Because of these strengths, Perl is a major component of much bioinformatics programming. At the same time, Perl is regarded by many computer scientists as an unsafe language in which it is easy to make programs do dangerous things. In addition, many regard the syntax and structure of most Perl programs to be of a nature that is hard to understand much after the fact. One obvious approach to data integration relies on technical standards that define representations of data and hence provide an understanding of data that is common to all database developers. For obvious reasons, standards are most relevant to future datasets.
Legacy databases, which have been built around unique data definitions, are much less amenable to a standards-driven approach to data integration. Standards are indeed an essential element of efforts to achieve data integration of future datasets, but the adoption of standards is a nontrivial task. Ideally, source data from these projects flow together into larger national or international data resources that are accessible to the community.
Adopting community standards, however, entails local compromises e. In this regard, funding agencies and journals have considerable leverage and through techniques such as requiring researchers to deposit data in conformance to community standards may be able to provide such incentives. At the same time, data standards cannot resolve the integration problem by themselves even for future datasets.
One reason is that in some fast-moving and rapidly changing areas of science such as biology , it is likely that the data standards existing at any given moment will not cover some new dimension of data. A novel experiment may make measurements that existing data standards did not anticipate. For example, sequence databases—by definition—do not integrate methylation data; and yet methylation is an essential characteristic of DNA that falls outside primary sequence information. As knowledge and understanding advance, the meaning attached to a term may change over time.
A second reason is that standards are difficult to impose on legacy systems, because legacy datasets are usually very difficult to convert to a new data standard and conversion almost always entails some loss of information. As a result, data standards themselves must evolve as the science they support changes. Because standards cannot be propagated instantly throughout the relevant biological community, database A may be based on Version It would be desirable if the differences between Versions In short, much of the devil of ensuring data integration is in the detail of implementation.
Experience in the database world suggests that standards gaining widespread acceptance in the commercial marketplace tend to have a long life span, because the marketplace tends to weed out weak standards before they become widely accepted.
Once a standard is widely used, industry is often motivated to maintain compliance with this accepted standard, but standards created by niche players in the market tend not to survive. This point is of particular relevance in a fragmented research environment and suggests that standards established by strong consortia of multiple players are more likely to endure. An important issue related to data standards is data normalization. Such problems can arise in many different contexts:. Section 4.
Microarray data related to a given cell may be taken by multiple investigators in different laboratories. Ecological data e. Neurological data e. The simplest example of the normalization problem is when different instruments are calibrated differently e. The procedure is not valid if the zeroing knob was jiggled accidentally after half of the measurements had been taken.
Such biases in the data are systematic.
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In principle, the steps necessary to deal with systematic bias are straightforward. The researcher must avoid it as much as possible. Because complete avoidance is not possible, the researcher must recognize it when it occurs and then take steps to correct for it. Correcting for bias entails determining the magnitude and effect of the bias on data that have been taken and identifying the source of the bias so that the data already taken can be modified and corrected appropriately.
In some cases, the bias may be uncorrectable, and the data must be discarded. However, in practice, dealing with systematic bias is not nearly so straightforward. Ball notes that in the real world, the process goes something like this:. There are many sources of systematic bias, and they differ depending on the nature of the data involved. They may include effects due to instrumentation, sample e. Section 3. There are many ways to correct for systematic bias, depending on the type of data being corrected. In the case of microarray studies, these ways include use of dye swap strategies, replicates and reference samples, experimental controls, consistent techniques, and sensible array and experiment design.
Yet all. Kepler, L. Crosby, and K. Hoffmann, T. Seidl, M. Colantuoni, G. Henry, S. Zeger, and J. Durbin, J.
Hardin, D. Hawkins, and D. Tran, D. Peiffer, Y. Shin, L. Meek, J. Brody, and K. Bilban, L.
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Buehler, S. Head, G. Desoye, and V. Data warehousing is a centralized approach to data integration.
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The maintainer of the data warehouse obtains data from other sources and converts them into a common format, with a global data schema and indexing system for integration and navigation. Such systems have a long track record of success in the commercial world, especially for resource management functions e. These systems are most successful when the underlying databases can be maintained in a controlled environment that allows them to be reasonably stable and structured.
Data warehousing is dominated by relational database management systems RDBMS , which offer a mature and widely accepted database technology and a standard high-level standard query language SQL. However, biological data are often qualitatively different from the data contained in commercial databases.
Furthermore, biological data sources are much more dynamic and unpredictable, and few public biological data sources use structured database management systems. Data warehouses are often troubled by a lack of synchronization between the data they hold and the original database from which those data derive because of the time lag involved in refreshing the data warehouse store.
Data warehousing efforts are further complicated by the issue of updates. Stein writes: 6.
One of the most ambitious attempts at the warehouse approach [to database integration] was the Integrated Genome Database IGD project, which aimed to combine human sequencing data with the multiple genetic and physical maps that were the main reagent for human genomics at the time. The integrated database was distributed to end-users complete with a graphical front end….
The IGD project survived for slightly longer than a year before collapsing.
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