What is bioinformatics

Bioinformatics is the application of computer technology to the management of biological information. Computers are used to gather, store, analyze and integrate biological and genetic information which can then be applied to gene-based drug discovery and development. The need for Bioinformatics capabilities has been precipitated by the explosion of publicly available genomic information resulting from the Human Genome Project. The goal of this project - determination of the sequence of the entire human genome (approximately three billion base pairs) - will be reached by the year 2002. The science of Bioinformatics, which is the melding of molecular biology with computer science, is essential to the use of genomic information in understanding human diseases and in the identification of new molecular targets for drug discovery. In recognition of this, many universities, government institutions and pharmaceutical firms have formed bioinformatics groups, consisting of computational biologists and bioinformatics computer scientists. Such groups will be key to unraveling the mass of information generated by large scale sequencing efforts underway in laboratories around the world.

The Indian success in software and pharmaceutical has kindled expectations that the next frontier of biotechnology will also be conquered. The logic is simple and appealing. The future of biotechnology is being rewritten with the onset of genetic mapping and the primary tool that powers this new era is informatics. Since India has proven competency in information technology, a competitive edge in biotechnology is within reach via the bioinformatics route.

Unlike software, biotechnology has had direct government support right from day one. The department of biotechnology was set up way back in 1986, Through these years around a score of government funded laboratories have taken basic research in biotechnology to internationally noticed levels. So while software started off only with trained manpower, the biotechnology base to go with it.

All this should mark success in biotechnology only a factor of time. But global competitiveness remains a somewhat open ended potential. A long distance remains to be covered and none can say with certitude that it will be. Meanwhile, the stakes are high- both because of the central government investment already sunk and also because the most forward looking of state governments are developing a stake in the area.

It includes anything that deals with living things. That take in conventional areas like fermentation, brewing and even biotechnology. By this definition the Indian biotechnology industry has a turnover of $ 2.5 billion (Rs 12,000 crore). On the other hand, the turnover of the new biotechnology sector, that which deals with the manipulation of genes and bioinformatics, accounts for less than Rs 500 crore. And what is more, it is not expected to grow at the 50 percent plus rate of IT enabled services. But global experts are constantly surprised by the level of energy that the Indian biotechnology industry displays and strongly affirm its initial advantages. Along with Korea, China, Australia and Israel, Indian is readily considered an emerging player in the industry.

Dr John Fagan, CEO of Genetic ID, an American company at the forefront of identification of genetically modified objects, describes the whole biotechnology spectrum thus. " At one end it is high tech-dealing with genomics, pharmacological drug discovery, transgenic crop development. At the other end are the biopesticides, biofertilisers, composting, enzymes, control agents. In between you have the marker directed breeding. So biotechnology stretches from high end genomics to agriculture itself."

There is indeed a new and old part to biotechnology but if you are talking about a frontier science that is exploding with the arrival of genetic mapping and sequencing of the human genome, that it is the new biotechnology that is the focus. And it is here that the reality on the ground is extremely thin. There are barely a handful of recombinant (derived through genetic engineering) products in the market. Volumes come from vaccines, particularly for hepatitis B marketed by companies like Shanta Biotech, Bharat Biotech and Wockhardt. As was the case with pharmaceuticals, reengineering of generic products is mostly what is being undertaken. There is also a bit of contract research. And there are a clutch of truly high tech startups like Avesthagen, Strand Genomics and Metahelix. But these are no more than promising startups, less than three years old.

The most significant and oldest player is Biocon, set up 20 years ago by an enterprising Indian entrepreneur, Kiran Mazumdar- Shaw. It is today a leading player in industrial enzymes and more. It has a strong R&D focus and its most recent development is a proprietary bioreactor for which it has obtained a US patent for it. In healthcare, it has combined its fermentation skills with synthetic chemistry to produce a number of speciality biogenerics. It has been particularly successful with statins, a group of cholesterol reducing drugs. One of its facilities for these has received US FDA approval. All very well but it has a projected turnover of Rs200 crore.

Indian capability in bioinformatics, needed to crunch the avalanche of genomic data to derive benefits it, is assumed simply because of the Indian success in software. But one dose not automatically flow from the other. The startups are into it but larger concerns will have to commercialize what they develop. The most important gap is the absence from the market yet of a single genetically engineered agricultural biotechnology product. It has taken six years for the regulator to clear BT cotton developed by Monsanto and Mahyco. Both Indian and China began work on biotechnology in the eighties but Chinese use of biotechnology products is miles ahead of India’s. India’s cautiousness and concern for safety is understandable but that does not mean its biotechnology has taken off.

India certainly has a great potential in biotechnology but it is still mostly in the laboratory. It will take enormous funding by a new generation of risk taking entrepreneurs to make a commercial success of it. Software needed little investment; same was the case with re-engineering pharmaceutical products. Biotechnology products, even those not covered under IPR, need comparatively large resources in money and time to develop. It can be done and India has passed the pre-qualification round but results will not start flowing the way already are in anther new field-IT enabled services. Amidst all the buzz about biotechnology, the long haul often does not get mentioned.

In June 2000, the working draft of the human genome was announced. The Human Genome Project’s success in sequencing the chemical bases of DNA opened a new frontier for the IT industry – Bioinformatics. A discipline representing the combined power of biology, mathematics, and computers which involves the study of data relating to three billion units of the human genome – the entire sequence of DNA made up of the four nucleotide based A C T and G spread across 23 chromosomes. DNA. Tipped to be a $25 billion market in the next five years in India, IT industries are gearing up to meet the challenges of data management and control for the bio-tech industry. Although the new buzz word for the IT industry, apart from a functional definition, of the field most techies are unaware of high throughput genomic computational technologies which are transforming biological sciences and the IT industry alike.

Mapping the Genome
Genomics is the study of chromosomes inhabiting the nuclei of human cells. All cells as we know have 46 chromosomes, one pair of sex chromosomes and 22 pairs of autosomes, which have no role in sex determination.

Each chromosome is made up of two strands of DNA which wrap around each other in the form of a double helix. Each chromosomal is made up of four DNA bases, or nucleotides: Adenine (A), thymine (T), guanine (G), and cytosine (C). When the individual chromosomal strands bond, adenine binds to thymine and guanine binds to cytosine. Each set of two bound nucleotides is a base pair. Ninety-seven per cent of these base pairs have no known function, and have been labeled “junk DNA” by scientists. Interspersed along the strands, however, are distinct nucleotide sequences called genes, each of which contains instructions that govern the body’s development and functions. The human genome — which refers to all the genes stretched along the chromosomes in cells — contains anywhere between 30,000 and 100,000 genes.

Genes, and the instructions they contain, express themselves by forming proteins, which are large molecules that give the body structure and carry out its functions. Examples of proteins which we are familiar with include enzymes, which facilitate, or catalyse, the reactions in cells; antibodies, produced by the immune system to fight infection when antigens invade the body; and hormones, which regulate functions such as growth and reproduction.

But for all the hype generated just as to understand meaning of a book it is not enough to know the number of pages, mere knowledge of the kinds of sequence and number of genes is by itself useless. To successfully apply the knowledge, various genes will have to be pieced together together with their corresponding proteins like a giant jigsaw puzzle identifying hidden biochemical pathways at work in health and disease. Normal genes, and variations known as polymorphisms, will be pieced together with their corresponding proteins like a giant jigsaw puzzle, identifying hidden biochemical pathways at work in health and disease. Some of these proteins will serve as drugs directly. Others will serve as new targets for drug intervention. Protein, antibody, and small-molecule (chemical) drugs will be developed to act on these targets with much more selectivity and potency than seen today.

Hence there is a tremendous opportunity for companies to intercede at various levels in this process with technologies and information that will revolutionise human health and fulfil the aspiration unleashed by the unraveling of the genomic sequence.

Areas in Genomics
The efforts in the near term post Genomic Sequence era will focus on the following areas:

Genetic variability or SNPs: The complete genetic makeup of an individual is known as a genotype. The way in which the genotype expresses itself, i.e., a person’s physical traits, is called a phenotype. The genotype of all humans is 99.9 per cent the same; only the .1 per cent difference in the genetic makeup of individuals accounts for their uniqueness, or their differing phenotypes. These genetic differences often come in the form of Single Nucleotide Polymorphisms (SNPs), so named because a single nucleotide in a base pair is different from what it should be.

This subsector is hence concerned with how the single nucleotide polymorphisms (SNPs) that constitute most of the genetic differences between individuals influence susceptibility to disease. Genetic variations might also explain why a given drug works well with some patients, but not with others

Functional genomics: With a catalog of the entire genome, gene sequences can be more rapidly isolated, but functional genomics – the process of determining the function of genes to find those that will make good targets for drug discovery – is going to be a growing field of research.

Lead generation and optimisation: Identifying targets for drug discovery from the total pool of genes will allow companies to produce and optimise drug leads using drug discovery methods such as antibody generation (the most direct route to finding an inhibitor of a given a target), combinatorial chemistry (for generating small-molecule compounds) and high-throughput screening (to find active small molecule compounds among the millions available in libraries).

Gene expression: All cells in the body, with the exception of red blood cells, contain all 23 chromosomes, which contain all genes. But not all genes are "turned on" to make their respective proteins. If a gene is turned on, it is said to be expressed. This is what makes a kidney cell a kidney cell and a nerve cell a nerve cell. For example, the insulin gene is turned on in specialized cells in the pancreas called islet cells. The insulin gene also resides in kidney cells, but it is turned off. In specialized kidney cells, the erythropoietin gene is turned on. Much is being learned about gene function by comparing levels of gene expression in body tissues and organs in states of health and disease. Scientists are also studying gene expression of tissues in response to drug therapy. This approach, known as pharmacogenomics, can provide important clues on toxicity and efficacy even before a drug enters human clinical testing.

Genes, and the instructions they contain, express themselves by forming proteins. Genes produce these proteins via a process called transcription. During transcription, the bonds that bind the DNA molecules break apart. Once separated, a molecule of messenger RNA (mRNA) moves along the individual DNA strand and copies its coding portions, the nucleotides that make up its genes.

When this transcription process is complete, the mRNA molecule will have formed a string of coding nucleotide bases. The mRNA then carries this string outside the nucleus and into the cytoplasm of the cell. Here, the string of bases is exposed to the ribosome, an organelle that carries out the work of translation—the process of carrying out the nucleotides’ instructions to form a protein.

The bases along the nucleotide string work three at a time. That is, each string of three bases, known as a codon, codes for a specific amino acid—the molecules that form proteins. As the bases perform this coding function, a molecule of transfer RNA (tRNA) carries the appropriate amino acid bases to the ribosome. The various amino acids—each of which was coded for by a different set of codons—are then strung together by the ribosome to form a protein.

By analysing genes and their expression, scientists can gain insight into the causes of illness. By looking at genetic variabilities, they can ascertain why a given population is more susceptible to certain diseases than others. Such analysis can also give them clues as to how to customize treatments to fit individual genotypes.

Commercialising the genome
The genomics industry is trying to leverage the enormous and limitless potential of these dicoveries. Apart from Bioinformatics, the general categories of future genomic product revenues are described below.

Genome sequence: Genome sequence is an ordered map of the DNA molecules in a given organism. It is the ultimate description of the genome, as the periodic table of the elements is for chemistry or as an alphabet is for a language. Now complete, the sequence of the human genome will be made available free of charge to the public, both by Celera and by the DOE/NIH Human Genome Project. The commercial value of the sequence will be realised as additional layers of genomic information are added to it. As information accumulates from other downstream genomics activities -- such as polymorphism discovery, disease association, genotyping, pharmacogenomics, and phenotyping -- the information will be added to the databases and annotated. These databases will become important repositories of information for biotechnology and pharmaceutical companies.

Gene discovery and function: Newly discovered genes may be patented if their function is known, as may the proteins for which they code. These proteins may be useful as protein drugs in and of themselves, an approach that served as the basis for the founding of the biotechnology industry. For example, the erythropoietin gene codes for the protein hormone erythropoietin, which Amgen turned into a blockbuster drug (Epogen) used to stimulate the bone marrow to produce red blood cells for the treatment of anemia.

Human Genome Sciences is on the forefront of this strategy, with three novel protein drugs currently in Phase II clinical testing. Lower organisms may be subject to genetic manipulations that are highly revealing of the roles of a gene, but that are not feasible in humans for obvious ethical reasons. An example is the use of genetically engineered mouse models. It is possible to engineer a mouse so that it is incapable of expressing a gene, potentially revealing how other proteins in the organism are affected by such a loss. Alternatively, expression of the gene can be manipulated up or down, or regulated in a time or location-dependent manner. Additional constraints on the utility of such models, beyond the appropriateness of the model itself, derive from the size of the organism, the reproductive turnover time, and similar factors.

Other proteins serve as cellular receptors that mediate specific cellular processes. Once associated with disease, these receptors can serve as important targets for development of new protein, antibody, and small-molecule drugs. Abnormal genes can also serve as diagnostic markers for associated diseases.

Expression Profiling: No gene functions in an isolated fashion. Genes, and the proteins they encode, are parts of larger systems, interacting with each other in complicated ways. These systems are known as pathways, and as the expression of a particular gene is regulated by the body to occur at specific times and in specific locations, so the expression of entire pathways of genes is likewise regulated. The aim of expression profiling is to use the patterns of gene expression as a clue for understanding the underlying pathways.

Genotyping: Progress in the hunt for disease genes depends on the ability to access individuals with disease and analyze their genetic makeup. Several methods based on target signal amplification technology have now emerged that can rapidly test for specific SNPs. The process, broadly known as genotyping, will soon reach industrial scale within several companies. High-throughput factories will comb through hundreds of tissue samples per day looking for genetic needles in disease haystacks

Pharmacogenomics: Pharmacogenomics is the study of genotype and its relationship to drug action. The goal is to use the right drug for the right person at the right time. Surprisingly, fewer than half of patients experience the intended benefit of most drugs, despite taking them as directed. Not surprisingly, most drugs have side effects, ranging from the merely bothersome (like rash and fatigue) to life-threatening reactions. Why do some patients benefit and some not? Why do some patients have violent reactions while others are unfazed? The answers are increasingly believed to be genetic.

Variations in drug-metabolizing enzymes, transporters, receptors, and other drug targets explain these individual responses. Pharmacogenomic approaches apply this information throughout the drug development process to define subsets of patient populations who are both likely to benefit from a drug and not experience adverse events. Already the concept is in place with Herceptin — only patients who have the HER2 gene and receptor are treated.

Soon, diseases that have been viewed as one, like hypertension and depression, will be seen as many. The day will come when doctors will diagnose several kinds of hypertension, each with a specific therapy known through pharmacogenomic information to be effective. The upside of this for the pharmaceutical industry is that many drugs that failed to show benefit in broad clinical trials may be resurrected with new trials tailored to treat individuals who are genetically likely to benefit.

Proteomics: An offshoot of the genomics revolution has been the advent of proteomics. As genomics is the study of the genome, the complete collection of genes in an organism, proteomics is defined as the study of the proteome, the complete complement of proteins. Information about a protein includes its amino acid sequence, its mass, and other physical properties that might be thought of as protein chemistry. Other investigations deal more with the proteome as a whole, such as large-scale pathway interaction assays, which look at large numbers of physical protein-protein interactions in parallel. Proteomics is highly complementary to genomics. For example, a proteomic lead is typically strong in potential, but difficult to follow up on. The ability to work backward from proteomics to genomics and discover the gene responsible for that protein opens many avenues of further investigation.

Bioinformatics: High-throughput approaches to data gathering necessitate substantial data-sorting abilities. The expanding field of bioinformatics involves the application of computing techniques toward processing all of this data and making it accessible and useful. Computers are used to reassemble the pieces of DNA sequence that come out of sequencing efforts, to process the enormous data sets arising from expression profiling, and at all stages of the increasingly automated discovery process to manage the robotic systems that perform the various procedures. And, of course, bioinformatics offers a means of interpreting the results of all this manipulated data.

A particularly large potential application uses computers to predict the significance of sequence data. The idea of using sequence to predict function starts with the observation that many genes fall into what are known as families, similar both in sequence and in function. Usually the genes, and the proteins that they encode, have retained certain critical, defining features over the course of evolution. These similarities go all the way down to the DNA sequence level, meaning that the family as a whole can be defined by a sort of DNA fingerprint. An analysis of new genomic sequence data that looks for similarities to known gene families can shed light on the function of a gene absent any other knowledge. Given the fact that the DNA sequence of a gene alone is sufficient to determine the properties of a protein, it makes sense that researchers would attempt to understand this translation to the level necessary to predict the characteristics of a gene based solely on that sequence. As simple as this might sound, the difficulty of such an endeavor is fearsome, and such attempts remain for the most part rudimentary. In truth, sequence analysis never relies solely on the sequence, since it is necessarily a comparative enterprise reliant upon the "wet biology" that has preceded it. The problem of sequence-function predictive analysis is multifarious, reflecting both a lack of computational power and a lack of accumulated knowledge on the basis of which to model the systems involved. One example is the relatively small amount that we know about the final three-dimensional structures of proteins, due to the difficulty involved in such structure determination. Nevertheless, there is considerable optimism that such attempts, known collectively as structural genomics, will eventually become an essential tool in the biologist’s toolbox.

Genomics Business Models: Four primary business models have emerged in the genomics industry. Structural Genomics Information companies, Functional Genomics Information companies, target drug dicovery companies and enabling genomic technology companies.

Structural Genomics Information Companies: These are the genomics information companies, defining the structure of the human genome and its related proteins. They seek to become the "Bloombergs" of the pharmaceutical industry, providing must-have genomic sequence, variation, and function information in gigantic databases. Initially, companies like Celera and Incyte have pursued a database subscription model for deep-pocketed large pharmaceutical customers. As with the genomic technology companies, the key challenge is to stay ahead of the information obsolescence curve.

Functional Genomics: Information Companies These companies start life as genomics services providers to pharmaceutical companies. The pharmaceutical deals validate the technology and pay the bills as they build their own pipelines of proprietary drugs in an effort to skip into the genomic drugs category. Like other genomics participants, functional genomics companies are meaningful competitors in the intellectual property race. Because of customer demand, they are at the forefront of pharmacogenomics, applying genomic technology to the evaluation of big pharma pipeline drugs even before they enter human clinical testing. The key challenges for these companies are to preserve enough of their discoveries and intellectual property to remain competitive as standalone entities. Examples include CuraGen, Tularik, Myraid, Genset, Lexicon Genetics, and Gene Logic.

Target Drug Discovery Companies: These are the companies at the forefront of developing tomorrow’s genomic drugs. For example, Human Genome Sciences has identified hundreds of proteins that have potential for use directly as injectable drugs. Three of these are currently undergoing Phase II clinical testing. The other major player, Millennium Pharmaceuticals, is target-based, with antibody drugs in human clinical testing today and several small-molecule compounds directed to new genomic targets in preclinical development. The central challenge for this model is success in clinical trials.

Enabling Genomic Technology Companies: These are the tool companies providing the picks and shovels of the genomics industry. New research tools, gene sequencers, chips, and hardware have enabled the entire industry in a mere short decade. The business models are similar to the hardware and processor models in the technology industry, with the addition of diagnostic and reagent sales. The key challenge for these companies will be to remain on the cusp of the innovation curve as yesterday’s technologies become commodified.

he deluge of data generated by the Human Genome Project (HGP) and other genomic research presents a broad array of commercial opportunities One such opportunity is Bioinformatics - information control for the Biotechnology industry. The successful mapping of the human genome by scientists has released detailed, complex and voluminous data that needs to be read and analyzed correctly for the initial research to make headway. For example, the National Center for Biotechnology Information processes nearly 3 million requests a day from biologists and other researchers. Compounding this is the fact that there are as many kinds of biological data as they are experiments. Currently gene and genome sequences are the most abundantly collected data types, followed by protein atomic coordinates, DNA sequences, etc. Industries involved in Genomes, Pharmaceutics, Proteomics Gene Expression, Genotyping etc are heavily dependant on timely development of computational analysis, effective data management and analysis of data.

It is in these areas that the industries spawned by the Human Genome Project are looking for the services of IT professionals or Bioinformaticians, professional data analysts who can work with the avalanche of data generated by the experimental biological community and by a growing number of data factory projects eg genome sequencing projects. Their work involves: designing sophisticated databases that can accurately represent map information (linkage, STSs, physical location, disease loci) and sequences (genomic, DNA’s, proteins) and linking them to each other and to bibliographic text databases of the scientific and medical literature; improving database design, software for database access and manipulation and data-entry procedures and mining the data base to develop new hypotheses, new models of how biological systems function and even rules and patterns which can be used to analyze data sets Companies like IBM, Satyam and Wipro in India, seeking a role in the information revolution with DNA at its core, are already extending their IT services to companies interested in the potential for targeting and applying genome data. Apart from the bellwethers dozens of small companies have sprung up to sell information, technologies, and services to facilitate basic research into genes and their functions. These new entrepreneurs offer an abundance of genomic services and applications, including additional databases with DNA sequences from humans, animals, plants, and microbes.

Key Bioinformatic Areas
They are a variety of areas in the yet emerging territory of Bioinformatics:

Seamless High Performance Computing: Megabases of DNA sequence being analyzed each day will strain the capacity of existing supercomputing centers. Interoperability between high-performance computing centers will be needed to provide the aggregate computing power, managed through the use of sophisticated resource management tools. The system must be fault-tolerant to machine and network failures so that no data or results are lost.

Sequence Annotation: Computers can be used very effectively to indicate the location of genes and of regions that control the expression of genes and to discover relationships between each new sequence and other known sequences from many different organisms. The process is referred to as sequence annotation. Annotation (the elucidation and description of biologically relevant features in the sequence) is the essential prerequisite before the genome sequence data can be useful and the quality with which annotation is done will directly affect the value of the sequence.

Simulation: The process involves using known information about a system along with a mathematical or physiochemical model to simulate properties of the system. The category is incredibly diverse from simulating the motion of interacting protein molecules to modelling the flow of chemicals through biochemical pathways.

Data Mining and Information Retrieval: Methods are needed to locate and retrieve information relevant to newly discovered genes. If similar genes or proteins are discovered through sequence comparison, often experiments have been performed on one or more homologues that can provide insight into the newly discovered gene or protein. Relevant information is contained in more than 100 databases scattered throughout the world, including DNA and protein sequence databases, genome mapping databases, metabolic pathway databases, gene expression databases, gene function and phenotype databases, and protein structure data-bases. This data can provide insight into a gene’s biochemical or whole organism function, pattern of expression in tissues, protein structure type or class, functional family, metabolic role, and potential relationship to disease phenotypes.

The target data resources are very heterogeneous (i.e., structured in a variety of ways), and some are merely text-based and poorly formatted, making the identification of relevant information and its retrieval difficult. Intelligent information retrieval technology is being applied to this domain to improve the reliability of such systems. One challenge here is that information relevant to an important gene or protein may appear in any database at any time. As a result, systems now being developed dynamically update the descriptions of genes and proteins in our data warehouse and continually poll remote data resources for new information.

Data Warehousing: The information retrieved by intelligent agents or calculated by the analysis system must be collected and stored in a local repository from which it can be retrieved and used in further analysis processes, seen by researchers, or downloaded into community databases. Numerous data of many types need to be stored and managed in such a way that descriptions of genomic regions and links to external data can be maintained and updated continually. In addition, large volumes of data in the warehouse must be accessible to the analysis systems running at multiple sites at a moment’s notice.

Visualization for Data and Collaboration: The sheer volume and complexity of the analyzed information and links to data in many remote databases require advanced data visualization methods to allow user access to the data. Users need to interface with the raw sequence data; the analysis process; and the resulting synthesis of gene models, features, patterns, genome map data, anatomical or disease phenotypes; and other relevant data. In addition, collaborations among multiple sites are required for most large genome analysis problems, Even more complex and hierarchical displays are needed that that will be able to zoom in from each chromosome to see the chromosome fragments (or clones) that have been sequenced and then display the genes and other functional features at the sequence level. Linked (or hyperlinked) to each feature will be detailed information about its properties, the computational or experimental methods used for its characterization, and further links to many remote databases that contain additional information. Analysis processes and intelligent retrieval agents will provide the feature details available in the interface and dynamically construct links to remote data.

Parallel Algorithms for Sequence Analysis: The recognition of important features in a sequence, such as genes, must be highly automated to eliminate the need for time-consuming manual gene model building. Five distinct types of algorithms (pattern recognition, statistical measurement, sequence comparison, gene modeling, and data mining) must be combined into a coordinated toolkit to synthesize the complete analysis. One of the key types of algorithms needed is pattern recognition. Methods need to be designed to detect the subtle statistical patterns characteristic of biologically important sequence features, such as genes or gene regulatory regions. DNA sequences are remarkably difficult to interpret through visual examination.

In genomics and computational biology, pattern recognition systems often employ artificial neural networks or other similar classifiers to distinguish sequence regions containing a particular feature from those regions that do not. Machine-learning methods allow computer-based systems to learn about patterns from examples in DNA sequence. They have proven to be valuable because our biological understanding of the properties of sequence patterns is very limited. Also, the underlying patterns in the sequence corresponding to genes or other features are often very weak, so several measures must be combined to improve the reliability of the prediction.

High-speed sequence comparison represents another important class of algorithms used to compare one DNA or protein sequence with another in a way that extracts how and where the two sequences are similar. Many organisms share many of the same basic genes and proteins, and information about a gene or protein in one organism provides insight into the function of its “relatives” or “homologues” in other organisms.

Experiments in simpler organisms often provide insight into the importance of a gene in humans, so sequence comparison is a very important tool. Often the most accurate and sensitive methods for making this comparison are carried out using massively parallel computational platforms.

Key skills: Knowledge of relational databases like Oracle and Sybase, ability to work comfortably in a command line scripting environment and knowledge of programming languages such and C, C++ and a scripting language such as Perl are fundamental key skills. A detailed list of skills needed for various posts are listed below

Software Engineer Informatics: If you want to be a software engineer (informatics) you must possess knowledge of relational databases like Oracle Sybase or SQL Strong object related design and development skills in Java or C++ would be of great help.

Software Engineer Bioinformatics: Strong object oriented design and skills in Java C, C++ along with knowledge of Oracle PL/SQL. XML middleware or application servers are a plus.

Support Engineers: Here again strong object oriented design and skills in Java C, C++ along with knowledge of Oracle PL/SQL are needed. XML middle ware or application servers are a plus.

Quality Engineers: Familiarity with sequence analysis tools such as BLAST, FAST A is desired. Other desired skills are Perl and Shell programming. Oracle SQL and Unix computing.

Programmer Analyst: Knowledge of Unix operating environment and database management system like SQL, Sybase and Oracle is a plus. Knowledge of user application software such as PC database packages spreadsheets and word processing programs would also be helpful.

With the crash in the US markets, IT companies are desperately looking for a new emerging area that can help them tide over the rough times. With the biotechnology industry being flooded with funds, Bioinformatics may just be it.

(Assure Consulting acknowledges help of existing sources, especially Biospace’s Genomics Primer in compiling this article.)

Recent years have seen an explosive growth in biological data. Large sequencing projects are producing increasing quantities of nucleotide sequences. The contents of nucleotide databases are doubling in size approximately every 14 months. The latest release of GenBank (V.102) exceeded one billion base pairs. Not only the size of sequence data is rapidly increasing, but also the number of characterized genes from many organisms and protein structures doubles about every two years. To cope with this great quantity of data, a new scientific discipline has emerged: bioinformatics, biocomputing or computational biology.

But how to become a bioinformatics expert?

Bioinformatics combines the tools and techniques of mathematics, computer science and biology in order to understand the biological significance of a variety of data. So if you like to get into this new scientific field you should be fond of these 'classic' disciplines. Because the field is so new, almost everyone in it did something else before. Some biologist went into bioinformatics by picking up programming but others entered via the reverse route.

Now you don't have to go through university twice. More and more interdisciplinary programs emerge, for example at the Computer Science and Biotechnology Department at Bielefeld University, Germany. The introductory courses in its bioinformatics program are similar to those of 'classical' computer science: algorithms and data structures, theoretical computer science, computer architecture, and programming practicals. You will also have mathematics courses on linear algebra, analysis, differential equations, applied maths, and statistics. Introductory biology courses are included as well. Later on the amount of biology courses increases, and the student will get also 'hands-on' experience in laboratory work. Here the student gets some sort of idea about the biologist's everyday work and sometimes realizes what computing tools are available and what tools are missing. The ideas for many of them were born during laboratory work!

In USA, anyone you ask about job prospects in the world of bioinformatics for young scientists will give the same answer: This field is hot! It is far from being overpopulated (at least in 1997 :-), and the number of jobs is growing further. Some of the biggest drug firms - like SmithKline Beecham, Merck, Johnson & Johnson, and Glaxo Wellcome - are hunting for bioinformatics experts while smaller firms have difficulties to get the staffers they want. In Europe, especially in Germany, the situation is less enthusiastic but we're hopefully catching up. While traditional scientific job markets are shrinking, here might be the opportunity many young scientists are looking for.

The Indian government launched a bioinformatics program nearly 15 years ago.Recent government and industry initiatives are leading to a spurt in bioinformatics activities. This report describes current research in universities and public-funded labs, profiles some industry participants and discusses challenges that India faces in its attempts to emerge as a leading

Biotechnology, Chemistry, Computer Software, Government Policy on Science and Technology, High Performance Computing

The Indian government launched a bioinformatics program nearly 15 years ago. India's Department of Biotechnology (DBT) initiated a program to promote the application of information technology (IT) in biotechnology research and began to create a network-based infrastructure that now extends across 57 universities and public-funded institutions. This DBT initiative spawned research groups involved in database creation, molecular modeling and algorithm development. Several groups have also been applying bioinformatics tools to address real-world problems in the biological sciences. DBT has pledged US$65 million (M) for genomics research over the next five years and has announced plans to enhance the infrastructure for bioinformatics research in public-funded institutions. The past year has also witnessed the entry of the private industry into genomics and bioinformatics. Some of these start-ups are working toward the generation of intellectual property, including bioinformatics products. A few companies are also pursuing contract research services for domestic and international clients in the biotechnology and pharmaceutical sectors. This report describes India's public-funded and industry initiatives in bioinformatics, citing examples from both these sectors and outlining industry trends. The report also discusses the challenges that India will face in its attempt to emerge as a leading participant in bioinformatics. ATIP offers a full range of information services including reports, assessments, briefings, visits, sample procurements, workshops, cultural/business sensitivity training, and liaison activities, all performed by our on-the-ground multilingual experts.

As the IT industry slows down in the US and India, Bioinformatics has become a new opportunity for many an Indian. The worldwide Bioinformatics and related research market is expected to be worth $20 billion ! Like in the IT industry, India's advantage is its large pool of English speaking, tech savvy, and relatively inexpensive workforce and like in the IT industry many Indian Companies are already part of this industry.

What is Bioinformatics?

Bioinformatics is an interdisciplinary research area, which may be defined as the interface between biological and computational sciences. Thus, the people working in this field in most cases either have training in biology or computer science, and they learned about the other field by dealing with problems or using the tools of the other one. Although the term 'Bioinformatics' is not really well defined, you could say that this scientific field deals with the computational management of all kinds of biological information, Most of the bioinformatics work that is being done can be described as analyzing biological data.

How it all began

Human Genome Project- An Overview.
Objectives of Research : The found human genome in every cell of a human being consists of 23 pairs of chromosomes.These chromosomes constitute the 3 billion letters of chemical code that specify the blueprint for a human being. The world Human Genome Project, a vast endeavor aimed at reading this entire DNA code will completely transform biology, medicine and biotechnology. The entire code will be available on our computers, all 30,000 human genes will be identified; all 5000 inherited diseases will become diagnosable and potentially curable; drug design will be completely transformed; and our understanding of ourselves will move into a new dimension. The Genome Project focuses on two main objectives: mapping - pinpointing the genomic location of all genes and markers; and DNA sequencing - reading the chemical "text" of all the genes and their intervening sequences. DNA sequences are entered into large databases, where they can be compared with the known genes, including inter-species comparisons. The explosion of publicly available genomic information resulting from the Human Genome Project has precipitated the need for Bioinformatics capabilities. The science of Bioinformatics, which is the melding of molecular biology with computer science, is essential to the use of genomic information in understanding human diseases and in the identification of new molecular targets for drug discovery.

Biology Vs Computers ?

The jobs currently available in Bioinformatics involve the design and implementation of programs and systems for the storage, management and analysis of vast amounts of DNA sequence data. Such positions require in-depth programming and relational database skills, which very few biologists possess, and so it is largely the computational specialists who are filling these roles. This is not to say the computer-savvy biologist doesn't play an important role. As the bioinformatics field matures there will be a huge demand for outreach to the biological community to sift through gigabases of genomic sequence in search of novel targets. It will be in these areas that biologists with the necessary computational skills will find their niche.
Programming Skills
In addition to extensive knowledge of molecular biology packages (GCG, BLAST, FASTA etc.), one will need to learn web and programming skills including HTML, Perl, JAVA and C++ and be familiar with a variety of operating systems (especially UNIX and Linux). Relational database skills like SQL and database application such as Sybase or Oracle will be highly advantageous.

What are the job profiles like?

1. Data mining - Huge amount of unorganized biological data to be sorted.
2. Relational Databases - Gene Banks , Protein Data Banks etc.
3. Diagnostic Kits - Develop kits for predicting diseases or predisposition of an individual for particular disease.
4. Bioinformatics Software - Custom made software for various scientific needs.
5. Proteomics - Structure and function of Proteins.
6. Genomics - Expression and function of genes.

Indian Companies into Bioinformatics

Satyam, TCS, DSQ Biotech, Dr.Reddy's Laboratories, Nicholas Piramal, MBT etc.

Graphics Support Specialist - Biocomputing - The Graphics Support Specialist will provide training and support to Whitehead Institute scientists in the use of graphics and image processing software and hardware, and in creating print materials. Responsibilities include instructing scientists in the use of graphics software and hardware packages, and developing and giving training sessions on graphics software. Other responsibilities include evaluating and introducing new graphics related hardware and software, creating illustrations for scientists, maintaining image processing hardware in the user room, and developing and maintaining web-based help pages on the use of software and hardware tools for graphics and image processing. Bachelor's degree in biology or related discipline and relevant experience in art, graphic design, or computer graphics required. Must have a minimum of two years of directly related graphics experience. Experience with Adobe Photoshop, Adobe Illustrator, Macromedia Dreamweaver, Macromedia Freehand or similar graphics software packages on multiple platforms also required. Demonstrated skills in instruction and training and the ability to work well with all levels of the Institute staff. Must have excellent interpersonal, verbal and written communication skills. (#42-02)

Team Leader, Haplotype LIMS Informatics - Center for Genome Research - Directs a small team of Software Engineers and Programmers to develop data processing software and hardware that supports the objectives of a high throughput molecular biology program. Provides leadership in designing and managing team projects, programming software and hardware, and in mentoring and supervising team members. Bachelor’s degree in computer science or related field required. At least 3 years of experience with software development in one or more of the following areas is required: Java, Perl, SQL, C/C++, relational modeling, and Unix environments. A general interest in science is a must. Strong written and verbal communication skills in addition to strong organizational skills are required. Advanced degree preferred. Prior management experience is preferred, but not required. Knowledge of molecular biology a plus. (#G40-02)

Senior Software Engineer - Center for Genome Research - Designs and develops complex data management, workflow, and or data analysis systems that support genomics research projects in accordance with best practices and new technical concepts. Works with users to guide determination of system and user specification. Gathers user feedback and modifies project plans accordingly. Bachelor’s degree in Computer Science or related field and 5 to 8 years of software development experience required. Relevant graduate work may be considered. Demonstrated knowledge of any combination of the following languages required: Java, Perl, C, C++ required. Knowledge of Oracle and SQL a plus. Prior involvement or interest in biology or related environment a plus. Excellent communication skills and the ability to perform effectively in a fast-paced environment required. Must be able to handle a variety of tasks, effectively solve problems with numerous and complex variables, and shift priorities rapidly. (#G48-02)

Software Engineer - Center for Genome Research - Designs and develops data management, workflow and data analysis systems for high throughput genomic research goals. Develops novel solutions to computational problems, which may involve both algorithmic and software architecture issues. Designs and implements oracle applications and sets up database reporting environment. Bachelor’s degree in Computer Science or related field and 4 years of development experience or relevant advanced degree required. Must have demonstrated work experience with some combination of Perl, C, C++, and Java in a Unix environment. Knowledge of Oracle and SQL strongly preferred. Must be familiar with mfact resampling. Willing to learn process of data acquisition relevant to algorithm and software development. Should be technically flexible and comfortable with development tactics involving rapid protoyping and frequent user interaction. Excellent written and verbal communication skills and able to listen to user requirements and respond to rapidly changing user needs. Knowledge of standard good software development and project planning practices required. (#G25-02)

Software Engineer - Center for Genome Research - Will develop, implement, and maintain information systems for genomic research in Perl, C, C++, Java, and other programming languages in UNIX, NT and WWW environments. Will write codes aimed at designing and optimizing software programs, work with biologists to determine the needs and priorities for software applications, and check code and software for quality. MS or Ph.D. in computer science or biology with hands on software development experience or BS with 2+ years of hands on software development. Must be proficient in Perl, C, C++, and Java. Must be comfortable in gathering and adapting to feedback from biologists and users. Good written and verbal communication skills required. Educational background in biology or lab or Bioinformatics work experience preferred, but not required. (#G53-01), (#G145-01), (#G147-01)

Data Analyst - Center for Genome Research - Performs computer analysis of DNA sequencing projects prior to their submission in sequencing databases. Ensures that projects are properly assembled and completed by examining data quality and ordering necessary lab work to resolve issues of poor data quality. Bachelor’s degree required; concentration or coursework in molecular biology or recombinant DNA techniques preferred. Experience with NT and Unix environments required; familiarity with public sequencing databases or software preferred. Must be attentive to detail and possess an affinity for problem solving. Must be able to work independently and as part of a team. (#G56-02)

Scientific Programmer - Center for Genome Research - Programs and maintains robust data processing systems that optimize laboratory results analysis and support the research goals of a high through put molecular biology laboratory. Gathers feedback from lab users to identify problems and project needs, updates and programs hardware and software specifically to meet such needs. Other related tasks as required. BachelorÕs degree required. Must be proficient in some combination of the following languages: C, C++, Java, Oracle, SQL, and Perl. Must have experience in Unix operating systems. Must be able to communicate well with an interdisciplinary group of biologists, engineers, and technicians. Some experience with molecular biology or recombinant DNA techniques preferred. (#G150-01, #G148-01)

Is the title of a ‘Software Superpower’ justified for India Inc? This question will be asked repeatedly year after year as India Inc continues to push its brand as the lowest priced destination. Industry analysts say that if it was Y2K earlier, it is Business Process Outsourcing (BPO) now. The recent Nasscom 2002 event too saw India’s IT majors going into overdrive on the BPO boom. While no one doubts the potential of the BPO boom for India, it is a fact that the country is a preferred destination because of its lower price. Sooner or later, nations like China or Philippines can compete on price and take away the market from India. While Nasscom is pushing the BPO opportunity aggressively, it has also identified several emerging sectors like bioinformatics, embedded technologies and multimedia content management, which could see India Inc moving away from its image of a low-cost supplier to a value-added service player. But the fastest growing and most promising of these sectors is undoubtedly bioinformatics. For the uninformed, bioinformatics refers to the applications of IT in data analysis, mainly in the creation of extensive electronic databases on genomes and protein molecules, and in three-dimensional modelling of biomolecules and biologic systems. Though bioinformatics is still in a nascent stage in India, industry analysts are extremely gung-ho on its market potential. And the reasons behind this bullishness are based on simple market estimates. Today, pharmaceutical and life sciences firms are increasingly placing emphasis on IT investments related to research, to gain competitive advantage by reducing time for drug discovery. Starting off with analysis of macromolecules, bioinformatics has advanced to a stage where it is playing an increasingly important role in the human genome project, drug discovery and prediction of protein structure and function. Computational analysis for interpretation of functions of genes and DNA sequences involves processing huge amounts of data that need to be analysed and interpreted for drug discovery. By the study of genes, pharmaceutical firms can determine the cause of various deformities, diseases and their remedies. Bioinformatics through the use of computer databases and algorithms speeds up and enhances biological research projects. A case in point is the human genome project, which is an effort to identify a mammoth 80,000 genes in Human DNA through the use of bioinformatics. Just imagine a group of scientists doing it manually! No wonder, people trained in the field of bioinformatics are in great demand in countries like the US where leading pharmaceutical companies are competing aggressively with each other for developing cures for gene-based diseases. Undoubtedly, career opportunities for professionals trained in the field of bioinformatics are high, as pharmaceutical research and drug development is a high growth area. Agrees Prof Deepti Deobagkar, director, Bioinfo-rmatics Centre, University of Pune, “Bioinformatics is the convergence of two technological revolutions, The upsurge in biological data due to the advances in biotechnology, paralleled by the phenomenal growth in information technology. The CPU power of computers and the size of the GenBank (a genetic sequence database) have been doubling at the same rate. With the completion of sequencing of human genome and genomes of various plants and organisms, bioinformatics is well poised to take up the challenges of the post-genomic era and hence the need for well-trained human resource is greater. There is hence a huge demand for trained bioinformaticians in USA, Europe, Australia and India.” A Frost & Sullivan report estimated the US bioinformatics market to be worth US $ 1.4 billion in 2000 which is expected to grow to US $6.9 billion by the year 2007. In India alone professionals trained in this field can expect to earn salaries upwards of Rs 2,00,000 per annum. A key indicator is seen by the fact that students who have completed the bioinformatics course at the Pune University have been lapped up by various Indian and multinational biotechnology and pharmaceutical industries, such as AstraZeneca Research Centre, Bangalore, GVK Biosciences, Hyderabad, DSQ Biotech, Bangalore and reputed research institutes like the Centre for Cellular and Molecular Biology (CCMB), Hyderabad and the Centre for Development of Advanced Computing (C-DAC), Pune. Indian software companies hit hard by the US slowdown have been quick to spot this opportunity. For example, Satyam Computer Services is working in developing software for pharmaceutical research. It has already tied up with the Centre for Cellular and Molecular Biology for joint exploration of IT enabled services opportunities in bioinformatics. India’s largest exporter of software, Tata Consultancy Services (TCS), has tied up with the Centre for DNMA Fingerprinting and Diagnostics in Hyderabad, for collaborative research in the areas of pattern recognition, clustering techniques and development of new techniques in genomics and proteomics. The Indian IT industry can address opportunities in the field of automated genome analysis, modelling of protein structures from primary sequences and software development for molecular modelling. As bioinformatics involves implementation of software for creation, storage and analysis of vast amount of DNA sequence data, the professionals are required to have programming and database skills along with an in-depth knowledge of biology. Likewise, since drug development involves studying the behaviour of complex molecules and developing tools to find out their capabilities, a person with a background in chemistry could play a key role in drug development. Also eligible are computer scientists and software professionals who are well trained in database tools, since bioinformatics uses computer software tools for database creation, data management, data warehousing, data mining and data analysis. Career opportunities for bioinformatics professionals include database design, database management and computational analysis. Typical skill sets required are strong knowledge of databases and operating systems like UNIX with programming skills like C, C++, Java or Perl. Almost all the institutes in India have formulated extremely detailed courses supported by good infrastructure for propagating bioinformatics as the next big career option. For example, the course conducted by the University of Pune includes an introduction to biological systems, mathematical and statistical techniques, database systems (Oracle & Developer 2000), biological databanks and sequence analysis, computer networking, biomolecular structure and dynamics, molecular modelling and simulations, HTML, C and Java programming, parallel computing, numerical methods and optimisation techniques, neuronal computing, graphics and visualisation. Looking at the immense potential of the new sunrise sector, the Government of India has funded several institutes across India to carry out research and education in this field. Notable ones are the Jawaharlal Nehru University, New Delhi, The Indian Institute of Science, Bangalore, Bose Institute, Calcutta, Institute of Microbial Technology, Chandigarh, Centre for Cellular and Molecular Biology, Hyderabad and the University of Pune. The most notable among these institutes is the University of Pune, which since its establishment in 1987 has played a key role in the promotion of bioinformatics activity in India. The primary function of the centre has been to provide up-to-date and accurate information in the area of biotechnology. Besides this, several software and pharmaceutical companies are conducting in-house courses for interested candidates. But before India Inc hopes to cash in on the ‘Science of the future’ and exploit opportunities in the emerging field of bioinformatics, it faces a serious impediment with the lack of efficient manpower. Agrees Deobagkar, “The availability of well-trained human resource in bioinformatics is inadequate. There is an urgent need to train the next generation in a more formal, academic manner in the area of bioinformatics.” With an estimated shortage of one million professionals, the need for professionals trained in bioinformatics is undoubtedly high. And hopefully, this time around, India Inc will cash in on the opportunity and move up the value chain.

The Human Genome Project, which was started in 1987, has grown into a multinational effort aimed at developing a resource for ultra high speed genomic mapping and sequencing. The partial or complete sequences for approximately 46,000 genes are now available in public databases, with perhaps twice that number in private databases. The number has increased by an order of magnitude in just three years. High resolution physical and genetic maps have be come available in the past two years, and 8 complete genomes of microorganisms have been sequenced, with new ones becoming available almost monthly.

The genome project, as it was initially conceived, will be completed within 5 years. This revolution in data generation is a now the major driving force in drug discovery. By the turn of the century, DNA databases will be the source of most, if not all, new drug targets. The atlas of the human genome will revolutionize medical practice and biological research in the 21st century. All human genes will eventually be identified, and this will facilitate the understanding of most inherited diseases. The determination of the underlying biology of genome organization and gene regulation will promote the understanding of how humans develop from single cells to adults, why this process sometimes goes awry, and the changes that take place as people age. New technologies developed for genome research will also find myriad applications in industry, as well as in projects to map and improve the genomes of economically important farm animals and crops.

Bioinformatics: The science of developing computer databases and algorithms for the purpose of speeding up and enhancing biological research. Bioinformatics is being used most noticeably in the Human Genome Project, the effort to identify the 80,000 genes in human DNA . New academic programs are training students in Bioinformatics by providing them with backgrounds in molecular biology and in computer science, including database design and analytical approaches

Bioinformatics now look like hot cake. Prognosis...Good, very good...The number of jobs advertised for bioinformatics in Science magazine from 1996 to 1997 increased by 96% to over 350 bioinformatics job advertisements. Masters students salaries range between $40,000 to $65,000 for persons with top masters training (Stephan and Black, 1998). In addition, Masters students reportedly have received hiring bonuses and relocation assistance. Of course you are ultimately responsible for landing a great job and without your effort nothing will be accomplished

Explore and master the fusion of sciences, Bio-informatics, A futuristic realm of knowledge, it combines, biology, computer science and information technology to break new ground in research, help mankend lead a healthy and happy life. Over the past few years, this science has been a sphere of vital breakthroughs. Its impact has been felt in major areas of significance like agriculture, medicine, pharmacy, manufacturing of commercial products, to name a few. In future, its applications will encompass almost every aspect of life and thus, the potential for truly lasting careers can only be assumed as huge.

nisiet offers the most advanced one of its kind, Industry relevant course in Bio-informatics. It is being offered with Technical Expertise & Collaborative efforts of leading research and development Institutes, reputed universities from India and abroad. Expertise will also be drawn from Industry leaders in Biotechnology, Pharmaceutical & Research.

This association will provide the student the widest exposure of the theory and practical on high end computers as per the Industry requirements.

We are currently pursuing negotiation with leading companies and universities in the world for higher exposure of our course and to adopt to latest requirements of the Bio-Informatics and molecular Modelling Industry.

Bioinformatics is the field of science in which biology, computer science, and information technology merge into a single discipline. The ultimate goal of the field is to enable the discovery of new biological insights as well as to create a global perspective from which unifying principles in biology can be discerned. There are three important sub-disciplines within bioinformatics:

· the development of new algorithms and statistics with which to assess relationships among members of large data sets;

· the analysis and interpretation of various types of data
including nucleotide and amino acid sequences, protein domain and protein structures; and

· the development and implementation of tools that enable
efficient access and management of different types of
information.

How important are bioinformatics skills to the general molecular biologist looking to make the transition from academia to industry?

Increasingly so. All the Big Pharma and many biotech firms now have their own bioinformatics groups who will be responsible for the major data-mining and keyboard pounding efforts. However, as companies continue to utilize genomics in the identification of novel targets, it will become increasingly important for bench scientists to be comfortable using in-house and web-based molecular biology software to perform their own analyses.

. The answer to this question depends on whether you are talking to a computer scientist who 'does' biology, or a molecular biologist who 'does' computing. Most of what you will read in the popular press is that the importance of interdisciplinary scientists cannot be over-stressed, and that the young people getting the top jobs in the next few years will be those graduating from truly interdisciplinary programs. However, there are many types of bioinformatics jobs available, so no one background is ideal for all of them. The fact is that many of the jobs available CURRENTLY involve the design and implementation of programs and systems for the storage, management and analysis of vast amounts of DNA sequence data. Such positions require in-depth programming and relational database skills which very few biologists possess, and so it is largely the computational specialists who are filling these roles. This is not to say the computer-savvy biologist doesn't play an important role. As the bioinformatics field matures there will be a huge demand for outreach to the biological community, as well as the need for individuals with the in-depth biological background necessary to sift through gigabases of genomic sequence in search of novel targets. It will be in these areas that biologists with the necessary computational skills will find their niche.

In addition to extensive knowledge of the the run-of-the mill molecular biology packages (GCG, BLAST etc.), you will need to learn web and programming skills including HTML, Perl, JAVA and C++, and be familiar with a variety of operating systems (especially UNIX). Relational database skills are very much sought after, so knowledge of SQL and a major database application such as Sybase or Oracle will be highly advantageous. One area of bioinformatics which is set to expand is the determination of relationships between structures and sequence. If you wish to enter this field, you will need to learn all you can about structural biology and modeling, mathematical optimization, computer graphics theory and linear algebra.

Sounds somewhat daunting? Don't despair - very few people are in possession of all these skills when making the transition. In a nutshell, you will need to be able to readily pick up, use and understand the tools and databases designed by computer programmers, and to communicate biological science requirements to core computer scientists. By acquiring at least a subset of the skills outlined above, you will be in a position to demonstrate to a prospective employer your ability to meet these criteria.

If you are working already as a biologist and want to expand your computational skills, try investigating local universities and colleges - many of them offer UNIX, C++, HTML and Java courses. Seek out opportunities at your workplace to work on bioinformatics and genomics related subjects, and to interact with others working in these fields

If you are coming at bioinformatics from the computational side, your skills are likely in such high demand that registering for courses in molecular biology related topics is not a requirement, though this would certainly give you a competitive edge. Read all you can about molecular biology and the applications of genomics and bioinformatics. Endeavor to become an expert in SQL and in one of the major database applications such as Sybase.

India best suited for bioinformatics: Brian Tempest

RANBAXY Laboratories Ltd (RLL), among the world's top 100 global companies, has soldiered on since 1961 to make its mark in the highly fragmented domestic pharmaceutical industry. With its global operations spanning 40 countries and a physical presence in 24 countries, the pharma-major seeks to register a global sales of $1 billion by the end of 2004. This would mean doubling the sales performance of the Ranbaxy group of companies, including its global subsidiaries from $507 million that was registered in the business year 2000.

In a free-wheeling interview to Business Line - at the sidelines of the Confederation of Indian Industry's recent conference on Biotechnology - Dr Brian Tempest, Ranbaxy's President (Pharmaceuticals) elaborated on how the company sought to achieve the targets set for itself. Dr Tempest was recently inducted onto the board of directors, at a board-meeting here early this month.

What are Ranbaxy's plans for bio-technology (BT), anything on the anvil, any new products?

In terms of what Ranbaxy has already done, we have analysed this area in great detail and are determining what we believe is the right way to go forward.

In terms of analysis, direction, we have some very clear plans, but in terms of products and strategy, which products we would actually deliver in the market place...that's a long way off. One of the issues is on the meaning of biotechnology. If you go to Europe, you will find BT companies which actually are research & development (R&D) groups of maybe 500 R&D scientists.

The other area in BT is that of vaccines and there are lot of opportunities in this area. There is also the issue of bio-pharmaceuticals, where there are many technologies available from a host of countries around the world.

We have been to many of them and have sent teams to evaluate them and we think we know what we want to do. Other areas like molecular biology and gene testing, which are innovative, are areas we would aspire to be in. At this point of time we do not have any products, but we have a very clear plan of action.

It depends on the segments that we want to enter as each one has different aspects that need to be considered. We might be partnering as we think that it is the way to carry out business globally. We do believe that in certain areas, we need to partner with a company in an overseas location and in other areas we will have a licensing arrangement or go with our own technology. Each segment requires a totally different strategy that needs to be followed.

Vaccines and bio-pharmaceuticals we hope to be in very soon. But in terms of the state of the art innovative areas like genes and mono-clonals, these are ongoing things for a pharmaceutical company and products will take time.

Bioinformatics is a very popular ...and you can get into it with a small team of people and use existing IT technology. Opinions are that India is precisely the best place for this research because of its population. And from what I understand a lot of companies are interested in it, including Ranbaxy.

The molecule is licensed to Bayer and it is conducting clinical trials in the US. Bayer has the option to take in other sources of technology at the same time. They are also carrying out clinical trials on another source product.

Contractual terms between Bayer and Ranbaxy is quite complex and at the moment all we want to say is that our product is better and is in clinical trials phase III in the US. We don't expect any milestone payments this year.

Ranbaxy's got other technologies, of which Ciprofloxacin is one. So we have a strong platform technology base. Ranbaxy has a number of different platforms which will be applied to over a number of molecules. And we will be entering the market, for instance we will be coming out with three Novel Drug Delivery System (NDDS) products this year. Since we are exceptionally strong in NDDS products, we are going to launch them and see what the opportunities are. What ever the issue with Bayer, it is not an overnight issue.

Will you launch these NDDS products in India through a co-marketing alliance or on your own?

In India we are very strong. We will launch it through our own operations. But we will also network with other companies. So for instance, when we launch the once-a-day products, we will bring in other partners in the Indian markets, so that, we can take advantage of the intellectual property of the discovery and get better returns.

Companies venturing into bioinformatics are realising that the sector does not lend itself easily by replication of the business models of software service companies. Experts point out that to succeed in bioinformatics, companies will have to rethink their business models in favour of collaborative frame works. For one, bioinformatics is not as ubiquitously applicable as software services – the market is small and concentrated. And Indian companies seem to be missing the most obvious link needed to penetrate this narrow market: they have little interface with companies in the West, the end users of their IT products. “Indian bioinformatics companies need to establish quickly an overseas interface to gauge what the customers really want. Collaborate with large bioinformatics companies in the US or in the Europe in an effort to source some of their business. Start talking to pharma and biotech companies in the west,” stresses Rabo India’s Associate Director Aloka Gupta. Some however, are quite pessimistic about the future of the Indian bioinformatics companies. They feel that the technological and scientific problems at stake are complex. Moreover, there are other problems that the market does not realise. “There are already leading companies in most sectors of bioinformatics. India has no natural advantage here, either in talent, training or cost. By flocking to this sector, VCs and entrepreneurs show that they have not learned the lessons from the herd mentality in dotcoms and IT services - -they seem to be fated to fail here too,” says Mahesh Murthy of venture capital fund, Passionfund. Murthy is quite skeptical of India achieving a technological edge. “If somebody comes up with a better way to visualise protein structure than what 10,000 other researchers the world over have managed after years of a head start, then we have some potential here,” he says. However, not all find the scene quite so dismal. “Pharma companies need a lot of legacy data management and clinical trails data management. Indian companies could start off by building their services around these segments,” explains Boston-based bioinformatics company Neugenesis’s Chairman Ajit Nagral. Companies like Spectramind have identified exactly such a niche segment. Extending its core competence of IT-enabled services, the company now employs several scientists to survey biological literature and create database for pharma companies. Other companies are edging higher up on the value chain, providing data mining programmes in addition to hardware tools to accelerate bioinformatics algorithms. Another business model being attempted is of providing lead molecules for drug targets to pharmaceutical companies. This involves starting with the identification of a drug target and ending with simulating the network of reactions a drug molecule is likely instigate. Success in this last ambitious business model demands seamless integration with biology laboratories – a fact that brings experts back to the crucial ingredient that Indian companies need to succeed - interdisciplinary and overseas collaboration.

New York, June 16: India is among the key players in the emerging bio-technolgy industry, along with Australia, China and Singapore, which house over 500 such companies, a report by leading finance services company said.

In one of its key findings, a report by Ernst and Young released last week said biotechnology is an emerging sector in Asia/Pacific experiencing notable expansion in India, Australia, China, and Singapore.

While 72 per cent of the public company revenues were generated in the US, Emerging biotech sectors in Europe, Canada, Asia and Pacific regions have experienced significant growth in the number of companies, the report said.

It is estimated that by 2005 the European biotech market could double from current valuations to more than $100 billion.

IBM, Compaq and Sun Microsystems are among the leading IT companies looking for a foothold in the Indian bioinformatics arena. Sun Microsystems, which isinvolved in bioinformatics research in the West, now wants to collaborate with bioinformatics centres in India. According to the Director of the Department of Biotechnology (DBT), Sun Microsystems has approached DBT for setting up a centre of excellence in bioinformatics. The company has already submitted its preliminary proposal for the same and is now working on recommendations from DBT.

The Global Scenario	The Indian Context

Overview	Bio-informatics New Opportunities
What is Bioinformatics	News Capsules
Background Information	India a Key player
Bioinformatics Research Computing	Career Opportunities
Bio-computing Softwares	Bioinformatics Expert
	DBT Centres