Some intriguing high-throughput DNA sequence variants prediction over protein functionality

• Main Authors: Kheirkhah, Atabak (Author), Mohd. Daud, Salwani (Author), Ahmad @ Salleh, Noor Azurati (Author), Mohd. Sam, Suriani (Author), Abas, Hafiza (Author), Shariff, Sya Azmeela (Author), Md. Yusof, Yusnaidi (Author)
• Format: Article
• Language: English
• Published: Penerbit UTM, 2016.
• Subjects: T Technology (General)
• Online Access: Get fulltext

 This paper intends to review computational methods and high throughput automated tools for precisely prediction various functionalities of uncharacterized proteins based on their desired DNA sequence information alone. Then proposes a hybrid weighted network and Genetic Algorithm to improve prediction purpose. The main advantage of the method is the protein function and DNA sequence prediction can be computed precisely using best fitness parent in genetic algorithm. With the accomplishment of human genome sequencing, the number of sequence-known proteins has increased exponentially and the pace is much slower in determining their biological attributes. The gap between DNA sequence variants and their functionalities has become increasingly large. However, detection of sequences based on protein data bank has become benchmark for many researchers. As amount of DNA sequence data continues to increase, the fundamental problem stay at the front of genome analysis. In the course of developing these methods, the following matters were often needed to consider: benchmark dataset construction, gene sequence prediction, operating algorithm, anticipated accuracy, gene recommender and functional integrations. In this review, we are to discuss each of them, with a different focus on operational algorithms and how to increase the accuracy of DNA sequence variants prediction