Bioinformatics is a new field that centers on the development and application of computational methods to organize, integrate, and analyze gene -related data. The Human Genome Project (HGP) was an international effort to determine the deoxyribonucleic acid (DNA) base sequence of the entire human genome, which includes about thirty thousand protein -encoding genes, their regulatory elements, and many highly repeated noncoding sections. In 1985, a group of visionary scientists led by Charles DeLisi, who was then the director of the office of health and environmental research at the U.S. Department of Energy (DOE), realized that having the entire human genome in hand would provide the foundation for a revolution in biology and medicine. As a result, the 1988 presidential budget submission to
The Human Genome Project and other genome projects have generated massive data on genome sequences, disease-causing gene variants, protein three-dimensional structures and functions, protein-protein interactions, and gene regulation. Bioinformatics is closely tied to two other new fields: genomics (identification and functional characterization of genes in a massively parallel and high-throughput fashion) and proteomics (analysis of the biological functions of proteins and their interactions), which have also resulted from the genome projects. The fruits of the HGP will have major impacts on understanding evolution and developmental biology, and on scientists' ability to diagnose and treat diseases. Areas outside of traditional biology, such as anthropology and forensic medicine, are also embracing genome information.
Knowing the sequence of the billions of bases in the human genome does not tell scientists where the genes are (about 1.5 percent of the human genome encodes protein). Nor does it tell scientists what the genes do, how genes are regulated, how gene products form a cell, how cells form organs, which mutations underlie genetic diseases, why humans age, and how to develop drugs. Bioinformatics, genomics, and proteomics try to answer these questions using technologies that take advantage of as much gene sequence information as possible. In particular, bioinformatics focuses on computational approaches.
Bioinformatics includes development of databases and computational algorithms to store, disseminate, and rapidly retrieve genomic data. Biological data are complex and abundant. For example, the U.S. National Center for Biotechnology Information (NCBI), a division of the National Institutes of Health, houses central databases for gene sequences (GenBank), disease associations (OMIM), and protein structure (MMDB), and publishes biomedical articles (PubMed). The best way to get a feeling for the magnitude and variety of the data is to access the homepage of NCBI via the World Wide Web ( http://ncbi.nlm.nih.gov ). A bioinformatics team at NCBI works on the design of the databases and the development of efficient algorithms for retrieving data and comparing DNA sequences.
Bioinformatics also covers the design of genomics and proteomics experiments and subsequent analysis of the results. For instance, disease tissues (such as those from cancer patients) express different sets of proteins than their normal counterparts. Therefore protein abundance can be used to diagnose diseases. Moreover, proteins that are highly (or uniquely) expressed in disease tissues may be potential drug targets.
Genomics and proteomics generate protein abundance data using different approaches. Genomics determines gene abundance (which is a good indicator of protein abundance) using DNA microarrays, also known as DNA chips, which are high-density arrays of short DNA sequences, each recognizing a particular gene. By hybridizing a tissue sample to a DNA chip, one can determine the activities of many genes in a single experiment. The design of DNA chips—that is, which gene fragments to use in order to achieve maximum sensitivity and specificity, as well as how to interpret the results of DNA chip experiments—are difficult problems in bioinformatics.
Proteomics measures protein abundance directly using mass spectroscopy , which is a way to measure the mass of a protein. Since mass is not unique enough for identifying a protein, one usually cuts the protein with enzymes (that cut at specific places according to the protein sequence) and measures the masses of the resulting fragments using mass spectroscopy. Such "mass distributions" for all proteins with known sequences can be generated using computers and stored. By comparing the mass distribution of an unknown protein sample to those of known proteins, one can identify the sample. Such comparisons require complex computational algorithms, especially when the sample is a mixture of proteins. Although not as efficient as DNA chips, mass spectroscopy can directly measure protein abundance. In fact, spectrometric identification of proteins has been the one of the most significant advances in proteomics.
Bioinformatics can lead to discovery of new proteins. When the cystic fibrosis gene (CF) was first identified in 1989, for example, researchers compared its DNA sequence computationally to all sequences known at that time. The comparison revealed striking homology (sequence similarity) to a large family of proteins involved in active transport across cell membranes. Indeed, the CF gene encodes a membrane-spanning chloride ion channel, called the cystic fibrosis transmembrane regulator, or CFTR. The identification of gene function by searching for sequence homology is a widely used bioinformatics method. When no homology is found, one may still be able to tell if a gene codes for membrane-spanning channels using computational tools. Membranes are bilayers of lipid molecules, which are water insoluble. An ion channel typically has regions outside the membrane (water soluble) and regions inside the membrane (water insoluble) arranged in a certain pattern. Computer algorithms have been developed to capture such patterns in a gene sequence.
By thinking boldly and by setting ambitious goals, the Human Genome Project has brought about a new era in biological and biomedical research. Many revolutionarily new technologies are being developed, most of which have significant computational components. The avalanche of genomic data also enables model-based reasoning. The bright future of bioinformatics calls for individuals who can think quantitatively and in the meantime love biology—an unusual combination.
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