Review Article (Open access)

SSR Inst. Int. J. Life Sci., 9(2): 3195-3205, March 2023

Review on Applicability of Bioinformatics in Current Research and Database Management


Ishani Morbia1, Richa Dubey2*, Shivangi Mathur3

1Research Scholar, Department of Biotechnology, Indian Institute of Technology, Gandhinagar, Gujarat, India

2Assistant Professor, Department of Microbiology, President Science College, Affiliated to Gujarat University, Shayona Campus Ahmedabad, Gujarat, India

3Assistant Professor, Department of Biotechnology, President Science College, Affiliated to Gujarat University, Shayona Campus Ahmedabad, Gujarat, India


*Address for Correspondence: Dr. Richa Dubey, Assistant Professor, Department of Microbiology, President Science College, Affiliated to Gujarat University, Shayona Campus Ahmedabad, Gujarat, India



ABSTRACT- A generation of new science has evolved with the development of bioinformatics and computational biology, which have molecular biology as an integrated part. In the past decade, technological advances have promoted a prominent development in expertise and knowledge in the molecular basis of phenotypes. In Bioinformatics, biological data is evaluated by computational science and processed in a more statistical and meaningful way. It includes the collection classification storage and evaluation of biochemical and organic statistics using computers in particular as implemented in molecular genetics and genomics. Computational Biology and Bioinformatics are emerging branches of science and include the use of techniques and concepts from informatics statistics, mathematics, chemistry, biochemistry, physics and linguistics. Therefore, bioinformatics and computational biology have sought to triumph over many challenges of which a few are listed in this overview. This evaluation intends to provide insight into numerous bioinformatics databases and their uses in the analysis of biological records exploring approaches emerging methodologies strategies tools that can provide scientific meaning to the information generated.

Key Words: Data analysis, Databases, Genomics, Sequence analyses, Systems biology

INTRODUCTION- Biological science has evolved unprecedently with advances in technology, which has generated a large amount of ‘omic’ data [1]. Making sense of this large amount of data is a great challenge. Bioinformatics aims at developing tools and databases to facilitate researchers in understanding the functionality of the raw data [2]. 

As the data that is generated is heterogenous, it becomes quite important to segregate it into different databases. Also, various tools need to be developed to search and mine these databases. The application of computational tools is to organize, analyze, understand, visualize, and store information associated with biological macromolecules (Fig. 1). This review aims to present a brief overview of these tools and databases and their respective utilities in various aspects. We also seek to highlight various areas that bioinformatics has given rise to and aided too.

Fig. 1: Applied approaches of Bioinformatics

Organization of Information

Segregation into Databases- To make biological information (DNA, RNA, and Protein sequences) available for research, it is necessary to store them in an organized way. Primary databases are a collection of results of experimental databases, whereas Secondary databases are a compilation and interpretation of data obtained from primary databases [3]. GenBank at NCBI, DNA Database of Japan (DDBJ) and European Molecular Biology Laboratory (EMBL) are the main primary databases [2]. These databases share the deposited information with each other on daily basis [3]. Protein Information Resource (PIR), UniProt/ SwissProt, Protein Data Bank (PDB), and Prosite are secondary databases [4].


Tools and Database

Gene Identification and Sequence Analyses- Sequence analyses refer is the understanding of different aspects of biomolecules like nucleic acids or proteins, which gives unique function to it. First, the sequences of the respective molecule(s) are taken from public databases. They are then subjected to various tools for refinement and prediction of their features such as function, structure, evolutionary history, or identification of homologues [5]. The choice of tool to be used depends on the nature of the analysis to be done (Table 1).


Table 1: Primary sequence analyses tools




Basic Local Alignment Search Tool

It is an algorithm for comparing DNA, RNA, protein, or amino acid sequences based on identity.

ORF Finder

Open Reading Frame Finder

It is a program that identifies all open reading frames or the possible protein-coding regions in a sequence.


Hidden Markov Models

Identification of homologous protein and nucleotide sequences by performing sequence alignments.


Various physico-chemical properties of proteins can be computed using this tool.


Single Nucleotide Polymorphisms

Single nucleotide polymorphisms in the DNA can be found using this tool.

Clustal Omega

This tool enables us to perform multiple sequence alignments.


Sequence profiling can be performed using this tool.


Genes and predict the splicing sites can be found using this tool.


Animal, plant, and bacterial genomes can be annotated using this tool and the structure and function of RNA and proteins can also be predicted.


Prokaryotic Promoter

Prediction Tool

Promoter sequences lying upstream of bacterial genes can be predicted using this tool.



Web Genome Scanner for Terminators

Transcription terminator sequences are contained in this database, which helps in the prediction of termination sites of the genes during transcription.


Predicts intron and exon sequences within the genome.

Virtual Footprint

Allows recognition of single or composite DNA patterns. Enables prediction of genome-based regulons and analysis of individual promoter regions.

Phylogenetic analyses- Phylogenetic analyses are used to infer evolutionary relationship among a group of related molecules or organisms, for the prediction of unknown functions, to determine gene flow, and to establish genetic relatedness. This can then be used in creating a phylogenetic tree. The principle of phylogeny is to group living organisms according to the degree of similarity: the higher the similarity, the closer the organisms would appear on the tree. A phylogenetic tree can be constructed by the following methods: distance methods, parsimony methods, and likelihood methods [6] (Table 2).


Table 2: Phylogenetic Analysis Tools




Molecular Phylogenetics

The tool is based on the maximum likelihood method for phylogenetic analyses.


Phylogeny Inference Package

It is a package of 35 portable computational phylogenetic programs.


Molecular Evolutionary Genetic Analysis

This tool enables the construction of phylogenetic trees to find evolutionary relationships.


Software to view the phylogenetic trees can be viewed with the help of this software, with an alternative of changing view.


Phylogenetic Analysis by Maximum Likelihood

It analyzes phylogenetic relations based on maximum likelihood.


It helps in the refinement of multiple performed alignments.


Sequence Databases- With the advancement of high throughput sequencing techniques, a massive amount of data is generated every day. To make this data freely available to the scientific community, Primary, Secondary, or Composite databases are constructed. The data in a primary database is experimental, a secondary database contains curated information and a composite database contains information from different primary sources (Table 3).


Genome Sequence Databases- The GenBank, built by the NCBI, collects genome sequences of over 2,50,000 species. Each sequence carries information about the literature, bibliography, organism, and a set of various other features, which include coding regions, promoters, untranslated regions, terminators, exons, introns, repeat regions, and translations (Table 4).


Table 3: Nucleotide Sequence Databases




DNA Data Bank of Japan

It is an integral member of the International Nucleotide Sequence Database Collaboration (INSDC) that collects DNA sequences.


It is a member of the International Nucleotide Sequence Database Collaboration (INSDC) and is an annotated collection of all publically available nucleotide sequences.

European Nucleotide


It is a collection of information related to experimental workflows based on nucleotide sequencing and a comprehensive record of sequence assembly information and functional annotation.


RNA Families

A collection of RNA families, each represented by multiple sequence alignments, consensus secondary structures and covariance models.


Table 4:  Genome Sequence Databases




It contains annotated genomes of eukaryotes including humans, vertebrates, and other model organisms.


Protein Information Resource

It is the largest, most comprehensive, annotated protein sequence database in the public domain.

Protein Sequence Databases- The most significant protein sequence databases are SWISS-PROT (Swiss Protein) Databank, TrEMBL (translation of DNA sequences in EMBL), UniProt (Universal Protein Resource), PIR (Protein Information Resource) and wwPDB (worldwide Protein DataBank) [7] (Table 5).

Table 5: List of protein sequence databases.




It is a part of UniProt knowledgebase that consists of annotated protein sequences.

Protein Data Bank

It consists of experimentally-determined structures of nucleic acids and proteins.


It is one of the biggest collections of protein sequences.


Collection of protein families, conserved domains, and actives sites of proteins.


PRoteomics IDEntification


It is a public data repository of mass spectrometry-based proteomics data, containing functional characterization and post-translation modification of proteins and peptides.



Protein Families

It is a database of protein families.


Collection of protein families, domains and functional sites for the functional characterization of new protein sequences.


Table 6: Miscellaneous Databases




It is a database of reactions, pathways and biological processes largely focused on humans and certain specific organisms.


The Arabidopsis Information Resource

It is a community resource and online model organism database of genetic and molecular biology data for the model plant Arabidopsis thaliana.


It is an interactive database and analysis resource for medicinally important herbs.


It is an online literature search and curation platform that enables biocurators to mine full-text literature searches of model organism research and to identify new allele and gene names and human disease gene orthologs.


Database for Dictyostelium discoideum.

Table 7: Signaling and Metabolic pathway Databases




Complement Map Database

It is a resource that uses transcriptional expression data to probe the relationship between diseases, cell physiology and therapeutics and thus generate gene expression profiles.


Pathway Interaction Database

It is a growing collection of human signalling and regulatory pathways curated from peer-reviewed literature. It can be used to study various cellular pathways, especially those related to cancer.


Kyoto Encyclopedia of Genes and Genomes

It is a collection of manually drawn pathway maps representing molecular interaction, reaction and relation networks for metabolism, cellular processes, human diseases, drug development, organismal processes, environmental information processing and genetic information processing.



Human Metabolome Database

It contains detailed information about small molecule metabolites found in the human body. It is intended to be used in applications in metabolomics, clinical chemistry, and biomarker discovery. The database is designed to contain or link three kinds of data: 1) chemical data, 2) clinical data and 3) molecular biology/biochemistry data.


Signalling Gateway Molecule Pages

It provides structured data on proteins which exist in different functional states participating in signal transduction pathways.

Protein structure and function prediction Databases- Proteins must fold up into a three-dimensional (3D) structure to become biologically active. So, insight into protein 3D structure is required to know its function. 3D structures are normally determined by X-ray crystallography or NMR. But as these techniques are costly, difficult and time-consuming, a protein's 3D structure can be predicted using various bioinformatics tools. These approaches help in the easy identification of the secondary structure of protein sequences like helices, sheets, domains, strands and coils. The most widely used approach to predict the 3D structure of a protein molecule is comparative modelling. In this approach, a related known sequence (with at least 30% sequence identity with target protein) is selected to predict the unknown structure [8]. The below given link is a list of protein prediction tools, (Table 8).

Table 8: Protein structure and function prediction tools




It is a neural network system to predict protein secondary structure, relative solvent accessibility and transmembrane helices.


It is used for homology or comparative modelling of protein 3-D structures.


It facilitates secondary, tertiary and contact prediction for protein sequences without close homologs in the Protein Data Bank.


Based on Class, Architecture, Topology & Homology, it is a hierarchical domain classification of protein structures in the PDB.

Phyre & Phyre 2

Protein Homology/Analogy Recognition Engine

It investigates known homologues, builds a hidden Markov model (HMM) of the targeted sequence based on the detected homologues and scans it against a database of HMMs of known protein structures.



It is a protein secondary structure prediction server. Also, it predicts solvent accessibility and coiled regions.


Hidden Markov Model for local sequence STRucture

It is a hidden Markov model to predict sequence-structure correlations in proteins.



Advanced Protein Secondary Structure Prediction Server

Predicts the secondary structure of proteins from their amino acid sequence.

Molecular interactions Databases- Discovering interaction among molecules is important to elucidate their biological function. Protein-protein interactions are vital for cellular activities like signalling, transportation, metabolism, etc. Bioinformatics can predict protein-protein interactions without the involvement of costly, and time-consuming methods like X-ray crystallography and Nuclear Magnetic Resonance (NMR) spectroscopy. The parameters influencing protein-protein interactions are then studied [9]. A list of selected tools to study protein-protein interactions is given in Table 9.


Table 9: Molecular Interactions study tool




It is a network alignment and search tool for comparing protein interaction networks across species to identify protein pathways and complexes that have been conserved by evolution.


It predicts protein-ligand interaction.


Search Tool for the Retrieval of Interacting Genes/Proteins

It is a database of known and predicted protein-protein interactions.


Biomolecular Interaction Network Database

It defines the molecular interaction of proteins and bio-complexes.


It is a database for the storage, presentation, and analysis of protein interactions, both in textual and graphical formats.


It is a program for locating and visualizing overlapping, densely inter-connected groups of nodes in undirected graphs and allowing the user to easily navigate between the original graph and the web of these groups. It can be used to predict the function of a single protein and to discover novel modules.


High Ambiguity Driven DOCKing

It can deal with multiple molecules (for docking), a capability that will be required to build large macromolecular assemblies.



Molecular Operating Environment

It is an integrated drug discovery software. It tracks design ideas and ligand modifications with property models, produces correlation plots to visualize structure, property, activity relationships and visualize hydrophobic and charged protein surface to study aggregation-prone regions.


Molecular Interaction Maps Overlap

It offers a flexible and efficient graph-matching tool for comparing complex biological pathways.



It can be used for multiple network alignment that allows the generalization of existing alignment scoring schemes and the location of conserved network topologies.


Simple Modular Architecture Research Tool

Used for the identification and analysis of protein domains within protein sequences.


Molecular COmplex Detection

It is a graph theoretic clustering algorithm that detects densely connected regions in large protein-protein interaction networks that may represent molecular complexes.


Drug designing Databases- As the traditional process of drug discovery is quite slow and expensive, bioinformatics tools have been developed to achieve the same. The process can be divided into four different steps: identification of drug target, validation of target, lead identification, and lead optimization [10]. The target is a small biomolecule upon which the drug molecule acts to produce a desired effect. So, the first step in the drug-designing process is the identification of a target. Many databases have been developed for the search for new drug targets. After the selection of potential targets, the role of those targets in a particular disease is studied. This is called target validation. Bioinformatics tools for modelling enable the prediction of the efficiency of compounds to bind at a particular site [11]. Then a certain compound-lead compound is to be found which can alter the action of the target. Bioinformatics tools allow the virtual screening of a large number of compounds that could manipulate a protein. Many times, the identified compound does not have the required properties, but it can be 'refined' to produce the desired effect with reduced side effects. This process is called 'lead optimization’ (Table 10).


Table 10: Drug-Target interaction study databases



Therapeutic Target


It is a database to provide information about known and explored therapeutic protein and nucleic acid targets, the targeted disease, pathway information and corresponding drugs directed at each of these targets.

Drug Bank

It is a comprehensive database containing information on drugs and drug targets. It combines detailed drug data i.e. chemical, pharmacological and pharmaceutical with comprehensive drug target information i.e. sequence, structure and pathway.


It provides an analysis of the structural information available in the PDB, relating to drug molecules and their protein targets.


It is a manually curated database of bioactive molecules with drug-like properties. It brings together chemical, bioactivity and genomic data to aid the translation of genomic information into effective new drugs.


Manually Annotated Targets and Drugs Online Resorce

It is a database for protein-chemical interactions. It differs from DrugBank in its inclusion of as many direct and indirect interactions as we could find. DrugBank usually contains only the main mode of interaction.

TDR Target


Tropical Disease Research

It facilitates rapid identification and prioritization of molecular targets for drug development, focusing on pathogens responsible for neglected human diseases. It integrates pathogen-specific genomic information with functional data i.e. expression, and phylogeny for genes collected from various sources.

TB Drug Target


It contains information on anti-tubercular drugs and target proteins for the treatment of Tuberculosis.


Potential Drug Target Database

It associates informatics data with structural database of known and potential drug targets. It focuses principally on drug targets with known 3-D structures.

Molecular dynamic simulation Databases- Biological activities occur due to molecular interactions in a time-dependent manner. The time dependency of a molecule can be studied bioinformatics tools called Molecular Dynamics Simulations (MDS). These tools provide detailed information on fluctuations, dynamic cellular processes, and conformational changes of proteins and nucleic acids. They also help in determining structures from experimental approaches like XRD and NMR spectroscopy [12] (Table 11).


Table 11: Molecular Simulation study tools





It is a suite of software for simulating small molecules and macromolecular systems, ligand design, pharmacophore modelling, structure-based design, macromolecule design and validation, macromolecule engineering and predictive toxicity.


It can be used for the prediction of the effect of point mutations or human SNPs on protein stability or protein complexes and to design proteins to improve stability or modify affinity or specificity.


It is a molecular modelling program for performing biomolecular dynamics simulations of proteins, DNA, and ligands.


Assisted Model Building with Energy Refinement


It is a set of molecular mechanical force fields for the simulation of biomolecules.


It is a program for molecular building, graphics, dynamics, and optimization, with an interface to quantum chemistry.

Applications of Bioinformatics Databases

Human Genome Project- Human Genome Project (HGP) was aimed towards sequencing the human genome and mapping every gene on every chromosome and developing tools for storing and analyzing this information. HGP employed the shotgun sequencing technique for whole genome sequencing. The enormous amount of data that was generated during this process was segregated, curated, and stored in various functional bioinformatics databases.

e.g. Functional Mapping: Agricultural, evolutionary, and biomedical genetic research is requiring the knowledge of genetic controls governing various phenotypes. Quantitative trait loci (QTLs) responsible for a complex trait can be known using a statistical mapping framework, called functional mapping [8,13].

Oncology- Oncology is the study of tumour cells and tumour environment. It is a big challenge to discover the molecular and cellular mechanisms underlying tumour metastasis. Analysing alterations of protein levels in the tumour and correlating it to metastasis helps in facilitating the development of therapeutic strategies and clinical management of cancer. Biomarker prediction and discovery also remain an important aspect here [14].

e.g. The Cancer Genome Atlas: The Cancer Genome Atlas (TCGA) holds tumour gene expression data, along with clinical information, which enables researchers to gather information on prominent genomic alterations occurring during the development and metastasis of a tumour.


Gene therapy- Gene therapy is a method of efficient introduction of a functional gene into the cells of the patient to cure diseases related to the deficiency or over-production of that gene product. These procedures primarily require knowledge of the organism’s annotated genome, which is provided by bioinformatics [15].

SNP Detection- A single nucleotide polymorphism (SNP) results due to variation of a single nucleotide at a particular position in the genome. It has been established that SNPs are associated with the susceptibility of the individual to specific diseases. Human genome sequences shed light on such SNP data associated with certain diseases and have led towards the development of predictive preventive personalized medicine [8].


Personal medicine- Personalized medicine is based upon an individual's genetic makeup to decide the amount and type of medications to be prescribed for the prevention and treatment of disease [16]. Translational bioinformatics is a field which deals with this area of healthcare. Research in personalized medicine aims to discover solutions based on the susceptibility profile of everyone [17].

RNA Sequencing- Genome-wide gene expression and regulatory mechanisms underlying basic physiological traits of various human pathologies are nowadays studied using RNA-Seq experiments. But, as these are complex analyses, the processing of the obtained data requires the assistance of various bioinformatics tools [8].

BBB Permeation- Prediction of blood-brain barrier (BBB) permeation is vital for designing drug molecules acting on the central nervous system (CNS). The process of permeation is complicated as compounds can cross the BBB both by passive diffusion and/or active transport. Hence, as an alternative to invasive animal experiments, in silico-screening methods have been developed for designing central nervous system active drugs by establishing their BBB permeation [8].

Agriculture- Stressful conditions lead to reduced plant growth, delayed seed germination, and decreased crop yield. Organ-specific proteomic analyses can be used to identify proteins that accumulate in plants under such conditions [18]. These conditions can then be subjected to genetic engineering to produce stress-resistant plant varieties [19].

Insect Resistance- Insect resistance was introduced in many plants by incorporating certain genes. An insect-killing gene was isolated from the genome of a bacteria called Bacillus thuringiensis and was incorporated into plants to make them insect-resistant. Corn, cotton, brinjal, soybean and potatoes have been made insect resistant so far.

Nutritional Quality- Increasing population demands a higher supply of food, but as agricultural land is limited, the solution to overcome this issue is to produce nutritionally enriched and enhanced food [20]. Golden rice is an important achievement in this area. Here, the genes to increase Vitamin A levels are increased in the crop. This has solved the problem of malnutrition quite well [21].

Radioactive waste clean-up- Bioinformatics tools are important to understand various metabolic pathways [22]. The bio-degradative pathways in the bacteria Deinococcus radiodurans were explored using these tools. It was then used to break down organic chemicals, solvents, and heavy metals in radioactive waste sites.

Forensic Science- Forensic science includes the study regarding identification and relatedness of individuals. Conventional techniques include fingerprinting and others. These have now advanced to DNA fingerprinting techniques, which use bioinformatics tools and techniques [23]. DNA fingerprinting works on the principle of comparison of repetitive DNA sequences which are unique to everyone. Criminal databases store DNA profiles of respective individuals to be compared [24].

Bioenergy/Biofuels- Bioinformatics aids in the understanding of biofuel-producing pathways. Recent studies in algal genomics, along with other 'omics' approaches, have proved to be potential targets in the development of genetically engineered microalgal strains producing biofuels [25].

Antibiotic resistance- Enterococcus faecalis is known to cause infection, attributing to a virulence region comprising of antibiotic-resistant genes contributing to the bacterium’s transformation from a harmless gut bacterium to a pathogen. The Discovery of such useful biomarkers for detecting pathogenic strains can establish controls to prevent the spread of infection.


CONCLUSIONS- Bioinformatics aids modern-day biology by sorting big biological data into functional databases and uncovers various aspects of different biomolecules. It provides scopes for the development of crucial fields such as drug development and screening, genetic engineering, genome annotation and others.

There is merely any area which remained untouched by bioinformatics and computational biology and thus the bright future of Biology will have a lot to owe to it.


Acknowledgement- The authors gratefully acknowledge guides and mentors from President Science College for their valuable guidance and support.



Research article concept- Dr. subey

Research design- Ms. Ishasni Morbia

Supervision- Dr. Shivangi Mathur

Data analysis and interpretation- Ms. Ishasni Morbia

Literature search- Ms. Ishasni Morbia

Writing article- Ms. Ishasni Morbia

Critical review- Dr. Shivangi Mathur

Article editing- Dr. Richa Dubey

Final approval- Dr. Shivangi Mathur


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