Introduction to Bioinformatics and Basic Concepts

Bioinformatics is a multidisciplinary branch of science that deals with the analysis, storage and processing of biological data (especially genome and proteome data) using computer technology and mathematical approaches. Bioinformatics is at the intersection of branches of biology such as genetics, proteomics and molecular biology and technical disciplines such as computer science and statistics (1). Bioinformatics is used to better understand biological systems and obtain meaningful results from complex data. For example, it interprets DNA and RNA sequences obtained from genome sequencing projects, studies cellular mechanisms by modeling protein structures and functions, and performs data analysis to understand disease mechanisms or discover biomarkers. Bioinformatics operates in various subfields. Sequence analysis covers the alignment and comparison of DNA, RNA and protein sequences, while structural bioinformatics focuses on the prediction and analysis of three-dimensional structures of proteins and nucleic acids. Genomics and epigenomics analyze the organization of genetic material and the functions of epigenetic changes, while transcriptomics examines gene expression profiles using technologies such as RNA-Seq (2). These subfields diversify the approach of bioinformatics to biological data and provide solutions to many scientific questions. Bioinformatics has a wide range of applications. In medical genetics, the analysis of genetic variations associated with diseases and the development of personalized treatment methods are provided. In cancer research, it is used to examine tumor genomes and discover biomarkers. In agricultural biotechnology, disease-resistant and productive plant species are developed in light of genomic data. In addition, environmental microbial communities are analyzed through metagenomic studies and potential drug candidates are identified by modeling protein-ligand interactions in drug discovery processes (3).

Figure 1. The image highlights the interdisciplinary nature of bioinformatics. Biological molecules, data, graphics, and tools arranged around DNA symbolize the technologies and branches of science used in processing genetic information. (Image generated using artificial intelligence).

History of Bioinformatics

The first algorithm that could be used to compare protein or DNA sequences was published by Needleman and Wunsch in 1970 (4). In 1977, the genome of the bacteriophage ΦX174 was first completely sequenced (5). The term bioinformatics was first coined in 1978 and defined as "Bioinformatics for the Study of Computational Processes in Biotic Systems" (6). In 1981, an algorithm for local sequence alignment was published by Smith and Waterman (7). In 1986, the polymerase chain reaction (PCR) technique was developed (8). This technique made it possible to amplify a specific region of DNA millions of times, revolutionizing genetic research. In the same year, the term genomics was coined by Tom Roderick to describe the large-scale study of genes and genetic material (9). The Human Genome Project was launched in 1990 and greatly encouraged the development and use of bioinformatics tools (10). In 1995, the complete genome sequence of the bacterium Haemophilus influenzae was published, the first time that a living organism’s genome was completely sequenced (11). By 2000, the Human Genome Project was largely complete, and bioinformatics had become a discipline that forms the basis of fields such as genomics, proteomics, and systems biology (10). In particular, databases (e.g., GenBank) and analysis tools (e.g., BLAST) have enabled scientists to easily analyze large biological data sets. In the following years, bioinformatics has been further integrated with technologies such as artificial intelligence and machine learning. Bioinformatics has also begun to play a key role in the field of genetic engineering, especially with the development of gene editing technologies such as CRISPR-Cas9 (12). In addition, personalized medicine and big data analytics continue to expand the application areas of bioinformatics.

Some Basic Terms Used in Bioinformatics

Base Pair (bp): It refers to the pairing of two mutual nucleotides found in the structure of the DNA molecule. It is usually used to express the length of DNA sequences; for example, 200bp is a DNA sequence that is 200 base pairs long (13). Kilobase (kb) refers to 1000 base pairs. It is used to indicate the length of large DNA or RNA segments in genomic and genetic studies (14). Megabase (Mb) refers to 1 million base pairs. It is used to describe genetic lengths at the chromosome level (15). Gigabase (Gb) refers to 1 billion base pairs. It is used to describe entire genome lengths (16).

Codon: In DNA or RNA, units consisting of three nucleotides that code for a specific amino acid. For example, the AUG codon codes for the amino acid methionine and initiates protein synthesis (17).

Anticodon: A triple nucleotide sequence found in the structure of tRNA that matches the codon of mRNA. It plays an important role in transporting amino acids to the ribosome during protein synthesis (18).

ORF (Open Reading Frame): A DNA or RNA sequence that starts at a start codon (e.g., AUG) and ends at a stop codon (e.g., UAA, UAG, UGA) (19).

Promoter: A region of DNA that initiates gene expression. Promoter regions allow RNA polymerase to bind and control the start of transcription (20).

Introns: Sections of a gene that do not code for protein but are cut out of RNA (21).

Exons: Sections of a gene that code for protein or are converted into a functional RNA molecule. After transcription, exons are combined to form mature RNA (22).

Genome: Refers to the entire genetic material of an organism. The genome includes both coding (contributing to protein synthesis) and non-coding DNA regions. The branch of science that studies the structure, function, and evolution of genomes is called genomics (25).

Proteome: The total of all proteins in an organism. The proteome includes all proteins expressed at a given time or under a given condition. The branch of science that studies the proteome is called proteomics (26).

Transcriptome: The total of all RNA molecules expressed in a cell at a given time. The transcriptome is critical for understanding gene expression levels and gene regulation. The branch of science that analyzes transcriptomes is called transcriptomics (27).

Metabolomics: It deals with the analysis of all small molecules (metabolites) found in a cell, tissue, or organism. Metabolomics is used to understand the functioning of metabolic pathways and the organism's response to environmental changes (28).

Metagenomics: Analyzes the genetic material of all microorganisms (such as bacteria, viruses, and archaea) in an environmental sample. Metagenomics is used to study microbial diversity and the functional properties of ecosystems (29).

Viromics: Analyzes the genetic material of all viruses found in an organism or the environment. Viromics is used to understand viral diversity and discover new virus species (30).

Indel: The addition (insertion) or deletion (deletion) of one or more bases in a DNA sequence (23).

Gene: A unit of genetic information located in an organism's DNA that is responsible for the production of a protein or RNA molecule. Genes carry the codes necessary for the synthesis of proteins and form the basic building blocks of biological functions (24).

Epigenomics: Examines how gene expression is regulated by analyzing DNA methylation, histone modifications, and other epigenetic changes (31).

Phylogenetics: The branch of science that studies the evolutionary relationships between species or genes. Phylogenetic analyses use phylogenetic trees to understand the evolutionary history of organisms (32).

Figure 2. An example phylogenetic tree image (33).

Sequence Alignment: The process of comparing DNA, RNA, or protein sequences to determine similarities (34).

Global Alignment: Compares the entire sequence (e.g., Needleman-Wunsch algorithm) (35).

Local Alignment: Focuses only on similar regions (e.g., Smith-Waterman algorithm) (36).

Homology: Describes the similarities between two biological sequences (DNA, RNA, or protein) that result from a common ancestor. Homologous sequences usually have similar functions (37).

SNP (Single Nucleotide Polymorphism): Genetic variations resulting from a change in a single nucleotide in the DNA sequence. SNPs are important sources of genetic diversity and are used to determine disease risks (25).

NGS (Next Generation Sequencing): Next generation sequencing technologies allow DNA or RNA sequences to be read quickly and with high accuracy. Examples of NGS technologies include Illumina, Oxford Nanopore, Ion Torrent, etc. (38).

Coverage Depth: In NGS projects, it refers to the number of times a genome region is sequenced. High coverage depth provides more accurate sequencing. For example, 30x coverage means that each region of the genome is sequenced an average of 30 times (39).

Database: These are systems where genetic and biological information is stored in bioinformatics. GenBank can be given as an example (40).

Some Software and Tools Used in Bioinformatics

Alignment and Genomic Analysis Tools

BLAST: It is one of the basic bioinformatics tools used to perform similarity analysis by comparing DNA, RNA or protein sequences (41).

Clustal Omega: It is used for multiple sequence alignment (42).

BWA (Burrows-Wheeler Aligner): It is a fast tool used for aligning short genomic sequences with long reference genomes (43).

Analysis of Transcriptomic Data

tRNAscan-SE: Used to find and analyze tRNA genes (44).

HISAT2: A rapid tool for aligning RNA sequences (RNA-Seq) to reference genomes (45).

Cufflinks: Used to estimate gene expression levels from RNA-Seq data (46).

StringTie: Provides transcript-level gene expression analysis and combined transcript annotation (47).

Phylogenetic and Evolutionary Analysis

MEGA (Molecular Evolutionary Genetics Analysis): Used in phylogenetic analyses and molecular evolution studies (48).

PhyML: Creates phylogenetic trees using the maximum likelihood method (49).

RAxML: Used in phylogenetic analyses of large data sets (50).

Metagenomic Analysis

QIIME: A toolkit used to analyze microbial samples (51).

Kraken: A software used for rapid and accurate metagenomic sequencing classification (52).

Metabat: Used to predict individual genomes from metagenomic sequences (53).

Some Databases Used in Bioinformatics

NCBI (National Center for Biotechnology Information): The official source of tools and databases such as BLAST, GenBank (54).

ENSEMBL: ENSEMBL focuses specifically on vertebrate genomes and provides information such as gene predictions, variations, and regulatory elements. It also provides tools for comparative analysis of genomes (55).

UCSC Genome Browser: It presents genome maps in an interactive manner and allows users to examine data such as genes, transcripts, variations, and regulatory regions. It is one of the most commonly used browsers for the human genome (56).

KEGG (Kyoto Encyclopedia of Genes and Genomes): KEGG focuses specifically on biochemical pathways (metabolic pathways, signal transduction pathways, etc.) and focuses on the functional analysis of genes, proteins, and metabolites (57).

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