Yeast, perhaps the most thoroughly-studied eukaryote, already has a number of comprehensive online databases containing functional annotation information for close to two-thirds of all known or predicted open reading frames (ORFs) in the genome, and numerous large-scale experimental results covering practically every gene/protein (eg., 89). In many cases there are links between the data sources, though the majority of these are at the individual gene level. For example, the Comprehensive Yeast Genome Database at the Munich Information Center for Protein Sequences (MIPS) lists five catalogues encompassing a curation of several thousand publications and spanning functional classification, known protein complexes, protein classes, mutant phenotypes and subcellular localization 2. Similarly, the Gene Ontology (GO) Consortium organizes the same information under the categorizations: Biological Process, Molecular Function and Cellular Component 10. Other online knowledge bases encompass computationally-derived gene properties, such as domain and motif information (e.g. SMART 11, Pfam 12). In addition, there are now mass stores of high-throughput biological data accumulating from experimental results such as synthetic lethal 13, protein interactions by yeast two hybrid 1415 or mass spectrometry 1617, immunolocalization 18, large-scale phenotypic analysis 19, and microarray analysis (eg., 8).

A listing of the current databases that can be evaluated with FunSpec is given in Table 1. The published experimental information that can be queried is given in Table 2. Through our own analyses of recent literature on ribosomal RNA processing, additional categories are provided to elicit enrichment of pre-ribosome components. Also, a separate category has been added to encompass genes that are essential for viability 20.

Upon collection of data resulting in clusters of genes/proteins having similar properties, responses, or other associations, often a researcher may wish to summarize and interpret the current state of knowledge of genes in such a cluster. A cluster of genes can be said to be "functionally enriched" for an attribute if the proportion of genes within the cluster known to have that attribute exceeds the number that could reasonably be expected from random chance. For example, 215 of the 6267 genes in yeast 1 are known to function in ribosome biogenesis. If, in a clustering analysis of gene expression profiles, one finds a cluster of 100 genes to be co-regulated and of those, 60 are known to function in ribosome biogenesis, then intuitively the cluster is enriched for ribosome biogenesis (60% in the cluster versus 3% in the genome). The degree of functional enrichment for a given cluster and category can be quantitatively assessed by the hypergeometric distribution 21. For each category, the probability (p-value) of observing such an overlap by chance is calculated as:

where G is the size of the genome, C is the number of genes in the genome having that attribute, n is the size of the query cluster, of which k are known to possess the attribute.

If this probability is sufficiently low for a given category, then a list of genes (e.g., a cluster) is said to be enriched for that attribute (see examples below). FunSpec inputs a list of genes and computes the hypergeometric P-value in many knowledge sources (MIPS, GO, SMART and Pfam domains, published protein complexes, 2-hybrid interactions, and subcellular localizations). A Bonferroni correction may be applied to compensate for multiple testing over many categories of a knowledge base.

In this section, we describe instances where FunSpec may be useful. In Figure 1, a 2-dimensional clustering of gene expression data from 8, covering 237 genes and 271 experiments is presented. The 76 genes indicated by the red bar were submitted to MIPS Functional Classification database on the FunSpec website. The categories "amino acid biosynthesis" and "amino acid metabolism" were deemed most unlikely to occur by chance (p < 10^-14 in both cases). To show the specificity of evaluating clusters in this manner, the 6 genes indicated in blue (isolated by visual inspection) have the same overall expression profile as genes generally involved in amino acid metabolism, but with a few distinct differences. FunSpec identifies these as involved in "methionine metabolism." (p = 1.0 × 10^-7).

Components of purified protein complexes can also be interpreted using FunSpec. It is noteworthy that functional annotations, most of which are based on phenotypic rather than biochemical data, generally correlate with large protein complexes in the literature (rand index = 0.94, adjusted rand index = 0.15; rand index measures the similarity of two partitions where 0 represents no similarity and 1 is complete correspondence; the adjusted rand index is a much more conservative measure 22) (data not shown). This underscores the utility of interpreting one data type with information from another 2324. Subcellular localization correlates positively with membership in protein complexes, as would be expected to enable physical association (among all protein complexes in the MIPS database, 17% are enriched (P < 0.01) for a specific subcellular compartment in the Kumar experimental data 18, in comparison to 1% for random lists of proteins with the same size distribution) (data not shown). For newly-identified complexes, FunSpec provides not only information about localization and potential function, but also a measure of confidence regarding the biological validity of the complex.