Micro Sequence Identification of DNA Data Using Pattern Mining Techniques

Abstract

A rapid development of NGS(Next generation Sequencing) technologies can be able to produce large amounts of sequence data,which is leading in to a wide range of new applications. Sequence matches between a translated nucleotide sequence and a well known biological protein can able to provide a strong evidence for the presence of homologous coding region, and such similarities often be identified even between a distantly related genes. Common techniques often restrict indels in the alignment to improve time,speed, whereas more Flexible aligners are too slow for large-scale applications. Moreover, many current aligners are becoming inefficient as generated reads grow ever larger. To perform such high dimensional process, it requires a special hardware implementation & designing, such implementation can also increases a complexity of efficiency and hardware. The Field Programmable Gate Array is the well-known to design and we propose a high efficient algorithm for sequence detection in any of bioinformatics data. Unlike previous methods, the proposed pattern matching algorithm can identifies the sequence of each factor on the basis of their occurrences. This method can computes the multi-level similarity measure with an available sequences. Based on the multi-level sequence similarity measure computed a single sequence of bioinformatics data can be identified. The proposed method produces efficient result in sequence searching and detection and improves the hardware utilization which in terms reduces the time complexity as well.