Concepts of
Bioinformatics
1. Introduction
Bioinformatics is
the field of
science in which
biology, computer science,
and information technology merge to
form a single discipline. It is the emerging field that deals with
the application of
computers to the
collection, organization, analysis, manipulation,
presentation, and sharing of biologic data to solve biological problems on the molecular level. According to Frank Tekaia,
bioinformatics is the mathematical, statistical
and computing methods that aim to solve biological problems using DNA and amino acid sequences and related information.
Fig 1. Concepts of Bioinformatics
The
term bioinformatics
was coined by Paulien Hogeweg in 1979 for the study of
informatic processes in
biotic systems. The
National Center for
Biotechnology
Information
(NCBI, 2001) defines bioinformatics as: "Bioinformatics is
the field of
science in
which biology, computer
science, and information
technology merge into a single discipline.
There are three
important sub-disciplines within
bioinformatics: the development of new algorithms and statistics with
which to assess relationships among members of large
data sets; the
analysis and interpretation of
various types of data including
nucleotide and amino acid sequences, protein domains, and protein structures;
and the
development and implementation of
tools that enable
efficient access and management
of different types of information.”
Concepts of
Bioinformatics
Bioinformatics is a
scientific discipline that has emerged in response to accelerating demand for a
flexible and intelligent means of storing,
managing and querying large and complex biological data
sets. The ultimate aim of bioinformatics is to enable the discovery of new
biological insights as well
as to create a global perspective from which unifying principles in
biology can be discerned. Over
the past few decades rapid developments in
genomic and other molecular research technologies and
developments in information
technologies have combined to produce a tremendous amount of
information related to molecular biology. At the beginning of the
genomic revolution, the main
concern
of bioinformatics was
the creation and
maintenance of a
database to store
biological
information such as nucleotide
and amino acid sequences. Development of this type of database
involved not only design issues but the
development of an interface whereby researchers could both
access existing data
as well as
submit new or
revised data (e.g.
to the NCBI,
http://www.ncbi.nlm.nih.gov/). More
recently, emphasis has shifted towards the analysis of large
data sets, particularly those stored in
different formats in different databases. Ultimately, all of this information must be combined to form a
comprehensive picture of normal cellular activities so that researchers may
study how these activities are altered in different disease states. Therefore,
the field of bioinformatics has evolved such that the most pressing task now
involves the analysis and interpretation
of various types of data, including nucleotide and amino acid sequences,
protein domains, and protein structures.
2. Origin & History of Bioinformatics
Over a century ago, bioinformatics
history started with an Austrian monk named Gregor Mendel. He
is known as the ―Father of Genetics". He
cross-fertilized different colors of the same species of
flowers. He kept careful records of the colors of flowers that he
cross-fertilized and the color(s) of
flowers they produced. Mendel
illustrated that the
inheritance of traits
could be more
easily
explained if it was controlled by
factors passed down from generation to generation.
After this discovery of Mendel, bioinformatics and genetic record
keeping have come a long way.
The understanding of genetics has advanced remarkably in the
last thirty years. In 1972, Paul
Berg
made the first recombinant DNA molecule using ligase. In that same year,
Stanley Cohen, Annie Chang
and Herbert Boyer produced the first recombinant DNA organism. In 1973, two important things happened in
the field of genomics:
1.
Joseph Sambrook led a team that refined DNA electrophoresis using
agarose gel, and
2. Herbert Boyer
and Stanely Cohen invented DNA cloning. By 1977, a method for sequencing
DNA was discovered and the first
genetic engineering company, Genetech was founded.
During 1981,
579 human genes had been
mapped and mapping by in situ hybridization
had
become a standard method. Marvin Carruthers
and Leory Hood made a huge leap in bioinformatics
when they invented a
method for automated DNA
sequencing. In 1988, the
Human Genome
Organization (HUGO) was
founded. This is an international organization of scientists involved in
Human Genome Project. In 1989, the
first complete genome
map was published
of the
bacteria Haemophilus influenza .
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Concepts of
Bioinformatics
The following year, the
Human Genome Project was started. In 1991, a total of 1879 human genes had been mapped.
In 1993, Genethon,
a human genome
research center in
France produced roduced a
physical map of the human genome. Three years later, Genethon published the
final version of the Human Genetic Map which concluded the
end of the first phase of the Human Genome
Project.
Bioinformatics
was fuelled by the need to create huge databases, such as GenBank and EMBL and DNA Database of Japan to store and
compare the DNA sequence data erupting from the human genome and other genome sequencing projects. Today,
bioinformatics embraces protein structure analysis,
gene and protein functional information, data from patients, pre-clinical and
clinical trials, and the metabolic
pathways of numerous species.
3. Importance
The
greatest challenge facing the molecular biology community today is to make
sense of the wealth of
data that has been produced by the genome sequencing projects. Cells have a
central core called nucleus, which is storehouse of an important molecule known
as DNA. They are packaged in units known as chromosomes. They are together known as the genome. Genes are specific regions of the genomes (about
1%) spread throughout
the genome, sometimes
contiguous, many times noncontiguous. RNAs similarly contains
informations, their major purpose is to copy information from DNA selectively and to bring it out of
the nucleus for its use. Proteins are made of amino acids, which are twenty in count (researchers
are debating on increasing this count, as couple of new ones are claimed to be identified).
The gene
regions of the DNA in the nucleus of the cell is copied (transcribed) into the
RNA and RNA
travels to protein
production sites and is translated into proteins is the Central Dogma of
Molecular
Biology. Portions of DNA Sequence are
transcribed into RNA. The first step of a cell is to copy a
particular portion of its DNA nucleotide sequence (i.e. gene) which is shown in
Fig 2 and Fig 3.
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Fig 2. Biological Systems
Bioinformatics, being
an interface between
modern biology and
informatics it involves
discovery,
development and implementation of computational algorithms and software
tools that
facilitate an understanding of the biological
processes (Fig 3.) with the goal to
serve primarily
agriculture and
healthcare sectors with
several spinoffs. In
a developing country
like India,
bioinformatics has a key role to play
in areas like agriculture where it can be used for increasing
the nutritional content, increasing the volume of the agricultural produce and
implanting disease
resistance etc. In the pharmaceutical sector, it can be used to reduce
the time and cost involved in
drug discovery process particularly for
third world diseases, to custom design drugs and to develop
personalized medicine.
Fig 3. Biological Processes
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Fig 4. Information on Sequence Data
Traditionally,
molecular biology research was carried out entirely at the experimental
laboratory
bench but the huge
increase in the scale of data being produced in this genomic era has seen a
need
to incorporate computers into this research
process. Sequence generation, its subsequent storage,
interpretation and analysis are entirely
computer dependent tasks. However, the molecular biology of
an organism is a very complex issue
with research being carried out at molecular level. The first
challenge facing the bioinformatics community today is the intelligent
and efficient storage of this
massive data. Moreover, it is essential to
provide easy and reliable access to this data. The data itself
is meaningless before analysis and it is impossible for even a trained
biologist to begin to interpret it
manually. Therefore,
automated computer tools
must be developed
to allow the
extraction
of meaningful biological
information. There are three
central biological processes around which
bioinformatics tools must be
developed:
DNA sequence which determines protein
sequence
Protein sequence which determines protein
structure
Protein structure which determines protein
function
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Fig. 5. Hypothesis-generating bioinformatics
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4. Difference between Bioinformatics and
Computational Biology
Both
Bioinformatics and Computational
Biology are Computers
and Biology. Biologists
who
specialize in
use of computational
tools and systems
to answer problems
of biology are
bioinformaticians.
Computer scientists, mathematicians, statisticians, and engineers who
specialize in
developing theories,
algorithms and techniques
for such tools
and systems are
computational
biologists. The actual process of analyzing and
interpreting data is referred to as computational
biology. Important sub- disciplines within bioinformatics and computational
biology include:
the development and implementation of tools
that enable efficient access to, and use and
management of, various types of information.
the
development of new
algorithms (mathematical formulas)
and statistics with
which to
assess
relationships among members of
large data sets, such as methods to locate a gene
within
a sequence, predict protein structure and/or function, and
cluster protein sequences
into families of related sequences
Bioinformatics has become
a mainstay of
genomics, proteomics, and
all other *.omics (such as
phenomics) and many
information technology companies have entered the business or are considering
entering the business, creating an IT (information technology) and BT
(biotechnology) convergence. A bioinformaticist is an expert who not only knows how to use
bioinformatics tools, but also knows how to
write interfaces for effective use of the tools. A bioinformatician, on the other hand,
is a trained individual who only knows to use bioinformatics tools without a
deeper understanding.
5. Biological databases
Biological databases are huge data bases of mostly
sequence data pouring in from many genome sequencing
projects going on all over the world. They are an important tool in assisting
scientists to understand and explain a host of biological phenomena from the
structure of biomolecules and their interaction, to the whole metabolism
of organisms to understanding the evolution of species. This knowledge helps facilitate to fight against
diseases, assists in the development of medications and in discovering basic
relationships amongst species in the history of life.
The information
about DNA, proteins
and the function
of proteins must
be stored in an
intelligent
fashion, so that
scientists can solve problems
quickly and easily using all available
information. Therefore,
the information is stored in databanks, many of which are accessible to
everyone on the
internet. A few examples are a databank containing protein structures (the PDB
or
Protein Data Bank), a databank containing
protein sequences and their
function (Swiss-Prot), a
databank with information about enzymes and
their function (ENZYME), and a databank with
nucleotide sequences
of all genes
sequenced up to
date (EMBL). Due to the current state of
technology,
there are large differences between the sizes of databanks. EMBL, the
nucleotides
database contains many
more sequences than the number of protein structures registered in the PDB.
The reason for this is
that it is a lot simpler to sequence a gene, than to find out which protein is
encoded by this gene and
what its function is. Also it is more difficult to determine the structure of
the protein.
Using databanks,
one can perform
all kinds of
comparisons and search
queries. If, for
example, you know a protein which causes a disease in humans, your might look
into a databank to
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Concepts of
Bioinformatics
see if a similar protein has
previously been described and what this protein does in the human body.
Using known information
will make it easier and quicker to develop a drug against the disease or a test to detect the disorder in an early stage.
The Biological data can be broadly
classified as:
Biological
Databases Information
they contain
1.
Bibliographic databases Literature
2.
Taxonomic databases Classification
3.
Nucleic acid databases DNA
information
4.
Genomic databases Gene
level information
5.
Protein databases Protein
information
6. Protein families, domains and
functional sites Classification of
proteins and identifying
domains
7.
Enzymes/ metabolic pathways Metabolic
pathways
There are many different
types of database but for routine sequence analysis, the following are initially the most important
1. Primary
databases: Contain sequence data such as
nucleic acid or protein. Example of
primary databases include:
Protein
Databases Nucleic
Acid Databases
•
SWISS-PROT •
EMBL
• TREMBL • Genbank
• PIR •
DDBJ
2. Secondary databases: These are also known as
pattern databases contain results from the
analysis
of the sequences in the primary databases. Example of secondary databases
include: PROSITE, Pfam,
BLOCKS, PRINTS.
6. Introduction to NCBI and Entrez
The
web-site of National Center for Biotechnology Information (NCBI) is one of the
world's premier
website for biomedical
and bioinformatics research (http://www.ncbi.nlm.nih.gov/). Based
within
the National Library of Medicine at the
National Institutes of Health, USA, the NCBI hosts many
databases used by biomedical and research professionals. The
services include PubMed (the
bibliographic database); GenBank (the
nucleotide sequence database); and the BLAST algorithm
for sequence comparison, among many
others. It is established in 1988 as a national resource
for molecular biology
information. NCBI creates
public databases, conducts
research in
computational biology,
develops software tools
for analyzing genome
data, and disseminates
biomedical information all for the better understanding of molecular
processes affecting human
health and disease.
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Concepts of
Bioinformatics
Every
database has a unique identifier. Each entry in a database must have a unique
identifier EMBL Identifier
(ID), GENBANK Accession Number (AC). This database stores information along
with the sequence. Each
piece of information is written on it's own line, with a code defining the
line. For example, DE (description); OS (organism species); AC (accession number). Relevant biological
information is usually described in the feature table (FT).
Fig 6. International Sequence Database
Collaboration
7. The
Entrez Search and Retrieval System
Entrez
is the text-based search and retrieval system used at NCBI for all of the major
databases, including
PubMed, Nucleotide and
Protein Sequences, Protein
Structures, Complete Genomes, Taxonomy, OMIM, and many others. Entrez is at once an
indexing and retrieval system, a collection of data from many sources, and an organizing principle
for biomedical information. These general concepts are the focus of this section (Fig 7.).
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Fig 7. NCBI - RDBMS
8. The
Nucleotide Sequence database
The GenBank
sequence database is
an annotated collection
of all publicly
available
nucleotide sequences and their protein
translations. This database is produced at National Center for
Biotechnology Information
(NCBI) as part of an international collaboration with the European
Molecular
Biology Laboratory (EMBL)
as given in
Fig. 8, data library
from the European
Bioinformatics
Institute (EBI) and the DNA Data Bank of Japan (DDBJ) given in Fig. 9. GenBank
and its collaborators receive sequences
produced in laboratories throughout the world from more
than 100,000 distinct organisms. GenBank
continues to grow
at an exponential
rate, doubling
every
10 months. Release 134, produced in February 2003, and
contained over 29.3 billion
nucleotide bases
in more than 23.0
million sequences. GenBank is built by direct submissions
from individual laboratories, as well
as from bulk submissions from large-scale sequencing centers.
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Fig. 8 EMBL Nucleotide Sequence Database
Fig. 9. DNA Data Bank of Japan
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9. The
Bibliographic Database
PubMed is a database
developed by the NCBI. The database was
designed to provide
access to
citations (with abstracts)
from biomedical journals. Subsequently, a linking feature was added to
provide access to full-text journal
articles at Web sites of participating publishers, as well as to other
related Web resources. PubMed is the
bibliographic component of the NCBI's Entrez retrieval
system.
MEDLINE is
NLM's premier bibliographic database
covering the fields
of medicine, nursing, dentistry, veterinary medicine, and the
preclinical sciences. Journal articles are indexed for MEDLINE, and their
citations are searchable, using NLM's controlled vocabulary, MeSH (Medical Subject Headings). MEDLINE contains all citations
published in Index Medicus, and corresponds in part to the International
Nursing Index and the Index to Dental Literature.
10. Macromolecular Structure Databases
The
resources provided by NCBI for studying
the three-dimensional (3D)
structures of proteins
center
around two databases: the Molecular Modeling Database (MMDB), which provides
structural
information about
individual proteins; and the Conserved Domain Database (CDD), which provides
a directory
of sequence and
structure alignments representing conserved
functional domains
within proteins(CDs). Together,
these two databases
allow scientists to
retrieve and view
structures, find
structurally similar proteins to a protein of interest, and identify conserved
functional
sites.
11. Computer Programming in Bioinformatics: JAVA
in Bioinformatics
The geographical scattered
research centres all around the globe ranging from private to academic
settings, and a range of hardware and OSs are
being used, Java is emerging as a key player in
bioinformatics. Physiome Sciences'
computer-based biological simulation technologies and
Bioinformatics Solutions'
PatternHunter are two examples of the
growing adoption of Java in
bioinformatics.
12. Perl
in Bioinformatics
String
manipulation, regular expression matching, file parsing, data format
interconversion etc are
the common
text-processing tasks performed in bioinformatics. Perl excels in such tasks
and is being
used by many developers. Yet, there are no
standard modules designed in Perl specifically for the
field of bioinformatics. However, developers
normally designed several of their own individual
modules for any specific purpose, which have become quite popular and are
coordinated by the
BioPerl project.
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Concepts of
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13. Measuring biodiversity
Biodiversity
Databases are used to collect the species names, descriptions, distributions,
genetic
information, status &
size of populations, habitat needs, and how each organism interacts with
other species etc.
Computer simulations models are useful to study population dynamics, or
calculate
the cumulative genetic
health of a
breeding pool (in
agriculture) or endangered
population
(in
conservation). Entire DNA sequences or genomes of endangered species can be
preserved, allowing the
results of Nature's genetic experiment to be remembered in silico.
In these days of growing
human population and habitat destruction, knowledge of centers of high
biodiversity is critical for rational
conservation decisions to be made. The major problem area is that
this information is largely unavailable to the decision makers. It is ironic that most of these data
are in the great museums, which are located
in the cool temperate parts of the world whereas; most
of the organisms are in the warm humid
parts of the world. The data that exist
are paper based.
Descriptions by collectors and curators,
herbarium sheets, diagrams and photographs, and of course,
pickled and preserved specimens with their labels. If a researcher wishes to
consult these data he/she
has to travel to the museum in
question. For people who need a breadth of information to make
decisions, this is obviously not an
option. There are two areas in biology where enormous amounts
of information are generated. One is
in molecular biology which deals with base sequences in
DNA
and amino acid
sequences in proteins,
and the other
is the biodiversity information
crisis. Mathematics
and computers are
being used to
tackle these problems
with procedures
which come under the label of Bioinformatics.
Fig 10. Biodiversity Hotspots regions
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14. Sequence analysis and alignment
The
most well-known application of bioinformatics is sequence analysis. In sequence analysis, DNA
sequences of
various organisms are
stored in databases
for easy retrieval
and comparison. The
well-reported Human
Genome Project (Fig. 11) is
an example of
sequence analysis
bioinformatics.
Using massive computers and various methods of collecting sequences, the entire
human genome was sequenced
and stored within a structured database. DNA sequences used for bioinformatics
can be collected in a number of ways. One method is to go through a genome and search out individual sequences to record and store.
Another method is to compare all fragments for finding whole sequences by overlapping the redundant segments. The latter
method, known as shotgun sequencing,
is currently the most popular because of its ease and speed. By comparing known sequences of a genome to
specific mutations, much information can be assembled about undesirable
mutations such as
cancers. With the
completed mapping of
the human genome, bioinformatics has become very
important in the research of cancers in the hope of an eventual cure. Computers are also used to collect and store
broader data about species. The Species 2000 project, for example, aims to collect a large amount of
information about every species of plant, fungus, and animal on the earth. This information can then be
used for a number of applications, including tracking changes in populations and biomes.
Fig. 11. Human Genome Project
With the growing amount of
data, earlier it was impractical to analyze DNA sequences manually. Nowadays,
many tools and techniques are available provide the sequence comparisons
(sequence alignment) and analyze the alignment product to understand the
biology. For example, BLAST is used to
search the genomes of thousands of organisms, containing billions of
nucleotides. BLAST is software which
can do this using dynamic programming, as fast
as google searches for
your keywords, considering
the length of query words of bio-sequences.
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Concepts of
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Sequence
Alignment: The sequence
alignment can be categorized into two groups i.e. global and local alignment
Global Alignment
Input:
two sequences S1, S2
over the
same alphabet Output: two sequences S’1, S’2 of equal length
(S’1, S’2
are S1, S2 with possibly additional gaps)
Example:
u S1= GCGCATGGATTGAGCGA
u S2= TGCGCCATTGATGACC
u A possible alignment:
S’1= -GCGC-ATGGATTGAGCGA
S’2= TGCGCCATTGAT-GACC—
Local Alignment
Goal:
Find the pair of substrings in two input sequences which have the highest
similarity Input: two
sequences S1, S2
over the
same alphabet
Output: two sequences S‟ 1,
S‟
2 of equal length
(S’1, S’2 are
substrings of S1, S2 with possibly additional gaps)
Example:
u
S1= GCGCATGGATTGAGCGA
u S2= TGCGCCATTGATGACC
u A possible alignment:
S’1=
ATTGA-G
S’2=
ATTGATG
FASTA: In bioinformatics, FASTA format is a
text-based format for representing either nucleotide
sequences
or peptide sequences, in which base pairs or amino acids are represented using
single-
letter codes. The format also allows for sequence names and comments to precede
the sequences.
The FASTA format may be used to represent
either single sequences or many sequences in a single
file. A series of single sequences,
concatenated, constitute a multisequence file. A sequence in
FASTA format is represented as a
series of lines, which should be no longer than 120 characters and
usually do not exceed 80 characters.
This probably was because to allow for preallocation of fixed
line sizes in software: at the time, most users relied on DEC VT (or
compatible) terminals which
could display 80 or 132 characters per line.
Most people would prefer normally the bigger font in 80-
character modes and so it became the
recommended fashion to use 80 characters or less (often 70) in
FASTA lines. The first line in a FASTA file starts either with a
">" (greater-than) symbol or a ";"
(semicolon) and was taken as a comment.
Subsequent lines starting with a semicolon would be
ignored by software. Since the only comment used was the first, it
quickly became used to hold a
summary description of the sequence, often starting with a unique library
accession number, and
with time
it has become commonplace use to always use ">" for the
first line and to not use ";"
comments (which would otherwise be ignored).
>gi|5524211|gb|AAD44166.1|
cytochrome b [Elephas maximus maximus]
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Concepts of Bioinformatics
LCLYTHIGRNIYYGSYLYSETWNTGIMLLLITMATAFMGYVLPWGQMSFWGATVITNLFSA
IPYIGTNLVEWIWGGFSVDKATLNRFFAFHFILPFTMVALAGVHLTFLHETGSNNPLGLTSDS
DKIPFHPYYTIKDFLGLLILILLLLLLALLSPDMLGDPDNHMPADPLNTPLHIKPEWFLFAYAI LRSVPNKLGGVLALFLSIVILGLMPFLHTSKHRSMMLRPLSQALFWTLTMDLLTLTWIGSQP
VEYPYTIIGQMASILYFSIILAFLPIAGXIENY
15. Prediction of protein structure
Proteins play crucial functional roles in all biological
processes: enzymatic catalysis, signaling
messengers, structural elements. Function
depends on unique 3-D structure. It is
easy to obtain
protein sequences
but difficult to
determine structure. Protein
structure prediction is
another
important application of
bioinformatics. The amino acid sequence of a protein, the so-called primary
structure, can be easily determined
from the sequence on the gene that codes for it. In the vast
majority of cases, this primary structure uniquely determines a
structure in its native environment.
Knowledge of this structure is vital in understanding the function of the
protein. For lack of better
terms, structural information is usually
classified as one of secondary, tertiary and quaternary
structure. Protein structure prediction is the prediction of the
three-dimensional structure of a protein
from its
amino acid sequence i.e, the prediction of its tertiary structure from its
primary structure.
Protein structures are being determined with increasing speed. Consequently,
automated and fast
bioinformatics tools are required for
exploring structure-function relationships in large numbers of
proteins. These are necessary both
when the function has been characterized experimentally and
when it must be predicted.
Fig.
12. Protein Structure Prediction
In the genomic branch of bioinformatics,
homology is used to predict the function of a gene: if the
sequence of gene A, whose function is known, is
homologous to the sequence of gene B, whose
function is unknown, one could infer that B may share A's function. In
the structural branch of
bioinformatics,
homology is used to determine which parts of a protein are important in
structure
formation and interaction with other
proteins. In a technique called homology modeling, this
information is used to predict the
structure of a protein once the structure of a homologous protein is
known. One example of this is the similar protein homology between hemoglobin
in humans and the
hemoglobin in legumes (leghemoglobin). Both serve the same purpose of transporting
oxygen in the
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Concepts of Bioinformatics
organism.
Though both of these proteins have completely different amino acid
sequences, their protein structures are virtually identical,
which reflects their near identical purposes.
16. Molecular docking
Fig.
13 Protein-ligand Docking
In the last
two decades, tens
of thousands of
protein three-dimensional structures
have been
determined by X-ray crystallography and Protein nuclear magnetic resonance
spectroscopy (protein
NMR). One central question for the biological scientist is whether it is
practical to predict possible
protein-protein interactions only based on
these 3D shapes, without doing protein-protein interaction
experiments. A variety
of methods have
been developed to tackle the
Protein-protein docking
problem, though it seems that there is
still much work to be done in this field. We are interested in
information about our DNA, proteins
and the function of proteins. Genes and proteins can be
sequenced, so the sequence of bases in
genes or amino acids in proteins can be determined. This
information must be stored in an
intelligent fashion, so that scientists can solve problems quickly
and easily using all available
information. Therefore, the information is stored in databanks, many
of which are accessible to everyone on the internet. A few examples are a
databank containing
protein structures (the PDB
or Protein Data
Bank), a databank
containing protein sequences
and their function (Swiss-Prot), a databank with information
about enzymes and their function
(ENZYME), and a
databank with nucleotide
sequences of all
genes sequenced up
to date
(EMBL).
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17. Bioinformatics in Agriculture
The most critical tasks in bioinformatics
involves the finding of genes in the DNA sequences of
various organisms, developing methods to predict the structure and
function of newly discovered
proteins and structural
RNA sequences, clustering
protein sequences into
families of related
sequences, development of protein models,
aligning similar proteins and generating phylogenetic
trees to
examine evolutionary relationships. The sequencing of the genomes of microbes,
plants and
animals should have enormous benefits for the agricultural community.
Computational analysis of
these sequence data generated by genome
sequencing, proteomics and array-based technologies is
critically important. Bioinformatics tools can be used to search for the
genes within these genomes
and to elucidate their functions.
The sequencing of the genomes of plants and
animals should have enormous benefits for the agricultural community. Bioinformatic tools
can be used
to search for
the genes within
these genomes and to elucidate their functions. This specific genetic knowledge could
then be used to produce
stronger, more drought,
disease and insect
resistant crops and
improve the quality of livestock making them healthier,
more disease resistant and more productive.
18. Bioinformatics in India
Studies of IDC points out that India will be a
potential star in bioscience field in the coming years after considering
the factors like
bio-diversity, human resources,
infrastructure facilities and governments initiatives.
Bioinformatics has
emerged out
of the inputs
from several different
areas such as
biology, biochemistry, biophysics,
molecular biology, biostatics, and computer science. Specially designed algorithms and organized databases is the core of
all informatics operations. The requirements for such an activity make heavy and high level demands on both the hardware
and software capabilities. This sector
is the quickest growing field in the country.
The vertical growth is because of the linkages between IT and
biotechnology, spurred by the human genome project. The promising startups are already there in Bangalore, Hyderabad,
Pune, Chennai, and Delhi. There are over 200 companies functioning in these places. IT majors such as Intel, IBM, Wipro
are getting into this segment
spurred by the promises in technological developments.
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Fig.
14. Applications and Challenges in Bioinformatics
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References
1. Benson,D.A., Karsch-Mizrachi,I., Lipman,D.J., Ostell,J.,
Wheeler,D.L.(2005) GenBank.
Nucleic
Acids Research, 33, D34-D38.
2.
Bioinformatics in the 21st century (1998). A
report to the
research resources and
Infrastructure working
group subcommittee on
biotechnology national science
and Technology council white house office of science and technology
policy Bioinformatics.
3. Crick F. (1970). Central Dogma of Molecular
Biology. Nature, 227, 561-563.
5. Human genome project and beyond
(ww.ornl.gov/hgmis/)
6. Indigenous knowledge,
Bioinformatics and Rural
Agriculture. (2005). 9th ICABR
International Conference on Agricultural
Biotechnology: ten years later , Ravello (Italy), July
6 to July 10.
7.
Jayaram B and Priyanka D. Bioinformatics
for a better tomorrow. Department of chemistry
& Supercomputing facility for bioinformatics &
computational biology, Indian Institute of
Technology.
8.
Maglott D., Ostell
J., Pruitt K.
D. and Tatusova
T. (2005) Entrez
gene: gene-centered
information
at NCBI, Nucleic Acids Research,
33, D54-D58.
9. Mcentyre J.
and Ostell J. (2005). The NCBI Handbook. Bethesda (MD): National Library of
Medicine
(US)
10. McEntyre
Jo, Jim O.,
National Center for
Biotechnology Information Bethesda (MD):
National Center for Biotechnology Information (US); 2002. The
NCBI Handbook. Medicine (US), NCBI.
11. Ronald M. A., Knegtel, Irwin
D. Kuntz and Oshiro C. M. (1997). Molecular Docking to
Ensembles of Protein Structures. Journal
of Molecular. Biology. 266, 424-440
12. Wheeler,D.L., Benson,D.A., Bryant,S.,
Canese,K., Church,D.M., Edgar,R.,
Federhen,S.,
Helmberg,W., Kenton,D., Khovayko,O. et al. (2005) Database resources of the National
Center for Biotechnology
Information: Update. Nucleic Acid Research,
33, D39-D45.
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