INTRODUCTION

Bioinformatics refers to the application of computers in analyzing and interpreting biological and biomedical data. Bioinformatics belongs to a discipline that stores, interprets, and analyzes big data which is usually generated or collected from clinical experiments. Moreover, various biological data are utilized in bioinformatics namely transcriptomics, proteomics, phenomics, and chemoinformatics. Bioinformatics is a vast field that requires a diverse range of skill sets. Biologists, computer scientists, mathematicians, statisticians, and physicists are among the professionals driving this diverse subject. The process that is involved in bioinformatics may include creating databases to store experimental results, modelling chemical reactions in a cell and also predicting protein folding, and many more.

Sequence analysis is an essential procedure in bioinformatics that involves subjecting DNA or RNA sequences to a variety of analytical tools in order to find sequence similarity and comprehend evolutionary links.. The term sequence analysis refers to the computational evaluation or analysis of sequences of DNA or RNA to understand its features, biological function, structure, and evolution of living organisms. To conduct sequence analysis successfully, it is necessary to first comprehend the source of the data such as the various experimental procedures utilized to determine the biological sequence. Therefore, we must employ different analytical methodologies based on whether the sequence is genomic, transcriptomic, or proteomic.

Genomic sequence analysis has the ability to reveal the presence of genetic variants, such as single nucleotide polymorphisms or perhaps copy number variations. These variants can provide vital information about a person’s vulnerability to specific diseases, treatment response, and risk of passing genetic abnormalities on to their children. Furthermore, researchers may use genome sequence analysis to learn how genes are expressed in various tissues and under different situations. This knowledge can help us understand the molecular pathways that underpin both normal biological functions and disorders. In addition, genome sequence analysis can aid in the identification of genetic variants that cause illnesses as well as the understanding of the molecular pathways behind the disease. This understanding has led to the development of innovative treatments. Also, personalized medicine could be developed using this analysis method. Apart from that, evolutionary relationships between various species could be determined as well. Overall, genome sequence analysis is a crucial method in genetics.

Traditional Approaches for Genomic Sequence Analysis

The traditional approaches of genomic sequencing are the methodology that was developed before the recent advancement of technologies resulting in high-speed and more efficient sequencing techniques. During the era of lack of industrialization the traditional approaches of genomic sequencing was used to analyze the genetic makeup of organisms. Techniques such as Sangers sequencing, the Maxam-Gilbert method, pyrosequencing are some of the examples of the traditional approaches which allowed accurate identification of sequences of DNA. Despite being old-fashioned, these methods are still being utilized in research laboratories and in clinical settings due to their accuracy and reliability.

When Watson and Crick published their key paper revealing the double helix structure of DNA in 1953, Sanger and Coulson created the ‘chain-termination’ or dideoxy technique for DNA sequencing in 1974. This methodology is conducted by utilizing four reaction mixes containing the template DNA, a primer, DNA polymerase and four deoxynucleotides where one of them is radiolabelled. This technique makes use of dideoxynucleotides, modified nucleotides that stop DNA synthesis when integrated into a developing DNA chain. The DNA sequence can be identified by detecting the light signals released after labeling the ending nucleotides with various fluorescent dyes. Similar to Sanger’s technique, another method for DNA sequencing was introduced by Maxam and Gilbert in 1977. The Maxam and Gilbert technique is a process that uses chemical components to break down the DNA sequences at specific regions. Unlike Sanger sequencing, this technique did not rely on DNA polymerase. Furthermore, since the technical difficulty of this method is high and it involves the usage of potentially harmful chemicals, this method has fallen out of favor in the current era.

Pyrosequencing is a second-generation genomic sequencing technique founded by Mostafa Ronaghi, Mathias Uhlen and Pȧl Nyŕen in 1996. This automated technique is based on the measurement of luminescence produced due to pyrophosphate synthesis during sequencing (sequencing-by-synthesis technology). There were several benefits of this technique compared to the previous techniques, where pyrosequencing is able to be performed using natural nucleotides and can be observed in real-time without the need for lengthy electrophoreses.

Traditional gene sequence analysis techniques have been utilized extensively and have greatly improved the understanding of genetics. However, these techniques have their own limitations that result in multiple challenges in the current era. Conventional approaches often require significant time and financial resources to study a single gene or genomic region. When evaluating massive amounts of genetic data, this can represent a major setback. Conventional approaches are frequently limited in their capacity to detect genomic differences such as in single nucleotide polymorphisms (SNPs). Traditional techniques, like Sanger sequencing, may have a high error rate, which lowers their efficiency. In addition, using traditional methods can be time-consuming and expensive because they sometimes need a lot of manual labor and skill.

The low throughput and high expense of these early sequencing techniques made them unsuitable for sequencing big genomes. Regardless, the traditional genome sequencing techniques have greatly contributed to the sequencing of smaller DNA fragments, such as genes or particular regions of interest. Furthermore, they have helped lay the foundation for the creation of more advanced, effective sequencing techniques.

ADVANCES IN DATA STRUCTURE FOR GENOMIC SEQUENCE ANALYSIS

Analyzing vast volumes of genomic data to discern essential knowledge about genes, their functions and interconnections is known as Genomic sequence analysis. In order to competently achieve this task, the capacity for genomic data storage, retrieval and manipulation is crucial; with underlying data structures being key components in these operations. Data structures are pivotal in representing and manipulating genetic data more effectively by organizing it systematically. The convoluted connections among different aspects of the genomics sphere, encompassing gene regulatory architectures, interactions between various proteins and even evolutionary linages might present a formidably labyrinthine synthesis on occasions. Nonetheless, through the use of appropriate data structures these interactions can be depicted accurately which ultimately sharpens both the precision as well as efficiency of the subsequent analyses performed by researchers.

To ensure effective data handling and storage, it is essential to possess a thorough comprehension of the fundamental building blocks, universally known as data structures. The value placed on these components cannot be overstated, bearing in mind the variety of choices available – each with its set of positives and negatives. One such option deserving mention is an array – essentially like an exquisitely organized directory that houses analogous information by numerical locations enabling access through indexing techniques.Constant-time access to elements is one of the arrays’ main advantages, which makes them perfect for applications that need speedy access to elements. The fixed size of arrays, however, restricts their versatility and necessitates additional memory allocation to support more components.

Meanwhile, a linked list is made up of several elements, each of which has a reference to the element after it. As linked lists can grow dynamically and do not have a fixed size, they are appropriate for applications that often add or remove members. However, because elements in linked lists must be traversed in a particular order to reach another member, access times are slower than for arrays. Using the Last-In-First-Out (LIFO) method, a stack is a group of elements that can be accessed. Stacks are helpful for applications like depth-first search algorithms that demand a particular order of element access. Nevertheless, compared to other data structures, stacks have limited functionality, and their size is constrained by the amount of underlying RAM.

Queues are very handy for applications like breadth-first search algorithms that demand a particular order of element access. Queues offer less functionality than other data structures, similar to stacks. Nodes and edges which are a data structure of type hierarchical make up a

tree. Trees are reliable for applications such as simulated annealing that brings efficiency in insertion/deletion and searching. However, compared to other data structures, trees can be more difficult to create and maintain, and the tree’s height might affect how well it performs. A hash function is a data structure that is used to map keys to values in a hash table. The applications that require rapid lookups, for instance, archives or dictionaries, and hash tables comes in handy as they provide reliable access to elements. However, hash tables can experience collisions, when several keys map to the same value, and therefore need extra memory to hold hash values.

To recap, data structures are a crucial tool for effectively organizing and working with genetic data. The exact problem being solved and the properties of the data being used determine the data structure to be used. By being aware of the advantages and disadvantages of various data structures, researchers can decide on the best course of action for their genomic sequence analysis projects and streamline the analysis procedures.

ADVANCES IN ALGORITHMS FOR GENOMIC SEQUENCE ANALYSIS

The rapid advancement in sequencing technologies, the field of biology concerning genomic disciplines had progressed so much that it had allowed the scientific community to examine and analyze DNA sequences rapidly at a massive scale. With this advancement achieved it had led to the development and initiation of various state-of-the-art algorithms tools for genomic sequencing such as machine algorithm learning that utilized advanced computational tools that transcend across different fields and disciplines.

Firstly the most recognisable advancement in algorithms for genomic sequence analysis is by the machine learning algorithm. Deep learning and convolutional neural networks are two machine learning methods that have been used to identify genetic variations and forecast gene expression levels. Such algorithms could swiftly and accurately evaluate massive datasets, resulting in quicker and more accurate genomic analysis. For example, understanding the biological roles of RNA molecules and creating medications that target them requires the capacity to anticipate their three-dimensional structure. But still, because RNA molecules are complex and dynamic, predicting their tertiary structure still poses some challenges. Hence the development of computational tools is integral for us to understand the complex structure more, where Ray (2019) has developed a new computational model for predicting the tertiary structure of RNA molecules. Their model utilizes a two-step approach that first generates coarse-grained models of RNA structures, which will be refined into full-atom models. They also demonstrated the effectiveness of their model by applying it to a set of 25 RNA molecules of varying complexity, achieving high accuracy in predicting their tertiary structures which could promise a more prominent result when using this algorithm method compared to traditional methods.

Next is the concept of Graph-based algorithms, such as de Bruijn graphs and overlap graphs, which are commonly used for genome assembly, which involves piecing together short DNA reads into longer contiguous sequences. These algorithms can improve the accuracy and completeness of genome assemblies. For example, Jiang et al. (2021) propose a deep learning model called DeepPhys that predicts the properties of molecules directly from their two-dimensional representations, using convolutional neural networks. The model is trained on

a large dataset of molecular structures and their corresponding physicochemical properties. They highlight the efficacy of their findings by demonstrating that DeepPhys surpasses standard approaches in terms of accuracy and efficiency, with a significant drop in the processing time required for the prediction approach when compared to other cutting-edge algorithms. Moreover, their approach has significant uses in drug discovery and development, especially in the early phases of drug design with a precise prediction of physicochemicals.

The third advancement for genomic sequencing is in alignment algorithms. Alignment algorithms, such as BLAST, and Clustal Omega, are used to compare genomic sequences to identify similarities and differences. Clustal Omega is a revised version of the popular Clustal program series for aligning multiple sequences, which was completely rewritten and updated in 2011. Due to its utilization of the mBed algorithm for guide-tree calculation, it has the capability to handle a vast amount of DNA/RNA or protein sequences, reaching into the tens of thousands. The algorithm permits swift resolution of alignment issues, even when running on personal computers, in situations where there is a large number of sequences to align. Clustal Omega boasts enhanced accuracy compared to previous Clustal programs, which is a result of using the HHalign method for profile hidden Markov models alignment. The program can be utilized either via the command line or online (Sievers & Higgins, 2021). The Basic Local Alignment Search Tool (BLAST) is a bioinformatics tool that allows the scientific community to evaluate biological sequences, such as DNA or protein sequences, using a vast database of sequences available in the BLAST database. It works where BLAST identifies sequences that are similar to the query sequence and offers information on their function and evolutionary links.

Apart from that, the network-based methods algorithms will be based on networks such as gene co-expression networks and protein-protein interaction networks, which are used to discover functional relationships between proteins and genes. According to Yadav and Jadhav (2019), machine learning and deep learning algorithms could open up new avenues for comprehending complicated biological systems, finding disease processes, and generating tailored therapeutics. These methods may be used in a variety of biomedical research fields, including genomics, proteomics, medical imaging, and drug development.

In conclusion,collaboration among members of the scientific communities is necessary in order to produce more robust and accurate improvements for machine learning and algorithms.

This is to ensure that such a system can provide more advanced and promising outcomes, as well as accurate insights into the complex world of biological system.

APPLICATIONS OF GENOMIC SEQUENCE ANALYSIS

Genomic sequence analysis refers to the process of analyzing and interpreting the DNA sequences of organisms’ genomes. Genome sequence analysis has revolutionized the field of biology and also has a wide range of applications in various fields.

First and foremost, genomic sequence analysis is employed in medical genetics. Genomic sequencing analysis plays an essential role in identifying potential mutations that might be responsible for genetically related diseases. Besides that, genomic sequencing is capable of

detecting almost all DN variations present in the human genome. It has been proven that genome sequencing can diagnose up to 6000 conditions, such as cystic fibrosis, haemophilia, Duchene muscular dystrophy, Marfan syndrome, and many more. Although genome sequencing is a more accurate and less expensive approach to the diagnosis of a disease, patients’ accurate clinical information and history are required to correctly interpret the results.

Genomic sequence analysis is also utilized in drug design. This is done by screening compounds for target genes. Besides that, the adverse effects of a new drug could be identified using genomic analysis. For instance, the animal’s gene expression pattern in the liver is used to determine whether the pathways of the gene are related to toxicity. Such information is crucial and may influence the decision about whether or not to discontinue drug development. Hence, this enables the acceleration of the drug development process. Moreover, the application of genomic sequencing in drug design may result in drugs with higher efficacy with a better safety profile.

Meanwhile, genomic sequencing is also essential in terms of agriculture. Improvements in DNA sequencing technology have enabled the decoding of whole genomes for a wide range of plant species. Currently, approximately 400 genomes from various terrestrial plant species have been deposited in GenBank. Grape and cucumber were the first plant genomes to be constructed utilizing a mix of Sanger and NGS techniques, with short reads generated, respectively. Also, understanding the plant genome using genome sequencing techniques enables the agriculture industry to come up with various breeding strategies that may deliver higher genetic gain and improve the crop’s adaptability, which would eventually save costs. The identification of GMO species in agriculture may be achievable with the use of DNA sequencing technologies. With the use of DNA sequencing, any tiny differences or mutations in the plant genome may be found. This will aid in the diagnosis of various plant illnesses and the production of pathogen-free plants.

Apart from that, DNA sequencing is important in forensic science. Many new and old techniques are used to sequence DNA in forensic science. Examples of it may include next-generation sequencing, PCR, and many more. For instance, next-generation sequencing, which is the latest technique used to sequence DNA, enables forensic laboratory staff to yield high-quality forensic DNA profiles. The use of DNA profiling, or genetic fingerprinting, is a key part of modern forensics. Forensic DNA profiles comprise size measurements, which are also inferred as the total count of repeats at short tandem repeat (STR) markers. These new tests

will enable forensic investigators to sequence STR markers, improving the overall capacity to distinguish suspects in complicated mixes.

Overall, genomic sequence analysis is a strong sequencing method that could be utilized for understanding the genetic basis of diverse biological processes, which can lead to novel discoveries and applications in a variety of domains.

FUTURE DIRECTIONS IN GENOMIC SEQUENCE ANALYSIS

The advancements in genomics have transformed our knowledge of life and illness. There are huge amounts of genetic data being created by using the development in next-generation sequencing methods. The huge amount of this data, however, creates substantial computing hurdles, and effective data structures and algorithms are required for its processing. This part of the report will be discussing the potential future developments in data

structures and algorithms for genomic sequence analysis, and also the potential applications or impact of future developments in genomic sequences.

Firstly, one of the possible areas for development in the sequence analysis is the usage of graph-based data structures. The previous techniques to sequence alignment frequently depend on the linear sequence representations, which might get mistakes and gaps in the constructed genome results. The graph-based representations such as overlap graphs and de Brujin graphs, have shown the high possibilities in getting an accurate result and can reduce the processing costs more. These structures will effectively identify the possible repeated sections and changes occurring in the genome, which is especially very important in the research of cancers due to genomic instability. Additionally, graph-based data structures can also tend to help in the investigations of complicated genomic events such as gene fusions and structural variations which are very difficult to find out using the linear approaches (Simpson et al., 2015).

Moreover, the use of machine learning algorithms for genomic sequence analysis is also a vast area of development. This particular expert system advances towards deep learning and reinforcement learning. These both learnings have had promisingly improvised the accuracy of sequence classification, prediction and also annotation. For instance, deep structured learning models have been operated to foresee the effects of genetic variants on protein function which directly leads into refined understanding of disease mechanisms and possible drug targets (Alipanahi et al., 2015). On top of that, reinforcement learning algorithms have been relevant to enhance experimental outline for gene editing and synthetic biology, which in the long run leads to the evolution of new curative targets. Eventually, when more genomic information becomes accessible, this machine learning application is distinctly possible to become dominant for efficiency and accuracy for analysis purposes.

Besides that, another prospect of implementation in these occurrences will be in the area of preciseness of medicine. The major objective of this request is to customise the medical aid to individual patients based on their genetic composition. The verge of using graph-based information structures and machine learning algorithms is because this specific procedure can make the recognition of disease-causing mutations much smoother and the expansion of personalized remedy. By the way of illustration, this graph-based perspective could possibly lead to more precise genomic analysis, which indirectly increases our understanding of the genetic base ailment and also opens the door for major evolution of productive treatments. Furthermore, machine learning algorithms could assist to pinpoint new drug targets and

enhance personalized medicine by forecasting which therapy would possibly be effective for specific patients (Gomez-Rubio et al., 2018). As well as that, these developments can vastly upgrade our perception towards complex diseases such as cancer and provide a route for better diagnosis and treatment choices. This can help us venture into new technologies that could be helpful in abundant fields such as agriculture and land management.

To summarise, the improved data structures and algorithms will tend to give an accurate genetic data processing. Graph-based data structures and also machine learning methods have increased the accuracy in the results, reduced the processing costs and also allows for the identification of difficult genomic processes. These advancements have a greater chance of changing the genetic disciplines in terms of improved diagnosis and potential therapies for numerous diseases throughout the world.

CONCLUSION

Our comprehension of genetics and diseases has been augmented by recent discoveries and the development of genomic analysis techniques. These changes are the result of the so-called development of new sequencing techniques, computational methods and data structures that they enable the study of complex and extensive data sets. Arrays, linked lists, trees, charts, and shortcuts tables are just a few examples of data structures that played a key role in the development methods of genomic sequence analysis. They can handle huge volumes of genetic data, which was previously impossible. To advance our understanding of genetics and disease, genomics research and development sequence analysis and data formats are required. New calculation methods and data structures will be needed to organize and evaluate genetic data as it becomes more complex and extensive. Scientists will gain new insights into genetics and disease as a result of development of new tools and methods that can manage larger and more complex data sets.

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