Preface

Genomic variations are the basis for phenotypic variations of individual organisms of the same species. These phenotypic variations could be of clinical importance in humans and medically relevant organisms. Therefore detection of genomic variations, and interpretation of their phenotypic effects and pathogenic potentials, has become a growing field in both biomedical research and clinical medicine.

*Bioinformatics Tools for Detection and Clinical Interpretation of Genomic Variations* is an up-to-date compilation of chapters on application of data analysis and mining tools for identification of clinically important genomic variations.

Chapter 1 discusses the application of non-decimated wavelet transform (NDWT) coupled with elastic net domains and Hurst exponent in identification of genetic diversity. Chapter 2 describes a comprehensive workflow for analysis of whole exome and whole genome sequencing data. It also presents the steps needed for variant discovery workflow with a particular focus on germline short variants and germline short insertion and deletion events. Additionally, this chapter outlines methods for analysis of somatic and structural variations.

Chapter 3 discusses local ancestry deconvolution and dating admixture events and the possible gaps in the knowledge that lead to the current challenges. Chapter 4 addresses the value of multiomics-based molecular patterns and the concept of pattern recognition and pattern biomarkers in cancer diagnosis and prognosis. It also explores the application of these concepts in personalized medicine. Chapter 5 addresses the genetic diversity of the hepatitis C virus and discusses its genotyping and concurrent variant profiling, as identification of resistance-associated variants of this virus determines the choice of anti-viral regimes in infected patients.

We would like to thank all the authors for their contributions and time in preparing this valuable collection. Also, we would like to extend our thanks to Mr. Luka Cvjetković for his great help during the editing of this book and to IntechOpen for their commitment and support.

#### **Ali Samadikuchaksaraei, MD, PhD**

Departments of Medical Biotechnology and Tissue Engineering & Regenerative Medicine, Iran University of Medical Sciences, Tehran, Iran

**Morteza Seifi, PhD** 

Alberta Children's Research Institute (ACRI), Calgary, Alberta, Canada

**1**

**Chapter 1**

**Abstract**

genetic variability.

**1. Introduction**

Exponent

The Bioinformatics Tools for

*Tesfahun Alemu Setotaw and Juliano Lino Ferreira*

**Keywords:** wavelet, genome, NDWT, elastic net, Hurst exponent

The genome era allowed us to evaluate different aspects on genetic variation, with a precise manner followed with a valuable tip to guide the improvement of knowledge and direct to upgrade to human life. In order to scrutinize these treasured resources, some bioinformatics tools permit us a deep exploration of these data. Among them, we display the significance of the discrete non-decimated wavelet transform (NDWT). The wavelets they possess improved capability to identify occult constituents of biological data and do a well-organized connection amid biological systems and the mathematical items used to designate them. The decomposition of signals/sequences at diverse stages of resolution allows

*Leila Maria Ferreira, Thelma Sáfadi,* 

Discovery of Genetic Diversity by

Means of Elastic Net and Hurst

The genome era allowed us to evaluate different aspects on genetic variation, with a precise manner followed by a valuable tip to guide the improvement of knowledge and direct to upgrade to human life. In order to scrutinize these treasured resources, some bioinformatics tools permit us a deep exploration of these data. Among them, we show the importance of the discrete non-decimated wavelet transform (NDWT). The wavelets have a better ability to capture hidden components of biological data and an efficient link between biological systems and the mathematical objects used to describe them. The decomposition of signals/ sequences at different levels of resolution allows obtaining distinct characteristics in each level. The analysis using technique of wavelets has been growing increasingly in the study of genomes. One of the great advantages associated to this method corresponds to the computational gain, that is, the analyses are processed almost in real time. The applicability is in several areas of science, such as physics, mathematics, engineering, and genetics, among others. In this context, we believe that using R software and applied NDWT coupled with elastic net domains and Hurst exponent will be of valuable guideline to researchers of genetics in the investigation of the
