MAPPFinder: using Gene Ontology and GenMAPP to create a global gene-expression profile from microarray data

Short abstract

MAPPFinder is a tool that creates a global gene-expression profile across all areas of biology by integrating the annotations of the Gene Ontology (GO) Project with the free software package GenMAPP.

Abstract

MAPPFinder is a tool that creates a global gene-expression profile across all areas of biology by integrating the annotations of the Gene Ontology (GO) Project with the free software package GenMAPP http://www.GenMAPP.org. The results are displayed in a searchable browser, allowing the user to rapidly identify GO terms with over-represented numbers of gene-expression changes. Clicking on GO terms generates GenMAPP graphical files where gene relationships can be explored, annotated, and files can be freely exchanged.

Background

DNA microarray experiments simultaneously measure the expression levels of thousands of genes, generating huge amounts of data. The analysis of these data presents a tremendous challenge to biologists and new tools are needed to help gain biological insights from these experiments. Although the data are generated for individual genes, examining a dataset on a gene-by-gene basis is time consuming and difficult to carry out across an entire dataset. One way of accelerating the pace of data analysis is to approach the data from a higher level of organization. This can be done using data-driven methods, such as hierarchical clustering and self-organizing maps [1,2], which identify groups of genes with similar expression patterns. A complementary approach is to view the data at the level of known biological processes or pathways. Identifying those groups of biologically related genes that are showing a large number of gene-expression changes will create an informative description of the biology that is occurring in a particular dataset, making it possible to generate new hypotheses and identify those specific areas of biology that warrant more detailed investigation.

One tool that assists in the identification of important biological processes is GenMAPP (Gene MicroArray Pathway Profiler) [3], a program for viewing and analyzing microarray data on microarray pathway profiles (MAPPs) representing biological pathways or any other functional grouping of genes. When a MAPP is linked to a gene-expression dataset, GenMAPP automatically and dynamically color codes the genes on the MAPP according to criteria supplied by the user. GenMAPP is a useful starting point for pathway-based analysis of gene-expression data, but there are several critical requirements to be met before this tool can be used to identify correlated gene-expression changes across all biology. On a practical level, pathway-based analysis of microarray data needs to be automated, so that all possible pathways can be explored. Identifying correlated gene-expression changes in an individual pathway is often interesting, but it is necessary to know if the gene-expression changes seen on a particular pathway are unique to this pathway or are occurring in many other pathways. Equally important to automation is expanding the pathway information that is digitally represented. GenMAPP currently has over 50 MAPP files depicting various biological pathways and gene families, but this is still only a small fraction of all known biology [3]. Several other pathway programs such as KEGG [4], EcoCyc/MetaCyc [5], Pathway Processor (which uses KEGG) [6] and ViMAc [7] are available for integration with microarray data analysis, but these programs focus on well-defined metabolic pathways, and like GenMAPP, would benefit from a broader base of pathway information.

To address this issue, we have used information available from the Gene Ontology (GO) Consortium [8]. The GO Consortium is creating a defined vocabulary of terms describing the biological processes, cellular components and molecular functions of all genes. The GO is built in a hierarchical manner, with a parent-child relationship existing between GO terms. Curators at the public gene databases are assigning genes to GO terms to provide annotation and a biological context for individual genes. In addition to providing gene annotation, GO also provides a structure for organizing genes into biologically relevant groupings. These groupings can serve as the basis for identifying those areas of biology showing correlated gene-expression changes in a microarray experiment. While GO has been used to annotate microarray data both by hand and by some software packages [9,10,11], there has been no automated way to use it for pathway-based analysis.

We have developed a tool called MAPPFinder that dynamically links gene-expression data to the GO hierarchy. For each of the 11,239 ([12]; as of May 6, 2002]) GO biological process, cellular component and molecular function terms, MAPPFinder calculates the percentage of the genes measured that meet a user-defined criterion. This is done for each specific GO node, and for the cumulative total of the number of genes meeting the criterion in a parent GO term combined with all of its children, giving a complete picture of the number of genes associated with a particular GO term. Using this percentage and a z score (see Materials and methods), the user can rank the GO terms by their relative amounts of gene-expression changes. MAPPFinder therefore generates a gene-expression profile at the level of biological processes, cellular components and molecular functions, rapidly identifying those areas of biology that warrant further study (Figure 1).

Figure 1.

How MAPPFinder works. Microarray data is imported into MAPPFinder as a GenMAPP gene-expression dataset. Using a relational database and the gene-association files from GO, MAPPFinder assigns the thousands of genes in the dataset to the thousands of GO terms. Using a user-defined criterion for a significant gene-expression change, MAPPFinder calculates the percentage of genes meeting the criterion and a statistical score for each GO term. Using the ranked list and GO browser generated by MAPPFinder the user can quickly identify interesting GO terms with high levels of gene-expression changes. The specific genes involved in these GO terms can be examined on automatically generated MAPPs using GenMAPP.

MAPPFinder and GenMAPP are both available free-of-charge at [13].

Results and discussion

To demonstrate the utility of MAPPFinder, we used the program to analyze the publicly available mouse microarray dataset, the FVB benchmark set for cardiac development, maturation and aging [14]. This dataset measures gene-expression levels in the hearts of 12.5-day embryos and adult mice. We have used the 12.5-day embryonic time point to identify those biological processes that show differentially expressed genes between embryonic and adult hearts. We ran the MAPPFinder analysis on this dataset using two criteria, either an increase (fold change > 1.2 and p < 0.05) or decrease (fold change < -1.2 and p < 0.05) in gene expression for the 12.5-day embryo. We chose this dataset for demonstration because of the large number of differences in gene expression observed in the 12.5-day embryo compared to the adult mouse heart tissue.

MAPPFinder linked the 9,946 probe sets measured in this experiment to the 11,239 GO terms [12] in the hierarchy and calculated the percentage of genes meeting the criterion and a z score for each GO term. Table 1 gives an overall summary of the linkages made between the dataset and GO and calculations carried out by MAPPFinder. Nearly half of the 9,946 probe sets measured in the FVB benchmark dataset were connected to a GO term, representing approximately 70% of the mouse genes associated with GO terms [15] and covering a good portion of what is currently known about mouse biology. The proportion of genes in the microarray dataset that link to GO terms will increase as more GO terms and gene associations are added by the Mouse Genome Database (MGD) [16].

Table 1.

Numbers of genes used in the MAPPFinder calculations

	FVB benchmark dataset for development
Genes measured	9,946
Genes linked to MGD directly	6,267
Genes linked to MGD via UniGene	220
Genes linked to GO terms	5,120
Unique genes linked to GO	4,574
Genes measured/associated in GO process	3,544/4,962 (71.4%)
Genes measured/associated in GO component	3,238/4,691 (69.0%)
Genes measured/associated in GO function	3,999/5,846 (68.4%)
	12.5-day embryo
	Increased	Decreased
Genes changed	2,219	1,775
Genes linked to GO process	806	711
Genes linked to GO component	726	657
Genes linked to GO function	885	783

Of the 9,946 genes measured by this array, 6,267 were linked to the MGD database via the GenBank accession numbers referenced by MGD. An additional 220 genes were linked to MGD using UniGene as an intermediate step (see Materials and methods). Of these 6,487 genes, 5,120 were found in the mouse GO gene-association files. Once duplicate probes were removed, 4,574 unique genes were used for the MAPPFinder analysis. This dataset comprised 71.4% of the 4,962 genes associated with GO process terms, 69% of the 4,691 genes associated with GO component terms, and 68.4% of the 5,846 genes associated with GO function terms [15]. In the 12.5-day embryo, 2,219 genes met the criterion for increased gene expression, 806 having process annotation, 726 having component annotation, and 885 having function annotation. The decreased criterion found 1,775 genes, 711 in process, 657 in component, and 783 in function.

After MAPPFinder assigns the genes in the microarray dataset to the GO structure, it calculates for each GO term the percentage and z score (see Materials and methods) for the genes that meet the user's criterion. These two values can be used to identify GO terms with an over- (or under-) represented number of gene-expression changes. The MAPPFinder results are displayed in two forms. The first is a GO browser that graphically displays the MAPPFinder results in the structure of the GO hierarchy (Figures 2a,3a). The second is a text file listing all the GO terms measured, ranked by the z score. The number of genes meeting the criterion, the number of genes measured in the experiment, and the number of genes assigned to each GO term by MGD are given, along with the respective percentages and z score, in the text file and GO browser (Figure 2b). Table 2 shows the list of process, component and function terms with a z score greater than 2 for the significantly increased and decreased criteria at the 12.5-day embryonic time point. GO terms that had fewer than 5 or more than 100 genes changed were removed from the list because these terms were either too specific or too general for our data analysis. This filter identified the top 108 (8.0%) GO terms for the significantly increased criterion and the top 63 (4.8%) GO terms for the significantly decreased criterion. The stringency of this filter can be increased or decreased by raising or lowering the z score cutoff, or by including terms with larger or smaller numbers of genes. The filtered list was then pruned by hand for related GO terms to remove any over-represented branches of the GO hierarchy (for the complete results, see Additional data files). When both a parent and a child term were present in the list, the parent term was removed if its presence was due entirely to genes meeting the criterion for the child term. The remaining terms on the list still have a large degree of interrelatedness, but have been retained here for completeness.

Figure 2.

The MAPPFinder browser. (a) The branch of the GO hierarchy rooted at the biological process term 'RNA processing' is shown. The terms are colored with the MAPPFinder results for genes significantly increased in the 12.5-day embryo versus the adult mice. Terms with 0-5% of genes changed are colored black, 5-15% purple, 15-25% dark blue, 25-35% light blue, 35-45% green, 45-55% orange, and greater than 55% red. The term RNA processing is highlighted in yellow, indicating that it meets the search or filter requirements. (b) The MAPPFinder results. The term RNA processing is shown with the various MAPPFinder results labeled. The percentage of genes meeting the criterion and the percentage of genes in GO measured in this experiment are calculated. The results are calculated for both this node individually and in combination with all of its child nodes (that is, nested results). The z score indicates whether the number of genes meeting the criterion is higher or lower than expected. A positive score indicates that more genes are changed than expected; a negative score means fewer genes are changed than expected, and a score near 0 indicates that the number of changes approximates to the expected value for that GO term.

Figure 3.

Linking MAPPFinder to GenMAPP. (a) The MAPPFinder browser displaying the 12.5-day embryo increased results for the GO process term 'glycolysis'. Color-coding of GO terms is the same as in Figure 2. (b) Clicking on the GO term glycolysis in the MAPPFinder browser builds the corresponding GenMAPP MAPP file. This MAPP file contains a list of genes associated with this term and all of its children. (c) Genes in the GO list were rearranged with the tools in GenMAPP to depict the glycolysis pathway with the metabolic intermediates and cellular compartments. Color-coding of genes for (b) and (c) is as follows: Red, fold change >1.2 and p < 0.05 in the 12.5-day embryo versus adult mice. Blue, fold change < -1.2 and p < 0.05. Gray, neither of the above criteria met. White, gene not found on the array.

Table 2.

MAPPFinder results for genes significantly increased and significantly decreased in 12.5-day embryos versus adult mice

GO name	Number changed	Number measured	Number in GO	% Changed	% Present	z score
Significantly increased
Process
Mitotic cell cycle	44	70	89	62.9	78.7	8.1631
DNA metabolism	67	135	163	49.6	82.8	7.6807
mRNA splicing	19	21	30	90.5	70	7.4868
RNA processing	29	41	60	70.7	68.3	7.4411
RNA metabolism	30	44	66	68.2	66.7	7.3038
Cell cycle	98	240	291	40.8	82.5	7.0096
mRNA processing	24	33	45	72.7	73.3	6.9456
Protein biosynthesis	52	104	152	50	68.4	6.8095
Macromolecule biosynthesis	57	121	172	47.1	70.3	6.5863
DNA replication	28	46	55	60.9	83.6	6.2752
DNA replication and chromosome cycle	29	49	62	59.2	79	6.1944
Ribosome biogenesis	19	28	37	67.9	75.7	5.7749
Biosynthesis	89	242	334	36.8	72.5	5.4866
DNA dependent DNA replication	13	18	22	72.2	81.8	5.0697
Mitosis	13	18	24	72.2	75	5.0697
Nuclear division	14	21	30	66.7	70	4.8663
DNA packaging	20	36	46	55.6	78.3	4.7782
Cell organization and biogenesis	74	207	294	35.7	70.4	4.6913
M phase	15	25	36	60	69.4	4.5110
mRNA splice site selection	7	8	8	87.5	100	4.4125
DNA replication initiation	6	7	7	85.7	100	4.0138
Chromosome organization and biogenesis (sensu Eukarya)	18	37	51	48.6	72.5	3.8338
DNA repair	21	46	53	45.7	86.8	3.7895
Protein folding	12	22	31	54.5	71	3.6157
Cytoplasm organization and biogenesis	56	169	241	33.1	70.1	3.3912
Establishment and/or maintenance of chromatin architecture	13	27	35	48.1	77.1	3.2089
Protein synthesis elongation	6	9	37	66.7	24.3	3.1815
Chromatin assembly/disassembly	10	20	25	50	80	2.9585
Biological process unknown	34	98	250	34.7	39.2	2.9354
Protein-ligand dependent protein degradation	17	43	58	39.5	74.1	2.6968
Ubiquitin-dependent protein degradation	16	42	57	38.1	73.7	2.4404
Protein-nucleus import	5	9	10	55.6	90	2.3820
Ubiquitin cycle	6	12	16	50	75	2.2896
Nucleocytoplasmic transport	6	12	17	50	70.6	2.2896
Actin cytoskeleton organization and biogenesis	6	12	19	50	63.2	2.2896
Transmembrane receptor protein Ser/Thr kinase signaling pathway	10	25	31	40	80.6	2.1081
Induction of apoptosis	7	16	24	43.8	66.7	2.0449
Component
Spliceosome	17	20	42	85	47.6	6.7175
Cytosolic ribosome (sensu Eukarya)	19	26	33	73.1	78.8	6.2032
Cytosol	40	85	112	47.1	75.9	5.4872
Ribosome	35	71	93	49.3	76.3	5.4624
Chromosome	19	36	55	52.8	65.5	4.3772
Nuclear envelope-endoplasmic reticulum network	9	12	17	75	70.6	4.3676
Adherens junction	6	7	14	85.7	50	4.0138
Endoplasmic reticulum membrane	7	9	13	77.8	69.2	3.9811
Chromatin	15	28	41	53.6	68.3	3.9579
Cellular component unknown	41	117	291	35	40.2	3.3057
Nucleolus	10	19	34	52.6	55.9	3.1587
26S proteasome	11	22	23	50	95.7	3.1036
Endoplasmic reticulum	39	117	141	33.3	83	2.8569
20S core proteasome	9	19	19	47.4	100	2.6078
Nuclear membrane	6	11	18	54.5	61.1	2.5536
Cytoskeleton	64	223	306	28.7	72.9	2.2918
Collagen	10	25	31	40	80.6	2.1081
Golgi membrane	7	16	18	43.8	88.9	2.0449
Actin cytoskeleton	16	46	63	34.8	73	2.0140
Function
RNA binding	51	113	155	45.1	72.9	5.8498
Cyclin-dependent protein kinase	17	24	33	70.8	72.7	5.6944
Structural constituent of ribosome	39	83	101	47	82.2	5.4055
Cyclin-dependent protein kinase, regulator	12	17	24	70.6	70.8	4.7646
Structural molecule	77	223	278	34.5	80.2	4.4306
Pre-mRNA splicing factor	7	8	12	87.5	66.7	4.4125
mRNA binding	10	14	19	71.4	73.7	4.3979
Protein serine/threonine kinase	62	181	243	34.3	74.5	3.8821
Actin binding	25	58	83	43.1	69.9	3.7927
Proteasome endopeptidase	11	19	19	57.9	100	3.7096
DNA-directed DNA polymerase	7	10	15	70	66.7	3.6069
RHO small monomeric GTPase	7	10	10	70	100	3.6069
Nucleotidyltransferase	16	33	41	48.5	80.5	3.5964
Kinase regulator	15	33	42	45.5	78.6	3.1777
DNA dependent adenosinetriphosphatase	8	14	16	57.1	87.5	3.1151
Cytoskeletal protein binding	33	93	144	35.5	64.6	3.0423
DNA repair protein	11	23	27	47.8	85.2	2.9232
Translation factor, nucleic acid binding	14	32	43	43.8	74.4	2.8970
Transcription co-activator	6	10	14	60	71.4	2.8483
Chromatin binding	5	8	11	62.5	72.7	2.7166
Kinase	89	311	394	28.6	78.9	2.6983
Phosphotransferase, alcohol group as acceptor	87	305	386	28.5	79	2.6301
Protein kinase	76	263	336	28.9	78.3	2.5796
Exonuclease	6	11	15	54.5	73.3	2.5536
Small monomeric GTPase	15	38	46	39.5	82.6	2.5247
GTP binding	43	141	201	30.5	70.1	2.3248
Peptidylprolyl cis-trans isomerase	6	12	16	50	75	2.2896
Translation elongation factor	6	12	16	50	75	2.2896
Transcription factor binding	11	27	43	40.7	62.8	2.2838
Guanyl nucleotide binding	46	155	219	29.7	70.8	2.1927
Adenosinetriphosphatase	12	31	38	38.7	81.6	2.1763
Molecular_function unknown	29	91	230	31.9	39.6	2.1739
Protein binding	99	368	539	26.9	68.3	2.1328
Chaperone	16	45	62	35.6	72.6	2.1166
Extracellular matrix structural constituent conferring tensile strength	10	25	31	40	80.6	2.1081
DNA-directed RNA polymerase	5	10	11	50	90.9	2.0897
Structural constituent of cytoskeleton	21	63	79	33.3	79.7	2.0838
Transferase, transferring one-carbon groups	8	19	29	42.1	65.5	2.0570
GTPase	25	78	95	32.1	82.1	2.0488
Isomerase	12	32	42	37.5	76.2	2.0468
Significantly decreased
Process
Fatty acid metabolism	19	30	41	63.3	73.2	5.9082
Main pathways of carbohydrate metabolism	20	39	50	51.3	78	4.8600
Energy derivation by oxidation of organic compounds	23	50	66	46	75.8	4.5739
Catabolic carbohydrate metabolism	18	36	45	50	80	4.4754
Tricarboxylic acid cycle	6	8	10	75	80	3.8664
Hexose metabolism	18	41	49	43.9	83.7	3.8016
Lipid metabolism	42	127	167	33.1	76	3.6708
Lipid transport	5	7	11	71.4	63.6	3.3807
Glycolysis	12	26	32	46.2	81.2	3.3091
Peroxisome organization and biogenesis	7	12	15	58.3	80	3.2972
Glucose metabolism	15	36	42	41.7	85.7	3.2247
Lymph gland development	8	15	17	53.3	88.2	3.2043
Cell proliferation	10	21	34	47.6	61.8	3.1400
Humoral immune response	15	37	79	40.5	46.8	3.0982
Carbohydrate metabolism	31	95	135	32.6	70.4	3.0557
Regulation of cell proliferation	5	8	15	62.5	53.3	2.9848
Muscle contraction	9	20	28	45	71.4	2.7716
Muscle development	13	34	43	38.2	79.1	2.6328
Mesoderm development	28	90	111	31.1	81.1	2.6096
Potassium transport	17	49	60	34.7	81.7	2.5450
Metal ion transport	24	77	100	31.2	77	2.4230
Monovalent inorganic cation transport	21	67	88	31.3	76.1	2.2935
Complement activation	8	20	23	40	87	2.2132
Cation transport	28	98	135	28.6	72.6	2.0923
Electron transport	25	87	113	28.7	77	2.0075
Component
Mitochondrion	88	187	293	47.1	63.8	9.3508
Peroxisome	18	29	42	62.1	69	5.6381
Mitochondrial inner membrane	19	36	60	52.8	60	4.8922
Mitochondrial electron transport chain complex	10	14	32	71.4	43.8	4.7848
Mitochondrial membrane	20	40	72	50	55.6	4.7195
Cytochrome C oxidase	6	8	16	75	50	3.8664
Mitochondrial matrix	9	22	33	40.9	66.7	2.4283
Basement lamina	5	11	11	45.5	100	2.0910
Cytoskeleton	57	223	306	25.6	72.9	2.0527
Function
Hydrogen ion transporter	11	15	33	73.3	45.5	5.1373
Primary active transporter	27	64	107	42.2	59.8	4.4175
Cation transporter	17	36	61	47.2	59	4.0585
Ion transporter	19	43	79	44.2	54.4	3.9406
Cytochrome c oxidase	6	8	16	75	50	3.8664
Oxidoreductase	48	149	207	32.2	72	3.7213
Major histocompatibility complex antigen	13	30	54	43.3	55.6	3.1700
Oxidoreductase, acting on the aldehyde or oxo group of donors	7	13	16	53.8	81.2	3.0285
Carrier	40	131	196	30.5	66.8	2.9960
Complement component	8	16	19	50	84.2	2.9770
P-type ATPase	5	9	11	55.6	81.8	2.6467
Hydrolase, acting on acid anhydrides, catalyzing transmembrane movement of substances	15	42	67	35.7	62.7	2.5199
Nucleobase, nucleoside, nucleotide kinase	7	16	19	43.8	84.2	2.3531
Phosphotransferase, phosphate group as acceptor	5	10	13	50	76.9	2.3520
Glutathione transferase	5	10	13	50	76.9	2.3520
P-P-bond-hydrolysis-driven transporter	17	52	78	32.7	66.7	2.2609
ATP-binding cassette (ABC) transporter	11	30	50	36.7	60	2.2573
Potassium channel	15	45	56	33.3	80.4	2.2093
Carbon-carbon lyase	5	11	18	45.5	61.1	2.0910

All terms with a z score of 2 and at least 5, but less than 100 genes meeting the criterion are shown.

The MAPPFinder results present a global picture of the biological processes, cellular components and molecular functions that are increased and decreased in the 12.5-day embryo compared with the adult mouse (Table 2). Using the criterion for a significantly increased gene-expression change, MAPPFinder primarily identified GO terms involved in cell division and growth. Notable GO terms include the processes 'mitotic cell cycle' (62.9% of 70 genes, z score of 8.1), 'mRNA splicing' (90.5% of 21 genes, z score of 7.5), and 'protein biosynthesis' (50% of 104 genes, z score of 6.8). The top-ranked component and function terms reflected the same biological processes. For example, the component term 'spliceosome' shows that 17 out of 20 genes (85%, z score of 6.7) were upregulated. The upregulation of these processes is consistent with the fact that cardiomyocytes remain mitotically active throughout embryonic development [17]. Apart from processes involved in cell division and growth, the MAPPFinder results indicate that the processes 'transmembrane receptor protein serine/threonine kinase signaling pathway' and 'induction of apoptosis' are upregulated, with a z score of approximately 2. The presence of the term 'transmembrane receptor protein serine/threonine kinase signaling pathway' is due to the upregulation of genes involved in transforming growth factor-β (TGFβ) receptor signaling, which is thought to regulate the induction of apoptosis required for morphogenesis during heart development [18,19].

Genes involved in energy metabolism showed the highest levels of downregulation in the 12.5-day embryo heart versus the adult heart. In particular, the process terms 'fatty acid metabolism' (63.3% of 30 genes, z score of 5.9) and 'main pathways of carbohydrate metabolism' (51.3% of 39 genes, z score 4.8), which is the parent of the terms 'glycolysis' and 'tricarboxylic acid cycle', indicate that metabolic genes as a whole are downregulated in an embryo when compared to an adult mouse. In addition, the component term 'mitochondrion' shows that 88 out of 187 genes (47.1%, z score of 9.1) are downregulated. The downregulation of genes involved in fatty-acid metabolism is consistent with research that has shown that the developing heart, unlike the adult heart, does not derive its energy from fatty acids [20].

Overall, the MAPPFinder results provide a global perspective of the processes that are up- and down-regulated in the 12.5-day embryonic heart compared to an adult heart. The results confirmed what was expected: when compared to the adult heart, the embryonic heart is undergoing increased cell division and growth and has decreased energy metabolism. In addition, the global gene-expression profile presented by MAPPFinder allows the gene-expression changes observed for cell division and growth and energy metabolism to be put in the context of other regulatory and developmental processes such as TGFβ signaling and apoptosis.

The MAPPFinder browser

Viewing the MAPPFinder results as a ranked list is informative, but it does not take full advantage of the fact that GO is arranged in a hierarchy. MAPPFinder also presents the results in the context of the GO hierarchy (Figures 2a,3a) showing the entire hierarchy, color-coded by the percentage of genes changed. Users can step through the hierarchy, expanding those branches of the tree that are showing gene expression changes, moving from broad terms to more specific categories. Often the ranked list of terms will show many interrelated terms, and it is necessary to view the results in the hierarchy to identify the relationships among them. For example, the terms 'RNA metabolism', 'RNA processing', 'mRNA processing', and 'mRNA splicing' appear as upregulated in Table 2. However, the tree view (Figure 2a) clearly shows that mRNA splicing is a child term of both RNA splicing and mRNA processing, which are in turn child terms of RNA metabolism. Similarly, the terms 'main pathways of carbohydrate metabolism', 'catabolic carbohydrate metabolism', and 'glycolysis' also appear as downregulated in Table 2. The MAPPFinder browser (Figure 3a) shows that 'glycolysis' is related to 'main pathways of carboyhydrate metabolism' through the hierarchical relationship between these terms.

The MAPPFinder browser also provides three search and navigation functions. First, the user can search by a keyword or an exact GO term name. Second, the user can search by a gene identifier to find which GO term(s) the gene is associated with. For example, searching for the gene alpha-myosin heavy chain using its SWISS-PROT identifier MYH6_MOUSE or its MGD identifier MGI:97255 finds the GO process terms 'striated muscle contraction', 'cytoskeleton organization and biogenesis', 'protein modification', and 'muscle development'. Third, the user can expand the GO tree automatically to show all nodes with a minimum number of genes or minimum percentage of genes meeting the criterion or with a minimum z score. The terms meeting the filter are highlighted in yellow to clearly indicate the results of the search.

Once the GO terms of interest have been identified with MAPPFinder, the user will want to know exactly which genes are associated with these terms and exactly which genes are being differentially expressed. This can be accomplished using GenMAPP. Selecting a GO term in the MAPPFinder browser automatically builds a MAPP containing the genes associated with that GO term and all of its children, and opens this MAPP in GenMAPP. Figure 3b shows the MAPP generated by selecting the GO term 'glycolysis' in the MAPPFinder browser. The genes on the MAPP are color-coded with the same criteria used to calculate the MAPPFinder results, significantly increased and decreased at the 12.5-day embryo time point. Clicking on a gene on the MAPP opens a 'back page' containing annotations, gene-expression data and hyperlinks to that gene's page in the public databases. By integrating GenMAPP and MAPPFinder, it is possible to seamlessly move from a global gene-expression profile at the level of all biological processes, components and functions to a detailed description of the gene-expression levels for the specific genes involved. For example, a closer examination of the glycolysis MAPP indicates that hexokinase I is upregulated in the 12.5-day embryo and isoforms II and IV are downregulated, as compared with the adult heart. This is consistent with hexokinase I being the predominant isoform in the embryonic heart [21].

Expanding MAPPFinder beyond GO

GO is a good starting point for analyzing microarray data in the context of biological pathways, but this is by no means the only way to group related genes. Instead of representing each GO process as an alphabetical list on a MAPP, it would be more useful to represent the relationships between these genes as a fully delineated pathway. As a start in this direction, GenMAPP.org [13] has created over 50 MAPPs depicting metabolic pathways, signaling pathways and gene families. MAPPFinder can incorporate any MAPP file into its analysis to augment the GO hierarchy. For the FVB benchmark developmental dataset, we have run MAPPFinder on an archive of 54 mouse MAPPs available from [13] (see Additional data files for the complete results). These results for the 12.5-day embryonic time point agree with the GO results, showing that the expression of genes involved in the metabolic pathways 'tricarboxylic acid cycle' (83.3% of 12 genes measured, z score of 5.91) and 'fatty acid degradation' (69.2% of 13 genes measured, z score 4.82) is significantly decreased. In addition, the significantly increased criterion identified genes encoding ribosomal proteins (71.1% of 45 genes, z score 6.75) and genes involved in the cell cycle (53.3% of 15 genes, z score 2.4).

The archive of MAPPs provided by GenMAPP is in no way comprehensive. The growth of this archive depends on assistance from the entire biological community. Our hope is that, as MAPPFinder users see the added utility of viewing the GO biological processes as fully delineated pathways, they will use GenMAPP to organize the gene lists into more descriptive biological pathways. Figure 3c gives an example of how the genes from the GO term 'glycolysis' can be rearranged using the tools in GenMAPP to depict the full pathway showing the direction of the enzymatic cascade, metabolic intermediates and cellular compartments. GenMAPP.org is currently accepting submissions of new MAPP files. MAPPs contributed by the community will be included in the downloadable MAPP archive.

MAPPFinder is a necessary complement to current analysis tools

By approaching large datasets from a higher level or organization, MAPPFinder helps to ease the data analysis and shorten the time necessary to gain a biological understanding of the microarray data. MAPPFinder has greatly expanded current pathway-based tools by using the large amount of annotations available from the GO. This broad analysis will help identify biological processes that have not yet been implicated in a particular experimental condition and begin to make connections between biological processes previously thought to be unrelated.

MAPPFinder is available for yeast, mouse and human data. We plan to extend the program to many of the other species that are in GO and updates will be available at [13].

Materials and methods

Gene-expression data

The publicly available mouse microarray dataset, the FVB benchmark set for cardiac development, maturation and aging, was obtained from the CardioGenomics Program for Genomics Applications [14]. These data compare healthy mouse hearts at different time points during development, using male and female FVB/N mice. Specifically, this dataset examines heart tissue from 12.5-day embryos, 1-day neonatal mice, 1-week mice, 4-week mice, and adult mice at 5 months and 1 year. Our analysis focused on the 12.5-day embryonic time point and the control adult mice. Three Affymetrix U74A version 1 arrays were used for each time point. For the embryonic time point, three hearts were pooled for each array because of their small size. To improve the statistical power in our analysis, the 5-month and the 1-year mice were combined into a single group of normal adult mice. Signal intensity values were obtained with Affymetrix MAS 5.0 software. Signal values less than 20 were raised to 20 and the log base 2 was taken. Log folds were determined from the average of each time point when compared with the average of the combined control group. P values were calculated with a permutation t test. The statistical analysis was done using the multest package of the R statistical programming language [22]. These data were imported into GenMAPP, and the resulting GenMAPP Expression Dataset file (.gex) was exported to MAPPFinder.

MAPPFinder requires a user-defined criterion for a meaningful gene-expression change. In this case we combined a fold change with a statistical filter to determine significance. We are using a fold change of greater than 1.2 with a p value of less than 0.05 to define a significant gene-expression increase, and a fold change of less than -1.2 with a p-value of less than 0.05 to define a significant gene-expression decrease. To determine the overall number of gene-expression changes in each GO term, an additional criterion of a fold change greater than 1.2 or less than -1.2 and a p value of less than 0.05 is used (data not shown).

It is important to note that while we have used gene-expression data generated from Affymetrix GeneChips, data from other microarray platforms and other techniques such as SAGE (serial analysis of gene expression) can be used equally easily.

Linking the expression data to Gene Ontology

MAPPFinder builds a local copy of the GO hierarchy using the three ontology files (Process, Component and Function) available from GO [12]. The directed acyclic graph (DAG) structure of GO [23] allows a node to be a child of multiple parents. This makes the navigation, visualization and computation of the MAPPFinder results more difficult than if the GO were stored in a classical tree structure. To ease the programming necessary to implement the MAPPFinder algorithm, the DAG structure was converted to a classical tree. For each node of the DAG that contained multiple parents, multiple copies were inserted into the tree representation of the GO using local identifiers to handle duplicate GO terms. This tree structure maintains the 'true path' rule enforced in the GO DAG structure. MAPPFinder handles this conversion internally, and to the user the GO hierarchy seen in the MAPPFinder browser will be identical to that seen in other GO browsers.

The links between the GO terms and the genes in the expression dataset are made with the gene-association files [15]. These associations are taken from the European Bioinformatics Institute [24] for human genes, the Mouse Genome Database (MGD) [16] for mouse genes, and the Saccharomyces Genome Database (SGD) [25] for yeast genes. Currently, the genes in the input data must be identified with GenBank, SWISS-PROT or SGD identifiers.

MAPPFinder uses a relational database to link the expression dataset to the gene-association files. The MAPPFinder database relates gene-expression data to the appropriate gene-identifrcation systems for each species (Figure 1). For human data, the gene-association files use SWISS-PROT identifiers, requiring a SWISS-PROT-to-GenBank relational table to link datasets using GenBank accession numbers to the GO annotations. For yeast data, the gene-association files use SGD identifiers. A SWISS-PROT-to-SGD relational table is also included for expression datasets using SWISS-PROT identifiers. For mouse data, the GO gene-association files use MGD identifiers, requiring a GenBank-to-MGD relational table, and a SWISS-PROT-to-MGD relational table. MAPPFinder takes advantage of the fact that MGD is also related to UniGene, allowing additional ESTs that are not in the MGD-GenBank relational table to be used as gene identifiers. With this intermediate step, many more GenBank identifiers can be linked to GO annotations. Currently, there is no direct relationship between SWISS-PROT and UniGene, so a similar intermediate step was not used for human data.

Calculating the MAPPFinder results

MAPPFinder calculates the percentage of genes measured within each GO term that meet a user-defined criterion, and this measurement is known as the 'percent changed'. MAPPFinder also calculates the percentage of the genes associated with a GO term that are measured in the experiment, and this measurement is known as the 'percent present'. Calculating the percent present is necessary to determine how well represented a GO term is in the dataset.

The GO gene-association files [17] are potentially problematic, because they treat each GO term independently, removing the implicit parent-child relationship. As a result, looking at the GO terms individually is often uninformative because the number of genes associated with any one term is smaller than the actual number of genes involved in that process, component, or function. To address this issue, we calculate the nested percentage for a parent term with all its children below it in the hierarchy. By combining the child terms with their parent, the results incorporate genes associated with the entire branch of the hierarchy, providing a much more accurate representation of the number of genes involved in that process, component or function. As more specific branches of the GO are examined, the denominator of the two equations will become smaller and the user can find their desired level of specificity. One complication that arises from this method is that in some cases a gene is associated with both the parent and child terms or multiple child terms. When the percentages are calculated for the sub-tree, we ensure that each gene is only counted once, so that genes with multiple annotations are not weighted more heavily.

Another complication that arises while calculating the MAPPFinder results is the issue of multiple probes of the same gene on the array. In this case, the features or duplicate genes are clustered to one unique gene. If any of the instances of this gene on the array meet the user-defined criterion, then that gene meets the user-defined criterion. The number of unique genes is also used to calculate the z score, meaning that the statistics are based only on a single occurrence of each gene in the dataset.

A statistical rating of the relative gene-expression activity in each MAPP and GO term is also provided. It is a standardized difference score (z score) using the expected value and standard deviation of the number of genes meeting the criterion on a GO term under a hypergeometric distribution. The z score is useful for ranking GO terms by their relative amounts of gene expression changes. Positive z scores indicate GO terms with a greater number of genes meeting the criterion than is expected by chance. Negative z scores indicate GO terms with fewer genes meeting the criterion than expected by chance. A z score near zero indicates that the number of genes meeting the criterion approximates the expected number. Extreme positive scores suggest GO terms with the greatest confidence that the correlation between the expression changes of the genes in this grouping are not occurring by chance alone. P values are not assigned to the GO terms or MAPPs because, while such a standardized difference score could approximate a normal z score for an individual MAPP, the lack of independence between GO terms and the multiple testing occurring among them most certainly makes the normal p value for such a z score unreliable. As a result, p values are not assigned to the GO terms and MAPPs.

The z score is calculated by subtracting the observed number of genes in a GO term (or MAPP) meeting the criterion from the expected number of genes, and dividing by the standard deviation of the observed number of genes. The equation used is

or

where N is the total number of genes measured, R is the total number of genes meeting the criterion, n is the total number of genes in this specific MAPP, and r is the number of genes meeting the criterion in this specific MAPP.

Therefore, if two GO terms contain the same number of genes, the term with the greater number of genes meeting the criterion will receive a higher score. Dividing by the standard deviation adjusts for the size of the GO term, ranking a GO term (or MAPP) with a large number of genes meeting the criterion higher than a GO term (or MAPP) with the same percentage of genes changed, but fewer total genes.

The MAPPFinder results are generated in the GO browser for analysis in the context of the GO hierarchy and as tab-delimited text files that can be used for sorting and filtering the data in a spreadsheet program.

Additional data files

The following additional data files are available:

The FVBN developmental data in the form of a GenMAPP expression dataset file (.gex). It contains the microarray dataset and the criteria used to define increased and decreased gene-expression change. It can be opened for editing in GenMAPP and is the appropriate data type for use with MAPPFinder.

The FVBN developmental data as a database file generated by MAPPFinder (.gdb). It contains the relationships between the genes in the dataset and the GO hierarchy. The file can be opened for viewing in Microsoft Access. This file must be present to build GenMAPP MAPPs from existing MAPPFinder results.

The MAPPFinder results for the 12.5-day embryos versus the adult mice are contained in the files: 12.5-day Embryo - significantly increased - Gene Ontology results, 12.5-day Embryo - significantly increased - Local results, 12.5-day Embryo - significantly decreased - Gene Ontology results, 12.5-day Embryo - significantly decreased - Local results, 12.5-day Embryo - All Changes - Gene Ontology results, 12.5-day Embryo - All Changes - Local Results. These text files contain the MAPPFinder results for both criteria and both the GO hierarchy and the GenMAPP.org MAPPs. These files can be loaded into MAPPFinder for view in the MAPPFinder GO browser. These files are tab-delimited and can also be viewed as tables in Microsoft Excel. The 'All Changes' files contain the results for a criteria looking for either increased or decreased gene expression changes. A zip file containing all aditional data files is available.

Supplementary Material

Additional data file 1

The FVBN developmental data in the form of a GenMAPP expression dataset file (.gex). It contains the microarray dataset and the criteria used to define increased and decreased gene-expression change. It can be opened for editing in GenMAPP and is the appropriate data type for use with MAPPFinder.

Additional data file 2

The FVBN developmental data as a database file generated by MAPPFinder (.gdb). It contains the relationships between the genes in the dataset and the GO hierarchy. The file can be opened for viewing in Microsoft Access. This file must be present to build GenMAPP MAPPs from existing MAPPFinder results.

Additional data file 3

12.5-day Embryo - significantly increased - Gene Ontology results

Additional data file 4

12.5-day Embryo - significantly increased - Local results

Additional data file 5

12.5-day Embryo - significantly decreased - Gene Ontology results

Additional data file 6

12.5-day Embryo - significantly decreased - Local results

Additional data file 7

12.5-day Embryo - All Changes - Gene Ontology results

Additional data file 8

12.5-day Embryo - All Changes - Local results

Additional data file 9

A zip file containing all aditional data files.