Visualizing Virus Population Variability From Next Generation Sequencing Data
2011 IEEE Symposium on Biological Data Visualization (BioVis), page 135--142 — oct 2011
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" }