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<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/yyucbg6w6bhgfejbzekrytd7da" style="color: black;">Studies in Fuzziness and Soft Computing</a>
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 chapter presents fuzzy methods for multiple genome sequence assembly of cultured genomes (single organism) and<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-540-89968-6_2">doi:10.1007/978-3-540-89968-6_2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/dhn5s7xzsnc6datdonyzb6wazy">fatcat:dhn5s7xzsnc6datdonyzb6wazy</a> </span>
more »... nvironmental genomes (multiple organisms). 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. 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. 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 an environmental genome.
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