisition and screening; image processing; information management; data modeling and statistical analysis; and system-biology integration.[3] In the acquisition phase, informatics tools "must be capable of capturing the raw data (the individual pixels) and the metadata around the acquisition methodology itself, including instrument settings, exposure details etc."[4] During image processing, the informatics tools must be able to read well over 80 proprietary file formats for optical microscopy and other imaging techniques. The information management stage requires roomy databases capable of organizing and holding terabytes of data.[4] Modeling and statistical analysis tools must be extensible and capable of being applied in different combinations to different images.[5] Finally, an integrated approach towards various biological tools and informatics systems is required to ensure the other four stages take place optimally.

Open-source bioimage informatics tools are helping to expand the scope of biomolecular researchers' work.[4] Examples include OMERO — software for visualizing, managing, and analyzing biological microscope images — and FARSIGHT, a toolkit for associative image analysis.

External links

References

  1. 2.0 2.1 2.2 Peng, Hanchuan (September 2008). "Bioimage informatics: a new area of engineering biology". Bioinformatics 24 (17): 1827–36. doi:10.1093/bioinformatics/btn346. PMC 2519164. PMID 18603566. http://bioinformatics.oxfordjournals.org/content/24/17/1827.full. Retrieved 17 June 2014. 
  2. Wu, Ling-Yun; Zhou, Xiaobo; Wong, Stephen T. C. (2010). "Chapter 17: Computational Imaging and Modeling for Systems Biology". Elements of Computational Systems Biology. John Wiley & Sons. pp. 381–401. ISBN 9780470556740. http://books.google.com/books?id=UO3VM58KLrkC&pg=PA381. Retrieved 17 June 2014. 
  3. 4.0 4.1 4.2 Swedlow, Jason R.; Eliceiri, Kevin W. (November 2009). "Open source bioimage informatics for cell biology". Trends in Cell Biology 19 (11-3): 656–660. doi:10.1016/j.tcb.2009.08.007. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2789254/. Retrieved 17 June 2014. 
  4. Swedlow, Jason R.; Goldberg, Ilya; Brauner, Erik; Sorger, Peter K. (04 April 2003). "Informatics and Quantitative Analysis in Biological Imaging". Science 300 (5616): 100–102. doi:10.1126/science.1082602. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3522889/. Retrieved 17 June 2014.