Mohit Kumar Sharma
Bioinformatics is a meeting point of two key contemporary disciplines – Biology and Computer science. It has had a pivotal role in high throughput projects such the human genome project. Bioinformatics is the computer-assisted data management discipline that helps us gather, analyze, and represent this information in order to edify ourselves, knowing the life’s progression in the healthy and disease conditions, and find novel or better drugs. Bioinformatics is being practiced across the globe by educational groups, companies, national and international research organizations. This discipline should be thought of as core of current and future biotechnology. The demands and opportunities for interpreting these data are expanding more than ever. Bioinformatics is a discipline which uses computational techniques to analyze the biological problems; the science of developing and utilizing computer databases and algorithms to accelerate and enhance biological research. It’s commonly referred as dry lab work which accelerates the wet lab work drastically. Bioinformatics is a tool to solve the Biological problems based on existing data. It is a mode to solve the Biological outcomes based on existing experimental results. It creates the way for the Biologists to store all the data. It makes some lab experiments easy by predicting the outcome of the lab experiment. Sometimes it shows the initial way to start the lab experiment from existing results. It helps the researchers to get an idea about any lab experiments before they start. Computers have become an essential component of modern biology.
Bioinformatics has established itself in just a few years as a foundation of modern biotechnology. Its focus on biological information management, independent of origin or representation, is enabling life science and novel drug discovery to progress much faster. Still, there are significant issues that challenge the industry. Integration of data acquisition, storage, manipulation, analysis and dissemination in a cross-platform manner, unambiguous gene sequence assignment, and development and application of new, language and ontology-based approaches are all at the leading edge of the field, with many companies and other institutions advancing these areas. The ultimate prize is the computer-aided empowerment of researchers to work seamlessly with multiple types of biological information, as close to real time as possible. Once all this is achieved at multiple levels, including genes, proteins, cells, tissues, diseases, population variations, plants, animals and other organisms, then the promise will have been fulfilled.
Career path & opportunities in Bioinformatics
Today’s bioinformaticists are in for a real treat. With a seemingly endless stream of biological data being generated across sectors, there is high demand for talented, experienced professionals at the crossroads of biology, statistics, and computer science. Scientists who can analyze large amounts of information and present it in a clear manner to decision makers are finding the sky is the limit in terms of jobs and career pathways, especially in the big pharma and biotech sectors.
For life scientists with expertise and an interest in bioinformatics, computer science, statistics, and related skill sets, the job outlook couldn’t be rosier. Big pharma, biotech, and software companies are clamoring to hire professionals with experience in bioinformatics and the identification, compilation, analysis, and visualization of huge amounts of biological and health care information. With the rapid development of new tools to make sense of life science research and outcomes, spurred by innovative research in bioinformatics itself, scientists who are entranced by data can pursue more career options than ever before.
When people refer to the field of bioinformatics, they’re referring to what might be aruged as two overlapping areas. The first is what you would call “bioinformatics”, which is more technical, and examples are creating tools to analyse data for biologists, or specific databases to store and retrieve information. For example if you created a new tool that could analyse sequencing in a way that hasn’t been done previously, then this is bioinformatics. Many journals such as Nature and Bioinformatics have sections purely for articles about new methods and tools.
The second path is what you might call “computational biology”, which is all about doing biological research, using a computer instead of a pipette. A strong understanding of biology is important, as well as the ability to phrase, then answer a research question. For example, if you believed that duplicate genes were less well conserved compared with non-duplicates, and you tested this hypothesis across a set of genomes, then this would be computational biology.
These two fields are not distinct, and overlap a fair amount. Some universities have bioinformatics departments in both the computer science and life science faculties, indicating the types of research carried out in each.
Bioinformatics specialists must acquire an unusual background, an eclectic blend of molecular biology, chemistry, and computer science. They work in close collaboration with bench scientists, helping them to plan and organize experiments and data collection so as to maximize the production of reliable and useful information. They are found in academic, Government, and industrial research labs.
There is no such thing as a typical career path in this field. Bioinformaticians need to perform two critical roles: develop IT tools embodying novel algorithms and analytical techniques, and apply existing tools to achieve new insights into molecular biology. However, you must remember that although powerful and highly specialised in itself, bioinformatics is only a part of biotechnology.
Specific areas that fall within the scope of bioinformatics
Sequence assembly:
The genome of an organism is assembled from thousands of fragments that must be correctly “stitched” together sophisticated computer-based methods, is carried out by a specialist in bioinformatics.
Database design and maintenance:
Many pharmaceutical companies maintain private databanks of gene sequences and other biological and chemical information. These repositories must be continually updated with data generated internally and from outside sources. This is a challenging task, and the design and maintenance of these complex databases has become an important part of bioinformatics.
Sequence (gene) analysis:
Once the DNA sequence of a fragment of the genome has been determined, the work has just begun; one must next understand the function of the gene. This involves locating regions of the gene that code for a protein product that is involved in regulation and control and also finding those sections of the gene (introns) that are clipped out and discarded. The gene may be compared against databases of known genes with well-understood functions to find clues to its role in health or disease. All of these analyses are carried out using powerful computers and specialized software, and many would consider this activity the most important area of focus within bioinformatics.
Pharmacogenomics:
It is now realized that single-point mutations (alterations in the genome at specific positions) can be associated not only with particular disease states (for example, sickle cell anemia) but also with reduced or increase sensitivity to particular drugs or with side-effects to those medications. Databases of these single nucleotide polymorphisms (SNPs) are rapidly evolving and promise to play an important role in future drug development efforts and in the design of clinical trials. Again, experts in bioinformatics are at the forefront of efforts to collect, analyze, and apply this crucial data.
Proteomics:
A relatively new area, proteomics studies not the entire genome but rather the portion of the genome that is expressed in particular cells. This often involves cutting-edge technology, such as the use of microarrays (“DNA-on-a-chip”) that allow the expression level of thousands of genes in a cell sample to be quickly determined. Once a large and diverse database of expression data has been collected, the next step is to identify connections between the patterns of expression of genes and a particular disease state. In this way, likely targets for drug and/or gene therapy can be located. Bioinformatics specialists work closely with bench scientists to accomplish the “data mining” that lies behind this next wave of the pharmaceutical industry.
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