DNA Fragment Assembly Algorithms: Toward a Solution for Long Repeats

In this work, we describe our efforts to seek optimal solutions for the DNA Fragment Assembly Problem in terms of assembly accuracy and runtime efficiency. The main obstacles for the DNA Fragment Assembly are analyzed. After reviewing various advanced algorithms adopted by some assemblers in the bioinformatics industry, this work explores the feasibility of assembling fragments for a target sequence containing perfect long repeats, which is deemed theoretically impossible without tedious finishing reaction experiments. Innovative algorithms incorporating statistical analysis proposed in this work make the restoration of DNA sequences containing long perfect repeats an attainable goal

Dna Sequence Assembly And Genetic Algorithms New Results And Puzzling Insights.

Applying genetic algorithms to DNA sequence assembly is not a straightforward process. Significantly improved results in terms of performance, quality of results, and the scaling of applicability have been realized through non-standard and even counter-intuitive parameter settings. Specifically, the solution time for a 10kb data set was reduced by an order of magnitude, and a 20kb data set that was previously unsolved by the genetic algorithm was solved in a time that represents only a linear increase from the 10kb data set. Additionally, significant progress has been made on a 35kb data set representing real biological data. A single contig solution was found for a 752 fragment subset of the data set, and a 15 contig solution was found for the full data set. This paper discusses the new results, the modifications to the previous genetic algorithm used in this study, the experimental design process by which the new results were obtained, the questions raised by these results, and some preliminary attempts to explain these results

A Study of Ordered Gene Problems Featuring DNA Error Correction and DNA Fragment Assembly with a Variety of Heuristics, Genetic Algorithm Variations, and Dynamic Representations

Ordered gene problems are a very common classification of optimization problems. Because of their popularity countless algorithms have been developed in an attempt to find high quality solutions to the problems. It is also common to see many different types of problems reduced to ordered gene style problems as there are many popular heuristics and metaheuristics for them due to their popularity. Multiple ordered gene problems are studied, namely, the travelling salesman problem, bin packing problem, and graph colouring problem. In addition, two bioinformatics problems not traditionally seen as ordered gene problems are studied: DNA error correction and DNA fragment assembly. These problems are studied with multiple variations and combinations of heuristics and metaheuristics with two distinct types or representations. The majority of the algorithms are built around the Recentering- Restarting Genetic Algorithm. The algorithm variations were successful on all problems studied, and particularly for the two bioinformatics problems. For DNA Error Correction multiple cases were found with 100% of the codes being corrected. The algorithm variations were also able to beat all other state-of-the-art DNA Fragment Assemblers on 13 out of 16 benchmark problem instances