Bioinformatics principles and applications

BIOINFORMATICS

P

RINCIPLES
&

A

PPLICATIONS

AUTHOR: EUGENE MADZOKERE
Bachelor of Science Honors Degree in Biotechnology Student (2013)
Chinhoyi University of Technology (CUT)
Zimbabwe
Correspondence: madzokere@gmail.com

SUMMARY: Bioinformatics Principles & Applications
MOLECULAR BIOLOGY
AGTC GENETIC AGUC
CODE

DNA

RNA

INFORMATION TECHNOLOGY
HIGH-THROUGHPUT SCREENING
(HTS)

NEXT-GENERATION SEQUENCING (NGS)
SEQUENCE ALIGNMENT, ANALYSIS &
INTERPRETATION

GENE FINDING
GENOME MAPPING & ASSEMBLY

Transcription
mRNA
Translation

PROTEIN

PROTEIN STRUCTURE ALIGNMENT &
PREDICTION
PREDICTION OF GENE EXPRESSION &
PROTEIN-PROTEIN INTERACTIONS
EVOLUTION MODELLING
DRUG DISCOVERY & DEVELOPMENT OF
BIOPHARMACEUTICALS

Bioinformatics Defined!
Bioinformatics is the acquisition, application of
computational
tools,
storage,
arrangement,
identification, archiving, analysis, interpretation,
visualization and communication approaches for
expanding the use of biological, medical, behavioral or
health data in molecular biology research
development.
 The term “bioinformatics” is short for “biological
informatics”.
 Antony Kerlavage of Celera Genomics defined
bioinformatics as “Any application of computation to
the field of biology including data management,
algorithm development, and data mining”.


Simply put, Bioinformatics is “the application of
information technology to the field of molecular
biology”.
 As a research field, Bioinformatics entails:
1. Creation & advancement of databases,
algorithms, computational & statistical
techniques and;
2. Creation and advancement of theory to solve
formal and practical problems arising from the
management and analysis of biological data.


Bioinformatics strives to further our knowledge of
biological systems and capacity to interpret biological
processes for utilization in different applications.
 This is evidenced by it’s development and use of
computationally intensive techniques
 That said, common activities include:
I.
Mapping & analyzing DNA, RNA, Protein, Amino
Acid, & Lipid sequences.
II. Sequence Alignment & Analysis.
III. Creation and Visualization of 3-D structure models
for biological molecules of significance e.g. protein.
IV. Genome Annotation.


Critical research areas include:
1. Sequence Alignment, Annotation, Analysis & Interpretation;
2. Gene Finding/Identification & Synthesis;
3. Genome Mapping and Assembly;
4. Protein Structure Alignment and Prediction;
5. Prediction of Gene Expression & Protein-Protein
Interactions;
6. Evolution Modeling &;
7. Drug Discovery and Development of Biopharmaceuticals.

These are dependent largely on high-throughput
screening (HTS), characterization, expression and next
generation sequencing (NGS) technologies.


Bioinformatics Databases Defined!









Interest in Bioinformatics was propelled by the necessity
to create databases of biological sequences.
The first database was created shortly after the Insulin
protein sequence was made available in 1956.
To store and manage efficiently the large amounts of data
generated in a genomic and molecular research era,
Information Technology (IT) has been & is being used to
develop & improve Bioinformatics/Biological Databases (BD).
A BD is a large, organized body of persistent data, usually
associated with computerized software designed to update,
query, and retrieve components of the data stored within the
system.
The Sequence BD’s are amongst the most synonymous.

Bioinformatics Databases Defined!






1.
2.
3.
4.




More specifically, a Biological database is a structured collection of
information (biological) consisting of basic units called “records” or
“entries”.
Each record consists of fields holding predefined data related to the
record.
The simplest database might be a single file containing many
records, each of which includes the same set of information.
E.g., A record associated with a Nucleotide Sequence Database
typically contains information such as:
A contact name;
The input sequence with a description of the type of molecule;
The scientific name of the source organism from which it was isolated;
and, often,
Literature citations associated with the sequence.
In such a case, nucleotide sequences would represent the record/entry,
whilst information such as the base count and origin, sequence length,
etc, constitutes the field!
Each Database has a summary or checksum line.

Purpose of Bioinformatics Databases!
Databases are designed to collect, archive,
visualize and organize data to enable
intelligent data description/interpretation,
discovery, retrieval & invocation.
 Databases exist as a way of ensuring
interoperability and integration between
different research institutes, research
databases, data mining tools, soft-wares and
ordinary end-user with minimal restriction.


Querying Bioinformatics Databases!
A database query is “a method to retrieve
information from the database”.
 Organization of database records into
predetermined fields, enables end-users to
query on fields.
 Database querying is made easier by
algorithms.


Purpose of Bioinformatics Algorithms!
An Algorithm is “a soft-ware programme
designed to improve sequence based database
query (search) by increasing the speed, precision,
accuracy and efficiency of identifying and
making sense of similarities and/or dissimilarities
from sequence alignments.”
 Examples of algorithms include:
1. Hidden Markov Models;
2. Smith-Waterman algorithm & the;
3. Needleman-Wunch algorithm.


Advances in Bioinformatics Algorithm Design!
However, although some algorithms are
highly sensitive and increase accuracy of
searches, they still take more time to
execute the search.
 For this reason, algorithms are now being
developed around biotechnologists and
bioinformaticians which permits:
1. Integration of diverse data and tools under a
common Graphic User Interface (GUI).
2. Sharing information &;
3. Creation of powerful solutions useful in data
archiving.


Value of Sequence Alignment Process!
Sequence Alignment informs us on the:
1. Function or activity of a new gene/protein.
2. Structure or shape of a new protein.
3. Location or preferred position of a protein.
4. Stability of a gene or protein.
5. Origin of a gene or protein.
6. Origin or phylogeny of an organelle.
7. Origin or phylogeny of an organism.


Bioinformatics Databases: Maintainer Status!

1.


1.

2.





Data submitted for storage in a database must be:
Easy to access & extract to answer a specific biological
question/research area.
Two forms of database maintainer status exist, namely:
Public repositories: have no legal restrictions & offer free public data
access and retrieval, however databases are often riddled with
redundancies because of limited error checking, curation, database
updating and lack of strict data submission rules.
Private repositories: have higher quality data & legal restrictions
attached to copyrights, patents, and often access is only available
upon some form of monetary payment to data managing
companies or their agents through a server network.
Data in Private repositories is regularly curated and updated and end
users follow strict data submission rules which limits redundancies
and errors.
“A databases maintainer status thus directly influences the quality,
access, dynamic nature, heterogeneity & type of data submitted for
storage”.

Bioinformatics Database Characteristics!

1.

2.

3.
4.
5.

6.

The Fundamental bioinformatics database characteristics include:
Hierarchical data organization: refers to data ranging from molecules,
molecular pathways, cells, tissues, to organisms and populations.
Complex data type: describes data existing in databases as text-based
sequences, blobs, images of cells and tissues, three dimensional
molecular structures and complex data structural biochemical
pathways.
Dynamic nature: describes the nature of data content and the
resultant constant changes in the database schema.
Quality: describe the integrity of constraints within databases, the
need to curate and update data constantly.
Heterogeneous content: most bioinformatics databases are
heterogeneous in their content and may even have common
semantic, database size, location, and syntactic differences (such as
storage format or the access method).
Accessibility: describes the necessity for internet access, a
search/browsing facility, flexibility to support external analysis tools
and federation.

SEQUENCE DATABASES DEFINED!








A Sequence Database (SD) is a large collection of
computerized (digital) nucleic acid sequences, protein
sequences and/or other sequences stored on a computer.
SD’s are an organized way of storing and managing
copious loads of sequence information accumulating
worldwide.
Annotation in SD’s is “the process of adding biological
information & predictions to a sequenced framework”.
Without this annotation, genome, nucleic acid, contig,
gene &/or protein sequences are virtually useless to
bioinformatics & molecular biology research
development.

SEQUENCE DATABASES DEFINED!
Sequence Databases are classified as:
1. Genome sequence databases.
2. Nucleic acid sequence databases.
3. Protein sequence databases.
4. Amino acid sequence databases.
 SD’s also fall into three database categories:
1. Primary databases;
2. Secondary databases;
3. Composite databases.


FUNDAMENTAL ELEMENTS OF SEQUENCE DATABASES
All of the following elements represent the “ideal minimal
content of annotation entry in a Sequence Database”
1.
Name :LOCUS, ENTRY, ID all unique identifiers
2.
Definition: A brief, one-line, textual sequence description.
3.
Accession: A constant data identifier.
4.
Version
5.
Gene identifier (GI)
6.
Comments & Keywords
7.
Source
8.
Organism & Taxonomy Information
9.
Literature References
10. Features table
11. Base count & Origin
12. And the Sequence itself!!!


LOCUS



DEFINITION Human mRNA for elongation factor 1 alpha subunit (EF-1 alpha).



GenBank and GenPept format —



ACCESSION X03558



VERSION



KEYWORDS



SOURCE




HSEF1AR

1506 bp

mRNA

linear PRI 12-SEP-1993

X03558.1 GI:31097
elongation factor; elongation factor 1.

human.

ORGANISM Homo sapiens Eukaryota; Metazoa; Chordata; Craniata; Vertebrata; Euteleostomi; Mammalia; Eutheria; Primates; Catarrhini; Hominidae; Homo.
REFERENCE 1 (bases 1 to 1506)



AUTHORS Brands,J.H., Maassen,J.A., van Hemert,F.J., Amons,R. and Moller,W.



TITLE



JOURNAL Eur. J. Biochem. 155 (1), 167-171 (1986)




The primary structure of the alpha subunit of human elongation……

MEDLINE 86136120
FEATURES
source

Location/Qualifiers
1..1506

/organism="Homo sapiens"
/db_xref="taxon:9606"
CDS

54..1442
/note="EF-1 alpha (aa 1-463)"
/codon_start=1
/protein_id="CAA27245.1"

/db_xref="GI:31098"
/db_xref="SWISS-PROT:P04720"
/translation="MGKEKTHINIVVIGHVDSGKSTTTGHLIYKCGGIDKRTIEKFEK
EAAEMGKGSFKYAWVLDKLKAERERGITIDISLWKFETSKYYVTIIDAPGHRDFIKNM
……VTKSAQKAQKAK"


BASE COUNT



ORIGIN

412 a

337 c

387 g

370 t

1 acgggtttgc cgccagaaca caggtgtcgt gaaaactacc cctaaaagcc aaaatgggaa
61 aggaaaagac tcatatcaac attgtcgtca ttggacacgt agattcgggc aagtccacca……….
1501 aactgt
//

1. The LOCUS field: It consists of five different subfields, namely:
 1a Locus Name (e.g. HSHFE) - It is a tag for grouping similar
sequences. The first two or three letters usually designate the
organism. In this case HS stands for Homo sapiens. The last several
characters are associated with another group designation, such as gene
product. In this example, the last three digits represent the gene
symbol, HFE. Currently, the only requirement for assigning a locus
name to a record is that it is unique.
 1b Sequence Length (12146 bp) – It is the total number of
nucleotide base pairs (or amino acid residues) in the sequence record.
 1c Molecule Type (e.g. DNA) - Type of molecule that was sequenced.
All sequence data in an entry must be of the same type.
 1d GenBank Division (PRI) - GenBank has different divisions. In this
example, PRI stands for primate sequences. Other divisions include ROD
(rodent sequences), MAM (other mammal sequences), PLN (plant, fungal,
and algal sequences), & BCT (bacterial sequences).
 1e Modification Date (23-July-1999) - Date of most recent
modification made to the record. The date of first public release is not
available in the sequence record. This information can be obtained only
by contacting NCBI at info@ncbi.nlm.nih.gov.

2. DEFINITION: – It is a brief description of the
sequence.
 The description may include source organism
name, gene or protein name, or designation as
untranscribed or untranslated sequences (e.g., a
promoter region).
 For sequences containing a coding region
(CDS), the definition field may also contain a
“completeness” qualifier such as "complete
CDS" or "exon 1."


3. ACCESSION (Z92910): – It is a unique
identifier assigned to a complete sequence
record.
 This number never changes, even if the
record is modified.
 An “accession number” is a combination of
letters and numbers that are usually in the
format of one letter followed by five digits
(e.g., M12345) or two letters followed by six
digits (e.g., AC123456).

4. VERSION (Z92910.1) – It is an identification number
assigned to a single, specific sequence in the
database.
 This number is in the format “accession.version.”
 If any changes are made to the sequence data, the
version part of the number will increase by one.
 E.g. U12345.1 becomes U12345.2.
 A version number of Z92910.1 for this HFE
sequence indicates that the sequence data has not
been altered thus it is an original submission.


5. Gene Identifier (GI) (1890179) - Also a
sequence identification number.
 Whenever a sequence is changed, the
version number is increased and a new GI
is assigned.
 If a nucleotide sequence record contains a
protein translation of the sequence, the
translation will have its own GI number.

6. KEYWORDS (haemochromatosis; HFE gene)  A “keyword” can be “any word or phrase used
to describe the sequence”.
 Keywords are not taken from a controlled
vocabulary. Notice that in this record the
keyword,
"haemochromatosis,"
employs
British spelling, rather than the American
"hemochromatosis."
 Many records have no keywords.
 A period is placed in this field for records
without keywords.


7. SOURCE (human) - Usually contains an
abbreviated or common name of the source
organism.
8. ORGANISM (Homo sapiens) - The scientific
name (usually genus & species) &
phylogenetic lineage.
 Refer to the NCBI Taxonomy Homepage for
more information about the classification
scheme used to construct taxonomic
lineages.

9. REFERENCE – It is a citation of publications by
sequence authors that supports information
presented in the sequence record.
 Several references may be included in one
record.
 References are automatically sorted from
the oldest to the newest.
 Cited publications are searchable by author,
article or publication title, journal title, or
MEDLINE unique identifier (UID).
 The UID links the sequence record to the
MEDLINE record.

•When the REFERENCE TITLE contains the words "Direct
Submission“, contact information for the submitter(s) is
provided.


10.The FEATURES Table:

11. BASE COUNT & ORIGIN:
 BASE COUNT - Base Count gives the total
number of adenine (A), cytosine (C), guanine
(G), and thymine (T) bases in the sequence.
 ORIGIN - Origin contains the sequence data,
which begins on the line immediately below
the field title.

NUCLEIC ACID SEQUENCE DATABASES (NASD)
NASD’s

are repositories that accept nucleic
acid sequence data and avail it for public use.
They hold heterogeneous (meaning the
source of material is either genomic &/or
cDNA), and either partially, completely or
un-annotated nucleic acid sequence data.

TYPES OF NUCLEIC ACID DATABASES
1.

PRIMARY NUCLEIC ACID DATABASES

Contain complete annotations of all the
nucleic acid sequence information of
organisms whose genomes have been
successfully sequenced.
 Examples include GenBank, DDBJ
and
EMBL.


International Nucleotide Sequence Database
Collaboration (INSDC)


These 3 combined make-up the
Collaboration (INSDC).

Collaboration (INSDC)
INSDC is a synchronization of GenBank, DDBJ and
EMBL databases done daily.
 Properties of INSDC include:
1. Consistent Accession numbers;
2. No legal restrictions. Although there are some
patented sequences stored and managed.
3. Holds both sequences submitted directly by
scientists and genome sequencing groups &
sequences taken from literature & patents.
4. Has very limited error checking thus there is a
fair amount of redundancy.
5. Access is provided via ftp & www interfaces; &
6. Sequences are listed in the 5’-3’ orientation.


[A] GenBank






1.
2.
3.
4.

5.
6.
7.
8.



GenBank (Genetic Sequence Databank) is one of the fastest growing
repositories of known genetic sequences.
It has a flat file structure that is an ASCII text file, readable &
downloadable by both humans and computers.
It is maintained by the National Center for Biotechnology (NCB).
Entry data contains information on:
The sequence;
Accession numbers;
The scientific and gene names;
Taxonomy/phylogenetic classification of the source organism;
A feature that identifies coding regions;
References to published literature;
Transcription units &;
Mutation sites.
There are approximately 286,730,369,256 sequence records in the
traditional GenBank divisions as of 2011.

[B] DNA Data Bank of Japan (DDBJ)

[B] DNA Data Bank of Japan (DDBJ)
Collects and supplies DNA data since its
inception in 1986.
 Data entry as in GenBank.
 DDBJ exchanges data via the SINET3
computer network.


[C] European Molecular Biology Laboratories
(EMBL)

[C] European Molecular Biology Laboratories
(EMBL)










It is a comprehensive database of DNA and RNA
sequences collected from the scientific literature and
patent applications and directly submitted from
researchers and sequencing groups.
Data collection is done in collaboration with GenBank
(USA) and the DNA Database of Japan (DDBJ).
It doubles in size every 18 months and as of June
1994 it contained nearly 2 million bases from 182,615
sequence entries.
It is maintained by the European Bioinformatics Institute
(EBI).
Data entry is friendly both to computers and humans.
Standard English used (explanations, descriptions etc).
Sequences are stored in the database as they would
occur in the biological state.

TYPES OF NUCLEIC ACID DATABASES
2. SECONDARY NUCLEIC ACID DATABASES
 They contain additional information
derived from analysis of data available in
primary repositories.
 They deal with particular classes of
sequences.
 Examples include UniGene, the HIV
sequence database and REBASE.

[A] UniGene SEQUENCE DATABASE
UniGene has records with unique gene
clusters.
 Each cluster contains: sequences
that
represent a unique gene and related
information e.g tissue types in which the gene
have been expressed.
 The database is populated with Expressed
Sequence Tags (EST’s).


[B] HIV SEQUENCE DATABASE


The HIV Sequence Database (HSD)
collects, curates & annotates HIV
sequence data.

PROTEIN SEQUENCE DATABABES
They consists of:
1. All the proteins that have been
translated from the RNA sequences and;
2. Protein sequenced.
 Three (3) types of protein sequence
databases exist, namely:
1. Primary protein databases;
2. Secondary protein databases and;
3. Composite protein databases.


PRIMARY PROTEIN SEQUENCE DATABASES
Synonymous examples of primary protein
sequence databases are:
1. SWISS-PROT &;
2. PIR.
 Both SWISS-PROT & PIR are curated.
 This means groups of designated curators
(database managers) prepare the entries
from literature and/ or contacts with
external experts prior to submission into
the respective databases.


[1] SWISS-PROT

1.
2.
3.
4.




a.

b.

Swiss-Prot provides high level notations describing:
Functions of a protein;
Protein domain structure;
Post-translational modifications; &
Protein variants and other variables (Bairoch and Apweiler, 2000).
It also provides a minimum level of redundancy & a high level of
integration with other databases.
It has legal restrictions in that entries are copyrighted, but freely
accessible and usable by academic researchers.
Commercial companies have to purchase a license from the Swiss
Institute of Bioinformatics (SIB) to access the database.
It classifies data into:
Core data (*Data - sequence references and taxonomic details)and;
The Annotation (*Annotation -sequence variants , functions,
domains, secondary and quaternary structures and post translational
modifications.).

[2] PROTEIN INFORMATION RESOURCE (PIR)
It is a division of the National Biomedical Research Foundation
(NBRF) in the US.
 It is a database that produces the NRL-3D (a database of
sequences extracted from the three dimensional
structures in the Protein Databank (PDB)).
 It’s existence allows sequence information in PDB to be
available for similarity searches & retrieval & provides cross
reference information for use with other PIR Protein
Sequence databases.
 It provides comprehensive, well organized, & accurate
information about proteins such as sequence similarity.
 It also maintains the PSD, NREF & the iProClass to support
researchers to understand genomic and proteomic
research.


SECONDARY PROTEIN SEQUENCE DATABASES
Major examples of Secondary protein sequence
databases are: TrEMBL, SP-TrEMBL, REM-TrEMBL
and SPRT (SWALL), Prosite and Pfam.
[1] TrEMBL:
TrEMBL stands for Translation of EMBL nucleotide
sequence database.
It is a computer-annotated supplement of SWISSPROT.
It contains all translations of EMBL nucleotide
sequence entries not yet integrated in SWISS-PROT.
TrEMBL speeds new sequence information to the
public.


[2] SP-TrEMBL:
 It focuses on entries to be incorporated later into
Swiss-Prot.
[3] REM-TrEMBL:
 It contains other data that will not be integrated
because it may be redundant or are truncated
or are not proteins or are fragments legitimately
translated in-vivo.
[4] SPRT (SWALL):
 It provides data by focusing on data currency in
Swiss-Prot, ignoring REM-TrEMBL, and by
performing sequence comparisons against a
database of all known isoforms.

[5] PROSITE:
It is a database of protein families and domains.
It consists of biologically significant sites, patterns
and profiles that help to reliably identify to which
known protein family (if any) a new sequence
belongs.
 It is part of and is maintained much like Swiss-Prot.
 It is based on regular expressions describing
characteristic subsequences of specific protein
families or domains.
 The Ras GTPase - activating protein signature pattern
is an example of a PROSITE regular expression.




[6] Pfam:









It is a database of protein families defined as domains
(contiguous segments of entire protein sequences).
For each domain, it contains a multiple alignment of a set of
defining sequences and the other sequences in Swiss-Prot and
TrEMBL that can be matched to that alignment.
Alignments can be converted into Hidden Markov Models
(HMM), which can be used to search for domains in a query
sequence.
It can be searched and used to identify domains in sequence.
It is licensed under the GNU General Public License making it
available to anyone,
However, Pfam imposes restrictions that derivative works (new
databases, &/or modifications) must be made available in
source form.

COMPOSITE DATABASES
They compile and filter sequence data
from different primary databases to
produce combined non-redundant sets
that are more complete than the
individual databases.
 An example of a composite database is
OWL.
 OWL combines 4 publicly available
primary sources: SWISS-PROT, PIR,
GenBank and NRL-3D.


SOFTWARE TOOLS FOR DATA MINING IN
BIOINFORMATICS








These range from simple command-line tools to
more complex graphical programs and standalone
web-services available from various bioinformatics
companies and public institutions.
The computational biology tool best-known among
biologists is probably BLAST.
BLAST is an algorithm for determining the similarity of
arbitrary sequences against other sequences, possibly
from curated databases of protein or DNA
sequences.
The NCBI provides a popular web-based
implementation that searches their databases.
BLAST is one of a number of generally available
programs for doing sequence alignment.

WEB SERVICES IN BIOINFORMATICS
A web service is “a program/software that can be
executed on a remote machine owning to the industry
efforts to standardize web service description,
discovery and invocation”.
 These efforts have led to standards such as
WSDL (Christenson et al, 2001), UDDI
(UDDI2002).
 The European Bioinformatics Institute (EBI) has
classified basic bioinformatics web services into
three categories:
1. SSS (Sequence Search Services);
2. MSA (Multiple Sequence Alignment) and;
3. BSA (Biological Sequence Analysis).


Availability of these service-oriented
bioinformatics resources demonstrates
the
applicability
of
web
based
bioinformatics solutions.
 The web services range from a collection
of standalone tools with a common data
format under a single, standalone or webbased interface, to integrative, distributed
and extensible bioinformatics workflow
management systems.


SOAP and REST-based interfaces have
been developed for a wide variety of
bioinformatics applications.
 This allows an application running on one
computer in one part of the world to use
algorithms, data and computing resources on
servers in other parts of the world.
 Also, end users do not have to deal with
software and database maintenance
overheads.


Transition:Web-based tools to Web Services


1.
2.
3.
4.
5.

Before Web-services came into force, web-based tools were
widely used to manage data.
However, web based tools alone, faced several hindrances
including:
Applications were not language and platform independent;
Lack of machine friendly web interface.
Non-standard input and output data format of the web
interfaces, application interface and message exchange protocol.
Transport protocol for the remote messaging were often not
fire-wall friendly.
Lack of automated service description, discovery and
integration.

Advantages of WEB SERVICES
Eliminate the need to develop and rely on adhoc screen scrapping mechanism.
2. Offer a single uniform method for the
application integration of through the internet.
3. Provide a model for web applications in which
their public interfaces and bindings are defined
and described using an XML standard format.
4. Use of XML-based messaging render the web
services infrastructure platform-and languageindependent and changes to the interface can
immediately be detected by client software.
1.

WEB SERVICE Architecture and Service Model
IN BIOINFORMATICS

Figure 1.0 The basic profile of the interoperability model of Web Services (WS-I)

The Web Service Description Language (WSDL)
(http://www.w3.org/TR/wsdl) uses the XML
standard format that describes a web service
interface and the exchange of messages between
the provider and requester in an abstract manner.
 Service providers are generally specialized genome”
centers such as National Center for Biotechnology
Information (NCBI), European Bioinformatics Institute
(EBI).
 Service consumers mostly are working in smaller
laboratories and research groups with smaller, nonspecialist resources.
1.

2. Simple Object Access Protocol (SOAP) is an
XML-based protocol for the stateless
message exchange which, in general, has
been developed on the top of HTTP.
 This makes WS -I firewall friendly as opposed
to the protocols used by (CORBA).

3.Universal Description, Discovery and Integration
(UDDI) are a standard protocol designed to publish
details about an organization and the web services.
 It provides a description and definition of web services
in a central repository, which functions as yellow
pages for web services.
 WSDL and SOAP are the W3C standards, while UDDI
is an Organization for the Advancement of Structured
Information Standards (OASIS) standard.
 For a client to use a web service it only needs WSDL
with SOAP that is commonly being used as the
default protocol.

Limitations of WEB SERVICES
The desired automation of the discovery,
composition and invocation of web services
and workflows is inhibited because:
1. UDDI search capabilities in its current form
are limited to the keyword-based matching.
It does not capture semantic relationships
between entries in its directories.


2. UDDI supports search based on only the high-level
information specified about businesses and services,
i.e., the final state specification.
 The transitory and intermediate capabilities of the
web service are not specified.
 However, UDDI service registrations may include
references to the WSDL descriptions, which may
facilitate the limited automation of the discovery and
invocation.
 But, the absence of any explicit semantic information
limits the automated comprehension of the WSDL
description to simple ontologies in domains without
contextual and conceptual differences.

3. With the parameterized input invocation for filtering
and delimiting the search domain is not available.
4. The search facilities in UDDI are restricted to exact
matches because the search is syntax based, and thus
discourages service composition and workflows.
5. Owning to the limitation of range imposed by nonsemantic descriptions, not all WSDL documents
describe the non-functional attributes such as
authenticity, currency, efficiency, performance,
scalability, etc. Even in the way WSDL+OWL-S, the
mapping OWL-S into WSDL may lose much semantic
information because WSDL can not express the
abundance semantics of OWL.

6. Both the service providers and service consumers
want to remain back-ward compatible to the legacy
formats.
 The service consumers want their data in legacy
formats so that the existing tools can operate over it.
 The service providers are wary of changing
requirements of myriad of the existing data formats.
 Although this is not a serious problem for the simple
data types, it has serious implications for most of the
biological data which is highly complex and internally
structured.

7. Scripts which are used to compose work
flows are monolithic and complex and hence
lack reusability.

Applications of BIOINFORMATICS
GENOMICS:
 Estimating the number of genes in an organism
basing on the number of nucleotide base pairs was
not reliable, due to the presence of high numbers of
redundant copies of many genes.
 Genomics has corrected this situation. Useful genes
can be selected from a gene library thus constructed
and inserted into other organisms for improvement or
harmful genes can be silenced.
 In the areas of Structural genomics, Functional
genomics and Nutritional genomics, bioinformatics
plays a vital role.
1.

a)

b)

c)

Structural Genomics:- Focuses on large scale
genome structure determination, gene
identification and characterization.
Functional Genomics:- Focuses on predicting
and identifying and characterization of
genes and genomes based on function.
Nutritional
Genomics:Focuses
on
characterizing and inferring nutritional
relevance to identified genes.

2. PROTEOMICS:
 Involves the sequencing of amino acids in a protein,
determining its three dimensional structure and relating it
to the function of the protein.
 Extensive data, generated through crystallography and
NMR, are required for proteomic studies.
 With such data on known proteins, the structure and its
relationship to function of newly discovered proteins can be
understood in a very short time.
 In such areas, bioinformatics has an enormous analytical
and predictive potential.
 It can help develop better understanding of how proteins
fold and interact with one another and with other biological
molecules which in turn will give scientists and doctors
better insight into diseases and ways to combat them.

3. CHEMINFORMARTICS & DRUG DESIGN:
 Cheminformatics involves organization of chemical data in a logical
form to facilitate the process of understanding chemical properties,
their relationship to structures and making inferences.
 In silico approaches now enable researchers:1.To identify and structurally modify a natural product;
2.To design a drug with the desired properties and;
3.To assess its therapeutic effects, theoretically.
 NB: [in silico (in the computer, based on silicon chip technology)].
 The risk involved in the earlier (in-vitro & in-vivo)random processes
of drug discovery methods is largely removed by bioinformatics.

4. MOLECULAR PHYLOGENIES:
 Phylogeny is the origin and evolution of organisms.
 Biological systems of classifications for the known organisms,
plants & animal included have been constructed.
 Amino acid sequences and characteristics of proteins are also
used in systematics.
 Similarly, new work is arising in areas to do with modeling the
phylogenetic evolution of microbes, viruses and sub-viral organisms
as a way of understanding, amongst other things:
1.
The effect of different patterns of severity on control of
infectiousness;
2.
Relationship between sub-type variation, infectivity and disease
progression and control.
3.
Factors triggering severity of infectiousness.

5. DRUG MODIFICATION:
 Several synthetic products are quite useful but cannot be
used by one and all for certain side effects in some people.
 E.g., Aspartame (marketed under different trade names) is
a dipeptide of aspartic acid and phenylalanine, and is 300
times sweeter than cane sugar.
 Aspartame is widely used as an alternate sweetener by
diabetics and others who cannot take sweeteners loaded
with calories.
 Unfortunately, pregnant women and people suffering from
phenylketonuria, a disorder due to an impaired metabolism
of phenylalanine, should not use aspartame.
 It would be useful if phenylalanine were substituted by
some other amino acid without affecting its sweetness, to
remove the restriction on its use.

The list of applications continues to grow daily and
can be seen in new “OMICS” areas such as:
1. Metagenomics - Analysis and manipulation of
microbial genomes without culturing.
2. Transcriptomics - Expression profiles of mRNA.
3. Metabolomics – Analysis, Modeling & Interpretation
of Signaling & Metabolic Pathways.
4. Cellinomics - Analysis, Modeling & Interpretation of
Cell-cell interactions.


CONCLUSION

…………

THE END……........

Bioinformatics principles and applications

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Bioinformatics principles and applications