GOFFA uses an ORACLE database containing the GO project data together with gene identifiers for human, mouse and rat from the NCBI Entrez gene database http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=gene. The database currently contains 16,389 mouse, 11,934 human and 11,599 rat genes. The genes from these three species can also be combined in analyses using the "cross-products" feature, where the same gene symbols from human, mouse and rat (regardless of case) are considered to share the same functional annotation in GO; in this case, the GOFFA database contains 26,564 unique gene symbols for cross species annotation.

Data from the GO website are downloaded in a structure called a directed acyclic graph (DAG), a name that denotes an unclosed structure where a particular child node associated with a GO term can have multiple parent nodes. GOFFA converts the DAG structure to a tree structure by constructing distinct paths from the highest parent node (least specific), successively down through progeny to the lowest (most specific) child node. In converting the data, GOFFA maintains the GO database's so-called true path rule by assuring that a gene product GO term applicable to a child node also applies to all parent terms. Thus, during the conversion to a tree structure, the DAG structure for each GO term can become many separate traversals from highest parent to lowest child. Each such traversal in GOFFA is called a GOFFA Tree Path, and each node along a GOFFA Tree Path is assigned a unique identification called a GOFFA ID. Consequently, the same GO term occurring in different GOFFA Tree Paths has a distinct GOFFA ID in each path. The restructuring of GO information in the GOFFA Tree Path format not only markedly speed up database queries but, most importantly, enable developing two unique utilities, GO Path and GO TreePrune (more in Results).

A fundamental step in analyzing DNA microarray data is to determine the differentially expressed genes (DEGs) for subsequent biological interpretation. GOFFA applies three statistical approaches to determine the significant GO terms for a given list of DEGs, two previously reported methods and one novel approach:

• Fisher's Exact Test – A right-sided Fisher's Exact Test http://www.matforsk.no/ola/fisher.htm is used to estimate the statistical significance of GO term i. Four lists of genes are needed to calculate the significance (i.e., p-value): The number of inputted genes (M) 25, the subset of M genes that belong to GO term i (m_i), the set of reference genes (N) and the subset of N genes that belong to GO term i (_ni). The accuracy of p-value is largely dependent on the choice of the set of reference genes. There are two options in GOFFA to determine N, depending on whether the genes derived from a known gene or not. For a known microarray chip, N is the total number of genes on the chip; in this case, p-value less than 0.01 normally is indicative of a statistically significant finding. GOFFA provides information associated with most commercial array platforms, including most GeneChip platforms from Affymetrix, most one- and two-channel array platforms from Agilent, as well as numerous other array platforms such as those from GE HealthCare CodeLink, Illumina BeadArray, and Applied Biosystems arrays, etc. If the microarray chip's genes are unknown, the total number of genes in the GOFFA database is assigned as N. In this case, the choice of N is dependent on the selected species, currently, 16,389 genes for mouse, 11,934 for human, 11,599 for rat, and 26,564 if combining all three species. Thus, the selection of N is an important factor to interpret p-value.

• Relative Enrichment Factor – GOFFA also calculates the Relative Enrichment Factor (E) for assessing the significance of GO term i for a given list of DEGs 5. The E-value is calculated as:

E = (m_i/M)/(n_i/N)     (1)

where m_i, M, n_i and N are defined the same as for Fisher's Exact Test described in the preceding paragraph. E provides a direct measure of the prevalence of a GO term i among the M significant genes compared to the prevalence of the same GO term i among N total genes. Accordingly, E = 1.0 corresponds to GO term i occurring among the DEGs at the same prevalence as among the N total genes. E = 2.0 indicates that GO term i occurring in the DEGs two times more than occurring in the N total genes, indicating significant findings.

• GOFFA Tree Path Ranking – Criteria based on Fisher's Exact Test and/or Relative Enrichment Factor sometimes fail to sufficiently condense and clarify results for effective interpretation, especially for large lists of significant genes. This provided the motivation of developing this unique function in GOFFA. The method applied the Fisher's inverse Chi-Squared method 2324 to sort GOFFA Tree Paths in accordance with their likely significance, and then renders an interactive graphic display for visualization and interpretation. The Fisher's inverse Chi-Square method uses the fact that given a uniform distributed p-value, -2log(p) has a chi-square distribution with two degrees of freedom, and hence the statistic

follows a chi-square distribution with 2K degrees of freedom when the joint null is true. In our case, p_k is the Fisher's Exact Test probability value of GO term k and K is a total number of GO terms within the traverse of the GOFFA Tree Path from the upper level of the tree down to GO term i. Thus, R_i is a relative metric of the prevalence of a GOFFA Tree Path from the upper level to the level GO term i belongs, given that the p_k values are known for each GO term on the path. The greater the value of R_i, the less likely it is that the significance of a GOFFA Tree Path is a chance occurrence.

GOFFA is available through ArrayTrack software http://edkb.fda.gov/webstart/arraytrack/.

The GOFFA's software GUI, shown in Figure 2, has three panels with different functions that are designed for intuitive and interactive use. The left panel (labeled 1) is for queries, the center panel (labeled 2) for tabular and/or graphical displays of and for interaction with the GO information, and the right panel (labeled 3) lists the individual genes associated with the GO information presented in the center panel.

Queries are initiated in the left panel by pasting DEG ID's into the query window, one gene per line. The input gene ID's must correspond to the "Select data type" option chosen by the user. Currently, GOFFA supports four types of gene identifiers: (1) GenBank Accession number, (2) Unigene ID, (3) LocusLink ID (or Entrez Gene ID) and (4) Gene Name. In addition, GOFFA supports two protein identifiers, IPI ID (EBI International Protein Index database) and Swiss-Prot accession number for proteomics data analysis. The GOFFA database currently contains GO annotation data for 105 microarray platforms that, with the "Select array type" option, is coupled with the GO analysis and available for display. Query results are displayed in the center panel in five interactive viewing windows, Tree, Terms, Genes, GO Path and GO TreePrune, that are activated with tabs at the top of the panel. These five windows provide the means for applying and iteratively re-applying statistical operators to the inputted (DEG) gene list, viewing statistical results, and viewing the results of GOFFA's Tree, GO Path, and GO PruneTree analysis. The data and results within both tables and plots are synchronized components, enabling mouse-click toggling between window views. For example, genes associated with GO terms that are selected through mouse clicks in the GOFFA Tree (panel 2, Figure 2) are displayed as a list in the right panel (panel 3, Figure 2).

The user can toggle between the center panel's five windows (panel 2, Figure 2), providing another level of iterative interactivity. Each window either displays information differently, or displays different information related to the inputted genes:

• Tree window – The Tree window is the default viewer that is launched after a search, and appears in the center panel. As shown in Figure 2, the Tree window displays GO terms in an outline-like hierarchical tree format (conventional view). The number of associated genes, the Relative Enrichment Factor (E-value), and the p-value from the Fisher's Exact Test are displayed for each GO Term at each GO hierarchical level. Since query results can form an extensive list, a flexible search capability is provided below the tree display. The user can search the tree by GO term, gene name/symbol, p-value, E-value, and their combination, and search results are then highlighted in blue within the display for easy location with associated gene (s) listed in the right panel.

• Terms and Genes windows – These two windows provide alternative, tabular presentations of the information contained in the tree window (Figure 3). Whereas the Tree window combines the three categories of GO path information, both the Terms window (Figure 3a) and Genes window (Figure 3b) separately display Molecular Function, Biological Process and Cellular Component category information, as chosen with a tab, and presents it in an excel-like spreadsheet format. As indicated by their names, the Terms window aggregates information by individual GO term, whereas the Genes window aggregates information by individual gene. Both windows display results of statistical operators (p-value and E-value). The Terms window displays the number of significant genes associated with each GO term, as well as the average hierarchical level at which the gene appears in the GO term. Tables in both windows can be sorted in either ascending or descending order of any column, and can be cut and pasted or exported to external software for further analysis.

• GO Path window – The GOFFA GO Path plot (Figure 4) visually presents the GOFFA Tree Paths estimated as the most relevant by equation 2. The GOFFA algorithm first rank-orders all GOFFA Tree Paths using equation 2 values, and then plots the 10 paths with the highest values, with the X-axis corresponding to descending hierarchical tree level, and the Y-axis corresponding to the log p value at each hierarchical level (Figure 4). Double clicking any GOFFA Tree Path in the graph or its color key located below the graph will launch a Tree window view (Figure 2, panel 2) with the GO terms corresponding to the GOFFA Tree Path highlighted in blue for easy recognition. The GO Path visualization could be considered as a condensed rendering of the most salient GO information associated with the DEG's data.

• GO TreePrune – This visualization tool display the GO terms in a node-like hierarchical tree structure, as shown in the Figure 5 example. Note that the plot is annotated with the p-value, E-value, and number of associated genes at each node of the tree. The number of genes associated with each node is also depicted in the pie chart as a fraction of the genes associated with the root node. The GO TreePrune plots can be very large and complex; as a result, GOFFA provides a tool for pruning the tree by assigning arbitrary and simultaneous cutoffs for p-value, E-value, and number of genes. Nodes below the cutoff values specified by the user are removed from the plot.

A dataset from a toxicogenomics study was used to demonstrate the utility of GOFFA. In this study, the renal toxicity and carcinogenicity associated with the treatment of aristolochic acid (AA) in rats was studied using DNA microarray. AA is an active component of herbal drugs derived from some plants that has been used for medicinal purposes since ancient time 26. The compound is a nephrotoxin and carcinogen in human and rodents. To investigate the effect of AA exposure on gene expression in rat kidney, a toxicogenomics study is conducted; the experimental protocol is described by Chen et al. in an accompanying paper of the same issue. Briefly, six-week old Big Blue rats were treated with AA and control vehicle for 3 month. One day after the last treatment, the animals were sacrificed and the kidneys were removed for microarray analysis using the Applied Biosystems Rat Genome Survey Microarray. Both treated and control samples had six biological replicates (rats). The data normalization and analysis were conducted using ArrayTrack. The DEG list was determined based on p < 0.01 and Fold Change > 2. Since GOFFA is fully integrated with ArrayTrack, the DEGs from ArrayTrack were directly passed to GOFFA for functional analysis. Of 1176 identified genes, 417 genes had GO information for analysis 25. The GOFFA results are summarized in Figures 2, 3, 4, 5.

The statistics based on a combination of Fisher's Exact Test (p < 0.05) and Relevant Enrichment Factor (E > 2) identified 52 enriched GO terms in the GO biological process. The majority of the terms are related to four functional categories, induction of apoptosis, defense response, response to stress, and amino acid metabolism. These four functional categories reflect the known biological and pharmacological responses of kidney to the AA treatment 26. Out of these four functional categories, GO Path ranked "defense response" as an important mechanism associated with the AA treatment (Figure 4), and similar results were obtained from GO TreePrune as well (Figure 5). This finding is consistent with the general understanding that defense response, which includes immune response, is a complex network response of a tissue to toxins and carcinogens (such as AA) for defending the body. Figure 2 gives the GO Path results in the Tree window, where the majority of genes involved in the defense response are up-regulated to oppose damage by AA. For example, the inhba gene (first gene in the right panel) is a growth factor with 4.1-fold increase in expression in kidney. This is a tumor-suppressor gene and it produces protein that increases arrest in the G1 phase of tumor cells 27. Therefore, its induction inhibits tumorigenesis in kidney treated with AA.