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.ppt   Bioinformatics.ppt (Size: 130.5 KB / Downloads: 349)


What is bioinformatics.
What is computational Biology.
Data mining.
Application of data mining.
Net accessible resources.
Sequence Analysis.
What can be Done with sequence Analysis.?
Identification of protein primary sequence from DNA sequence.
Tips for searching Database.
The process of Evolution.
Principle and their Importance.

What is Bioinformatics.

Bioinformatics describe any use of computer to handle biological information. In practice, the definition used by most people in narrower, bioinformatics to them is a syononym for computational molecular biology, the use of computers to characterize the molecular components of living.
What is data mining.

Data mining is the process by which testable hypothesis are generated regarding the function or structure of gene or protein of interest by idenfenite similar sequence in better characterized organism.
Application of data mining:-
Include fraud detection, credit card scoring and personal profile marketing. Skillful interpretation of data can enhance customer relation, direct marketing, trend analysis, financial market forecasting and international criminal investigations.

Net accessible resources:-

Two main world wide web sites provide information on data mining:-
The data mine: This includes pointers to FTP-able papers, and two large data mining bibliographies. It attempts to provide links to as much of the available data mining information on the net as is possible. Run by Pryke , at the University of Birmingham.
Knowledge discovery mine: The knowledge discovery mine has the KDD FAQ, a comprehensive catalog of tools for discovery in data ,as well as back issues of the KDD-Nugget mailing list. Run by leading KDD researcher Gregory Piatetsky-Shapiro.
What is sequence Analysis.
Sequence analysis is the process of trying to find out something about a nucleotide or amino acid sequence, employing in silico biology techniques. You may have sequenced a gene yourself, and wish to learn what the long string of letters representing base, actually code for. You may want to confirm that you indeed cloned a gene successfully, or you might want to learn about a sequence of DNA that you know absolutely nothing about. You may want to know if a worm has a similar protein to a human one..
What can be done now with sequence Analysis

Given the pessimistic view of sequence analysis presented in the previous section, why do we even bother with it? In the first place the attempted to find methods for successful sequence analysis is a research goal in its own right; one whose potential rewards are so vast as to make it of the first importance. In the second place, although there are many things that sequence analysis cannot yet do , there are many very worth while things that can currently be done with sequence analysis and these will be summarized in this section.
Identification of protein sequence from DNA sequence

The computer programs which are used to infer protein sequence from DNA sequence provide information which can be used to be help approach a solution. For example, if you are trying to find out in a DNA sequence a protein is encoded, it is very used to know what peptides would be encoded by all six reading frames. A stretch containing many stop codons is a poor candidate for encoding a protein. This will not absolutely tell you where the protein sequence starts and stops, but it will you guess where that might occur. Programs exist for doing these . In fact there are many factors you can used to guess where in a DNA sequence a protein sequence might reside; use of the expected codon bias, presents of characteristic sequences representing regulatory signals in the DNA and so forth. One family of programs integrates a variety of these approaches , and using either explicit algorithms or trained neural nets ,makes a prediction.
Tips for searching database.

Use latest database version
Use blast first, then a finer tool (fasta, search, blitz , sweep, block et al)
Search both strands when using FASTA. This is automatically done in GCG
Translate sequence where relevant
Search 6-frame translation of DNA database
EO<0.05 is statistically significant, usually biologically interesting
Check also 0.05 <EO< 10, as you might find interesting stuff
Pay attention to abnormal composition, t causes biased scoring
Split large queries
If>1000 for DNA,>200 for protein
If the query has repeated segments, delete them and repeat search
The process of evolution.
Indeed, homologous proteins arise from mutations in a common ancestor coding gene. Through the process of gene divergence, some gene mutations have been accepted by natural selection because they preserved the folding and function of the coded protein. This could be represented by schematic tree where several genes come from a common ancestor gene.
Principle and their importance

Sensitivity Versus Specificity
There are different ways to estimate similarity between two sequences, allowing us to modify the sensitivity and specificity of the results when performing a sequence database search with a query sequences . If the sensitivity is high, more distantly related sequence as the S. griseus protease will be retrieved.
However, unrelated sequences as the endochitinase will also be returned. On the other hand if the specificity is high , only closely related sequences will be returned but, in this case, distantly related ones will be missed . Thus, a researcher has to know how he could manage this problem .And this is one additional reason explaining why biologists should not treat software as a black box .

Window approaches

In particular, in comparing two sequences, a dot matrix can be used where one sequence is written out horizontally and the other is written out vertically . A dot I placed at the intersection of a row and a column for each matched pair of letters. If the frequency matched letters between two sequences is high, particularly in DNA sequences , which are composed of only four building blocks , the background noise is high . In order to reduce the noise, one can place a dot only when several joined letters are matched. The numbers of joined letters evaluated together is called the window size.
Efficient use of program

When performing a database search , a research must know that he can improve his results . If he knows the principles, the use of windows, he will be increase the sensitivity by decreasing the window size parameter. This will improve the ability of the program to recognize distantly related sequences . Alternatively , he will be able to increase the specificity by increasing the window size parameter ..

This is important for a researcher who wants to use the programs available for sequence analysis to acquire a reliable knowledge of biocomputing. Knowing the capabilities and the draw backs of the program will help us to use them in a more accurate and efficient way.

.ppt   jalgaon talk.ppt (Size: 2.58 MB / Downloads: 162)

Bioinformatics - a brief overview

Dr. Arun G.Ingale

Department of Biotechnology
School of life Sciences
North Maharashtra University, Jalgaon

What is bioinformatics?

Application of information technology to the storage, management and analysis of biological information
Facilitated by the use of computers
Presented by:
Mr. D. M. Patil

.ppt   LECTURE bioinfo1T2.ppt (Size: 1.08 MB / Downloads: 98)
• Bioinformatics is an integration of computer knowledge, mathematical and statistical methods to manage and analyze the biological information.
• Bioinformatics is currently defined as the study of information content and information flow in biological systems and processes.
• The science of developing computer databases and algorithms for the purpose of speeding up and enhancing biological research.
• Bioinformatics focuses more on the development of practical tools for biological data management and analysis
Bioinformatics evolved to deal with four related but still distinct works namely:
1. Handling and management of biological data including its organization, control, analysis and so forth.
2. Communication among people, projects and institutions engaged in biological research and applications. This may include email, file transfer, remote login, computer conferencing and establishment of web based information resources.
3. Organization, access, search and retrieval of biological information, documents and literature.
4. Analysis and interpretation of the biological data through the computational approaches including visualization, mathematical modeling and development of algorithms for highly processing of complex biological structures.
Application areas of bioinformatics:
• Post-genome applications
• Sequence analysis
• Protein structure prediction
• Data processing, data management
• Database searches
• Phylogenetic analysis
• Gene expression
• Recognition of genes and regulatory elements
• Modeling and simulation of metabolic pathways and regulatory networks
• Software tools
Human Genome Project
• Just a half-century ago very little was known about the genetic factors that contribute to human disease.
• In 1953, James Watson and Francis Crick described the double helix structure of deoxyribonucleic acid (DNA), the chemical compound that contains the genetic instructions for building, running and maintaining living organisms.
• Until the early 1970’s, DNA was the most difficult cellular molecule for biochemists to analyze.
• DNA is now the easiest molecule to analyze – we can now isolate a specific region of the genome, produce a virtually unlimited number of copies of it, and determine its nucleotide sequence overnight.
The human genome is made up of approximately three billion base pairs of deoxyribonucleic acid (DNA). The bases of DNA are adenine (A), thymine (T), guanine (G), and cytosine ©.
• In 1990, the National Institutes of Health (NIH) and the US. Department of Energy joined with international partners in a quest to sequence all 3 billion letters, or base pairs, in the human genome, which is the complete set of DNA in the human body. This concerted, public effort was the Human Genome Project.
Who is the U.S. Human Genome Project?
National Center for Human Genome Research
• Department of Energy (DOE) - Ari Patrinos
• National Institutes of Health(NIH)- Francis Collins
• DOE Joint Genome Institute
3 DOE national labs
• Baylor College of Medicine
• Sanger Centre
• Washington University Genome Sequencing Center
• Whitehead Institute/MIT Center for Genome Research
Where locally?
• University of Washington Genome Center
• University of Washington Multimegabase Sequencing Center
• A reference sequence - not an exact match for any one person
• Blood (female) or sperm (male) samples taken from a large number of donors.
• Ethnically diverse
• Few samples processed
• Names protected
• identify all the approximate 30,000 genes in human DNA,

• determine the sequences of the 3 billion chemical base pairs that make up human DNA,
• store this information in databases,
• improve tools for data analysis,
• transfer related technologies to the private sector, and
• address the ethical, legal, and social issues (ELSI) that may arise from the project.
An Independent Genome Project? 1998
• Celera Genomics Corporation CEO- Craig Venter
• Proposes to sequence human genome in 3 years for $3 million
• Invented new sequencing technologies
■ 1990: Project initiated as joint effort of U.S. Department of Energy and the National Institutes of Health
■ June 2000: Completion of a working draft of the entire human genome (covers >90% of the genome)
■ February 2001: Analyses of the working draft are published (First draft published in Science and Nature in February, 2001)
■ April 2003: HGP sequencing is completed and Project is declared finished two years ahead of schedule
Finished Human Genome sequence published in Nature 2003.
• The Human Genome Project’s goal was to provide researchers with powerful tools to understand the genetic factors in human disease, paving the way for new strategies for their diagnosis, treatment and prevention.
• From the start, the Human Genome Project supported an Ethical, Legal and Social Implications research program to address the many complex issues that might arise from this science.
• All data generated by the Human Genome Project were made freely and rapidly available on the Internet, serving to accelerate the pace of medical discovery around the globe.
• In April 2003, researchers successfully completed the Human Genome Project, under budget and more than two years ahead of schedule.
• Sequencing Progress Draft Sequence:
Completed June 26, 2000
• Joint announcement by Venter and Collins
What does the draft human genome sequence tell us?
By the Numbers
• The human genome contains 3 billion chemical nucleotide bases (A, C, T, and G).
• The average gene consists of 3000 bases, but sizes vary greatly, with the largest known human gene being dystrophin at 2.4 million bases.
• The total number of genes is estimated at around 30,000 to 40,000.
• Almost all (99.9%) nucleotide bases are exactly the same in all people.
• The functions are unknown for over 50% of discovered genes.
• Total length 3000 Mb
• ~ 40,000 genes (coding seq)
• Gene sequences < 5%
– Exons ~ 1.5% (coding)
– Introns ~ 3.5% (noncoding)
– Intergenic regions (junk) > 95%
– Repeats > 50%
How It's Arranged
• The human genome's gene-dense "urban centers" are predominantly composed of the DNA building blocks G and C.
• In contrast, the gene-poor "deserts" are rich in the DNA building blocks A and T. GC- and AT-rich regions usually can be seen through a microscope as light and dark bands on chromosomes.
• Genes appear to be concentrated in random areas along the genome, with vast expanses of non coding DNA between.
• Stretches of up to 30,000 C and G bases repeating over and over often occur adjacent to gene-rich areas, forming a barrier between the genes and the "junk DNA.”
• Chromosome 1 has the most genes (2968), and the Y chromosome has the fewest (231).
The Wheat from the Chaff
• Less than 2% of the genome codes for proteins.
• Repeated sequences that do not code for proteins ("junk DNA") make up at least 50% of the human genome.
• Repetitive sequences are thought to have no direct functions, but they shed light on chromosome structure and dynamics. Over time, these repeats reshape the genome by rearranging it, creating entirely new genes, and modifying and reshuffling existing genes.
• These genomes were sequenced by 2003
Genome Sizes (MegaBases)
• Model organisms
• Bacteria (E. coli, influenza, several others)
• Yeast (Saccharomyces cerevisiae)
• Plant (Arabidopsis thaliana)
• Roundworm (Caenorhabditis elegans)
• Fruit fly (Drosophila melanogaster)
• Mouse (Mus musculus)


Bioinformatics is a newly emerging interdisciplinary research area which is defined as the interface between biological and computational sciences. Bioinformatics itself is not a well-defined term, so we can say; this deals with the computational management of all kinds of biological information, whether it’s about genes and their products, whole organisms or ecological systems. Bioinformatics work as to gather, store, analyze and integrate biological and genetic information that is applied to developmental biology, evolutionary Biology, or to gene based drug discovery.

In last few decades, advances in molecular biology have allowed the increasingly rapid sequencing of large proteins of the genomes of several species like Bakers yeast. The human genome project, designed to sequence all 24 human chromosomes is also progressing, sequence databases, as Gene-bank and EMBL is growing at exponential rates. The most pressing task in bioinformatics involves the analysis of sequence information. “Computational biology”- the name given to this process. Set of genetic instruction, making an organism – its genome is contained in long thread like DNA molecules packed into chromosomes. These sequence of chemical units in the DNA is kind of code specifying the structure of protein molecules, which carry out most of the functions of living cells.

DNA sequencing, sequence alignment, biological database and retrieval systems are discussed in the preceding chapters. Another aspect of bioinformatics is DNA computation and formation of bio-chip. “Guinness world record” recently (2003) recognized a computer that performs 66billion operations/second with 99.8% accuracy. The area of 1c.c can contain 10 trillion DNA molecules and with this amount the computer can hold 10 terabytes of data and can perform 10 trillion calculations at a time.


Biology in the 21st century is being transformed from a purely lab based science to an information science as well.

Bioinformatics is a new global wave. Simply speaking bioinformatics is the management, analysis and interpretation of biological data. It is a convergence of plant and animal science, mathematics and information technology. Bioinformatics is application of, computer science, mathematical and statistical tools in the area of life science.

Thus bioinformatics is the field of science in which biology, computer science, and information technology merge to form a single discipline. The ultimate goal of the field is to enable a global perspective from which unifying principles in biology can discerned at the beginning of the “genomic revolution” a bioinformatics concern was the creation and maintenance of a database to store biological information, such as nucleotide and amino acid sequences. Development of this type of database involved not only design issues, but the development of complex interfaces whereby researchers could both access existing data as well as submit new or revised data.

Ultimately, however, all of this information must be combined to form a comprehensive picture of normal cellular activities so that researchers may study how these activities are altered in different disease state. Therefore, the field of bioinformatics has evolved such that the most pressing task now involves the analysis and interpretation of various types of data, including nucleotide and amino acid sequence, protein domains, and protein structures. The actual process of analyzing and interpreting data is referred to as “computational biology”.

Important sub-disciplines within bioinformatics and computational biology include:
- The development and implementation of tools that enable efficient access to, and use and management of, various types of information; and
- The development of new algorithm (mathematical formulae) and statistics with which to assess relationship among members of large data sets, such as methods to locate a gene within a sequence, protein structure and/or function, and cluster protein sequences into families of related sequences.

The “Guinness world records” recently (2003) recognized a computer that can perform 66billion operations a second with 99.8% accuracy, more than 100,000 times the speed of the fastest pc as “the smallest computing device” ever constructed. This amazing speed was feasible, thanks to DNA.

DNA Computation-THE ENIGMA.

DNA computing in the literal sense is the use of DNA (Deoxyribose Nucleic Acid) molecules, molecules that encode genetic information for all living beings, in computer. This is accomplished in a suspended solution of DNA, where certain combinations of DNA molecules are interpreted as a particular result to a problem encoded in the original molecules present. DNA computation relies on devising algorithms that solve problems using the encoded information in the sequence of nucleotides that make up DNA’s double helix-the bases Adenine, Guanine, Cytosine, and Thymine (A, G, C, and T, respectively) and then breaking and making new bonds between them to reach the answer, DNA computing is one of the fastest growing fields in both Computer Science and Biology, and its future looks extremely promising.

In the fore-front of this industry are Israeli scientists from the Weizmann Institute of Science in the Rheovot who developed this molecular computing machine composed of enzymes and DNA molecules instead of silicon microchips. The DNA computer is also self powered i.e. the new design made of -and fuelled by – DNA. The scientists discovered that single DNA molecule can yield all the energy needed to run a computation. The machine is so small that a tiny droplet could hold up to three trillion of these DNA computers, in total performing 66 billion operations a second.
Significance of DNA computing:

The computer became the first programmable autonomous computing in which the input, output, soft-ware, hard-ware were all made of DNA computer can work without an external energy source.

Conventional electronic computers process information – as electrical impulses – through circuits etched onto silicon chips, but technology is approaching the physical limits of miniaturization. Technologies except that sometime between 2010 and 2015, the long march of Moore’s Law- which states that “computing power doubles every 18 months or so- will come to a sudden halt.” This is where DNA computers will play vital role. The new computer design uses naturally occurring enzymes as the “hard-ware”. Each computational step requires two complementary DNA molecules –one that performs an input and other that performs a “soft-ware” role. DNA computers have the potential to take computing to new levels, picking up where Moore’s Law leaves off.

Advantages to using DNA instead of Silicon:

As long as cellular organisms, there will always be a supply of DNA makes it a cheap resource. Unlike the toxic materials used to make traditional microprocessors, DNA biochips can be made cleanly. DNA’s key advantage is that it will make computers smaller than any computer that has come before them, while at the same time holding more data. Speed (100 *faster than a fast supercomputer). The energy efficiency (Computers built by humans waste about a billion times more energy per operation). More than ten trillion DNA molecules can fit into an area no longer than one cubic centimeter (0.06 cubic inches). With this small amount of DNA, a computer would be able to hold ten terabytes of data, and perform ten trillion calculations at a time.


Dealing with the biological entities which are represented in mathematics and computer parlances, bioinformatics made the job easy for the scientists and researchers. It gave new dimensions with the help of which man can’t even think of where actually he is going to land. Bioinformatics is an excellent tool in the development of biology.. Here the importance of internet too comes into existence by the help of which many resources can be known. In very simple words we can say that the hard labor of scientists has been eased out by the help of the tools via internet.

Sequencing: -

Genome in just the sequence of all DNA and is a complete set of determined DNA sequence of the genetic material of the particular organism. There are several tools and techniques that involve the study of genome. Each of the tools and techniques so employed is based on some standard principles and fundamental percepts.
DNA sequencing means determining the number of nucleotide sequence of a DNA strand, the strand is labeled at one end and then spitted into one of the nucleotides. The fragmentation is done by the help of electrophoresis gel to find the length of the sequence and the presence of each nucleotide. Automated gel (florescent color emitter) based sequencing technology are also available to sequence the chromosomes and this process of detection is faster, quiet
accurate and economical. In the total sequence of the nucleotides one single line denotes the information about the protein created by amino acid sequence.
Manual sequencing is done by the help of the following steps:-
1. Single strand of DNA is prepared which is to be sequenced.
2. Template DNA is supplied with –
a. mixture of all four nucleotide in fixed quantities( DNA polymerase)
b. Mixture of all four di-deoxynucleotides.Finally the mixture is DNA polymerase +diNTPS for A, G, T, C.
3. The chain elongation proceeds normally until by chance DNA polymerase inserts a di-deoxynucleotide instead of deoxynucleotide. If the ratio is high then some DNA strands add several hundreds of nucleotides before the process come to halt.
4. After the reaction period, fragments are separated by length from longest to shortest.
Resolution is so high that the difference of one nucleotide is enough to separate the strand from the next shorter or next longer strand.


It is important for bioinformatics professional to know the means to identify the similarities and differences in sequences. There are several means to determine the similarities and homologies of the various sequences.

1. statement about evolutionary history 1. not so
2. two or more sequences have a common ancestors 2. two or more sequences are similar by same criterion
3. sequences are compared and it’s results have profound impact 3. sequences are just compared by some method and is logically weaker

Alignment: -

It is a hypothetical concept of positional homology between bases or amino acids. It represents evolutionary relationship between the protein sequences by placing them side by side. There are co dons that encode the starting and ending of the sequence. Alignment is “correct” if the events in historical past are represented. Correct alignment means the co dons matching with that of the ancestral proteins. Correct sequences can be reconstructed to get the ancestral sequences and indicates the substitutions, insertions and deletions.
This is a standardized approximation where we check the alignments through several ways. It is known to us that mutation in a gene could change the protein or gene in several ways –

1. Elimination of certain gene could produce a GAP in the sequence
2. Introduction of a new DNA could produce a new sequence where we can address it as an INSERTION.
3. Substitution of any DNA could also change the sequence.

Optimal alignment:

There is no rule for optimal alignment so there’s no such thing a single best alignment is possible. This forms the base line and the basis of which the sequences are compared. We can categorize the alignment into two forms:
1) Global alignment
2) Local alignment.
Global alignment assumes that the alignment of two proteins or genes is basically similar to the entire length of their corresponding sequences to each other, from one end to other.
Local alignment doesn’t work under any assumptions. Entire sequence of the corresponding proteins or genes searched and attempted to match segments out of sequences. No attempt is made to force a sequence the alignment. It merely compares the part that has good similarity.

Multiple sequence alignment is a mathematical model and is resorted to for the following reasons:
1) Generation of concise information
2) Illustration of dissimilarity between groups of segments. Multiple alignments arrange a set of sequences in a scheme where position are believed to be homologous are written in a common column and all similar sequences can be compared in one single figure or table.

There is an important biostatic tool that approximates the biological event with the mathematical formulae and models. This tool is called Substitution matrix. These are of three types which are widely used-a) Point aligned matrix b) Blocked substitution matrix c) Gonnet.


All biological sequence databases work on the percept of similarity tables either explicitly or implicitly. These databases make use of number of biological parameters: evolutionary model, structural properties and chemical properties such as charge, polarity, shape.
1.Alignment of pairs of sequences
Shows how much each pair is related
However it should be noted that each of the algorithm model is specific in application, utility, and environment. Thus, one needs to exercise judgment in choosing the algorithm and model.

4. List of common sequence changes

Used to improve sequence alignment
2. Alignment of a group of sequence
Shows which of the position correspond to one another in the sequences

3.List of alignment scores and changes in each position
Used to predict phylogenetic relationship amongst various sequences

Biological Databases:

A biological database consists of a large value of biological data which is organized in a consistent pattern. A database comprises of one or more files, each file has many records, and each record has same set of data fields and contains the same type of information in them. There are several types of biological database. Each of it has own purpose data elements and features. For the researchers point of view the data so stored in the databases needs to be easily accessed through standard means and the results so obtained should be free of unwanted data. Most of the databases organize their data by gene sequence, sometimes it varies. Classification of Biological Databases lists a few databases and depicts one of the methods of classification of biological databases:

1) Fundamental type of biological data:
a) Nucleotide sequences b) Protein sequences
c) Protein sequences patterns d) Macromolecular 3D structure
e) Gene expression data f) Metabolic pathways.

2) Primary / Derived data:
a) Primary databases-
The experimental results are fed directly into the database and stored in it where data isn’t verified for authenticity.
b) Secondary databases-
Data elements are stored in the database only on verification of its authenticity. c) Aggregate of many databases-links to other data items, combination of data, and consolidation of data.

3) Technical design:
a) Flat files
b) Relational database (SQL)
c) Objected-oriented database
d) Exchange/ publication technologies (FTP, HTML, CORBA, XML……).

Retrieval system:

It’s very important block in database. It’s a user interface and helps the user to retrieve data from the database through user friendly, easily understandable query formats. Sequence Retrieval System (SRS) and Entrez are examples of these databases. They are web-oriented systems, which have a well defined web interface for integrating
Heterogeneous databases. In many cases, biological databases are multitudes of heterogeneous databases which are interlinked. Search and retrieval of data in these databases is based on the principle of pre-made indexes. These are a set of all possible /relevant items that are found in the documents of that database.
There are various approaches for searching a data:
a) Using keywords
b) Through accession numbers and identifiers
c) Through reference of literature.

Data in bioinformatics is basically in the form of records having a number of fields. The very aim of storing data on computer databases is to facilitate searching. Typically every record has two names, each following a particular set of rules:
a) Accession code- its basically a number which have combination of numerals and alphabets
b) Identifier- is a generic string of letters and digits which is easily interpretable in some meaningful way by the human, for instance as a recognizable abbreviation of full protein or are name of gene
c) Sequence submission

The year 2003 marks the 50th anniversary of DNA discovery. Now, standing at this era of computers we can easily say it’s the dusk of computer era which waits the dawn of DNA era. From all the previous discussion we can conclude that the advancement made in the field of microbiology, biotechnology, genetics and use of computational techniques in the management of all kind of biological information and also the storage, organization , and indexing of sequence information leads to the formation of the interdisciplinary research area known as bioinformatics. We can infer from all the previous discussion that bioinformatics has rendered studies, involving sequence, easy. One needs to understand the methods by which biological entities, events, tests and the like are represented in mathematical and computer parlances. Bioinformatics is the new global wave. Drug firms like SmithKline Beecham, Pfizer etc are positive about contribution of bioinformatics in drug research area. Companies of agricultural produce like Monsanto, Cargill seeds etc are utilizing in the plant sciences domain. IT companies like IBM, sun micro system, wipro etc anticipate that bioinformatics will be an important revenue domain for them.


Search Engine: google, AltaVista, Lycos, hotbot.
Website: www.bioinformatics .org,,,,,
Magazine: Science reporter, Bioinformatics.
Newspaper: Science articles of The Telegraph, The Statesman, and Hindustan Times.
References: Michael Paul Stewart publications, bioinformatics at Chalmers,

Attached Files
.doc   Bioinformatics.doc (Size: 128 KB / Downloads: 81)

.doc   WAP.doc (Size: 56 KB / Downloads: 88)
breaking the medical barriers

Days aren’t far off when beauty saloons will perform fundamental body changes apart from customizing looks of the people . If you aren’t born perfect , free from any disease and deformity , you need not despair . If you lack mathematical aptitude it will be incorporated in your mind . If you aren’t tall enough stature will be changed accordingly . If you are weak and not sturdy enough , your physique could be improved . At the first sign of the defect and deformity , people will stop around for a better and stronger organically grown heart , brain kidney or the liver etc . Mankind will be able to prolong its life or for that matter even may live forever
If you think this is the script for a science fiction movie , you are mistaken .It’s the future reality . All this is possible with a technology called BIOINFORMATICS. The convergence of biology, computer science, electronics and mathematics into the bioinformatics domain will be the enabling factor behind such innovations.
Bioinformatics is the application of biology , computer science , electronics , IT and mathematical and statistical tools in the domain of life sciences .This paper comprises of the live examples of the advancement of bioinformatics and its areas of applications like Motorola and IBM etc.
By using Bioinformatics today, we have :
 DNA Chip : DNA Chip is small flat surface on which strands of one half of the DNA double-helix (called probes) are placed.
 Protein Chip : Protein Chip is similar to DNA chips , except that these sample individual proteins that makes up the DNA .
 Biochip : Biochip is an IC whose electrical and logical functions are performed by protein molecules appropriately manipulated.
 Gene Chip : Gene chip reclassify diseases on the basis of their underlying molecular signals rather than misleading surface symptoms.
The human gene study has unlocked the DNA code, resulting in unlocking secrets of 23 human chromosomes. This will enable life science engineers to realize new drugs to treat diseases. Electronics and life science engineers are taking advantages of the similarities between the bio/gene chips and electronic circuits to evolve novel manufacturing techniques.
If such is the pace of development of bioinformatics than in future we can expect humans to live much longer and even become immortal……
Days aren’t far off when beauty saloons will perform fundamental body changes apart from customizing looks of the people . If you aren’t born perfect , free from any disease and deformity , you need not despair . If you lack mathematical aptitude it will be incorporated in your mind . If you aren’t tall enough stature will be changed accordingly . If you are weak and not sturdy enough , your physique could be improved . At the first sign of the defect and deformity , people will stop around for a better and stronger organically grown heart , brain kidney or the liver etc . Mankind will be able to prolong its life or for that matter even may live forever If you think this is the script for a science fiction movie , you are mistaken .It’s the future reality .The future is often stranger than fiction . The convergence of biology , computational science , electronics and mathematics into the bioinformatics domain will be the enabling factor behind such innovations .
Dr . John Satini , a former researcher at Massachusetts Institute of Technology (MIT) , now heads a start-up company named Microchips that develops silicon chips with tiny wells filled with drug compounds . The drug compounds can be released in the human body in a controlled manner via the preprogrammed microchip .
What bioinformatics is ?
Bioinformatics is the application of biology , computer science , electronics , IT and mathematical and statistical tools in the domain of life sciences . Vijay S . Shukla , Director of Bioinformatics Institute of India , Noida , emphasizes the importance of advanced computational approaches in enabling bioinformatics researchers to organize , search , access , retrieve and analyze biological data .
Bioinformatics finds application in medicines for recommending individually tailored drugs based on an individual’s profile .
Bioinformatics has given birth to various chips working at DNA level . Bioinformatics helps in controlling DNA as under :
DNA Control via RF Signal :
Researchers at MIT have moved a step towards integrating electronics and biological functions . They have been able to control biomolecules using RF energy and nanocrystal antenna . They remotely controlled the behavior of DNA , the basic building block of humans and other forms of life , causing it to switch from one state to another at will .
How it was done ?
An electronic interface to the bio molecule was created . RF magnetic field was inductively coupled to a 1nm long nanocrystal antenna linked covalently to a DNA molecule . The inductive coupling , i.e. the transfer of energy to the nanocrystal energy , increased the local temperature of the bound DNA , allowing the change of state to take place , while leaving molecules surrounding the DNA relatively unaffected . The switching was fully reversible , as dissolved molecules dissipated the heat in less than 50 pikoseconds . Thus RF signal generated outside the body can control changes in DNA . The signal used in the experiment was 1 GHz.
Human Electronics :
The nucleus is the most obvious organelle in the human cell . With in the nucleus is the DNA (deoxyribonucleic acid) responsible for providing the cell with its unique characteristics . The DNA is similar in every cell of the body , but depending on the specific cell type , some genes may be turned on or off - that’s why a liver cell is different from a muscle cell .
In DNA the medium is a chain of two units (phosphate and ribose) , and the most easily recognizable message is provided by a sequence of letters (bases) attached to the chain . The DNA has two sequences of letters wrapped around each other in the form of a double helix . One is the compliment of the other , so that the sequence of one string (strand) can be inferred from the sequence of the other . The DNA sequence of bases encodes 20 amino acids . Under instructions received from DNA , amino acids join together in the same order as they are encoded in DNA to form proteins . Chains of amino acids , which folds in very complicated ways , play a very important role in determining how we interact with the environment .
Moving towards the chips :
“ Mapping the human genome is akin to transcribing the text of a very big book”, says Nicholas Naclerio ,VP and GM of the Biochip Systems Unit of Motorola . He adds , “We all have this text without understanding of what it means . Its like knowing the alphabets but not being able to read anything .There is to go a long way before it will make sense .” Sevaral electronics companies have joined the ranks of public biotech companies and started funding start-ups that develop Biochips . Agilent Laboratories ,a unit of Agilent Technologies , has been researching biochips . Its Chemical Analysis Group has introduced the first commercially available ‘lab-on-a-chip’. The device prepares biological samples , handles fluids and performs biochemical analysis……..all on one microchip .
The lab-on-a-chip functions like a microprocessor . It carries out complicated multistep processes . DNA chips use a chemical that causes the DNA to fluoresce when a match occurs . Electronics circuits can be incorporated on the chip to detect various stages of the DNA . DNA carries an electric charge . That charge can be read on the chip just like a memory array . Motorola manufactures lab-on-a-chip devices on a process development line akin to the one used for semiconductor and MEMS technology . The manufacturing process is a kind of MEMS technology . Semiconductor processes such as photolithography and etching are used to make chips . But since the feature size of these chips is much larger than of typical IC’s the related process equipments are similar to those used in the flat-panel industry or a high-density circuit board facility , rather than the chip fab .
DNA Chip :
DNA Chips are small flat surfaces on which strands of one half of the DNA double-helix (called probes) are placed. Because one-half of the DNA double-helix naturally bonds with its complementary other half (a process called hybridization) this chip can be used to identify the presence of particular genes in a biological sample . These chips , called micro arrays , can be manufactured using semiconductor technology on variety of surfaces (including glass and plastic) .
DNA Chip used to detect pathogens :
Detecting pathogens, whether from natural diseases or biological weapons, is about to get faster and more convenient, thanks to a new technique that can sense harmful DNA and immediately alert a doctor or scientist. The research, published in the April 9 issue of the Journal of the American Chemical Society, uses custom-designed loops of DNA that emit colored light in the presence of a specific creature's DNA. The loop-laden chip could be used to detect anything from a bacterium or virus, to the specific DNA of a plant or person.
The new chip is remarkable in that it eliminates many of the time-consuming steps normally taken in identifying an organism by its DNA. Traditionally, workers in a laboratory have to make thousands of copies of a piece of DNA they want to test. Then a complex series of steps must be performed to attach a special molecule to the DNA, which will act as a fluorescent beacon, making the DNA strand easy to detect. These beacon-outfitted pieces are then mixed with control DNA sequences to see if any match. Matching sequences would adhere to one another, betraying their presence via the beacon.
The Rochester team, Krauss and Benjamin Miller, associate professor of dermatology, and post doctoral fellow Hui Du, has created a new technique that is far simpler. A scientist might only have to place a drop of the solution in question onto a small chip or card and watch for a change of color to indicate whether specific DNA is present. The chips are sensitive enough that copying may be unnecessary, as are complex beacon attachments, and the chips could be easily manufactured so doctors could instantly detect dozens or hundreds of pathogens right in their office. Future soldiers would also be able to identify unknown biological substances quickly and surely on the battlefield.
A chip using the new method would be constructed like a field of wilted sunflowers-customized sequences of DNA are bent like hairpins, with one end "planted" into a layer of metal and the other end hanging down alongside it . This dangling end contains a molecule called a flourophore , which , like the brilliant head of a sunflower, shines brightly when properly lighted. With all of the sunflowers' heads bent down to the ground, the field as a whole looks green because the fluorophore is short-circuited when directly on the metal. When "watered" with the right DNA sequence, however, the flowers stand erect, turning the entire field-and thus the chip-from green to bright yellow.
The unfolding of the chip's detector DNA strands happens when new DNA with a precise sequence is dripped onto the chip. The chip's DNA is designed to prefer to be bonded with a specific DNA sequence, such as a sequence unique to anthrax, than to remain folded over on itself. The new DNA bonds along the length of many of the chip's DNA and the two form a sort of rigid stem that lifts the beacon. The all-important beacon is pre-attached to the detector strand of DNA, rather than needing to be attached to each and every strand of DNA being tested
Currently, the Rochester team has developed chips that can detect an antibiotic-resistant type of stalph bacteria, and they're working on chips that can detect the non-antibiotic-resistant strain as well. A laser is also needed at present to highlight the "sunflower head," but Miller and Krauss are working on ways to make the signal from the beacon more easily visible.
Protein Chips :
Protein Chips are similar to DNA chips , except that these sample individual proteins that makes up the DNA . The market for these devices is less immediate because medical science is far from identifying and mapping all the 100,00 proteins that makes up DNA .
“With experience and expertise in semiconductor technology , electronics , softwares , MEMS , high density interconnects and flat-panel manufacturing - Motorola has the capability to develop biochips in high volume ,” says Mr.Naclerio .He points out that electronics companies can take advantages of some parallels between electronics and biology .
Biochip and the chip electronics :
Biochip is an IC whose electrical and logical functions are performed by protein molecules appropriately manipulated. Advances in molecular biology and semiconductor micro fabrication have resulted in new formats for hybridization arrays. These arrays consists of several electrodes covered by a thin layer of agarose. Each micro electrode is capable of generating a controllable electric current that can be used to draw biological samples, reagents and probes to specific locations on the chip surface. The no. of genes covered by these arrays depends on the no. electrodes made with in the area of the array.
Cell microarrays are high-density arrays of microwells fabricated on an optical fibre plane with a packing density of 10 wells per Each individually addressed microwell can accommodate a single living cell . A charged-couple device detector is used to monitor and spatially receive flurescence signals from each cell. The technology provides a reliable method to validate new disease-infected cells for early evaluation of potential drugs. The micro total analysis system (micro TAS) or a lab-on-a-chip for integrated bio-chemical analysis periodically transforms biochemical information into an electronic or optic signal. The components on the lab-on-a-chip device can be divided into active and passive components. Biological fluids to be tested are activated using the electrokinetic principle. Electrokinetic flow is generated when electrodes, attached with computer-driven power supplies, are placed in reservoirs of each end of a channel and activated to generate electric current through the channel under these conditions. Fluid flow takes through electro-osmosis and electrophoresis

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