Bioinformatics Research Group at

Department of Computer Science

University of Nevada Reno

Utilizing Fuzzy Logic for Gene Sequence Construction from Sub Sequences and Characteristic Genome Derivation and Assembly

Modern genomic methods have been used to a small extent in genome sequencing. While these approaches are extremely informative when examining a single member of the community, they are not a practical approach for community level analysis. A method is needed for creating a characteristic representation (characteristic genome) from the sub sequences of DNA found in several sub variant of a bacteria of the same species. Such genome could be used for more efficient classification at a molecular level through the process of controlled generalization. Currently most of the sequence applications do not tolerate any kind of inexactness or errors in sub sequence matching. Quite often in real world data mining applications, especially in molecular biology, exact patterns do not exist and therefore, an approximate matching algorithm is required. Fuzzy Logic has been used extensively in approximate string matching using distance measures, etc. An accurate system reflects the mechanism responsible for the variations in the match. Current sequencing methods tend to be rejecting sequences that do not match with a high degree of similarity. This can lead to large amounts of data being rejected by algorithms that otherwise may be important in deriving a genomic sequence and the metabolic characteristics of such a sequence.

The goals of this research are the following:

i Given a collection of nucleotide sequences such as from multiple organisms, develop techniques based on fuzzy set theory and other methods for assembly of the sequences into the original full genome for each organism.
ii Using the techniques developed in goal 1, develop a generalized approach for creating a characteristic genome that represents a generalization of the original organisms that donated sequence data.

This research aims to contribute a better taxonomic classification of genomes by creating the ability to assemble a generalized genome and utilizing the similarity measures to compare and classify unknown genome sequences. It also aims to find more accurate and faster assembly methods of sequences in to a single genome.

Project 2
Team: Adrienne Breland Greg L Vert, Fred Harris, Alison Murray, Sara Nasser

Utilizing Biologically Significant Genetic Features and Fuzzy Logic for Bacterial Genome Fragment Assembly and Strain Differentiation of Environmental Genomic Sequences

(This is a new project hence we do not have any reports on it yet)

Literature and Publications

Run the Generator/Assembler