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Visualizing Virus Population Variability From Next Generation Sequencing Data

2011 IEEE Symposium on Biological Data Visualization (BioVis), page 135--142 - oct 2011
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Advances in genomic sequencing techniques allow for larger scale generation and usage of sequence data. While these techniques afford new types of analysis, they also generate new concerns with regards to data quality and data scale. We present a tool designed to assist in the exploration of the genetic variability of the population of viruses at multiple time points and in multiple individuals, a task that necessitates considering large amounts of sequence data and the quality issues inherent in obtaining such data in a practical manner. Our design affords the examination of the amount of variability and mutation at each position in the genome for many populations of viruses. Our design contains novel visualization techniques that support this specific class of analysis while addressing the issues of data aggregation, confidence visualization, and interaction support that arise when making use of large amounts of sequence data with variable uncertainty. These techniques generalize to a wide class of visualization problems where confidence is not known a priori, and aggregation in multiple directions is necessary.

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BibTex references

@InProceedings{CGOG11,
  author       = "Correll, Michael and Ghosh, Subhadip and O'Connor, David and Gleicher, Michael",
  title        = "Visualizing Virus Population Variability From Next Generation Sequencing Data",
  booktitle    = "2011 IEEE Symposium on Biological Data Visualization (BioVis)",
  pages        = "135--142",
  month        = "oct",
  year         = "2011",
  publisher    = "IEEE",
  doi          = "10.1109/BioVis.2011.6094058  ",
  projecturl   = "http://graphics.cs.wisc.edu/Vis/Layercake/",
  url          = "http://graphics.cs.wisc.edu/Papers/2011/CGOG11"
}

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