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Bioinformatics, MS - Career Outcomes
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.
If you're interested in this exciting field and not sure about whether you want to invest yourself into a PhD program, University of the Sciences is the place for you. Opportunities for bioinformaticists are especially abundant at the MS level.
The master's degree is often seen as an ideal compromise between the very lengthy training required for a PhD and the restricted skill level attained with the bachelor's, which often limits prospects of career advancement. At the same time, a master's degree provides an excellent foundation, in both coursework and practical experience, for further study leading to a doctoral degree.
Specific areas that fall within the scope of bioinformatics (all of which will be a part of your master's program) include:
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.
Once the DNA sequence of the genome has been determined, the work has just begun; one must next understand what RNA products are produced from the genome, and which of these code for proteins or other regulatory and structural molecules, as well as the pattern of expression for all these biomolecules as a function of tissue, developmental stage, and disease states. This is the study of the transcriptome. Modern high-throughput methods allow us to capture a snapshot of all the products of the genome in a tissue, and even for a single cell!
Many genes code for proteins with unknown function. These are compared against databases of known genes with well-understood functions, to find clues as to the role of the novel proteins in biochemical or signaling pathways, and 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.
Proteomics focuses on the proteins that are expressed in a cell, and their diverse roles in signaling, metabolism, regulation, and in maintaining the physical structure of cells and tissues. While genomic methods concentrate primarily on nucleotide and peptide sequence data, the goal of proteomics is to understand the structure and function of proteins, and the complexes they form, in atomic detail. The experimental methods in proteomics, ranging from X-ray diffraction studies to mass spectrometry, seek to reveal the details of the spatial organization of proteins, their physical interactions, the role of chemical modifications that take place after the protein is synthesized, and importantly, the ways a protein can interact with small molecules such as metabolites and drugs. The software tools used in proteomics differ dramatically from those in genomics; here, three-dimensional visualization is key, as are analyses based on detailed physics and chemistry. Proteomics methods form the core of modern computer-aided drug design, and many students take advantage of advanced experimental and computational resources available at USciences to open the door to this exciting discipline.
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 increased sensitivity to particular drugs, or with side-effects to those medications.
Databases of these single nucleotide polymorphisms (SNPs) along with genome-wide association studies (GWAS) that link SNPs to disease, 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.
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