A New Approach to DNA, RNA, and Protein Motifs Templates Visualization and Analysis via Compilation Technique

Abstract

Motifs template is the input for many bioinformatics systems such codons finding, transcription, transaction, sequential pattern miner, and bioinformatics databases analysis. The size of motifs arranged from one base up to several Mega bases, therefore, the typing errors increase according to the size of motifs. In addition, when the structures motifs are submitted to bioinformatics systems, the specifications of motifs components are required, i.e. the simple motifs, gaps, and the lower bound and upper bound of each gap. The motifs can be of DNA, RNA, or Protein. In this research, a motif parser and visualization module is designed depending on a proposed a context free grammar, CFG, and colors human recognition system. GFC describes the motif structure to parse the motifs, detect, debug the errors, and analyze the motifs template to its components. Many experiments are accomplished using motifs templates of various sizes arranged from 10 Kbase to 10 Mbase, various numbers of gaps arranged from 15 gaps to 15000 gaps, and different numbers of errors arranged from 100 errors to 1820 errors. The proposed systems, in all these experiments, exhibited linear behavior in parsing phase and visualization phase that indicates its scalability to motifs template sizes.

A survey of exact motif finding algorithms

“…Proteins are complex and big molecules that perform multiple key functions in the organism. There are 20 distinctive kinds of amino acids that may be combined into proteins which are adenine (A), thymine (T), cytosine (C), guanine (G), isoleucine (I), phenylalanine (F), serine (S), glutamine (Q), histidine (H), asparagine (N), aspartic acid (D), arginine (R), glutamic acid (E), lysine (K), leucine (L), valine (V), tryptophan (W), tyrosine (Y), methionine (M) and proline (P) [3].…”

“…While intergenic areas are very extensive and very varied, sequence motifs are frequently repeated and preserved, making it difficult to identify them. Therefore, the algorithms in this field vary significantly in the execution times, the amount of memory consumed, and the accuracy of the answers [3].…”

Protein Motifs to Hide GA-Based Encrypted Data

“…Typically, single-base representation is used to code databases. ese bases are adenine (A), thymine (T), cytosine (C), guanine (G), isoleucine (I), phenylalanine (F), serine (S), glutamine (Q), histidine (H), asparagine (N), aspartic acid (D), arginine (R), glutamic acid(E), lysine (K), leucine (L), valine (V), tryptophan (W), tyrosine (Y), methionine (M), and proline (P) [21,22]. In this paper, the bases of protein motifs will be used to hide data in specified bits of binary representation of a base.…”