Visualization of Biological Sequence Similarity Search Results

Authors:
Ed Huai-hsin Chi;Phillip Barry;Elizabeth Shoop;John V. Carlis;Ernest Retzel;John Riedl
Affiliations:
Computer Science Department, University of Minnesota, 4192 EE/CSci Building, Minneapolis, MN;Computer Science Department, University of Minnesota, 4192 EE/CSci Building, Minneapolis, MN;Computer Science Department, University of Minnesota, 4192 EE/CSci Building, Minneapolis, MN;Computer Science Department, University of Minnesota, 4192 EE/CSci Building, Minneapolis, MN;Computational Biology Centers, Medical School, University of Minnesota, Box 196, UMHC, 1460 Mayo Building, 420 Delaware Street S.E., Minneapolis, MN;Computer Science Department, University of Minnesota, 4192 EE/CSci Building, Minneapolis, MN
Venue:
VIS '95 Proceedings of the 6th conference on Visualization '95
Year:
1995

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Abstract

Biological sequence similarity analysis presents visualization challenges, primarily because of the massive amounts of discrete, multi-dimensional data. Genomic data generated by molecular biologists is analyzed by algorithms that search for similarity to known sequences in large genomic databases. The output from these algorithms can be several thousand pages of text, and is difficult to analyze because of its length and complexity. We developed and implemented a novel graphical representation for sequence similarity search results, which visually reveals features that are difficult to find in textual reports. The method opens new possibilities in the interpretation of this discrete, multi-dimensional data by enabling interactive investigation of the graphical representation.