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Integrating Genomics and Computer Vision: Unravelling Genetic Patterns and Analyzing Genomic Data

Neha Tanwar1, Sandeep Kumar2*, Garima Singh3 and Monika Bhakta4

1Department of Food Technology, Guru Jambheshwar University of Science and Technology, Hisar, India

2Engineering Cluster, Singapore Institute of Technology (SIT), 10 Dover Drive, Singapore, Singapore

3Department of Law, Bennett University, Greater Noida, India

4Department of Law, Sangam University, Bhilwara, Rajasthan, India

Abstract

In recent years, genomics and computer vision have undergone significant advancements that have profoundly influenced scientific research and healthcare. Genomics, which involves studying an organism’s complete DNA sequence, is crucial in understanding the genetic basis of diseases and designing personalized treatment strategies. Conversely, computer vision, a subfield of artificial intelligence, concentrates on creating algorithms and methodologies for analyzing and interpreting visual data. This chapter offers an overview of the convergence of genomics and computer vision, emphasizing the application of image analysis techniques for genomic data and the detection and analysis of genetic patterns using computer vision methods. The rapid progress in high-throughput sequencing technologies has led to a remarkable increase in the volume of genomic data generated. This abundance of genetic information necessitates efficient and accurate analysis methods, wherein computer vision techniques are indispensable.

A prominent area of research in integrating genomics and computer vision is using image analysis techniques for genomic data. The analysis and interpretation of complex genomic data require the development of sophisticated algorithms capable of identifying various types of genetic patterns. With their capability to extract meaningful features from visual data, computer vision methods have demonstrated their value in analyzing genomic sequences and identifying genetic variations. This interdisciplinary approach holds great promise for advancing genomic research and enhancing healthcare applications. The combination of genomics and computer vision has diverse applications, including detecting and analyzing genetic patterns. Computer vision algorithms can effectively uncover spatial or temporal relationships in genetic data, such as mutations or gene expression levels. This integration has revolutionized scientific research and healthcare, enabling more profound insights into disease biology. The collaboration between genomics and computer vision will drive future discoveries and innovations as genomics advances and generates vast amounts of data.

Keywords: Genomics, computer vision, machine learning, genetics, genome sequencing

1.1 Introduction

Computer vision is a specialized area within artificial intelligence which concentrates on the scientific and technological aspects of enabling machines to perceive and interpret the physical world through visual data. Computer vision is an interdisciplinary field that focuses on allowing computers to analyze and understand visual information from the world around us. Its primary goal is to empower computers with the capability to extract, research, and comprehend information from images or video sources.

Computer vision applications span multiple fields, including medicine, robotics, surveillance, and, more recently, genomic research. It uses digital images and videos as input data to replicate human vision capabilities, such as object recognition, scene understanding, and image analysis. Computer vision is crucial in various applications, including autonomous vehicles, facial recognition, medical imaging, surveillance systems, and robotics [2]. This technology has witnessed significant advancements in recent years, primarily driven by advances in deep learning and neural network architectures.

In the context of genomics, computer vision can be used to analyze and interpret genomic data, which includes DNA sequences, gene expression profiles, and genomic images obtained through advanced imaging techniques [3]. This field of research, known as genomic vision, can enhance our understanding of genomics and contribute to various aspects of biological and medical research, as shown in Figure 1.1.

Figure 1.1 Year-by-year progress in human genomics projects [1].

The roots of computational genomics are intertwined with those of bioinformatics. In the 1960s, Margaret Dayhoff and her colleagues at the National Biomedical Research Foundation compiled databases of homologous protein sequences to study evolution. They created a phylogenetic tree based on amino acid sequences to understand the changes required for one protein to transform into another. This led to developing a scoring matrix that assessed the likelihood of protein-relatedness [4]. Genomics, often called functional genomics, has a broad scope aiming to understand the functions of all genomic elements in an organism. This involves using genome-scale assays like genome sequencing, transcriptome profiling, and proteomics. Unlike hypothesis-driven approaches, genomics relies on data exploration to discover novel properties and associations from large-scale genomic data.

Due to the vast and complex nature of genomics data, more than a visual examination of pairwise correlations is required. Analytical tools, especially machine learning algorithms, are essential to uncover unexpected relationships, generate new hypotheses, and make predictions. Machine learning algorithms are well-suited for data-driven sciences, including genomics, as they automatically detect patterns in the data without relying on hard-coded assumptions or domain expertise. However, the effectiveness of machine learning algorithms heavily depends on how the data is represented, i.e., how the features are computed. The quality and relevance of these features significantly impact the performance of classification tasks. For example, in tumor classification from fluorescent microscopy images, handcrafted elements such as cell counts might not fully capture relevant visual characteristics like cell morphology, cell distances, or organ localization, leading to reduced classification accuracy. Thus, improving feature representation is a central concern in genomics research.

In the 1980s, genome sequence databases emerged, posing new challenges for searching and comparing gene information. Unlike simple text-searching algorithms used for regular websites, genetic similarity requires finding similar rather than identical strings. The Needleman-Wunsch algorithm was developed, utilizing scoring matrices from Dayhoff’s research to compare amino acid sequences [5]. Later, the BLAST algorithm was introduced for fast, optimized searches of gene sequence databases and remains widely used today. The term “computational genomics” gained popularity in the mid-to-late 1990s when complete sequenced genomes became available. The Annual Conference on Computational Genomics, initiated by scientists from The Institute for Genomic Research (TIGR) in 1998, distinguished this speciality from broader fields like genomics and computational biology [5]. Its first use in scientific literature was in nucleic acids research in the preceding year. Key conferences include Intelligent Systems for Molecular Biology (ISMB) and Research in Computational Molecular Biology (RECOMB).

The precise arrangement of nucleotides—the building blocks of DNA— in the genome of a given organism is referred to as its genomic sequence (as shown in Figure 1.2). The genome is an organism’s whole collection of genetic material, or DNA in most cases, which contains the instructions needed to develop, maintain, and operate that particular creature. Within the field of genomics, which focuses on the examination of complete genomes, a genomic sequence offers a guide for comprehending the genetic data contained in an organism’s DNA [7].

DNA is composed of four central nucleotides: adenine (A), thymine (T), cytosine (C), and guanine (G). A and C couple with T and G, respectively, in a complementary way to make pairs. The genomic sequence is the linear configuration of these nucleotides along the DNA strand [8]. Essential facts regarding chromosomal sequencing:

• Base pairs: typically, genomic sequences are shown as letters for nucleotides. A brief genetic sequence, for instance, might be represented as “ATCGGA.”
• Genes and non-coding areas: among other things, non-coding areas in genomic sequences perform regulatory roles. Coding regions, on the other hand, contain instructions for making proteins or genes.
• Variability: individuals within the same species can have dramatically different genomic sequences. Understanding genetic diversity, inheritance patterns, and disease risk is aided by studying these variants.
• Genomic information: the information needed for an organism’s growth, development, and operation is included in its genome sequence. Understanding the genetic underpinnings of different traits and disorders and finding genes and regulatory elements depend on deciphering these sequences.
• Technologies for mapping and sequencing genomes: genomics has been revolutionized thanks to sequencing and genome mapping advances, including next-generation sequencing (NGS). These technologies make large-scale genomic research possible, enabling quick and...