Bioinformatics
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 U.S. Congress requested funds to start the HGP. Momentum built quickly and by 1990, DOE and the U.S. National Institutes of Health had laid out plans for a fifteen-year project. An international public consortium and a private company announced completion of a rough draft of the human genome sequence on June 26, 2000, with papers describing the data pub­lished eight months later. This is the first generation bestowed with the “parts list” of life, as well as the daunting task of making sense out of it.
Data Management
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 andforensicmedicine, are also embracing genome information.
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. Biologi cal 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:/
cbi.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.
Applications
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.
 
References
Butler, Declan. “Are You Ready for the Revolution?” Nature 409 (15 February 2001): 758-760.
DeLisi, Charles. “The Human Genome Project.” American Scientist 76 (1988): 488-493.
Marshall, Eliot. “Bioinformatics: Hot Property: Biologists Who Compute.” Science 272 (1996): 1730-1732.
Roos, D. S. “Bioinformatics: Trying to Swim in a Sea of Data.” Science 291 (16 Feb­ruary 2001): 1260-1261.