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What is bioinformatics? An introduction and overview


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A flood of data means that many of the challenges in biology are now challenges in computing. Bioinformatics, the application of computational techniques to analyse the information associated with biomolecules on a large-scale, has now firmly established itself as a discipline in molecular biology, and encompasses a wide range of subject areas from structural biology, genomics to gene expression studies. In this review we provide an introduction and overview of the current state of the field. We discuss the main principles that underpin bioinformatics analyses, look at the types of biological information and databases that are commonly used, and finally examine some of the studies that are being conducted, particularly with reference to transcription regulatory systems. 2. Introduction Biological data are flooding in at an unprecedented rate (1). For example as of August 2000, the GenBank repository of nucleic acid sequences contained 8,214,000 entries (2) and the SWISS-PROT databas...
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What is bioinformatics? An introduction and overview
Nicholas M Luscombe, Dov Greenbaum & Mark Gerstein*
Department of Molecular Biophysics and Biochemistry
Yale University, 266 Whitney Avenue
PO Box 208114, New Haven CT 06520-8114, USA
* - corresponding author
For IMIA 2001 Yearbook
Web version
1. Abstract
A flood of data means that many of the challenges in biology are now challenges in computing.
Bioinformatics, the application of computational techniques to analyse the information associated
with biomolecules on a large-scale, has now firmly established itself as a discipline in molecular
biology, and encompasses a wide range of subject areas from structural biology, genomics to gene
expression studies.
In this review we provide an introduction and overview of the current state of the field. We discuss
the main principles that underpin bioinformatics analyses, look at the types of biological information
and databases that are commonly used, and finally examine some of the studies that are being
conducted, particularly with reference to transcription regulatory systems.
2. Introduction
Biological data are flooding in at an unprecedented rate (1). For example as of August
2000, the GenBank repository of nucleic acid sequences contained 8,214,000 entries (2)
and the SWISS-PROT database of protein sequences contained 88,166 (3). On average,
the amount of information stored in these databases is doubling every 15 months (2). In
addition, since the publication of the H. influenzae genome (4), complete sequences for
over 40 organisms have been released, ranging from 450 genes to over 100,000. Add to
this the data from the myriad of related projects that study gene expression, determine the
protein structures encoded by the genes, and detail how these products interact with one
another, and we can begin to imagine the enormous quantity and variety of information
that is being produced.
2.1 Bioinformatics a definition1
1 As submitted to the Oxford English Dictionary
As a result of this surge in data, many of the challenges in biology have actually become
challenges in computing. Such an approach is ideal because of the ease with which
computers can handle large quantities of data and probe the complex dynamics observed
in nature. Bioinformatics, the subject of the current review, is often defined as the
application of computational techniques to understand and organise the information
associated with biological macromolecules. This shotgun marriage between the two
subjects is largely attributed to the fact that biology itself is an information technology;
an organism’s physiology and behaviour are largely dictated by its genes, which at the
basic level can be viewed as digital repositories of information. At the same time, there
have been major advances in the technologies that supply the raw data; according to
Anthony Kerlavage of Celera, an experimental laboratory can easily produce over 100
gigabytes of data a day (5). This incredible processing power has been matched by
developments in computer technology; the most important areas of improvements have
been in the CPU, disk storage and Internet, allowing faster computations, better data
storage and revolutionalised the methods for accessing and exchanging of data.
2.2 Aims of bioinformatics
The aims of bioinformatics are three-fold. First, at its simplest bioinformatics organises
data in a way that allows researchers to access existing information and to submit new
entries as they are produced, eg the Protein Data Bank for 3D macromolecular structures
(6, 7). While data-curation is an essential task, the information stored in these databases
is essentially useless until analysed. Thus the purpose of bioinformatics extends far
beyond mere volume control. The second aim is to develop tools and resources that aid in
the analysis of data. For example, having sequenced a particular protein, it is of interest to
compare it with previously characterised sequences. This requires more than just a
straightforward database search. As such, programs such as FASTA (8) and PSI-BLAST
(9) must consider what constitutes a biologically significant resemblance. Development
of such resources requires extensive knowledge of computational theory, as well as a
thorough understanding of biology. The third aim is to use these tools to analyse the data
and interpret the results in a biologically meaningful manner. Traditionally, biological
studies examined individual systems in detail, and frequently compared them with a few
that are related. In bioinformatics, we can also conduct global analyses of all the available
data with the aim of uncovering common principles that apply across many systems and
highlight features that are unique to some.
: bioinformatics is conceptualising biology in terms of
molecules (in the sense of physical chemistry) and applying “informatics techniques
(derived from disciplines such as applied maths, computer science and statistics)
understand and organise the information associated with these molecules, on a
. In short, bioinformatics is a management information system for molecular
biology and has many practical applications.
In this review, we provide an introduction to bioinformatics. We focus on the first and
third aims just described, with particular reference to the keywords underlined in the
definition: information, informatics, organisation, understanding, large-scale and
practical applications. Specifically, we discuss the range of data that are currently being
examined, the databases into which they are organised, the types of analyses that are
being conducted using transcription regulatory systems as an example, and finally discuss
some of the major practical applications of bioinformatics.
3. “…the INFORMATION associated with these molecules…”
Table 1 lists the types of data that are analysed in bioinformatics and the range of topics
that we consider to fall within the field. Here we take a broad view and include subjects
that may not normally be listed. We also give approximate values describing the sizes of
data being discussed.
We start with an overview of the sources of information: these may be divided into raw
DNA sequences, protein sequences, macromolecular structures, genome sequences, and
other whole genome data. Raw DNA sequences are strings of the four base-letters
comprising genes, each typically 1,000 bases long. The GenBank repository of nucleic
acid sequences currently holds a total of 9.5 billion bases in 8.2 million entries (all
database figures as of August 2000). At the next level are protein sequences comprising
strings of 20 amino acid-letters. At present there are about 300,000 known protein
sequences, with a typical bacterial protein containing approximately 300 amino acids.
Macromolecular structural data represents a more complex form of information. There
are currently 13,000 entries in the Protein Data Bank, PDB, most of which are protein
structures. A typical PDB file for a medium-sized protein contains the xyz coordinates of
approximately 2,000 atoms.
Scientific euphoria has recently centred on whole genome sequencing. As with the raw
DNA sequences, genomes consist of strings of base-letters, ranging from 1.6 million
bases in Haemophilus influenzae to 3 billion in humans. An important aspect of complete
genomes is the distinction between coding regions and non-coding regions ‘junk’
repetitive sequences making up the bulk of base sequences especially in eukaryotes. We
can now measure expression levels of almost every gene in a given cell on a whole-
genome level although public availability of such data is still limited. Expression level
measurements are made under different environmental conditions, different stages of the
cell cycle and different cell types in multi-cellular organisms. Currently the largest
dataset for yeast has made approximately 20 time-point measurements for 6,000 genes
(10). Other genomic-scale data include biochemical information on metabolic pathways,
regulatory networks, protein-protein interaction data from two-hybrid experiments, and
systematic knockouts of individual genes to test the viability of an organism.
What is apparent from this list is the diversity in the size and complexity of different
datasets. There are invariably more sequence-based data than structural data because of
the relative ease with which they can be produced. This is partly related to the greater
complexity and information-content of individual structures compared to individual
sequences. While more biological information can be derived from a single structure than
a protein sequence, the problem is overcome in the latter by analysing larger quantities of
Data source Data size Bioinformatics topics
Raw DNA sequence
Protein sequence
Gene expression
8.2 million
(9.5 billion bases)
300,000 sequences
(~300 amino acids
13,000 structures
(~1,000 atomic
coordinates each)
40 complete
(1.6 million
3 billion bases each)
largest: ~20 time
point measurements
for ~6,000 genes
Separating coding and non-coding regions
Identification of introns and exons
Gene product prediction
Forensic analysis
Sequence comparison algorithms
Multiple sequence alignments algorithms
Identification of conserved sequence motifs
Secondary, tertiary structure prediction
3D structural alignment algorithms
Protein geometry measurements
Surface and volume shape calculations
Intermolecular interactions
Molecular simulations
(force-field calculations,
molecular movements,
docking predictions)
Characterisation of repeats
Structural assignments to genes
Phylogenetic analysis
Genomic-scale censuses
(characterisation of protein content, metabolic
Linkage analysis relating specific genes to
Correlating expression patterns
Mapping expression data to sequence, structural
and biochemical data
Other data
Metabolic pathways
11 million citations
Digital libraries for automated bibliographical
Knowledge databases of data from literature
Pathway simulations
Table 1. Sources of data used in bioinformatics, the quantity of each type of data that is
currently (August 2000) available, and bioinformatics subject areas that utilise this data.
4. “… ORGANISE the information on a LARGE SCALE …”
4.1 Redundancy and multiplicity of data
A concept that underpins most research methods in bioinformatics is that much of this
data can be grouped together based on biologically meaningful similarities. For example,
sequence segments are often repeated at different positions of genomic DNA (11). Genes
can be clustered into those with particular functions (eg enzymatic actions) or according
to the metabolic pathway to which they belong (12), although here, single genes may
actually possess several functions (13). Going further, distinct proteins frequently have
comparable sequences organisms often have multiple copies of a particular gene
through duplication and different species have equivalent or similar proteins that were
inherited when they diverged from each other in evolution. At a structural level, we
predict there to be a finite number of different tertiary structures estimates range
between 1,000 and 10,000 folds (14, 15) and proteins adopt equivalent structures even
when they differ greatly in sequence (16). As a result, although the number of structures
in the PDB has increased exponentially, the rate of discovery of novel folds has actually
There are common terms to describe the relationship between pairs of proteins or the
genes from which they are derived: analogous proteins have related folds, but unrelated
sequences, while homologous proteins are both sequentially and structurally similar. The
two categories can sometimes be difficult to distinguish especially if the relationship
between the two proteins is remote (17, 18). Among homologues, it is useful to
distinguish between orthologues, proteins in different species that have evolved from a
common ancestral gene, and paralogues, proteins that are related by gene duplication
within a genome (19). Normally, orthologues retain the same function while paralogues
evolve distinct, but related functions (20).
An important concept that arises from these observations is that of a finite “parts list” for
different organisms (21, 22): an inventory of proteins contained within an organism,
arranged according to different properties such as gene sequence, protein fold or function.
Taking protein folds as an example, we mentioned that with a few exceptions, the tertiary
structures of proteins adopt one of a limited repertoire of folds. As the number of
different fold families is considerably smaller than the number of gene families,
categorising the proteins by fold provides a substantial simplification of the contents of a
genome. Similar simplifications can be provided by other attributes such as protein
function. As such, we expect this notion of a finite parts list to become increasingly
common in the future genomic analyses.
Clearly, an essential aspect of managing this large volume of data lies in developing
methods for assessing similarities between different biomolecules and identifying those
that are related. Below, we discuss the major databases that provide access to the primary
sources of information, and also introduce some secondary databases that systematically
group the data (Table 2). These classifications ease comparisons between genomes and
their products, allowing the identification of common themes between those that are
related and highlighting features that are unique to some.
Database URL
Protein sequence
Protein sequence (composite)
Protein sequence (secondary)
Protein Data Bank (PDB)
Nucleic Acids Database (NDB)
HIV Protease Database
Nucleotide sequences
Genome sequences
Entrez genomes
Integrated databases
Sequence retrieval system (SRS)
Table 2. List of URLs for the databases that are cited in the review.
4.2 Protein sequence databases
Protein sequence databases are categorised as primary, composite or secondary. Primary
databases contain over 300,000 protein sequences and function as a repository for the raw
data. Some more common repositories, such as SWISS-PROT (3) and PIR-International
(23), annotate the sequences as well as describe the proteins’ functions, its domain
structure and post-translational modifications. Composite databases such as OWL (24)
and the NRDB (25) compile and filter sequence data from different primary databases to
produce combined non-redundant sets that are more complete than the individual
databases and also include protein sequence data from the translated coding regions in
DNA sequence databases (see below). Secondary databases contain information derived
from protein sequences and help the user determine whether a new sequence belongs to a
known protein family. One of the most popular is PROSITE (26), a database of short
sequence patterns and profiles that characterise biologically significant sites in proteins.
PRINTS (27) expands on this concept and provides a compendium of protein fingerprints
groups of conserved motifs that characterise a protein family. Motifs are usually
separated along a protein sequence, but may be contiguous in 3D-space when the protein
is folded. By using multiple motifs, fingerprints can encode protein folds and
functionalities more flexibly than PROSITE. Finally, Pfam (28) contains a large
collection of multiple sequence alignments and profile Hidden Markov Models covering
many common protein domains. Pfam-A comprises accurate manually compiled
alignments while Pfam-B is an automated clustering of the whole SWISS-PROT
database. These different secondary databases have recently been incorporated into a
single resource named InterPro (29).
4.3 Structural databases
Next we look at databases of macromolecular structures. The Protein Data Bank, PDB (6,
7), provides a primary archive of all 3D structures for macromolecules such as proteins,
RNA, DNA and various complexes. Most of the ~13,000 structures (August 2000) are
solved by x-ray crystallography and NMR, but some theoretical models are also included.
As the information provided in individual PDB entries can be difficult to extract,
PDBsum (30) provides a separate Web page for every structure in the PDB displaying
detailed structural analyses, schematic diagrams and data on interactions between
different molecules in a given entry. Three major databases classify proteins by structure
in order to identify structural and evolutionary relationships: CATH (31), SCOP (32), and
FSSP databases (33). All comprise hierarchical structural taxonomy where groups of
proteins increase in similarity at lower levels of the classification tree. In addition,
numerous databases focus on particular types of macromolecules. These include the
Nucleic Acids Database, NDB (34), for structures related to nucleic acids, the HIV
protease database (35) for HIV-1, HIV-2 and SIV protease structures and their
complexes, and ReLiBase (36) for receptor-ligand complexes.
4.4 Nucleotide and Genome sequences
As described previously, the biggest excitement currently lies with the availability of
complete genome sequences for different organisms. The GenBank (2), EMBL (37) and
DDBJ (38) databases contain DNA sequences for individual genes that encode protein
and RNA products. Much like the composite protein sequence database, the Entrez
nucleotide database (39) compiles sequence data from these primary databases.
As whole-genome sequencing is often conducted through international collaborations,
individual genomes are published at different sites. The Entrez genome database (40)
brings together all complete and partial genomes in a single location and currently
represents over 1,000 organisms (August 2000). In addition to providing the raw
nucleotide sequence, information is presented at several levels of detail including: a list
of completed genomes, all chromosomes in an organism, detailed views of single
chromosomes marking coding and non-coding regions, and single genes. At each level
there are graphical presentations, pre-computed analyses and links to other sections of
Entrez. For example, annotations for single genes include the translated protein sequence,
sequence alignments with similar genes in other genomes and summaries of the
experimentally characterised or predicted function. GeneCensus (41) also provides an
entry point for genome analysis with an interactive whole-genome comparison from an
evolutionary perspective. The database allows building of phylogenetic trees based on
different criteria such as ribosomal RNA or protein fold occurrence. The site also enables
multiple genome comparisons, analysis of single genomes and retrieval of information
for individual genes. The COGs database (20) classifies proteins encoded in 21
completed genomes on the basis of sequence similarity. Members of the same Cluster of
Orthologous Group, COG, are expected to have the same 3D domain architecture and
often, similar functions. The most straightforward application of the database is to predict
the function of uncharacterised proteins through their homology to characterised proteins,
and also to identify phylogenetic patterns of protein occurrence for example, whether a
given COG is represented across most or all organisms or in just a few closely related
4.5 Gene expression data
The most recent sources of genomic-scale data have been from expression experiments,
which quantify the expression levels of individual genes. These experiments measure the
amount of mRNA or protein products that are produced by the cell. For the former, there
are three main technologies: the cDNA microarray (42-44), Affymatrix GeneChip (45)
and SAGE methods (46). The first method measures relative levels of mRNA abundance
between different samples, while the last two measure absolute levels. Most of the effort
in gene expression analysis has concentrated on the yeast and human genomes and as yet,
there is no central repository for this data. For yeast, the Young (10), Church (47) and
Samson datasets (48) use the GeneChip method, while the Stanford cell cycle (49),
diauxic shift (50) and deletion mutant datasets (51) use the microarray. Most measure
mRNA levels throughout the whole yeast cell cycle, although some focus on a particular
stage in the cycle. For humans, the main application has been to understand expression in
tumour and cancer cells. The Molecular Portraits of Breast Tumours (52), Lymphoma
and Leukaemia Molecular Profiling (53) projects provide data from microarray
experiments on human cancer cells.
The technologies for measuring protein abundance are currently limited to 2D gel
electrophoresis followed by mass spectrometry (54). As gels can only routinely resolve
about 1,000 proteins (55), only the most abundant can be visualised. At present, data
from these experiments are only available from the literature (56, 57).
4.6 Data integration
The most profitable research in bioinformatics often results from integrating multiple
sources of data (58). For instance, the 3D coordinates of a protein are more useful if
combined with data about the protein’s function, occurrence in different genomes, and
interactions with other molecules. In this way, individual pieces of information are put in
context with respect to other data. Unfortunately, it is not always straightforward to
access and cross-reference these sources of information because of differences in
nomenclature and file formats.
At a basic level, this problem is frequently addressed by providing external links to other
databases, for example in PDBsum, web-pages for individual structures direct the user
towards corresponding entries in the PDB, NDB, CATH, SCOP and SWISS-PROT. At a
more advanced level, there have been efforts to integrate access across several data
sources. One is the Sequence Retrieval System, SRS (59), which allows any flat-file
databases to be indexed to each other; this allows the user to retrieve, link and access
entries from nucleic acid, protein sequence, protein motif, protein structure and
bibliographic databases. Another is the Entrez facility (39), which provides similar
gateways to DNA and protein sequences, genome mapping data, 3D macromolecular
structures and the PubMed bibliographic database (60). A search for a particular gene in
either database will allow smooth transitions to the genome it comes from, the protein
sequence it encodes, its structure, bibliographic reference and equivalent entries for all
related genes.
5. “…UNDERSTAND and organise the information…”
Having examined the data, we can discuss the types of analyses that are conducted. As
shown in Table 1, the broad subject areas in bioinformatics can be separated according to
the sources of information that are used in the studies. For raw DNA sequences,
investigations involve separating coding and non-coding regions, and identification of
introns, exons and promoter regions for annotating genomic DNA (61) (62). For protein
sequences, analyses include developing algorithms for sequence comparisons (63),
methods for producing multiple sequence alignments (64), and searching for functional
domains from conserved sequence motifs in such alignments. Investigations of structural
data include prediction of secondary and tertiary protein structures, producing methods
for 3D structural alignments (65, 66), examining protein geometries using distance and
angular measurements, calculations of surface and volume shapes and analysis of protein
interactions with other subunits, DNA, RNA and smaller molecules. These studies have
lead to molecular simulation topics in which structural data are used to calculate the
energetics involved in stabilising macromolecular structures, simulating movements
within macromolecules, and computing the energies involved in molecular docking. The
increasing availability of annotated genomic sequences has resulted in the introduction of
computational genomics and proteomics large-scale analyses of complete genomes and
the proteins that they encode. Research includes characterisation of protein content and
metabolic pathways between different genomes, identification of interacting proteins,
assignment and prediction of gene products, and large-scale analyses of gene expression
levels. Some of these research topics will be demonstrated in our example analysis of
transcription regulatory systems.
Other subject areas we have included in Table 1 are development of digital libraries for
automated bibliographical searches, knowledge bases of biological information from the
literature, DNA analysis methods in forensics, prediction of nucleic acid structures,
metabolic pathway simulations, and linkage analysis linking specific genes to different
disease traits.
In addition to finding relationships between different proteins, much of bioinformatics
involves the analysis of one type of data to infer and understand the observations for
another type of data. An example is the use of sequence and structural data to predict the
secondary and tertiary structures of new protein sequences (67). These methods,
especially the former, are often based on statistical rules derived from structures, such as
the propensity for certain amino acid sequences to produce different secondary structural
elements. Another example is the use of structural data to understand a protein’s function;
here studies have investigated the relationship different protein folds and their functions
(68, 69) and analysed similarities between different binding sites in the absence of
homology (70). Combined with similarity measurements, these studies provide us with an
understanding of how much biological information can be accurately transferred between
homologous proteins (71).
5.1 The bioinformatics spectrum
Figure 1 summarises the main points we raised in our discussions of organising and
understanding biological data the development of bioinformatics techniques has
allowed an expansion of biological analysis in two dimension, depth and breadth. The
first is represented by the vertical axis in the figure and outlines a possible approach to
the rational drug design process. The aim is to take a single protein and follow through an
analysis that maximises our understanding of the protein it encodes. Starting with a gene
sequence, we can determine the protein sequence with strong certainty. From there,
prediction algorithms can be used to calculate the structure adopted by the protein.
Geometry calculations can define the shape of the protein’s surface and molecular
simulations can determine the force fields surrounding the molecule. Finally, using
docking algorithms, one could identify or design ligands that may bind the protein,
paving the way for designing a drug that specifically alters the protein’s function. In
practise, the intermediate steps are still difficult to achieve accurately, and they are best
combined with experimental methods to obtain some of the data, for example
characterising the structure of the protein of interest.
Figure 1. Paradigm shifts during the past couple of decades have taken much of biology away from the
laboratory bench and have allowed the integration of other scientific disciplines, specifically computing.
The result is an expansion of biological research in breadth and depth. The vertical axis demonstrates how
bioinformatics can aid rational drug design with minimal work in the wet lab. Starting with a single gene
sequence, we can determine with strong certainty, the protein sequence. From there, we can determine the
structure using structure prediction techniques. With geometry calculations, we can further resolve the
protein’s surface and through molecular simulation determine the force fields surrounding the molecule.
Finally docking algorithms can provide predictions of the ligands that will bind on the protein surface, thus
paving the way for the design of a drug specific to that molecule.
The horizontal axis shows how the influx of biological data and advances in computer technology have
broadened the scope of biology. Initially with a pair of proteins, we can make comparisons between the
between sequences and structures of evolutionary related proteins. With more data, algorithms for multiple
alignments of several proteins become necessary. Using multiple sequences, we can also create
phylogenetic trees to trace the evolutionary development of the proteins in question. Finally, with the
deluge of data we currently face, we need to construct large databases to store, view and deconstruct the
information. Alignments now become more complex, requiring sophisticated scoring schemes and there is
enough data to compile a genome census a genomic equivalent of a population census providing
comprehensive statistical accounting of protein features in genomes.
The aims of the second dimension, the breadth in biological analysis, is to compare a
gene with others. Initially, simple algorithms can be used to compare the sequences and
structures of a pair of related proteins. With a larger number of proteins, improved
algorithms can be used to produce multiple alignments, and extract sequence patterns or
structural templates that define a family of proteins. Using this data, it is also possible to
construct phylogenetic trees to trace the evolutionary path of proteins. Finally, with even
more data, the information must be stored in large-scale databases. Comparisons become
more complex, requiring multiple scoring schemes, and we are able to conduct genomic
scale censuses that provide comprehensive statistical accounts of protein features, such as
the abundance of particular structures or functions in different genomes. It also allows us
to build phylogenetic trees that trace the evolution of whole organisms.
The distinct subject areas we mention require different types of informatics techniques.
Briefly, for data organisation, the first biological databases were simple flat files.
However with the increasing amount of information, relational database methods with
Web-page interfaces have become increasingly popular. In sequence analysis, techniques
include string comparison methods such as text search and 1D alignment algorithms.
Motif and pattern identification for multiple sequences depend on machine learning,
clustering and data-mining techniques. 3D structural analysis techniques include
Euclidean geometry calculations combined with basic application of physical chemistry,
graphical representations of surfaces and volumes, and structural comparison and 3D
matching methods. For molecular simulations, Newtonian mechanics, quantum
mechanics, molecular mechanics and electrostatic calculations are applied. In many of
these areas, the computational methods must be combined with good statistical analyses
in order to provide an objective measure for the significance of the results.
7. Transcription regulation a case study in bioinformatics
DNA-binding proteins have a central role in all aspects of genetic activity within an
organism, participating in processes such as transcription, packaging, rearrangement,
replication and repair. In this section, we focus on the studies that have contributed to our
understanding of transcription regulation in different organisms. Through this example,
we demonstrate how bioinformatics has been used to increase our knowledge of
biological systems and also illustrate the practical applications of the different subject
areas that were briefly outlined earlier. We start by considering structural analyses of how
DNA-binding proteins recognise particular base sequences. Later, we review several
genomic studies that have characterised the nature of transcription factors in different
organisms, and the methods that have been used to identify regulatory binding sites in the
upstream regions. Finally, we provide an overview of gene expression analyses that have
been recently conducted and suggest future uses of transcription regulatory analyses to
rationalise the observations made in gene expression experiments. All the results that we
describe have been found through computational studies.
7.1 Structural studies
As of August 2000, there were 379 structures of protein-DNA complexes in the PDB.
Analyses of these structures have provided valuable insight into the stereochemical
principles of binding, including how particular base sequences are recognized and how
the DNA structure is quite often modified on binding.
A structural taxonomy of DNA-binding proteins, similar to that presented in SCOP and
CATH, was first proposed by Harrison (72) and periodically updated to accommodate
new structures as they are solved (73). The classification consists of a two-tier system:
the first level collects proteins into eight groups that share gross structural features for
DNA-binding, and the second comprises 54 families of proteins that are structurally
homologous to each other. Assembly of such a system simplifies the comparison of
different binding methods; it highlights the diversity of protein-DNA complex geometries
found in nature, but also underlines the importance of interactions between α-helices and
the DNA major groove, the main mode of binding in over half the protein families. While
the number of structures represented in the PDB does not necessarily reflect the relative
importance of the different proteins in the cell, it is clear that helix-turn-helix, zinc-
coordinating and leucine zipper motifs are used repeatedly. This provides compact
frameworks that present the α-helix on the surfaces of structurally diverse proteins. At a
gross level, it is possible to highlight the differences between transcription factor domains
that “just” bind DNA and those involved in catalysis (74). Although there are exceptions,
the former typically approach the DNA from a single face and slot into the grooves to
interact with base edges. The latter commonly envelope the substrate, using complex
networks of secondary structures and loops.
Focusing on proteins with α-helices, the structures show many variations, both in amino
acid sequences and detailed geometry. They have clearly evolved independently in
accordance with the requirements of the context in which they are found. While
achieving a close fit between the α-helix and major groove, there is enough flexibility to
allow both the protein and DNA to adopt distinct conformations. However, several
studies that analysed the binding geometries of α-helices demonstrated that most adopt
fairly uniform conformations regardless of protein family. They are commonly inserted in
the major groove sideways, with their lengthwise axis roughly parallel to the slope
outlined by the DNA backbone. Most start with the N-terminus in the groove and extend
out, completing two to three turns within contacting distance of the nucleic acid (75, 76).
Given the similar binding orientations, it is surprising to find that the interactions
between each amino acid position along the α-helices and nucleotides on the DNA vary
considerably between different protein families. However, by classifying the amino acids
according to the sizes of their side chains, we are able to rationalise the different
interactions patterns. The rules of interactions are based on the simple premise that for a
given residue position on α-helices in similar conformations, small amino acids interact
with nucleotides that are close in distance and large amino acids with those that are
further (76, 77). Equivalent studies for binding by other structural motifs, like β-hairpins,
have also been conducted (78). When considering these interactions, it is important to
remember that different regions of the protein surface also provide interfaces with the
This brings us to look at the atomic level interactions between individual amino acid-base
pairs. Such analyses are based on the premise that a significant proportion of specific
DNA-binding could be rationalised by a universal code of recognition between amino
acids and bases, ie whether certain protein residues preferably interact with particular
nucleotides regardless of the type of protein-DNA complex (79). Studies have
considered hydrogen bonds, van der Waals contacts and water-mediated bonds (80-82).
Results showed that about 2/3 of all interactions are with the DNA backbone and that
their main role is one of sequence-independent stabilisation. In contrast, interactions with
bases display some strong preferences, including the interactions of arginine or lysine
with guanine, asparagine or glutamine with adenine and threonine with thymine. Such
preferences were explained through examination of the stereochemistry of the amino acid
side chains and base edges. Also highlighted were more complex types of interactions
where single amino acids contact more than one base-step simultaneously, thus
recognising a short DNA sequence. These results suggested that universal specificity, one
that is observed across all protein-DNA complexes, indeed exists. However, many
interactions that are normally considered to be non-specific, such as those with the DNA
backbone, can also provide specificity depending on the context in which they are made.
Armed with an understanding of protein structure, DNA-binding motifs and side chain
stereochemistry, a major application has been the prediction of binding either by proteins
known to contain a particular motif, or those with structures solved in the uncomplexed
form. Most common are predictions for α-helix-major groove interactions given the
amino acid sequence, what DNA sequence would it recognise (77, 83). In a different
approach, molecular simulation techniques have been used to dock whole proteins and
DNAs on the basis of force-field calculations around the two molecules (84, 85).
The reason that both methods have only been met with limited success is because even
for apparently simple cases like α-helix-binding, there are many other factors that must
be considered. Comparisons between bound and unbound nucleic acid structures show
that DNA-bending is a common feature of complexes formed with transcription factors
(74, 86). This and other factors such as electrostatic and cation-mediated interactions
assist indirect recognition of the nucleotide sequence, although they are not well
understood yet. Therefore, it is now clear that detailed rules for specific DNA-binding
will be family specific, but with underlying trends such as the arginine-guanine
7.2 Genomic studies
Due to the wealth of biochemical data that are available, genomic studies in
bioinformatics have concentrated on model organisms, and the analysis of regulatory
systems has been no exception. Identification of transcription factors in genomes
invariably depends on similarity search strategies, which assume a functional and
evolutionary relationship between homologous proteins. In E. coli, studies have so far
estimated a total of 300 to 500 transcription regulators (87) and PEDANT (88), a
database of automatically assigned gene functions, shows that typically 2-3% of
prokaryotic and 6-7% of eukaryotic genomes comprise DNA-binding proteins. As
assignments were only complete for 40-60% of genomes as of August 2000, these figures
most likely underestimate the actual number. Nonetheless, they already represent a large
quantity of proteins and it is clear that there are more transcription regulators in
eukaryotes than other species. This is unsurprising, considering the organisms have
developed a relatively sophisticated transcription mechanism.
From the conclusions of the structural studies, the best strategy for characterising DNA-
binding of the putative transcription factors in each genome is to group them by
homology and analyse the individual families. Such classifications are provided in the
secondary sequence databases described earlier and also those that specialise in
regulatory proteins such as RegulonDB (89) and TRANSFAC (90). Of even greater use is
the provision of structural assignments to the proteins; given a transcription factor, it is
helpful to know the structural motif that it uses for binding, therefore providing us with a
better understanding of how it recognises the target sequence. Structural genomics
through bioinformatics assigns structures to the protein products of genomes by
demonstrating similarity to proteins of known structure (91). These studies have shown
that prokaryotic transcription factors most frequently contain helix-turn-helix motifs (87,
92) and eukaryotic factors contain homeodomain type helix-turn-helix, zinc finger or
leucine zipper motifs. From the protein classifications in each genome, it is clear that
different types of regulatory proteins differ in abundance and families significantly differ
in size. A study by Huynen and van Nimwegen (93) has shown that members of a single
family have similar functions, but as the requirements of this function vary over time, so
does the presence of each gene family in the genome.
Most recently, using a combination of sequence and structural data, we examined the
conservation of amino acid sequences between related DNA-binding proteins, and the
effect that mutations have on DNA sequence recognition. The structural families
described above were expanded to include proteins that are related by sequence
similarity, but whose structures remain unsolved. Again, members of the same family are
homologous, and probably derive from a common ancestor.
Amino acid conservations were calculated for the multiple sequence alignments of each
family (94). Generally, alignment positions that interact with the DNA are better
conserved than the rest of the protein surface, although the detailed patterns of
conservation are quite complex. Residues that contact the DNA backbone are highly
conserved in all protein families, providing a set of stabilising interactions that are
common to all homologous proteins. The conservation of alignment positions that contact
bases, and recognise the DNA sequence, are more complex and could be rationalised by
defining a 3-class model for DNA-binding. First, protein families that bind non-
specifically usually contain several conserved base-contacting residues; without
exception, interactions are made in the minor groove where there is little discrimination
between base types. The contacts are commonly used to stabilise deformations in the
nucleic acid structure, particularly in widening the DNA minor groove. The second class
comprise families whose members all target the same nucleotide sequence; here, base-
contacting positions are absolutely or highly conserved allowing related proteins to target
the same sequence.
The third, and most interesting, class comprises families in which binding is also specific
but different members bind distinct base sequences. Here protein residues undergo
frequent mutations, and family members can be divided into subfamilies according to the
amino acid sequences at base-contacting positions; those in the same subfamily are
predicted to bind the same DNA sequence and those of different subfamilies to bind
distinct sequences. On the whole, the subfamilies corresponded well with the proteins’
functions and members of the same subfamilies were found to regulate similar
transcription pathways. The combined analysis of sequence and structural data described
by this study provided an insight into how homologous DNA-binding scaffolds achieve
different specificities by altering their amino acid sequences. In doing so, proteins
evolved distinct functions, therefore allowing structurally related transcription factors to
regulate expression of different genes. Therefore, the relative abundance of transcription
regulatory families in a genome depends, not only on the importance of a particular
protein function, but also in the adaptability of the DNA-binding motifs to recognise
distinct nucleotide sequences. This, in turn, appears to be best accommodated by simple
binding motifs, such as the zinc fingers.
Given the knowledge of the transcription regulators that are contained in each organism,
and an understanding of how they recognise DNA sequences, it is of interest to search for
their potential binding sites within genome sequences (95). For prokaryotes, most
analyses have involved compiling data on experimentally known binding sites for
particular proteins and building a consensus sequence that incorporates any variations in
nucleotides. Additional sites are found by conducting word-matching searches over the
entire genome and scoring candidate sites by similarity (96-99). Unsurprisingly, most of
the predicted sites are found in non-coding regions of the DNA (96) and the results of the
studies are often presented in databases such as RegulonDB (89). The consensus search
approach is often complemented by comparative genomic studies searching upstream
regions of orthologous genes in closely related organisms. Through such an approach, it
was found that at least 27% of known E. coli DNA-regulatory motifs are conserved in
one or more distantly related bacteria (100).
The detection of regulatory sites in eukaryotes poses a more difficult problem because
consensus sequences tend to be much shorter, variable, and dispersed over very large
distances. However, initial studies in S. cerevisiae provided an interesting observation for
the GATA protein in nitrogen metabolism regulation. While the 5 base-pair GATA
consensus sequence is found almost everywhere in the genome, a single isolated binding
site is insufficient to exert the regulatory function (101). Therefore specificity of GATA
activity comes from the repetition of the consensus sequence within the upstream regions
of controlled genes in multiple copies. An initial study has used this observation to
predict new regulatory sites by searching for over-represented oligonucleotides in non-
coding regions of yeast and worm genomes (102, 103).
Having detected the regulatory binding sites, there is the problem of defining the genes
that are actually regulated, commonly termed regulons. Generally, binding sites are
assumed to be located directly upstream of the regulons; however there are different
problems associated with this assumption depending on the organism. For prokaryotes, it
is complicated by the presence of operons; it is difficult to locate the regulated gene
within an operon since it can lie several genes downstream of the regulatory sequence. It
is often difficult to predict the organisation of operons (104), especially to define the gene
that is found at the head, and there is often a lack of long-range conservation in gene
order between related organisms (105). The problem in eukaryotes is even more severe;
regulatory sites often act in both directions, binding sites are usually distant from
regulons because of large intergenic regions, and transcription regulation is usually a
result of combined action by multiple transcription factors in a combinatorial manner.
Despite these problems, these studies have succeeded in confirming the transcription
regulatory pathways of well-characterised systems such as the heat shock response
system (99). In addition, it is feasible to experimentally verify any predictions, most
notably using gene expression data.
7.3 Gene expression studies
Many expression studies have so far focused on devising methods to cluster genes by
similarities in expression profiles. This is in order to determine the proteins that are
expressed together under different cellular conditions. Briefly, the most common methods
are hierarchical clustering, self-organising maps, and K-means clustering. Hierarchical
methods originally derived from algorithms to construct phylogenetic trees, and group
genes in a “bottom-up” fashion; genes with the most similar expression profiles are
clustered first, and those with more diverse profiles are included iteratively (106-108). In
contrast, the self-organising map (109, 110) and K-means methods (111) employ a “top-
down” approach in which the user pre-defines the number of clusters for the dataset. The
clusters are initially assigned randomly, and the genes are regrouped iteratively until they
are optimally clustered.
Given these methods, it is of interest to relate the expression data to other attributes such
as structure, function and subcellular localisation of each gene product. Mapping these
properties provide an insight into the characteristics of proteins that are expressed
together, and also suggest some interesting conclusions about the overall biochemistry of
the cell. In yeast, shorter proteins tend to be more highly expressed than longer proteins,
probably because of the relative ease with which they are produced (112). Looking at the
amino acid content, highly expressed genes are generally enriched in alanine and glycine,
and depleted in asparagine; these are thought to reflect the requirements of amino acid
usage in the organism, where synthesis of alanine and glycine are energetically less
expensive than asparagine. Turning to protein structure, expression levels of the TIM
barrel and NTP hydrolase folds are highest, while those for the leucine zipper, zinc finger
and transmembrane helix-containing folds are lowest. This relates to the functions
associated with these folds; the former are commonly involved in metabolic pathways
and the latter in signalling or transport processes (113). This is also reflected in the
relationship with subcellular localisations of proteins, where expression of cytoplasmic
proteins is high, but nuclear and membrane proteins tend to be low (114, 115).
More complex relationships have also been assessed. Conventional wisdom is that gene
products that interact with each other are more likely to have similar expression profiles
than if they do not (116, 117). However, a recent study showed that this relationship is
not so simple (118). While expression profiles are similar for gene products that are
permanently associated, for example in the large ribosomal subunit, profiles differ
significantly for products that are only associated transiently, including those belonging
to the same metabolic pathway.
As described below, one of the main driving forces behind expression analysis has been
to analyse cancerous cell lines (119). In general, it has been shown that different cell lines
(eg epithelial and ovarian cells) can be distinguished on the basis of their expression
profiles, and that these profiles are maintained when cells are transferred from an in vivo
to an in vitro environment (120). The basis for their physiological differences were
apparent in the expression of specific genes; for example, expression levels of gene
products necessary for progression through the cell cycle, especially ribosomal genes,
correlated well with variations in cell proliferation rate. Comparative analysis can be
extended to tumour cells, in which the underlying causes of cancer can be uncovered by
pinpointing areas of biological variations compared to normal cells. For example in
breast cancer, genes related to cell proliferation and the IFN-regulated signal transduction
pathway were found to be upregulated (52, 121). One of the difficulties in cancer
treatment has been to target specific therapies to pathogenetically distinct tumour types,
in order to maximise efficacy and minimise toxicity. Therefore, improvements in cancer
classifications have been central to advances in cancer treatment. Although the distinction
between different forms of cancer for example subclasses of acute leukaemia has
been well established, it is still not possible to establish a clinical diagnosis on the basis
of a single test. In a recent study, acute myeloid leukaemia and acute lymphoblastic
leukaemia were successfully distinguished based on the expression profiles of these cells
(53). As the approach does not require prior biological knowledge of the diseases, it may
provide a generic strategy for classifying all types of cancer.
Clearly, an essential aspect of understanding expression data lies in understanding the
basis of transcription regulation. However, analysis in this area is still limited to
preliminary analyses of expression levels in yeast mutants lacking key components of the
transcription initiation complex (10, 122).
Here, we describe some of the major uses of bioinformatics.
8.1 Finding Homologues
As described earlier, one of the driving forces behind bioinformatics is the search for
similarities between different biomolecules. Apart from enabling systematic organisation
of data, identification of protein homologues has some direct practical uses. The most
obvious is transferring information between related proteins. For example, given a poorly
characterised protein, it is possible to search for homologues that are better understood
and with caution, apply some of the knowledge of the latter to the former. Specifically
with structural data, theoretical models of proteins are usually based on experimentally
solved structures of close homologues (123). Similar techniques are used in fold
recognition in which tertiary structure predictions depend on finding structures of remote
homologues and checking whether the prediction is energetically viable (124). Where
biochemical or structural data are lacking, studies could be made in low-level organisms
like yeast and the results applied to homologues in higher-level organisms such as
humans, where experiments are more demanding.
An equivalent approach is also employed in genomics. Homologue-finding is extensively
used to confirm coding regions in newly sequenced genomes and functional data is
frequently transferred to annotate individual genes. On a larger scale, it also simplifies
the problem of understanding complex genomes by analysing simple organisms first and
then applying the same principles to more complicated ones this is one reason why
early structural genomics projects focused on Mycoplasma genitalium (91).
Ironically, the same idea can be applied in reverse. Potential drug targets are quickly
discovered by checking whether homologues of essential microbial proteins are missing
in humans. On a smaller scale, structural differences between similar proteins may be
harnessed to design drug molecules that specifically bind to one structure but not another.
8.2 Rational Drug Design
One of the earliest medical applications of bioinformatics has been in aiding rational drug
design. Figure 2 outlines the commonly cited approach, taking the MLH1 gene product as
an example drug target. MLH1 is a human gene encoding a mismatch repair protein
(mmr) situated on the short arm of chromosome 3 (125). Through linkage analysis and its
similarity to mmr genes in mice, the gene has been implicated in nonpolyposis colorectal
cancer (126). Given the nucleotide sequence, the probable amino acid sequence of the
encoded protein can be determined using translation software. Sequence search
techniques can then be used to find homologues in model organisms, and based on
sequence similarity, it is possible to model the structure of the human protein on
experimentally characterised structures. Finally, docking algorithms could design
molecules that could bind the model structure, leading the way for biochemical assays to
test their biological activity on the actual protein.
Figure 2. Above is a schematic outlining how scientists can use bioinformatics to aid rational drug
discovery. MLH1 is a human gene encoding a mismatch repair protein (mmr) situated on the short arm of
chromosome 3. Through linkage analysis and its similarity to mmr genes in mice, the gene has been
implicated in nonpolyposis colorectal cancer. Given the nucleotide sequence, the probable amino acid
sequence of the encoded protein can be determined using translation software. Sequence search techniques
can be used to find homologues in model organisms, and based on sequence similarity, it is possible to
model the structure of the human protein on experimentally characterised structures. Finally, docking
algorithms could design molecules that could bind the model structure, leading the way for biochemical
assays to test their biological activity on the actual protein.
8.3 Large-scale censuses
Although databases can efficiently store all the information related to genomes, structures
and expression datasets, it is useful to condense all this information into understandable
trends and facts that users can readily understand. Broad generalisations help identify
interesting subject areas for further detailed analysis, and place new observations in a
proper context. This enables one to see whether they are unusual in any way.
Through these large-scale censuses, one can address a number of evolutionary,
biochemical and biophysical questions. For example, are specific protein folds associated
with certain phylogenetic groups? How common are different folds within particular
organisms? And to what degree are folds shared between related organisms? Does this
extent of sharing parallel measures of relatedness derived from traditional evolutionary
trees? Initial studies show that the frequency of folds differs greatly between organisms
and that the sharing of folds between organisms does in fact follow traditional
phylogenetic classifications (21, 41). We can also integrate data on protein functions;
given that the particular protein folds are often related to specific biochemical functions
(68, 69), these findings highlight the diversity of metabolic pathways in different
organisms (20, 105).
As we discussed earlier, one of the most exciting new sources of genomic information is
the expression data. Combining expression information with structural and functional
classifications of proteins we can ask whether the high occurrence of a protein fold in a
genome is indicative of high expression levels (112). Further genomic scale data that we
can consider in large-scale surveys include the subcellular localisations of proteins and
their interactions with each other (127-129). In conjunction with structural data, we can
then begin to compile a map of all protein-protein interactions in an organism.
8.4 Further applications in medical sciences
Most recent applications in the medical sciences have centred on gene expression
analysis (130). This usually involves compiling expression data for cells affected by
different diseases (131), eg cancer (53, 132, 133) and ateriosclerosis (134), and
comparing the measurements against normal expression levels. Identification of genes
that are expressed differently in affected cells provides a basis for explaining the causes
of illnesses and highlights potential drug targets. Using the process described in Figure 2,
one would design compounds that bind the expressed protein, or perhaps more
importantly, the transcription regulator has caused the change in expression levels. Given
a lead compound, microarray experiments can then be used to evaluate responses to
pharmacological intervention, (135, 136) and also provide early tests to detect or predict
the toxicity of trial drugs.
Further advances in bioinformatics combined with experimental genomics for individuals
are predicted to revolutionalise the future of healthcare. A typical scenario for a patient
may start with post-natal genotyping to assess susceptibility or immunity from specific
diseases and pathogens. With this information, a unique combination of vaccines could
be prescribed, minimising the healthcare costs of unnecessary treatments and anticipating
the onslaught of diseases later in life. Regular lifetime screenings could lead to guidance
for nutrition intake and early detections of any illnesses (137). In addition, drug-based
treatments could be tailored specifically to the patient and disease, thus providing the
most effective course of medication with minimal side-effects (138). Given the present
rate of development, such a scenario in healthcare appears to be possible in the not too
distant future.
9. Conclusions
With the current deluge of data, computational methods have become indispensable to
biological investigations. Originally developed for the analysis of biological sequences,
bioinformatics now encompasses a wide range of subject areas including structural
biology, genomics and gene expression studies. In this review, we provided an
introduction and overview of the current state of field. In particular, we discussed the
types of biological information and databases that are commonly used, examined some of
the studies that are being conducted with reference to transcription regulatory systems
and finally looked at several practical applications of the field.
Two principal approaches underpin all studies in bioinformatics. First is that of
comparing and grouping the data according to biologically meaningful similarities and
second, that of analysing one type of data to infer and understand the observations for
another type of data. These approaches are reflected in the main aims of the field, which
are to understand and organise the information associated with biological molecules on a
large scale. As a result, bioinformatics has not only provided greater depth to biological
investigations, but added the dimension of breadth as well. In this way, we are able to
examine individual systems in detail and also compare them with those that are related in
order to uncover common principles that apply across many systems and highlight
unusual features that are unique to some.
10. Acknowledgements
We thank Patrick McGarvey for comments on the manuscript.
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... For example, after sequencing a specific protein, it is interesting to compare it to previously characterized sequences. The third goal is to use these tools to analyze data and interpret results in a biologically meaningful way (Rana, 2014;Luscombe et al., 2016). ...
Objective: Cancer is one of the horrendous diseases. Classifying cancer is founded on identifying cancer-causing mutations in gene sequences. Although genetic analysis can predict certain types of cancer, there is currently no effective method for predicting cancers. Therefore, the purpose of this paper is to predict the cancer types and to find a data mining technique that uses two different machine learning algorithms for classifying cancer. Moreover, earlier detection of the mutated tumor protein P53 gene can predict treatment and gene therapy techniques. Methods: (UMD-2010) the Universal Mutation Database is used to diagnose mutations in genes. The challenge, however, is that the database very basic. Besides, it is an excel format database. Due to its limitations, the data base cannot be used to classify cancer. In addition, bioinformatics techniques such as pairwise alignment and BLAST are used, followed by machine learning algorithms that use neural network algorithms to classify cancer based on malignant mutations in the TP53 gene, by selecting (12) out of (53) database fields for the TP53 gene database in the second stage. It should be noted that the (UMDCell-line2010) database does not have one of these twelve fields (Field of gene locus). Result: As a Utilizing MLP and SVM for training and testing a set number of fields, the Machin learning methods were found to be an effective way to classify cancers. Where the Relative Absolute Error for MLP and SVM is 83.6005 % ,65.6605 %, the accuracy is 90 %, 93.7% respectively. Conclusion: Following the learning and testing stages, the mean absolute error (MAE), used to measure the errors was found in the SVM less than the (MAE) in MLP algorithm. we can conclude that using SVM is considered better than the MLP algorithm because the accuracy in SVM better than the accuracy of MLP.
... Bioinformatics (BI) is a research area where biologists, computer scientists, physicians, mathematicians, and chemists blend their skills and fetch various fields, such as molecular biology, genetics, microbiology, mathematics, chemistry, biochemistry, physics, and informatics. The communication of these fields helps resolve distinct tasks to discover new evidence in complex biological systems to justify health organizations to better comprehend life and disease mechanisms [133][134][135]. BI was initially defined as comparing and databasing the genome (DNA), individual molecules such as RNA and proteins, and modeling the structure and function of existing and newly made proteins. ...
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Treponema pallidum subspecies pallidum causes syphilis, a sexually transmitted disease that infects more than 2.1 million pregnant women every year. Due to its maximum death rates and augmented risk of human immunodeficiency virus (HIV) infection, the disease is still a matter of debate in many low- and high-income countries. The infection has three stages that lead to several complications if left untreated and can lead to many tertiary complications in the brain, eyes, ears, heart, and pregnancy. Neurosyphilis is also known as the clinical result of infection of the central nervous system by Treponema pallidum subspecies pallidum. It can evolve at any time and from any stage of syphilis exposure. This review briefly explains the severe and multiple neurosyphilitic complications and recently identified cases related to neurosyphilis. We also explained computational neuroscience, neuroinformatics, and in silico models and techniques based on artificial intelligence and other computational and mathematical methods. These techniques have already been applied to several neurological and psychological brain complications and can be applied to neurosyphilis to better understand the persistence of the disease related to the brain that causes neurosyphilis.
... Usually public databases accumulate big amounts of information, and they are categorised into primary and secondary databases. The primary databases are composed of the findings of experimental data that are reported without any critical analysis related to previous publications (Luscombe et al., 2001;Prosdocimi, 2010). However, in the secondary databases, there is a collection and explication of data, called process of content curation. ...
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Bioinformatics is an amalgamation of biology, mathematics and computer science. It is a science which gathers the information from biology in terms of molecules and applies the informatic techniques to the gathered information for understanding and organizing the data in a useful manner. With the help of bioinformatics, the experimental data generated is stored in several databases available online like nucleotide database, protein databases, GENBANK and others. The data stored in these databases is used as reference for experimental evaluation and validation. Till now several online tools have been developed to analyze the genomic, transcriptomic, proteomics, epigenomics and metabolomics data. Some of them include Human Splicing Finder (HSF), Exonic Splicing Enhancer Mutation taster, and others. A number of SNPs are observed in the non-coding, intronic regions and play a role in the regulation of genes, which may or may not directly impose an effect on the protein expression. Many mutations are thought to influence the splicing mechanism by affecting the existing splice sites or creating a new sites. To predict the effect of mutation (SNP) on splicing mechanism/signal, HSF was developed. Thus, the tool is helpful in predicting the effect of mutations on splicing signals and can provide data even for better understanding of the intronic mutations that can be further validated experimentally. Additionally, rapid advancement in proteomics have steered researchers to organize the study of protein structure, function, relationships, and dynamics in space and time. Thus the effective integration of all of these technological interventions will eventually lead to steering up of next-generation systems biology, which will provide valuable biological insights in the field of research, diagnostic, therapeutic and development of personalized medicine.
... Simultaneously, there has been significant progress in technologies that provide raw data. According to studies by Kerlavage, the experimental lab can easily produce more than 100 GB of data per day (Magazine, 1999;Luscombe et al., 2001). Bioinformatics make it accountable for analysis, distribution, and the storage of biological information. ...
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Objective: As part of the bioinformatics studies, we utilized National Cancer Institute (NCI)'s Breast Cancer Risk Assessment Tool to estimate the five-year period and lifetime risk of breast cancer development among Iraqi risky women. Methods: Totally, 110 risky women aged 21-67 (mean=36±7.4) years were interviewed by a series of questions regarding the risk of breast cancer development. Moreover, 100 cases with mutation in the BRCA1 or BRCA2 genes were included. Results: Our results demonstrated that the patient's estimated risk of breast cancer development during the next five years and lifetime (until the age 90 years) included 0.96% (p=0.211) and 9.97% (p=0.002), respectively being relatively low. Accordingly, the lifetime risk for the breast cancer development was significantly higher (10.38%) than that of 5-year. However, the age of patients was not significantly associated to the breast cancer development as there was no significant difference among various age groups. Conclusion: It was concluded that long-term or lifetime period plays as a significant risk factor for developing breast cancer among female patients who had had a screening episode in Iraq.
... Bioinformatics is a management information system for molecular biology. [1]. It is an Information Technology field that develops methods for storing, retrieving, organizing and analyzing biological data. ...
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DNA (deoxyribose nucleic acid) is found in all living organisms human, animals, bacteria and many viruses have only four nucleobases : adenine (A), cytosine (C), guanine (G), and thymine (T). RNA (ribose nucleic acid) is different from DNA because it contains a base called uracil in place of thymine. James Watson and Francis Crick proposed DNA structure as: DNA molecule made of two strands running in opposite directions, twisted together in a long spiral structure called a double helix and Adenine always bonds with thymine, and cytosine always bonds with guanine. This article focuses on string algorithms for Counting DNA nucleotides, Transcribing DNA to RNA and Complementing a strand of DNA .
In the past few decades, bioinformatics has been extensively explored in many areas of biological sciences. It combines the principles of biology, computer science, mathematics, physics, and statistics to analyze and interpret biological data. It uses computation power, algorithm and software for extracting knowledge from biological data for analysis, prediction, imaging, and visualization purpose. In this chapter, we highlight recent developments in this field and how the potential of bioinformatics has harnessed in multiple disciplines.
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With advanced technology and its development, Bioinformatics is one of the avant-garde fields that has managed to make amazing progress in the pharmaceutical-medical field, by modeling the infrastructural dimensions of healthcare and integrating computing tools in drug innovation, facilitating prevention, detection / more accurate diagnosis and treatment of disorders, while saving time and money. The association between bioinformatics and pharmacovigilance promoted sample analyzes as well as the interpretation of drug side effects, also focusing on drug discovery and development (DDD), in which systems biology, personalized approach and drug repositioning were considered together with translational medicine. The role of bioinformatics has been highlighted in DDD, proteomics, genetics, modelling, miRNA discovery and assessment, and clinical genome sequencing. The authors have collaborated significant data from the most known online databases and publishers, narrowing also the diversified applications, in order to target four major areas (tetrad): DDD, anti-microbial research, genomic sequencing, and miRNA research and its significance in the management of current pandemic context. Our analysis aims to provide optimal data in the field, by stratification the information related to published data in key sectors and to capture attention of researchers interested in Bioinformatics-a field that has succeeded in advancing the healthcare paradigm, by introducing developing techniques and multiple database platforms, addressed in the manuscript.
MicroRNAs (miRNAs) are essential components of gene regulatory networks. miRNAs participate in the regulation of biological processes and thereby have promising potentials to act as valuable diagnostic biomarkers and even therapeutic molecules in various diseases. Bioinformatics analysis is a pivotal initial step of microRNAs research. The main functions of bioinformatics are extensive biodata mining, processing, and analysis of raw data to provide precious and accurate results based on in-silico statistical and mathematical methods. This work aims to evaluate the most widely used bioinformatics tools in microRNAs research in a critical viewpoint and to suggest a helpful list of tools according to the significant features. For this purpose, a wide range of microRNA-related bioinformatics tools in the field of “microRNA search/targets/pathway/mutation/disease/and biomarker discovery” were searched and studied. To examine the advantages and disadvantages of every applied tool “miR-183”, “miR-182”, “miR-96”, “ATOH1 (Atonal BHLH Transcription Factor 1)”, and Hearing loss disorder were used as a search keyword. As a result, among 162 studied bioinformatics tools, a total of 38 were selected based on distinct features, including result reliability, service accessibility, usefulness, and user satisfaction. The presence of a large number of currently available microRNAs-bioinformatics tools and the continuing development of new tools makes confusion and complexity in the selection of proper tools for a particular study. Here we have introduced and discussed the most helpful Bioinformatics Tools.
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The existence of Massive datasets that are generated in many applications provides various opportunities and challenges. Especially, scalable mining of such large-scale datasets is a challenging issue that attracted some recent research. In the present study, the main focus is to analyse the classification techniques using WEKA machine learning workbench. Moreover, a large-scale dataset was used. This dataset comes from the protein structure prediction field. It has already been partitioned into training and test sets using the ten-fold cross-validation methodology. In this experiment, nine different methods have been tested. As a result, it became obvious that it is not applicable to test more than one classifier from the (tree) family in the same experiment. On the other hand, using (NaiveBayes) Classifier with the default properties of the attribute selection filter has a great time consuming. Finally, varying the parameters of the attribute selections should be prioritized for more accurate results.
The docking of repressor proteins to DNA starting from the unbound protein and model-built DNA coordinates is modeled computationally. The approach was evaluated on eight repressor/DNA complexes that employed different modes for protein/ DNA recognition. The global search is based on a protein-protein docking algorithm that evaluates shape and electrostatic complementarity, which was modified to consider the importance of electrostatic features in DNA-protein recognition. Complexes were then ranked by an empirical score for the observed amino acid /nucleotide pairings (i.e., protein-DNA pair potentials) derived from a database of 20 protein/DNA complexes. A good prediction had at least 65% of the correct contacts modeled. This approach was able to identify a good solution at rank four or better for three out of the eight complexes. Predicted complexes were filtered by a distance constraint based on experimental data defining the DNA footprint. This improved coverage to four out of eight complexes having a good model at rank four or better. The additional use of amino acid mutagenesis and phylogenetic data defining residues on the repressor resulted in between 2 and 27 models that would have to be examined to find a good solution for seven of the eight test systems. This study shows that starting with unbound coordinates one can predict three-dimensional models for protein/DNA complexes that do not involve gross conformational changes on association. Proteins 33:535–549, 1998. © 1998 Wiley-Liss, Inc.
On the basis of a structural analysis of 240 protein-DNA complexes contained in the Protein Data Bank (PDB), we have classified the DNA-binding proteins involved into eight different structural/functional groups, which are further classified into 54 structural families. Here we present this classification and review the functions, structures and binding interactions of these protein-DNA complexes.
We have developed three computer programs for comparisons of protein and DNA sequences. They can be used to search sequence data bases, evaluate similarity scores, and identify periodic structures based on local sequence similarity. The FASTA program is a more sensitive derivative of the FASTP program, which can be used to search protein or DNA sequence data bases and can compare a protein sequence to a DNA sequence data base by translating the DNA data base as it is searched. FASTA includes an additional step in the calculation of the initial pairwise similarity score that allows multiple regions of similarity to be joined to increase the score of related sequences. The RDF2 program can be used to evaluate the significance of similarity scores using a shuffling method that preserves local sequence composition. The LFASTA program can display all the regions of local similarity between two sequences with scores greater than a threshold, using the same scoring parameters and a similar alignment algorithm; these local similarities can be displayed as a "graphic matrix" plot or as individual alignments. In addition, these programs have been generalized to allow comparison of DNA or protein sequences based on a variety of alternative scoring matrices.
High-throughput gene sequencing has revolutionized the process used to identify novel molecular targets for drug discovery. Thousands of new gene sequences have been generated but only a limited number of these can be converted into validated targets likely to be involved in disease. We describe here some of the approaches used at SmithKline Beecham to select and validate novel targets. These include the identification of selective tissue gene product expression, such as for cathepsin K, a novel osteoclast-specific cysteine protease. We also describe the discovery and functional characterization of novel members of the G-protein coupled receptor superfamily and their pairing with natural ligands. Lastly, we discuss the promises of gene microarrays and proteomics, developing technologies that allow the parallel analyses of tissue expression patterns of thousands of genes or proteins, respectively.
The Structural Classification of Proteins (SCOP) database provides a detailed and comprehensive description of the relationships of known protein structures. The classification is on hierarchical levels: the first two levels, family and superfamily, describe near and distant evolutionary relationships; the third, fold, describes geometrical relationships. The distinction between evolutionary relationships and those that arise from the physics and chemistry of proteins is a feature that is unique to this database so far. The sequences of proteins in SCOP provide the basis of the ASTRAL sequence libraries that can be used as a source of data to calibrate sequence search algorithms and for the generation of statistics on, or selections of, protein structures. Links can be made from SCOP to PDB-ISL: a library containing sequences homologous to proteins of known structure. Sequences of proteins of unknown structure can be matched to distantly related proteins of known structure by using pairwise sequence comparison methods to find homologues in PDB-ISL. The database and its associated files are freely accessible from a number of WWW sites mirrored from URL