Visualization of Biological Sequence Similarity Search Results

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Visualization of Biological Sequence Similarity Search Results

Ed Huai-hsin Chi , Phillip Barry , Elizabeth Shoop , John V. Carlis ,
Ernest Retzel , John Riedl

Computer Science Department, University of Minnesota
4-192 EE/CSci Building
Minneapolis, MN 55455

Computational Biology Centers
Medical School, University of Minnesota
Box 196, UMHC, 1460 Mayo Building
420 Delaware Street S.E.
Minneapolis, MN 55455


Biological sequence similarity analysis presents visualization challenges, primarily because of the massive amounts of data and the discrete, multi-dimensional nature of the 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.

Here is the paper in postscript. (gzipped, 50552 bytes)

Here is the first page of figures (gzipped, 121433 bytes) and second page of figures (gzipped, 147103 bytes) in color postscript. Warning: these figure files are gzipped. The first page of figures uncompresses to around 8 megabytes. The second page of figures also uncompresses to around 8 megabytes. If you print these on a black and white printer, they will turn up in greyscale. You also need a big printer with lots of memory, otherwise they will print very slowly.

Ed H. Chi (
Fri Apr 28 12:51:35 CDT 1995