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Bioinformatics came into eminence with the beginning of Human Genome Project with its central role in genome data storage, manipulation and analysis. It is the branch of science which through the use of high electronic beam screen computer technology summates all the biological data mainly the genetic aspects that has been produced over the years by the research scientists (Searls D.B.,2002). Traditionally, researches were carried out at the experimental laboratory, but with the huge increase in the data of the genomic area a need of incorporating the computers in the research process has been arisen. Now, it mainly involves the construction, updating and maintenance of the database containing a wealth of biological information and at the same time enables the users to analyze and explore the data. It includes aspects of computer science, software engineering, mathematics and molecular biology (Kim J.H.,2002). It has been used with different scopes including genomics, biosciences, clinical research along with biomedical and health informatics.
The term medical informatics deals with the application of fast growing computer technology to the field of medicine; it could be either medical education or for the purpose of medical research which ultimately enhances our understanding in the treatment of disease. "Health informatics is now tending to replace commoner term medical informatics reflecting a widespread concern to define an information agenda for health services which recognizes the role of citizens as agents in their own care, as well as the major information-handling roles of the non-medical healthcare professions"(Ross et al.,2003). The applications of Health informatics can be used in the progress of patient care, to increase productivity and to provide access to the knowledge of disease.
However, with the advancement of science and technology, bioinformatics now emerges as a new facet of science which combines the fields of Molecular Biology and Genetics, Biotechnology and Microbiology making them interpreted and explored by means of computer technology. The establishment of NCBI (national centre for biotechnology information) in 1988 as a public database, has helped in disseminating information across the world thereby facilitates in tracking out the molecular processes of the disease (Brazeil et al., 2003) (Sayers et al., 2010).
Image showing the homepage of NCBI
Fig 2: Figure showing the NCBI database page. The page shows the establishment of database in the year 1988 and the various parameters that can be explored using this database.
In the later phase many other databases were created such as Ensembl, Entrez , Unigene, CSIRO and BLAST aiding in tracking the genetic mechanism responsible for the pathogenesis of the disease.
Bioinformatics, genomics and Disease
The findings of the Human Genome Project showed that humans approximately have 30,0000 genes with the difference in the DNA sequence between two individuals only being 0.1% different, while the rest 99.9% being the same. Cracking the human genome code and the publication of 3.2 billion sequences making up the human chromosomes on the internet by the US department of energy (DOE) and National Institute of Health has revolutionized the field of pharmacogenomics (Ginsburg and Willard,2009). Knowledge generated by these biomedical databases enables the health care organisations to identify the citizens who are not only at the genetic risks for developing the diseases but also the ones who are the at risk of developing symptomatic diseases which could be reduced. These innovative approaches cannot be sustained without effectively dealing with the vast amounts of data generated in the laboratory in the functional and structural areas of genomics and proteomics. It helps the other scientist and doctors to understand the molecular DNA code (the blueprint of an individual) in detail. The major diseases targeted by the drug industries mainly are AIDS, mental illnesses, auto immune diseases, obesity , Alzheimer's, Parkinson's and Heart diseases (Shi E.,2001).
Image of NCBI showing genes and disease map.
Fig 3: Figure showing the human chromosomes 1, 2, 3 and 4 with the gene loci mapped responsible for the diseases. VHL gene on chromosome 3 mapped for example is a factor that participates sometimes in the pathogenesis of cancer.
Kang and co-workers developed a DNA microarray for the detection of abnormalities found in chromosomes causing genetic disorders like Down's syndrome, Patau syndrome, Edward syndrome, Turner Syndrome, Klinefelter syndrome, Cri-du-Chat Syndrome, William's Syndrome and Wolf-Hirscorrn Syndrome. A bacterial artificial chromosome chip (BAC) was used to diagnose chromosomal abnormalities (Searls D., 2001).
Also, Single nucleotide polymorphism (SNP) contributes to the 0.1 % differences for genetic makeup. Till now 3.2 million polymorphic SNPs have been mapped and these SNPs aids in finding the human predisposition to any particular diseases. They are serving as a genetic markers in correlating the drug efficacy and toxicity potential of any individual (Maojo and Martin-Sanchez, 2001).
Bioinformatics softwares are in current use to track these hidden SNPs in human genome. Copy number variations (CNVs) are also the other factor adding up another contribution in 0.1 % differences and causes diseases in humans.
Image showing SNP Genotyping system.
Fig 4: Figure showing the SNP detecting series wherein the software detects more than 10,000 SNPs and arranges them in a data.
The microarray technologies along with the array algorithm are in current use to track out these variations in SNPs. The microarray technology provides huge ranges of informatics opportunities. They are a tool for monitoring the gene expression levels for thousands of genes in parallel. The expression data obtained and stored is highly informative for diseased state. These can also be used for distinguishing tumour types, define new subtypes and identify misclassified cell lines and to predict prognostic outcomes (Mullner and Chung, 2006). This approach is particularly powerful in offering the promise of "personalised medicine", where in the specific underlined effect can be identified and the prognosis predicted.
Analysis of Microarray data
Fig 5: showing analysis of Microarrays data to discover new tumour classification and to build gene predictors for cancer diagnosis.
Till date more than 2.1 million SNPs have been detected and deposited into the SNP's database (http://snp.cshl.org/).
Figure 6: Homepage for the SNPs database showing the number of files available for the HapMap project.
Personalised medicine was defined by Francis Collins, head of the human genome project as "using information about the person's genetic makeup to tailor strategies for the detection, treatment or prevention of the disease". The concept of personalised medicine is becoming accepted by the medical profession, the FDA, health insurance organisations and pharmaceutical industries (Dudley and Butte,2009). This promise is to be used in the prevention of diseases in the future.
Fig 7: Flow Chart showing personalised medicine requires the integration of clinical data, genomics and phenotyping. Bioinformatics is crucial in translating these data into applications leading to improved diagnosis, prediction and prognosis with treatments.
Image explaining various processes require for Personalised Medicine
Figure 8: Personalised medicine includes the above processes until the outcome of the disease is known.
Bio-informatics and Proteomics:
The expression level of any particular gene is not correlated with the level of mRNA present in the cell. Also the human genes undergo alternative splicing and RNA editing mechanism, enhancing a gene potential in coding different proteins. Due to which studying mRNA and DNA sequences alone are not able to give the correct expression status of the genes dominating in a particular disease. Thus, the fields of bioinformatics have now collaborated with the science of proteins to analyze and explore every protein in the human body. Also, with the advent of X-ray crystallography and NMR (Natural Magnetic Resonance), it is possible to capture and study proteins on the screen in their 3D structure (Magalhaeis and Toussaint,2003). Today scientists are on the verge of creating human proteome map and if they will be successful in their proteome map there will be a better quality of life serving to minute details.
The figure below explains the differences found in the renal carcinoma and the healthier ones with the help of bioinformatics.
Figure 9: Figure showing the diagnosis of renal carcinoma using the 2D-Gel electrophoresis technique. Normal: indicates normal without carcinoma, RCC: indicates renal cell carcinoma.
Bioinformatics and Pharmacology
The 1994 noble prize winner for decoding cellular signalling mechanism, Alfred Gilman, a pharmacologist is now working on a project to trace the cellular signalling mechanism map and transcribed that knowledge on the computer screen. Inspired by him, now all the drug companies are using the technology of bioinformatics to develop more algorithms that can be used to predict the function of proteins encoded by newly discovered genes (Xie et al.,2005). These have been possible by the use of various multiple alignment software that is in current use to establish the phylogenetic relationships of the proteins under study.
Drug docking is the most powerful technology, possible through the bioinformatics knowledge are in most common use to find the docking site of the drug in the active cleft of the proteins. The proteins participating in the pathogenesis of diseases can be easily mapped out and docked on the computer screen which has revolutionized the field of pharmacology (Miller,2000). Scientists nowadays are able to map the mutations in proteins giving drug-resistant property to the cells. For e.g., scientists have even mapped out mutations in HIV protease and HIV reverse transcriptase isolated from heavily drug treated HIV I infected patients (Fig 9, 10). This will definitely enhance the knowledge of better understanding the drug resistant mechanism which are the common scenario observed in AIDS, Cancer and Tuberculosis. For tuberculosis, the genome of Mycobacterium tuberculosis has been sequenced and mapped to explore the ABC transporters proteins present in the bacteria giving drug resistant property (Douglas et al.,2007).
X Ray crystallography structure.
Figure 10: Figure showing the X-ray crystallography structure of HIV I protease enzyme. Red colour indicates the mutations in the active site of the enzyme while purple colour indicates accessory changes.
Figure 11: Figure showing the drug resistant potential of HIV I resistant drugs. The upper half showing the drug resistant against HIV I protease and the second half indicates drug resistant profiling against HIV I reverse transcriptase.
AZT: azidothymidine, DDI: dideoxyinosine, DDC:dideoxycytidine, D4T:stavudine, ABC;, 3TC:Lamivudine
Bio-informatics plays an essential role in decoding genomic, transcriptomic and proteomic data produced by experimental technologies, and organizing information from traditional biology and medicine. Also, biomarkers which are molecular signatures can be used to measure the progress of a disease and also the physiological effects of therapeutic interventions. They often serve as an early warning signs for various diseases including cancer, tuberculosis, cardiovascular diseases etc(Gatto J.,2003). Because of various types of biomarkers, and various methods to validate them, a number of bioinformatics tools are available for biomarker analysis. DNA, RNA, protein and antibody microarrays are generally used for analysis of biomarkers. For example, a recent bioinformatics method called Decision Forest analysis has been used to identify biomarkers for oesophageal cancer. It identifies various types of SNPs and SNP patterns in the oesophageal cancer.
Certain drug therapies are non-curative while some also cause substantial side effects. Some drugs also reveal individual differences in therapeutic responses, of which a substantial amount relates to genetic determination of an individual. Hence, a focus needs to be placed on subpopulations that demonstrate the same disease phenotypes but different genotypes or genetic profiles (Kasabov,2006). The slightest genetic difference makes a huge impact on the individual's susceptibility to diseases and its responsive treatment to the drugs.
From the discovery of the exchange of DNA strands by homologous chromosomes (Joshua Lederberg in 1958) that resulted in genetic variation and from the present day discovery by the Mario Capecchi and Oliver Smithies in 2007 who have applied the homologous recombination in mammalian cells, the homologous recombination has been proved to occur between introduced DNA, with which the cell chromosomes are repaired using the induced genes. Thus, now the researches are prominently focusing onto target the genes which are involved in mammalian organ development, helping in correction of several inborn errors that arise in the event of malformation (Martin S., 2003). If the experiments prove to be successful on the mouse model acting as a source for gene therapy, the art of medicine will be enriched with biology as a foundation of future developments in medicine.
Current Technologies in complete merging of informatics with medicine
However, the function of many genes encoded by the human genomes is yet to be determined. Bioinformatics failed to decode the functions of some novel genes which does not exhibit homology sequence pattern with the other genes published in the database. For this in vitro research (wet lab) is required to perform the necessary manipulations. The human genomes submitted by both public and private sectors have gaps in them. Also, 95 % of human DNA is junk. Bio-informatics failed to signify the importance of these non-coding sequences and their existence. We still do not have complete expression libraries that are a expression signature of the genes present in a tissue or organ. Proteins, their location and modification are not characterized(Kasalbov N.,2006). Not all diseased genes have been characterized so far and the genetic differences between normal and disease state are not known.NMR and X-ray crystallography techniques are labour intensive and required a month or two. Microarrays should become more affordable, convenient to practice, accurate and portable.
Bioinformatics has also failed to predict the functions of novel proteins whose structure are not similar to that of the other proteins present in the databases. The procedure of molecular docking is very much time consuming (Molidor R.,2003). Thus, a successful adoption in these principles will serve both beginners and experienced bioinformaticians alike in the development of scientific goals.
What is coming?
Although with the underlying shortcomings in the field of bioinformatics, an increased progress can give us an improved technique for the next generation sequencing. Environmental variables could be incorporated to correlate with the genotype and functional information for a particular gene sequence (Sanchez,2004). Epigenetics, nanoparticles and nanomedicine can advance at a much faster pace with bioinformatics.
However, the programming skills for Bioinformatics are becoming a necessity across the fields of medicine and biology. Integration of bioinformatics with the structure oriented world of small proteins and drugs and with the biological systems is driving this evolution, with the ultimate goals of efficient discovery and development of drugs that are safer and more effective (Molidor,2003). DNA-microarray based techniques have been applied to identification of markers and identification of disease causing infections.