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
E-mail: richa@presidentsciencecollege.org
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
|
Tools |
Utility |
|
BLAST Basic
Local Alignment Search Tool |
It
is an algorithm for comparing DNA, RNA, protein, or amino acid sequences
based on identity. https://blast.ncbi.nlm.nih.gov/Blast.cgi |
|
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. https://www.ncbi.nlm.nih.gov/orffinder/ |
|
HMMER Hidden
Markov Models |
Identification of homologous protein and nucleotide
sequences by performing sequence alignments. http://hmmer.org/ |
|
ProtParam |
Various physico-chemical properties of proteins can
be computed using this tool. https://web.expasy.org/protparam/ |
|
novoSNP 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. |
|
Sequerome |
Sequence
profiling can be performed using this tool. |
|
JIGSAW |
Genes and predict the splicing sites can be found
using this tool. |
|
Softberry |
Animal, plant, and bacterial genomes can be
annotated using this tool and the structure and function of RNA and proteins
can also be predicted. http://www.softberry.com/ |
|
PPP Prokaryotic
Promoter Prediction
Tool |
Promoter sequences lying upstream of bacterial genes
can be predicted using this tool. http://bamics2.cmbi.ru.nl/websoftware/ppp/ppp_start.php |
|
WebGeSTer 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. http://pallab.serc.iisc.ernet.in/gester/dbsearch.php |
|
Genscan |
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. http://www.prodoric.de/vfp/ |
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
|
Tools |
Utility |
|
MOLPHY Molecular Phylogenetics |
The
tool is based on the maximum likelihood method for phylogenetic analyses. https://sbgrid.org/software/titles/molphy |
|
PHYLIP Phylogeny Inference Package |
It
is a package of 35 portable computational phylogenetic programs. http://evolution.genetics.washington.edu/phylip/install.html |
|
MEGA Molecular
Evolutionary Genetic Analysis |
This
tool enables the construction of phylogenetic trees to find evolutionary
relationships. https://www.megasoftware.net/ |
|
Treeview |
Software
to view the phylogenetic trees can be viewed with the help of this software,
with an alternative of changing view. |
|
PAML Phylogenetic
Analysis by Maximum Likelihood |
It
analyzes phylogenetic relations based on maximum likelihood. |
|
Jalview |
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
|
Databases |
Utility |
|
DDBJ DNA
Data Bank of Japan |
It is an integral
member of the International Nucleotide Sequence Database Collaboration
(INSDC) that collects DNA sequences. |
|
GenBank |
It is a member of the
International Nucleotide Sequence Database Collaboration (INSDC) and is an
annotated collection of all publically available nucleotide sequences. https://www.ncbi.nlm.nih.gov/genbank/ |
|
European
Nucleotide Archive |
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. |
|
Rfam RNA
Families |
A collection of RNA families,
each represented by multiple sequence alignments, consensus secondary
structures and covariance models. |
Table 4: Genome Sequence Databases
|
Databases |
Utility |
|
Ensemble |
It contains annotated
genomes of eukaryotes including humans, vertebrates, and other model
organisms. https://m.ensembl.org/index.html |
|
PIR Protein
Information Resource |
It is the
largest, most comprehensive, annotated protein sequence database in the public
domain. https://proteininformationresource.org/ |
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.
|
Databases |
Utility |
|
SWISS
PROT |
It
is a part of UniProt knowledgebase that consists of annotated protein
sequences. http://www.ebi.ac.uk/swissprot/ |
|
Protein
Data Bank |
It
consists of experimentally-determined structures of nucleic acids and
proteins. https://www.rcsb.org/ |
|
Uniprot |
It
is one of the biggest collections of protein sequences. |
|
Prosite |
Collection
of protein families, conserved domains, and actives sites of proteins. http://www.expasy.org/prosite/ |
|
PRIDE PRoteomics
IDEntification Database |
It
is a public data repository of mass spectrometry-based proteomics data, containing
functional characterization and post-translation modification of proteins and
peptides. https://www.ebi.ac.uk/pride/ |
|
Pfam Protein
Families |
It
is a database of protein families. https://pfam.xfam.org/ |
|
InterPro |
Collection
of protein families, domains and functional sites for the functional
characterization of new protein sequences. |
Table 6:
Miscellaneous Databases
|
Databases |
Utility |
|
Reactome |
It is a database of reactions, pathways and
biological processes largely focused on humans and certain specific
organisms. https://reactome.org/ |
|
TAIR 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. https://www.arabidopsis.org/ |
|
Medherb |
It is an interactive database and analysis resource
for medicinally important herbs. |
|
Textpresso |
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. http://www.textpresso.org/tpc |
|
DictyBase |
Database for Dictyostelium discoideum. http://dictybase.org/ |
Table
7: Signaling and
Metabolic pathway Databases
|
Databases |
Utility |
|
CMAP 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. http://gmod.org/wiki/CMap |
|
PID 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. http://pid.nci.nih.gov |
|
KEGG 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. https://www.genome.jp/kegg/pathway.html |
|
HMDB 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. |
|
SGMP 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, http://www.biologie.unihamburg.de/bonline/library/genomeweb/GenomeWeb/prot-2-struct.html
(Table 8).
Table 8:
Protein structure and function prediction tools
|
Tools |
Utility |
|
PHD |
It
is a neural network system to predict protein secondary structure, relative
solvent accessibility and transmembrane helices. https://npsa-prabi.ibcp.fr/cgi-bin/npsa_automat.pl?page=/NPSAHLP/npsahlp_secpredphd.html |
|
MODELLER |
It
is used for homology or comparative modelling of protein 3-D structures. https://salilab.org/modeller/ |
|
RaptorX |
It
facilitates secondary, tertiary and contact prediction for protein sequences
without close homologs in the Protein Data Bank. |
|
CATH |
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. http://www.sbg.bio.ic.ac.uk/~phyre2/html/page.cgi?id=index |
|
JPred
|
It
is a protein secondary structure prediction server. Also, it predicts solvent
accessibility and coiled regions. |
|
HMMSTR Hidden
Markov Model for local sequence STRucture |
It
is a hidden Markov model to predict sequence-structure correlations in
proteins. http://www.bioinfo.rpi.edu/~bystrc/hmmstr/server.php |
|
APSSP
2 Advanced
Protein Secondary Structure Prediction Server |
Predicts
the secondary structure of proteins from their amino acid sequence. http://crdd.osdd.net/raghava/apssp/ |
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
|
TOOLS |
UTILITY |
|
PathBLAST |
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. http://www.pathblast.org/ |
|
AutoDock |
It
predicts protein-ligand interaction. http://autodock.scripps.edu/ |
|
STRING Search
Tool for the Retrieval of Interacting Genes/Proteins |
It
is a database of known and predicted protein-protein interactions. https://string-db.org/ |
|
BIND Biomolecular
Interaction Network Database |
It
defines the molecular interaction of proteins and bio-complexes. http://bind.ca |
|
IntAct |
It
is a database for the storage, presentation, and analysis of protein
interactions, both in textual and graphical formats. https://www.ebi.ac.uk/intact/ |
|
CFinder |
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. http://www.cfinder.org/ |
|
HADDOCK High
Ambiguity Driven DOCKing |
It
can deal with multiple molecules (for docking), a capability that will be
required to build large macromolecular assemblies. https://haddock.science.uu.nl/ |
|
MOE 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. https://www.chemcomp.com/Products.htm |
|
MIMO Molecular
Interaction Maps Overlap |
It offers a flexible and efficient
graph-matching tool for comparing complex biological pathways. |
|
Gremlin |
It can be used for multiple network
alignment that allows the generalization of existing alignment scoring schemes
and the location of conserved network topologies. http://gremlin.bakerlab.org/index.php |
|
SMART Simple
Modular Architecture Research Tool |
Used
for the identification and analysis of protein domains within protein
sequences. http://smart.embl-heidelberg.de/ |
|
MCODE 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. https://baderlab.org/Software/MCODE |
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
|
Databases |
Utility |
|
Therapeutic
Target Database |
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. http://bidd.nus.edu.sg/group/cjttd/ |
|
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. https://www.drugbank.ca/ |
|
DrugPort |
It provides an analysis of the
structural information available in the PDB, relating to drug molecules and
their protein targets. |
|
chEMBL |
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. https://www.ebi.ac.uk/chembl/ |
|
MATADOR 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. http://matador.embl.de/ |
|
TDR
Target Database 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.
https://tdrtargets.org/ |
|
TB
Drug Target Database |
It
contains information on anti-tubercular drugs and target proteins for the
treatment of Tuberculosis. |
|
PDTD 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
|
TOOLS |
UTILITY |
|
Discovery
Studio |
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. https://www.3dsbiovia.com/ |
|
FoldX |
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. |
|
Abalone |
It
is a molecular modelling program for performing biomolecular dynamics simulations
of proteins, DNA, and ligands. |
|
AMBER Assisted
Model Building with Energy Refinement |
It
is a set of molecular mechanical force fields for the simulation of
biomolecules. https://ambermd.org/ |
|
Ascalaph |
It
is a program for molecular building, graphics, dynamics, and optimization,
with an interface to quantum chemistry. http://www.biomolecular-modeling.com/Ascalaph/Ascalaph_Designer.html |
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.
CONTRIBUTION OF AUTHORS
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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