Genomic Sequence Analysis for Exon Prediction Using Adaptive Signal Processing Algorithms

✍ Scribed by Zia Ur Rahman, Srinivasareddy Putluri

• Publisher: CRC Press
• Year: 2021
• Tongue: English
• Leaves: 203
• Edition: 1
• Category: Library

✦ Synopsis

This book addresses the issue of improving the accuracy in exon prediction in DNA sequences using various adaptive techniques based on different performance measures that are crucial in disease diagnosis and therapy. First, the authors present an overview of genomics engineering, structure of DNA sequence and its building blocks, genetic information flow in a cell, gene prediction along with its significance, and various types of gene prediction methods, followed by a review of literature starting with the biological background of genomic sequence analysis. Next, they cover various theoretical considerations of adaptive filtering techniques used for DNA analysis, with an introduction to adaptive filtering, properties of adaptive algorithms, and the need for development of adaptive exon predictors (AEPs) and structure of AEP used for DNA analysis. Then, they extend the approach of least mean squares (LMS) algorithm and its sign-based realizations with normalization factor for DNA analysis. They also present the normalized logarithmic-based realizations of least mean logarithmic squares (LMLS) and least logarithmic absolute difference (LLAD) adaptive algorithms that include normalized LMLS (NLMLS) algorithm, normalized LLAD (NLLAD) algorithm, and their signed variants. This book ends with an overview of the goals achieved and highlights the primary achievements using all proposed techniques. This book is intended to provide rigorous use of adaptive signal processing algorithms for genetic engineering, biomedical engineering, and bioinformatics and is useful for undergraduate and postgraduate students. This will also serve as a practical guide for Ph.D. students and researchers and will provide a number of research directions for further work.

Features

• Presents an overview of genomics engineering, structure of DNA sequence and its building blocks, genetic information flow in a cell, gene prediction along with its significance, and various types of gene prediction methods

• Covers various theoretical considerations of adaptive filtering techniques used for DNA analysis, introduction to adaptive filtering, properties of adaptive algorithms, need for development of adaptive exon predictors (AEPs), and structure of AEP used for DNA analysis

• Extends the approach of LMS algorithm and its sign-based realizations with normalization factor for DNA analysis

• Presents the normalized logarithmic-based realizations of LMLS and LLAD adaptive algorithms that include normalized LMLS (NLMLS) algorithm, normalized LLAD (NLLAD) algorithm, and their signed variants

• Provides an overview of the goals achieved and highlights the primary achievements using all proposed techniques

Dr. Md. Zia Ur Rahman is a professor in the Department of Electronics and Communication Engineering at Koneru Lakshmaiah Educational Foundation (K. L. University), Guntur, India. His current research interests include adaptive signal processing, biomedical signal processing, genetic engineering, medical imaging, array signal processing, medical telemetry, and nanophotonics.

Dr. Srinivasareddy Putluri is currently a Software Engineer at Tata Consultancy Services Ltd., Hyderabad. He received his Ph.D. degree (Genomic Signal Processing using Adaptive Signal Processing algorithms) from the Department of Electronics and Communication Engineering at Koneru Lakshmaiah Educational Foundation (K. L. University), Guntur, India. His research interests include genomic signal processing and adaptive signal processing. He has published 15 research papers in various journals and proceedings. He is currently a reviewer of publishers like the IEEE Access and IGI.

✦ Table of Contents

Cover
Half Title
Title Page
Copyright Page
Table of Contents
Authors
Chapter 1 Introduction
1.1 Genomics Engineering
1.2 DNA Sequence Structure
1.3 Motivation for the Work
1.4 Objectives
1.5 Molecular Basis for Genomic Information
1.5.1 Understanding the Genome
1.5.2 Building Blocks of DNA
1.6 Gene Prediction
1.6.1 Significance of Gene Prediction
1.7 Types of Gene Prediction Approaches
1.7.1 Extrinsic Gene Prediction
1.7.2 Ab Initio Gene Prediction
1.7.3 Comparative Gene Prediction
1.8 DNA Representations for Genomic Sequence Analysis
1.9 Types of DNA Representations
1.9.1 Voss Mapping
1.9.2 Z-Curve Representation
1.9.3 Tetrahedron
1.9.4 Complex
1.9.5 Quaternion
1.9.6 Electron-Ion Interaction Potential
1.9.7 Inter-nucleotide Distance
1.9.8 Maximum Likelihood Estimate
1.8.1 Desirable Properties
1.10 Organization of Book
Chapter 2 Literature Review
2.1 Biological Background of Genomic Sequence Analysis
2.2 The Gene and Early Development of Genetics
2.3 Origin of Three-Base Periodicities in Genomic Sequences
2.4 DSP-Based Techniques for DNA Analysis
2.4.1 Application of Discrete Fourier Transform
2.4.2 Spectral Content (SC) Measure
2.4.3 Optimized Spectral Content (SC) Measure
2.4.4 Spectral Rotation (SR) Measure
2.4.5 Fourier Product Spectrum (FPS) Method
2.4.6 Digital Filters for Genomic Analysis
2.4.7 Autoregressive Models
2.5 Adaptive Algorithms for DNA Analysis
2.6 Conclusions
Chapter 3 Sign LMS Based Realization of Adaptive Filtering Techniques for Exon Prediction
3.1 Introduction
3.2 Theoretical Considerations of Adaptive Filtering Techniques in DNA Analysis
3.2.1 Adaptive Filter
3.2.2 Properties of Adaptive Algorithms
3.2.3 Need for Development of Adaptive Exon Predictors
3.3 Structure of Adaptive Exon Predictor for DNA Analysis
3.4 LMS Algorithm
3.5 LMF Algorithm
3.6 Variable Step Size LMS (VSLMS) Algorithm
3.7 Least Mean Logarithmic Squares (LMLS) Algorithm
3.8 Least Logarithmic Absolute Difference (LLAD) Algorithm
3.9 Simplified Algorithms Based on Signum Function
3.9.1 Sign-Based LMS Algorithms
3.10 Extension to Sign-Based Realizations of LMS-Based Variants
3.10.1 Sign-Based Least Mean Fourth (LMF) Algorithms
3.10.2 Sign-Based Variable Step Size LMS (VSLMS) Algorithms
3.10.3 Sign-Based Least Mean Logarithmic Squares (LMLS) Algorithms
3.10.4 Sign-Based Least Logarithmic Absolute Difference (LLAD) Algorithms
3.11 Computational Complexity Issues
3.12 Convergence Analysis
3.13 Results and Discussion for LMS-Based Variants
3.13.1 Gene Datasets from the NCBI Gene Databank for Gene Sequence Analysis
3.13.2 Analysis of Gene Datasets of NCBI Gene Databank
3.13.2.1 Nucleotide Densities of Monomers and Dimers in Gene Dataset
3.13.3 Performance Measures of Exon Prediction
3.13.4 Exon Prediction Results
3.14 Conclusions
Chapter 4 Normalization-Based Realization of Adaptive Filtering Techniques for Exon Prediction
4.1 Introduction
4.2 Normalized Adaptive Algorithms
4.3 Normalized LMS (NLMS) Algorithm
4.4 Error-Normalized LMS (ENLMS) Algorithm
4.5 Normalized Least Mean Fourth (NLMF) Algorithm
4.6 Variable Step Size Normalized LMS (VNLMS) Algorithm
4.7 Extension to Sign-Based Realizations of Normalized Algorithms
4.7.1 Sign-Based Normalized LMS (NLMS) Algorithms
4.7.2 Sign-Based Error-Normalized LMS (ENLMS) Algorithms
4.7.3 Sign-Based Normalized LMF (NLMF) Algorithms
4.7.4 Sign-Based Variable Step Size NLMS (VNLMS) Algorithms
4.8 Computational Complexity Issues
4.9 Convergence Analysis
4.10 Results and Discussion for Normalization-Based Variants
4.10.1 Exon Prediction Results
4.11 Conclusions
Chapter 5 Logarithmic-Based Realization of Adaptive Filtering Techniques for Exon Prediction
5.1 Introduction
5.2 Logarithmic Adaptive Algorithms
5.3 Normalized LMLS (NLMLS) Algorithm
5.4 Error-Normalized LMLS (ENLMLS) Algorithm
5.5 Normalized LLAD (NLLAD) Algorithm
5.6 Error-Normalized LLAD (ENLLAD) Algorithm
5.7 Extension to Sign-Based Realizations of Logarithmic Normalized Algorithms
5.7.1 Extension to Sign-Based Realizations of NLMLS-Based Variants
5.7.2 Extension to Sign-Based Realizations of ENLMLS-Based Variants
5.7.3 Extension to Sign-Based Realizations of NLLAD-Based Variants
5.7.4 Extension to Sign-Based Realizations of ENLLAD-Based Variants
5.8 Computational Complexity Issues
5.9 Convergence Analysis
5.10 Results and Discussion for Logarithmic Normalized Variants
5.10.1 Exon Prediction Results
5.11 Conclusions
Chapter 6 Conclusion and Future Perspective
6.1 Summary and Conclusions
6.2 Recommendations for Future Research
References
Index