Software

The analysis of genomic data is heavily dependent on computers, bioinformatics, and Internet technologies. In fact, analytic decisions can often be dictated by the limitations of the available software. To the extent that the investigator wishes to explore multiple analytic options, a familiarity with software applications, such as those shown in Table 5, is imperative (103). There are a variety of commercial and public-domain software applications for each step in the microarray experiment. In an optimal setting, a laboratory information management system (LIMS) will be in place to assist in tracking samples names, sample information, experimental conditions, and protocols. In so doing, the investigator can simplify data reporting at the time of publication as well as ensuring careful data management for the analysis. A variety of image analysis software is available depending on whether cDNA arrays or oligonucleotide arrays are used. Several authors have evaluated the relative strengths and weakness of the methods used by different programs; however, there is no current standard.

The major current focus of software development is in the analysis of microarray data. Microarray data can be analyzed using a range of traditional software from basic spreadsheets such as Microsoft's Excel or more devoted statistical packages such as SAS. In general, however, investigators have preferred dedicated applications with tools focused specifically at microarrays. One of the most flexible of these is the R statistical programming language. R is a programming language in which users can either adopt a wide variety of previously written packages to their specific needs or, if need be, write their own. These multiple applications can be performed without the need to transport data from one format to another, as all are executed within the R environment. Most other software applications offer a fixed number of analytic options without the ability to install or adapt them to the specific needs of the user. Although the user might be able to perform the range of applications desired by transferring data from one application to another, this usually requires reformatting entire datasets. The disadvantage to R is that compared to the stand-alone applications, it is relatively less user friendly.

Table 5 lists a variety of popular software options that perform many of the supervised and unsupervised learning techniques discussed in the previous sections, as well as gene normalization, gene filtering, and data display. Supervised and unsupervised learning algorithms generally divide samples based on lists of genes. For example, the k nearest-neighbors technique chooses a set of genes to define the gene space that best defines clear classes. Lists of genes generated in this way, although more manageable than the thousands of genes originally analyzed in the array experiment, can still prove difficult to interpret. The applications Genmapp, GoSurfer, and Mappbuilder deserve special mention, as they play a role in organizing the long lists of genes generated by clustering and classification algorithms. GenMapp, for example, allows the user to examine genomic data using the Gene Ontology Consortium's GO terms or other user specified functional annotation and provides another potentially useful look at differences between groups.

A final group of applications overlaps with those resources listed in Table 4. These are not stand-alone software but Internet-based tools. Some of these such as the BASE application serve a similar purpose to the programs listed in Table 5, allowing for data viewing or other analysis. More commonly though, these resources are interactive databases and search

Table 5 Software

Application Software title Source

Table 5 Software

Application Software title Source

Laboratory Information

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