Automated Genomic Signal Processing for Diseased Gene Identification

Genomic signal processing (GSP) is the engineering discipline for the analysis, processing, and use of genomic signals to gain biological knowledge, and the translation of that knowledge into systems-based applications that can be used to diagnose and treat genetic diseases. Statistical Computations on DNA Sequences is one of key areas in which GSP can be applied. In this paper, we apply DSP tools on trinucleotide repeat disorders (too many copies of a certain nucleotide triplet in the DNA) to classify any gene sequence into diseased/non-diseased state. Intially, we collected the Gene sequences responsible for trinucleotide repeat disorders from NCBI. Then, we applied GSP techniques to convert the given gene sequence into an indicator sequence, and furthermore we apply Fast Fourier transforms (FFTs) and Discrete Wavelet Transforms (DWTs), followed by statistical feature extraction and the obtained statistical features, fed into an Artificial Neural Network to predict the state of the input genomic sequence.

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Improved Algorithm for Analysis of DNA Sequences Using Multiresolution Transformation

The Scientific World JOURNAL ◽

10.1155/2015/786497 ◽

2015 ◽

Vol 2015 ◽

pp. 1-9 ◽

Cited By ~ 7

Author(s):

T. M. Inbamalar ◽

R. Sivakumar

Keyword(s):

Signal Processing ◽

Dna Sequences ◽

Background Noise ◽

Digital Signal ◽

Computational Techniques ◽

Discrete Wavelet ◽

Protein Coding ◽

Genomic Signal Processing ◽

Coding Regions ◽

Genomic Signal

Bioinformatics and genomic signal processing use computational techniques to solve various biological problems. They aim to study the information allied with genetic materials such as the deoxyribonucleic acid (DNA), the ribonucleic acid (RNA), and the proteins. Fast and precise identification of the protein coding regions in DNA sequence is one of the most important tasks in analysis. Existing digital signal processing (DSP) methods provide less accurate and computationally complex solution with greater background noise. Hence, improvements in accuracy, computational complexity, and reduction in background noise are essential in identification of the protein coding regions in the DNA sequences. In this paper, a new DSP based method is introduced to detect the protein coding regions in DNA sequences. Here, the DNA sequences are converted into numeric sequences using electron ion interaction potential (EIIP) representation. Then discrete wavelet transformation is taken. Absolute value of the energy is found followed by proper threshold. The test is conducted using the data bases available in the National Centre for Biotechnology Information (NCBI) site. The comparative analysis is done and it ensures the efficiency of the proposed system.

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DIFFERENT GENOMIC SIGNAL PROCESSING METHODS FOR EUKARYOTIC GENE PREDICTION: A SYSTEMATIC REVIEW

Biomedical Engineering Applications Basis and Communications ◽

10.4015/s1016237217300012 ◽

2017 ◽

Vol 29 (01) ◽

pp. 1730001 ◽

Cited By ~ 4

Author(s):

Mai S. Mabrouk ◽

Safaa M. Naeem ◽

Mohamed A. Eldosoky

Keyword(s):

Systematic Review ◽

Signal Processing ◽

Cancer Detection ◽

Dna Sequences ◽

Early Stage ◽

Gene Prediction ◽

Malignant Neoplasm ◽

Biological Information ◽

Genomic Signal Processing ◽

Genomic Signal

Bioinformatics field has now solidly settled itself as a control in molecular biology and incorporates an extensive variety of branches of knowledge from structural biology, genomics to gene expression studies. Bioinformatics is the application of computer technology to the management of biological information. Genomic signal processing (GSP) techniques have been connected most all around in bioinformatics and will keep on assuming an essential part in the investigation of biomedical issues. GSP refers to using the digital signal processing (DSP) methods for genomic data (e.g. DNA sequences) analysis. Recently, applications of GSP in bioinformatics have obtained great consideration such as identification of DNA protein coding regions, identification of reading frames, cancer detection and others. Cancer is one of the most dangerous diseases that the world faces and has raised the death rate in recent years, it is known medically as malignant neoplasm, so detection of it at the early stage can yield a promising approach to determine and take actions to treat with this risk. GSP is a method which can be used to detect the cancerous cells that are often caused due to genetic abnormality. This systematic review discusses some of the GSP applications in bioinformatics generally. The GSP techniques, used for cancer detection especially, are presented to collect the recent results and what has been reached at this point to be a new subject of research.

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Genomic signal processing for DNA sequence clustering

PeerJ ◽

10.7717/peerj.4264 ◽

2018 ◽

Vol 6 ◽

pp. e4264 ◽

Cited By ~ 9

Author(s):

Gerardo Mendizabal-Ruiz ◽

Israel Román-Godínez ◽

Sulema Torres-Ramos ◽

Ricardo A. Salido-Ruiz ◽

Hugo Vélez-Pérez ◽

...

Keyword(s):

Cluster Analysis ◽

Signal Processing ◽

Dna Sequences ◽

Digital Signal ◽

Unsupervised Classification ◽

Genomic Signal Processing ◽

Novel Approach ◽

Signal Processing Methods ◽

Genomic Signal

Genomic signal processing (GSP) methods which convert DNA data to numerical values have recently been proposed, which would offer the opportunity of employing existing digital signal processing methods for genomic data. One of the most used methods for exploring data is cluster analysis which refers to the unsupervised classification of patterns in data. In this paper, we propose a novel approach for performing cluster analysis of DNA sequences that is based on the use of GSP methods and the K-means algorithm. We also propose a visualization method that facilitates the easy inspection and analysis of the results and possible hidden behaviors. Our results support the feasibility of employing the proposed method to find and easily visualize interesting features of sets of DNA data.

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