Genomic repeats detection using Boyer-Moore algorithm on Apache Spark Streaming

Genomic repeats, i.e., pattern searching in the string processing process to find repeated base pairs in the order of Deoxyribonucleic Acid (DNA), requires a long processing time. This research builds a big-data computational model to look for patterns in strings by modifying and implementing the Boyer-Moore algorithm on Apache Spark Streaming for human DNA sequences from the Ensemble site. Moreover, we perform some experiments on cloud computing by varying different specifications of computer clusters with involving datasets of human DNA sequences. The results obtained show that the proposed computational model on Apache Spark Streaming is faster than standalone computing and parallel computing with multicore. Therefore, it can be stated that the main contribution in this research, which is to develop a computational model for reducing the computational costs, has been achieved

Parallel String Matching with Multi Core Processors-A Comparative Study for Gene Sequences

The increase in huge amount of data is seen clearly in present days because of requirement for storing more information. To extract certain data from this large database is a very difficult task, including text processing, information retrieval, text mining, pattern recognition and DNA sequencing. So we need concurrent events and high performance computing models for extracting the data. This will create a challenge to the researchers. One of the solutions is parallel algorithms for string matching on computing models. In this we implemented parallel string matching with JAVA Multi threading with multi core processing, and performed a comparative study on Knuth Morris Pratt, Boyer Moore and Brute force string matching algorithms. For testing our system we take a gene sequence which consists of lacks of records. From the test results it is shown that the multicore processing is better compared to lower versions. Finally this proposed parallel string matching with multicore processing is better compared to other sequential approaches

Application Of Exact String Matching Algorithms Towards SMILES Representation Of Chemical Structure.

Bioinformatics and Cheminformatics use computer as disciplines providing tools for acquisition, storage, processing, analysis, integrate data and for the development of potential applications of biological and chemical data. A chemical database is one of the databases that exclusively designed to store chemical information

Approximate string matching with reduced alphabet

Peer reviewe

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

Prospects and limitations of full-text index structures in genome analysis

The combination of incessant advances in sequencing technology producing large amounts of data and innovative bioinformatics approaches, designed to cope with this data flood, has led to new interesting results in the life sciences. Given the magnitude of sequence data to be processed, many bioinformatics tools rely on efficient solutions to a variety of complex string problems. These solutions include fast heuristic algorithms and advanced data structures, generally referred to as index structures. Although the importance of index structures is generally known to the bioinformatics community, the design and potency of these data structures, as well as their properties and limitations, are less understood. Moreover, the last decade has seen a boom in the number of variant index structures featuring complex and diverse memory-time trade-offs. This article brings a comprehensive state-of-the-art overview of the most popular index structures and their recently developed variants. Their features, interrelationships, the trade-offs they impose, but also their practical limitations, are explained and compared