Earticle

In this paper various methods of sequence analysis which include the alignment based and alignment free methods of tree generation are reviewed and these find distance/similarity among the sequences of different species. Alignment free method based on tuple count and set theory is proposed and the results are compared with the guide tree obtained using alignment based method. The proposed method is tested on DNA sequence of length below 1000bp (dataset1) and Sequence of length above 16000bp (dataset2). It achieves the similar performance as that of the alignment based method but without the alignment phase.

Sequence similarity in biological databases is used to characterize a newly discovered protein and confirming the existence of its homologs. This is often computationally very expensive. We have implemented a new algorithm that performs sequence similarity search using a pre-search phase. The proposed algorithm works in three phases. As a pre-preparation for Pre-Search, we locate a sequence, similar to the query sequence to extract all common words between the former and the latter. In the second phase, the pre-search phase, we locate all sequenes containing any of the randomly chosen common words. The list is further scanned in the third phase and the results obtained from the second phase are refined using Similarity Search (SS) algorithm, described in the paper. We have preprocessed the Uniref100.FASTA protein database containing 9,757,328 records downloaded from uniprot.org, to suit our application of sequence similarity search. The algorithm is simple and can be applied in various perspectives. These include searching in DNA and protein sequence databases, motif finding, and gene identification search. Pre-Search reduces the search space using much faster simpler algorithm. In large database search, its effect could be phenomenal.

Multiple sequence alignment (MSA) is an important tool in biological analysis. However, it is difficult to solve this class of problems, due to their exponential complexity. This paper presents an algorithm combining the genetic algorithm and a self-organizing neural network for solution to MSA. This approach demonstrates improved performance in long DNA and RNA data sets exhibiting small similarity.

Dynamic programming is a method for solving complex problems by breaking them down into simpler subproblems. This idea is very insightful for solving bioinformatics problems. Aligning distantly related protein sequences is a long-standing problem in bioinformatics and a key for successful protein structure prediction. A fast and valid algorithm can benefit the whole process of biology research. In this paper, we introduce an algorithm that given a certain evaluation function, will calculate the optimal alignment by dynamic programming.

We proposed a method complementing failure of combining DNA fragments, defect of conventional contig assembly programs. In the proposed method, very long DNA sequence data are made into a prototype of fragment of about 700 bases that can be analyzed by automatic sequence analyzer at one time, and then matching ratio is calculated by comparing a standard prototype with 3 fragmented clones of about 700 bases generated by the PCR method. In this process, the time for calculation of matching ratio is reduced by Compute Agreement algorithm. Two candidates of combined fragments of every prototype are extracted by the degree of overlapping of calculated fragment pairs, and then degree of combination is decided using a fuzzy inference method that utilizes the matching ratios of each extracted fragment, and A, C, G, T membership degrees of each DNA sequence, and previous frequencies of each A, C, G, T. In this paper, DNA sequence combination is completed by the iteration of the process to combine decided optimal test fragments until no fragment remains. For the experiments, fragments of about 700 bases were generated from each sequence of 10,000 bases and 100,000 bases extracted from ‘PCC6803’, complete protein genome. From the experiments by applying random mutations on these fragments, we could see that the proposed method was faster than FAP program, and combination failure, defect of conventional contig assembly programs, did not occur.

The traveling salesman problem (TSP) is one of the most studied in operations research and computer science. Research has led to a large number of techniques to solve this problem; in particular, genetic algorithms (GA) produce good results compared to other techniques. A disadvantage of GA, though, is that they easily become trapped in the local minima. In this paper, a cuckoo search optimizer (CS) is used along with a GA in order to avoid the local minima problem and to benefit from the advantages of both types of algorithms. A 2-opt operation was added to the algorithm to improve the results. The suggested algorithm was applied to multiple sequence alignment and compared with the previous algorithms.