Analysis of string-searching algorithms on biological sequence databases

String-searching algorithms are used to find the occurrences of a search string in a given text. The advent of digital computers has stimulated the development of string-searching algorithms for various applications. Here, we report the performance of all string-searching algorithms on widely used biological sequence databases containing the building blocks of nucleotides (in the case of nucleic acid sequence database) and amino acids (in the case of protein sequence database). The biological sequence databases used in the present study are Protein Information Resource (PIR), SWISSPROT, and amino acid and nucleotide sequences of all genomes available in the genome database. The average time taken for different search-string lengths considered for study has been taken as an indicator of performance for comparison between various methods

Advanced Searching Algorithms and its Behavior on Text Structures

This research investigates the behavior of the Boyer-Moore-Horspool (BMH) and the Boyer-Moore-Raita (BMR) string-matching algorithms using multilingual texts. The performance is computed based on searching for patterns in master strings. Experiments are conducted using a number of pattern lengths with many experiments repetition. The experimental results show that on average the number of comparisons per character passed in the case of the BMR is less than the number encountered by the BMH variant. The improvement is due to properties of the text structures. These experiments may lead to more theoretical and practical studies to develop new variants of algorithms. Using multilingual text structures provide more insight into the theory and structure of algorithms as multilingual text structures have different set of characters and dependencies, and the character properties have different type of structures. Since many applications of today depend on searching algorithms, therefore researchers need to explore every possibility that lead to improving the efficiency of searching and matching mechanisms. The time performance of exact string pattern matching can be greatly improved if an efficient algorithm is used. Considering, for example, the growing amount of text handled in the electronic patient records, it is worth and essential, in these cases and others, to searching for an efficient algorithm to deal with such huge items of information. Keywords: Matching, Boyer-Moore, Raita algorithm, Searching, multilingua

Improved algorithms for string searching problems

We present improved practically efficient algorithms for several string searching problems, where we search for a short string called the pattern in a longer string called the text. We are mainly interested in the online problem, where the text is not preprocessed, but we also present a light indexing approach to speed up exact searching of a single pattern. The new algorithms can be applied e.g. to many problems in bioinformatics and other content scanning and filtering problems. In addition to exact string matching, we develop algorithms for several other variations of the string matching problem. We study algorithms for approximate string matching, where a limited number of errors is allowed in the occurrences of the pattern, and parameterized string matching, where a substring of the text matches the pattern if the characters of the substring can be renamed in such a way that the renamed substring matches the pattern exactly. We also consider searching multiple patterns simultaneously and searching weighted patterns, where the weight of a character at a given position reflects the probability of that character occurring at that position. Many of the new algorithms use the backward matching principle, where the characters of the text that are aligned with the pattern are read backward, i.e. from right to left. Another common characteristic of the new algorithms is the use of q-grams, i.e. q consecutive characters are handled as a single character. Many of the new algorithms are bit parallel, i.e. they pack several variables to a single computer word and update all these variables with a single instruction. We show that the q-gram backward string matching algorithms that solve the exact, approximate, or multiple string matching problems are optimal on average. We also show that the q-gram backward string matching algorithm for the parameterized string matching problem is sublinear on average for a class of moderately repetitive patterns. All the presented algorithms are also shown to be fast in practice when compared to earlier algorithms. We also propose an alphabet sampling technique to speed up exact string matching. We choose a subset of the alphabet and select the corresponding subsequence of the text. String matching is then performed on this reduced subsequence and the found matches are verified in the original text. We show how to choose the sampled alphabet optimally and show that the technique speeds up string matching especially for moderate to long patterns

A Parallel Computational Approach for String Matching- A Novel Structure with Omega Model

In r e cent day2019;s parallel string matching problem catch the attention of so many researchers because of the importance in different applications like IRS, Genome sequence, data cleaning etc.,. While it is very easily stated and many of the simple algorithms perform very well in practice, numerous works have been published on the subject and research is still very active. In this paper we propose a omega parallel computing model for parallel string matching. The algorithm is designed to work on omega model pa rallel architecture where text is divided for parallel processing and special searching at division point is required for consistent and complete searching. This algorithm reduces the number of comparisons and parallelization improves the time efficiency. Experimental results show that, on a multi - processor system, the omega model implementation of the proposed parallel string matching algorithm can reduce string matching time