Bioinformatics - Solving Biological Problems Using DNA and Amino Acid Sequences

Data is ever increasing. We need a system to represent, store and manipulate complex information, detect correlations and patterns, construct data models etc. Furthermore, being independently maintained, data can change in time or even change its base structure, making it difficult for modelling systems to accommodate these changes. Current representation and storage systems are not very flexible in dealing with structural changes and also they are not powered with the ability of performing complex data manipulations of the sort mentioned above.

In this fast paced ever changing world, there is always a need to increase the efficiency and reduce the time required for a particular problem to be solved. Most biological experiments, apart from being time consuming, are unpredictable and difficult to maintain, which results in a wastage of resources. With the advent of computers and their increased application in many different fields of study, we can now theoretically test out the hypothesis before proceeding with the practical experiment. Computational biology, as it is termed, is paving the path towards achieving this. With the success of the human genome project, the need for biologists equipped with the ability to look at diseases from a computational and mathematical standpoint

- to determine the sequences of the 3 billion chemical base pairs of the human DNA

Health informatics is the bridging of computer science, information and the health care field. This interdisciplinary field can be applied to a range of medical fields such as nursing, biomedicine, medicine and subspecialties such as immunology (immunoinformatics). Informatics not only has roles to play in day-to-day areas of immunology such as data storage/retrieval, decision support, standards and electronic health care records but also in research and education such as data mining and simulation systems (Coiera, 2002). Informatics and more specifically, health informatics first started being used in in the late 1950s with the rise of computers (Ho, 2010). Technologies such as computers allowed practitioners and researches

Pharmacogenomics, a component of precision medicine, is the study of how genes affect a person’s response to particular drugs. It involves a combination of pharmacology and genomics, to manufacture medication and doses that are customized to fulfill the variations found in people’s genes. (Reference 2016)

Synaptic is a bio technology company that uses a combination of both Information Management (IM) and biological research to develop drugs based on proteins and peptides. Synaptic is comprised of a large Information Management department which consists of multiple divisions within the department. Each division within the IM is in charge of communications and computer systems services, in particular research, manufacturing and infrastructure. The company also employs computational scientists that aid with the research aspect of the product development. The development of the product makes it necessary for both set of parties – the Information Management and the scientists to interact with each

There was a broad consensus for five priority areas for future research, to integrate approaches across different technologies and to include diverse populations for future human studies. There is a need for creating new collaborations with versatile teams that combine bioinformatics, genetics, molecular biology, and cell physiology.

Thus an interdisciplinary approach should be utilized as a broad range of scientific discipline from biochemistry to informatics which will help us to identify and understand the mechanism for cancer progression and its prognosis. With the rapid changes in the technology, there has been various challenges which have come into the picture due to regulatory oversight and legislation. One of the major challenges is to manage large amount of data generated via assays thus it requires massive computational resources for data storage, processing and interpretation. Certain informatics resources such as ClinGen and ClinVar database of the National Center for Biotechnology Information (NCBI) has provided us with easily accessible data. The identification and routine deployment of molecular markers to improve classification of indeterminate lesions will be of great benefit to patients and surgeons and will reduce costs to the health care system by limiting unnecessary surgical procedures. More multidisciplinary research, closer collaborations is required between drug and cancer biomarker developers, clinicians, biologists, biostaticians and IT professionals, so to develop cancer biomarkers to improve patient outcome as well as to help the clinician in rapid clinical decision

The material covered in this PBS special related to our class because we have discussed the Human Genome Project, the structure of DNA, and how genetic mutations work. The Human Genome Project was an international research project that sequences all of the genes in humans. DNA

This method, as well as the Maxam and Gilbert method, for sequencing DNA are transforming the world of science, medicine and the views of people around the world.

Having completed the Human Genome Project, scientists now seek to uncover the secrets of the human proteome (Begley 1). It is "guesstimated" that the proteome, meaning all the proteins, will involve up to 1000 times more data than the genome did. But this again brings us to the question: What will the scientific and medical communities do with all this information?

These domains will enhance the way we practice medicine because there is so much information and knowledge being produce from all the data that is being collected and analyzed by translational bioinformatics. Translational bioinformatics is the area of biomedical informatics that has to do with collecting data from various databases. The data collected is stored and analyzed to produce information and knowledge about a disease or a health related issue

DNA sequencing is a method for determining the precise order of nucleotides present in a given DNA molecule [1]. The Sanger and Gilbert methods of sequencing DNA are referred to as first-generation sequencing because they were the first to be developed. The Sanger sequencing, also known as the chain terminating method, developed in 1975 by Edward Sanger, was considered the gold standard for nucleic acid sequencing for two and a half decades (Sanger et al., 1977). The Human Genome Project was accomplished with first-generation sequencing in 2003, known as Sanger sequencing. The Human Genome Project (HGP) was an international cooperative venture established to sequence the 3 billion base pair (~25,000 genes) in the human genome.

The growth and development of Biotechnology and Bioinformatics worldwide have tremendously impacted life science, especially in the field of animal production and animal health; however, I as the veterinarian officer, still have limited knowledge in these fields. In order to augment my knowledge and skill, further study in these related areas is a crucial step toward furthering my career. I work as a veterinarian officer at Bureau of Biotechnology in Livestock Production, Department of Livestock Development, Ministry of Agriculture and Cooperative in Thailand after having competed in and receiving a full scholarship, the Undergraduate Intelligent Scholarship from the Thai government to move forward into postgraduate study. It is most imperative to gain advanced knowledge of Biotechnology and Bioinformatics to combine with my veterinary background, conductive to expand my capabilities thus putting me in a position for more opportunities, experiences and knowledge. Moreover, I would like to become a highly competent woman in this career field. For these reasons, I would like to pursue my Master’s degree in Biotechnology

The importance of this method is due to it could be perform just using genomic DNA sequence and models of TFBS for the specific TF. But as we explained above, TFBS are short region sequence, and then motif are widespread along the genomes. Thus, this motif-based prediction lead to overstate the number of CRMs through many false positive. So, motif-based prediction for CRM prediction typically need other information to achieve realistic models. In that manner, a single CRM could have several sequence features, and these features are frequently conserved