Bioinformatics

Bioinformatics is applied to at least five major types of activities: data acquisition, database development, data analysis, data integration, and analysis of integrated data.

Data Acquisition.

Data acquisition is primarily concerned with accessing and storing data generated directly off of laboratory instruments. Many of these instruments are either automated or semi-automated high-throughput instruments that generate large volumes of data. The Human Genome Project utilized hundreds of DNA sequencers, producing enormous amounts of data. The data had to be captured in the appropriate format, and it had to be capable of being linked to all the information related to the DNA samples, such as the species, tissue type, and quality parameters used in the experiments. This area of bioinformatics primarily relates to the use of "laboratory information management systems," which are the computer systems used to manage the information needs of a particular laboratory.

Database Development.

Many laboratories generate large volumes of such data as DNA sequences, gene expression information, three-dimensional molecular structure, and high-throughput screening. Consequently, they must develop effective databases for storing and quickly accessing data. For each type of data, it is likely that a different database organization must be Predicted structure of a chemokine receptor in the membrane of a white blood cell. Knowing the amino acid sequence (represented by single letters) allows researchers to predict the three-dimensional structure of a protein. Adapted from <http://www.cdc.gov/ncidod/eid/vol3no3/smith.htm>. used. A database must be designed to allow efficient storage, search, and analysis of the data it contains. Designing a high-quality database is complicated by the fact that there are several formats for many types of data and a wide variety of ways in which scientists may want to use the data. Many of these databases are best built using a relational database architecture, often based on Oracle or Sybase.

A strong background in relational databases is a fundamental requirement for working in database development. Having some background in the molecular biology techniques used to generate the data is also important. Most critical for the bioinformatics specialist is to have a strong working relationship with the researchers who will be using the database and the ability to understand and interpret their needs into functional database capabilities.

Data Analysis.

Being able to analyze data efficiently requires having a good database design, allowing researchers to query the database effectively and letting them quickly obtain the types of information they need to begin their data analysis. If queries cannot be performed, or if performance is tediously slow, the whole system breaks down, since scientists will not be inclined to use the database. Once data is obtained from the database, the user must be able to easily transform it into the format appropriate for the desired analysis tools.

This can be challenging, since researchers often use a combination of publicly available tools, tools developed in-house, and third-party commercial tools. Each tool may have different input and output formats. Starting in the late 1990s, there have been both commercial and in-house efforts at pharmaceutical and biotech companies to reduce the formatting complexities. Such simplification efforts focus on building analysis systems with a number of tools integrated within them such that the transfer of data between tools appears seamless to the end user.

Bioinformatics analysts have a broad range of opportunities. They may write specific algorithms to analyze data, or they may be expert users of analysis tools, helping scientists understand how the tools analyze the data and how to interpret results. A knowledge of various programming languages, such as Java, PERL, C, C++, and Visual Basic, is very useful, if not required, for those working in this area.

Data Integration.

Once information has been analyzed, a researcher often needs to associate or integrate it with related data from other databases. For example, a scientist may run a series of gene expression analysis experiments and observe that a particular set of 100 genes is more highly expressed in cancerous lung tissue than in normal lung tissue. The scientist might wonder which of the genes is most likely to be truly related to the disease. To answer the question, the researcher might try to find out more information about those 100 genes, including any associated gene sequence, protein, enzyme, disease, metabolic pathways, or signal transduction pathway data.

Such information will help the researcher narrow the list down to a smaller set of genes. Finding this information, however, requires connections or links between the different databases and a good way to present and store the information. An understanding of database architectures and the relationship between the various biological concepts in the databases is key to doing effective data integration.

Analysis of Integrated Data.

Once various types of data are integrated, users need a good way to present these various pieces of data so they can be interpreted and analyzed. The information should be capable of being stored and retrieved so that, over time, various pieces of information can be combined to form a "knowledge base" that can be extended as more experiments are run and additional data are integrated from other sources. This type of work requires skills related to database design and architecture. It also requires specific programming skills in various computer languages, as well as expertise in developing interfaces between a computer and its user.