Traditional methods obtain a microorganism's DNA by culturing it individually. Recent advances in genomics have lead to the procurement of DNA of more than one organism from its natural habitat. Indeed, natural microbial communities are often very complex with tens and hundreds of species. Assembling these genomes is a crucial step irrespective of the method of obtaining the DNA. This dissertation presents fuzzy methods for multiple genome sequence assembly.

An optimal alignment of DNA genome fragments is based on several factors, such as the quality of bases and the length of overlap. Factors such as quality indicate if the data is high quality or an experimental error. Sequence assembly does not have crisp results and is based on degree of similarity. To address this challenge we propose a sequence assembly solution based on fuzzy logic, which allows for tolerance of inexactness or errors in fragment matching and that can be used for improved assembly.

Assembly of a single organism's genome is presented using a modified dynamic programming approach with fuzzy characteristic functions. The characteristic functions are used to select alignments of sequence fragments. Assembly of environmental genomes starts with the classification of mixed fragments from different organisms into homogeneous groups. Separating closely related species is a difficult task because the fragments contain many similarities. We propose fuzzy classification using modified fuzzy weighted averages to classify fragments belonging to different organisms within an environmental genome population. Our proposed approach uses DNA-based signatures such as GC content and nucleotide frequencies as features for the classification. This divide-and-conquer strategy also improves performance on larger datasets. We evaluate our method on artificially created environmental genomes to test various combinations of organisms and on environmental genomes obtained from acid mine drainage available at National Center for Biotechnology Information. The assembler is enhanced by a parallel version for high performance.