Genome comparison using Gene Ontology (GO) with statistical testing
© Cai et al; licensee BioMed Central Ltd. 2006
Received: 24 February 2006
Accepted: 11 August 2006
Published: 11 August 2006
Automated comparison of complete sets of genes encoded in two genomes can provide insight on the genetic basis of differences in biological traits between species. Gene ontology (GO) is used as a common vocabulary to annotate genes for comparison. Current approaches calculate the fold of unweighted or weighted differences between two species at the high-level GO functional categories. However, to ensure the reliability of the differences detected, it is important to evaluate their statistical significance. It is also useful to search for differences at all levels of GO.
We propose a statistical approach to find reliable differences between the complete sets of genes encoded in two genomes at all levels of GO. The genes are first assigned GO terms from BLAST searches against genes with known GO assignments, and for each GO term the abundance of genes in the two genomes is compared using a chi-squared test followed by false discovery rate (FDR) correction. We applied this method to find statistically significant differences between two cyanobacteria, Synechocystis sp. PCC6803 and Anabaena sp. PCC7120. We then studied how the set of identified differences vary when different BLAST cutoffs are used. We also studied how the results vary when only subsets of the genes were used in the comparison of human vs. mouse and that of Saccharomyces cerevisiae vs. Schizosaccharomyces pombe.
There is a surprising lack of statistical approaches for comparing complete genomes at all levels of GO. With the rapid increase of the number of sequenced genomes, we hope that the approach we proposed and tested can make valuable contribution to comparative genomics.
Comparison of two completely sequenced genomes sheds lights on the genetic basis of differences in biological traits between species. Of particular interest is the comparison of complete sets of genes and gene products encoded in two genomes. Manual comparison is important but time-consuming and labor-intensive at the whole-genome scale and thus must be aided by automated approaches.
Unambiguous automated comparison requires that both genomes be annotated with the same structured, controlled vocabulary. Currently, the most common choice for such a vocabulary is gene ontology (GO) . The November 15, 2005 version of GO contained 19,025 terms in three hierarchical structures—as Directed Acyclic Graphs (DAGs)—termed Biological Processes, Cellular Components, and Molecular Functions. Every branch in the graph represents a biological concept progressing from general to specialized with increasing graph depth. The depth of the branches in the graphs varies, with levels ranging from 2 to 15.
The GO web site currently lists 31 genomes that have been annotated with GO . The annotations that are of the highest quality and updated most frequently are usually carried out by researchers who sequence and study a particular species; these annotations are primarily stored in species-specific databases such as SGD  for Saccharomyces cerevisiae, FlyBase  for Drosophila melanogaster, WormBase  for Caenorhabditis elegans, MGI  for Mus musculus, and TAIR  for Arabidopsis thaliana. Since these species-specific databases are located in different sites on the web, there is need for integrated, searchable databases that contain annotations for multiple species. The GO Consortium has developed such a resource, called AMIGO , that allows users to search and browse GO annotations integrated from many species-specific databases. Additionally, the European Bioinformatics Institute (EBI) has developed the Gene Ontology Annotation (GOA) database  that provides GO annotations for non-redundant proteins from many species in UniProt [10, 11]. We compare these two resources in the Methods section. In addition to sequences annotated with GO, 15,754 functional domains in the InterPro domain database  have been linked to 2,627 GO terms .
Using the above-mentioned resources, there are two main types of methods developed to automatically annotate new gene products with GO terms: sequence similarity-based methods such as GOFigure , Goblet , OntoBlast , GOtcha , and Blast2GO , and sequence domain-based methods such as InterProScan  and GOTrees . For genome-scale GO annotations the similarity-based, in particular BLAST-based methods have been the preferred choice [17, 21–24]. BLAST is significantly faster than InterProScan and can annotate many more GO terms than InterProScan can. A recent evaluation showed that assigning GO terms of the top BLAST hit gave satisfactory results when compared with several more complex methods . Thus we chose the BLAST approach in our work.
After the sets of genes encoded in the two genomes are annotated with GO, they can then be compared. The goal is to find functional categories that differ between the two genomes, which may explain differences in biological traits or suggest interesting families for further detailed investigation. The most common practice is to use tools such as GOslim [26, 27] to tally the number of genes that fall within each functional category at the first level under Biological Processes, Cellular Components, and Molecular Functions, and then to compare between the two genomes. Because the two genomes usually differ in size, the absolute numbers of genes in each functional category need to be weighted before they are compared; they are often divided by the total number of genes in the respective genomes [28–30]. The results of the unweighted and weighted comparisons are usually presented as bar charts or fold changes.
The unweighted and weighted GO-based genome comparisons, although useful, have two drawbacks. First, focusing only on the high-level functional categories may miss differences that are detectable only at more refined levels. Second, bar charts or fold changes alone are not sufficient to separate true functional differences from those occurring by chance; thus, statistical testing of significance is necessary. Lessons can be learned from another, more extensively researched application of GO—the detection of significantly enriched GO categories in a set of co-expressed or differentially expressed genes in microarray experiments. Several tools have been developed to search complete GO trees (rather than just the high levels) and apply statistical testing of significance (e.g., Onto-Express ; FatiGO ; for an evaluation of these tools, see ref. ).
Contrary to the situation in microarray analysis, there is a surprising lack of statistical approaches for GO-based comparison of two genomes. Here we propose such a statistical approach to find reliable differences between the complete sets of genes encoded in two genomes at all levels of GO. For each GO term the abundance of genes in the two genomes is compared using chi-squared test followed by false discovery rate (FDR) correction. Furthermore, to analyze the reliability of the differences detected, we studied two important issues. First, when new sequences are assigned GO terms by similarity (as determined by BLAST) to other sequences having known GO assignments, the choice of BLAST cutoff may affect the results. We therefore analyzed the effects of employing a wide range of BLAST cutoffs. Second, we studied how the results vary when only subsets of the genes were used. To our knowledge, our work is the first to address all the aforementioned issues.
We used this statistical approach to compare two cyanobacterial genomes, Synechocystis sp. PCC6803 and Anabaena sp. PCC7120. Cyanobacteria (also called blue-green bacteria, blue-green algae, cyanophyceae, or cyanophytes) are important model organisms for the study of photosynthesis, nitrogen fixation, evolution of plant plastids, and survival in diverse environments [34–41]. Two of the most widely studied cyanobateria species are Synechocystis sp. PCC6803 and Anabaena sp. PCC7120. PCC6803 is a fresh water unicellular cyanobacterium incapable of nitrogen fixation ; PCC7120 is a filamentous, heterocyst-forming cyanobacterium that has long been used to study the genetics and physiology of cellular differentiation, pattern formation, and nitrogen fixation . These interesting biological differences as well as the appropriate evolutionary distance between PCC6803 and PCC7120 make them a popular pair of species to compare and contrast[34, 44–50]. We compared PCC6803 and PCC7120 genomes using our statistical method and evaluated the detected statistically significant differences against known biological differences. To analyze how results change when only subsets of the genes are used, a larger set of statistically significant differences is desirable and we used the comparison of human vs. mouse and that of Saccharomyces cerevisiae vs. Schizosaccharomyces pombe genomes.
Whole-genome GO annotation
Comparison of AMIGO and GOA
GO Annotation Database
European Bioinformatics Institute (EBI)
Total number of species
Total number of associations
Total number of non-redundant sequences
Total number of GO terms
Total number of other databases integrated
We were able to annotate 2,224 genes in the PCC6803 genome to 1,933 GO terms, and 3,348 genes in the PCC7120 genome to 1,947 GO terms.
Testing the statistical significance of detected differences between genomes
For each GO category, we used the chi-squared test to determine whether the numbers of genes from the two genomes were statistically significantly different . Since the total number of GO categories is large, a large number of tests is required. We adopted the widely used FDR correction (q-value cutoff = 0.01) to control the overall false positive rate . We chose rather strict criteria to ensure reliability of the results; they can be set differently by other users.
The PCC7120 genome contains significantly more genes in "cobalt ion transport" (GO:0006824) compared with PCC6803, likely a consequence of the multicellular nature of PCC7120. Close inspection showed that the statistically significant difference in parent nodes "transition metal ion transport" (GO:0000041), "di-, trivalent inorganic cation transport" (GO:0015674), and "metal ion transport" (GO:0030001) is a consequence of the difference in the subfamily "cobalt ion transport" (GO:0006824) rather than a cumulative effect of any other subfamilies. PCC7120 contains significantly more genes than PCC6803 in "protein amino acid phosphorylation" (GO:0006468). These genes are responsible for critical protein kinase functions in the multicellular PCC7120 [53–55]. The significantly greater number of genes in "nitrogen fixation" (GO:0009399) in PCC7120 is consistent with its ability to fix nitrogen, a function the simpler organism PCC6803 does not have. The "cellular biosynthesis" (GO:0044249) family differs from those above in that it is significantly more abundant in PCC6803 than in PCC7120. This result may be a consequence of PCC6803's rapid growth capability.
We compared the two genomes with regard to the GO molecular function category and obtained similar results. We then compared them with regard to the GO cellular component category and found three statistically significant differences: "cytoplasm" (GO:0005737), "integral to membrane" (GO:0016021), and "intrinsic to membrane" (GO:0031224), all of which are more abundant in PCC6803 than in PCC7120.
Effect of different BLAST cutoffs
Effect of partial data
BLAST and InterProScan are two most widely used automated GO annotation methods. BLAST is the preferred choice for genome-scale annotation because it runs much faster and, perhaps more importantly, can annotate many more GO terms than InterProScan can. We had used InterProScan to annotate and compare PCC6803 and PCC7120, and found that it missed some important differences including "nitrogen fixation, GO:0009399". However, BLAST has its own limitations. Accurate functional assignment is difficult in cases where the match is less well defined due to lower sequence similarity . In future research we will investigate how to combine results from BLAST and InterProScan to improve annotation quality and use grid computing to reduce computation time.
We used BLAST E-value cutoff as the criteria in assigning GO terms. Local sequence alignment programs such as BLAST may prefer short strong matches to long weak matches and may cause inaccurate GO assignment. The strict E-value cutoff we chose in our analysis ensured the relatively high quality of the results. It was reported that a match between two sequences is most likely reliable if the alignment is at least 70 residues in length with at least 40% sequence identity . We investigated the quality of the HSP (High scoring Segment Pair) in our BLAST results (detail provided in the Additional file 1). With E-value cutoff 1e-20, the minimum length of HSP was 64 and the minimum sequence identity was 68%. Thus the assignments in our results were reliable. It is possible that false negatives may occur with a strict cutoff. In our analysis we prefer accuracy to coverage. Others can use different criteria depending on their individual goals. The statistical testing we proposed in this paper is independent of the GO assignment method. We suggest doing the comparison and comparing the results using different E-value cutoffs and different subsets of the input gene sets to identify the most reliable differences between two genomes.
In any GO analysis, the quality of the original GO annotation is critical. The GO annotation data are continuously expanded; however, the present data are incomplete and noisy , and the annotation quality is uneven, with a mix of literature-supported annotations and those inferred automatically. We did not modify the GO annotation data for our present study, but further research will consider the quality of the original GO annotations when assessing the reliability of the results. One limitation of our approach is that it only compared the number of genes in each functional category. It cannot capture differences in the level of gene expression. Another inherent limitation of GO is that it does not map directly to pathways. As a result GO-based comparison cannot detect differences at the pathway level. We have recently used the KEGG Orthology (KO) as an alternative controlled vocabulary in a KO-Based Annotation System (KOBAS) and demonstrated that KOBAS is effective in automated annotation and pathway identification . In future research we will investigate KO-based comparison to compare two genomes at the pathway level.
Our goal is to achieve higher confidence in the differences detected between two genomes. Towards this end, we applied rigorous statistical testing followed by FDR correction instead of simply relying on fold changes. We also tested a wide range of BLAST cutoff values and different subsets of the input genes to provide additional measures of confidence in the results. If results beyond those having the highest confidence are required, then the cutoff values can be relaxed. The advantage of the statistical approach presented here is that, no matter what cutoff values are chosen, the resulting p-values, q-values, and sampling analysis can be used to assess the confidence in the results.
There are other procedures available to correct false positive rates resulting from multiple testing, including the Bonferroni correction, Sidak stepwise correction, Holm stepwise correction, Hochberg's stepwise correction, and others [61, 62]. We chose the FDR correction because of its overall high quality and computational speed [63, 64]. It is also the most common procedure used in GO-related and microarray analyses [62, 65, 66].
Contrary to the situation in microarray analysis, there is a surprising lack of statistical approaches used in GO-based comparison of two complete genomes. Our work is the first to propose and test a statistical approach to comparing the complete sets of genes in two whole genomes at all levels of GO and study the effect of varying BLAST cutoffs and using subset of the input gene sets. We believe that such an approach can provide a measure of confidence in the identified differences and help ensure the reliability of the results.
Supplementary materials and related programs for the paper are provided on-line [See Additional file 1].
Whole-genome GO annotation
We set the default BLAST cutoff E-value to be 1E-20. In Part 3 of results, we study the cutoff's effect on the final results. We parsed the BLAST result to obtain the GOA ID for the top hit and used the ID to query the GOA association database to retrieve the corresponding GO annotation and assign it to the query sequence. The result is written to a file in the format specified by the GO Consortium .
We parsed the gene ontology DAGs and stored the GO terms and their hierarchical relationships in a local data structure. The genes in a genome are linked to GO terms using the aforementioned approach; they are also linked to all parent GO terms by propagating the DAG structures. If a gene has been assigned more than one GO terms that have a common parent GO term, the gene is counted only once in the parent GO term. Finally, the numbers of genes assigned to each GO term in the DAGs are tallied, representing the abundance of genes in each GO function within the genome.
The complete set of known and predicted genes in PCC6803 and PCC7120 genomes were downloaded from Cyanobase . The PCC6803 genome contains 3,573,470 bp with 3,167 predicted ORFs; the PCC7120 genome contains 6,413,771 bp with 5,362 predicted ORFs.
Testing the statistical significance of detected differences between genomes
The goal is to identify all GO terms for which two genomes (A and B) are statistically significantly different. Define:
N = the total number of annotated genes in Genome A
n = the total number of annotated genes in Genome B
X = the number of genes in Genome A that are assigned the GO term currently under consideration
x = the number of genes in Genome B that are assigned the GO term currently under consideration
H0: p 0 = p 1 or
H1: p 0 ≠ p 1
The p- value is calculated as the upper tail probability of the chi-squared distribution with one degree of freedom using the CPAN Statistics::Distributions modules .
Because the number of tests performed equals the number of GO terms, which may be thousands, multiple hypotheses testing is important to control the overall Type I error rate. We used the commonly applied FDR correction. For every test result that is considered statistically significant, the FDR correction calculates a q- value to measure the minimum FDR when calling that result significant. A q- value cutoff, α (alpha), guarantees that the expected proportion of false positives is α (alpha) among the set of significant features produced [52, 66]. The default for α (alpha) was set to 0.01 in our study. The conservative FDR correction was implemented according to the GenTS package .
The statistically significantly different GO terms detected between two genomes are stored in text format, sorted by increasing q-value. We also modified the GO TermFinder package  to show the results graphically, with different colors showing different levels of significance.
All related programs are attached in Additional file 1
Effect of different BLAST cutoffs
We studied how the BLAST cutoff value can affect the comparison of results between two genomes of PCC6803 and PCC7120. We tested a wide range of BLAST E-value cutoffs, from 1E-100 to 10, and recorded the number of statistically significantly different GO terms between the two cyanobacterial genomes at each cutoff. We then recorded the number of common statistically significantly different GO terms between adjacent cutoffs to show how much the result changes when the cutoff is varied.
Effect of partial data
We performed the random sampling to study how the results are affected when only part of the data is used. For each sample, we randomly selected 90%, 80%, 70%, and 60% of the annotated genes in each genome, and recomputed the statistically significantly different GO terms. We then compared the result of each sampling with that for the complete data sets and counted the numbers of common and unique GO terms. Because comparison of the two cyanobacteria resulted in too few significant GO terms to make this analysis meaningful, we analyzed the comparison of human vs. mouse and Saccharomyces cerevisiae vs. Schizosaccharomyces pombe. The GO annotations for these four genomes were retrieved from the Gene Ontology Consortium web site.
This work was supported by the China Ministry of Science and Technology "863" grants. We thank Drs. Arthur Grossman and Devaki Bhaya for insightful discussions. We thank the two anonymous reviewers for helpful suggestions.
- Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, Harris MA, Hill DP, Issel-Tarver L, Kasarskis A, Lewis S, Matese JC, Richardson JE, Ringwald M, Rubin GM, Sherlock G: Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet 2000, 25: 25–29. 10.1038/75556PubMed CentralView ArticlePubMedGoogle Scholar
- Current annotated genomes in GO web site[http://www.geneontology.org/GO.current.annotations.shtml]
- Apweiler R, Bairoch A, Wu CH, Barker WC, Boeckmann B, Ferro S, Gasteiger E, Huang H, Lopez R, Magrane M, Martin MJ, Natale DA, O'Donovan C, Redaschi N, Yeh LS: UniProt: the Universal Protein knowledgebase. Nucleic Acids Res 2004, 32 Database issue: D115–9. 10.1093/nar/gkh131View ArticleGoogle Scholar
- Camon E, Magrane M, Barrell D, Lee V, Dimmer E, Maslen J, Binns D, Harte N, Lopez R, Apweiler R: The Gene Ontology Annotation (GOA) Database: sharing knowledge in Uniprot with Gene Ontology. Nucleic Acids Res 2004, 32 Database issue: D262–6. 10.1093/nar/gkh021View ArticleGoogle Scholar
- Mulder NJ, Apweiler R, Attwood TK, Bairoch A, Barrell D, Bateman A, Binns D, Biswas M, Bradley P, Bork P, Bucher P, Copley RR, Courcelle E, Das U, Durbin R, Falquet L, Fleischmann W, Griffiths-Jones S, Haft D, Harte N, Hulo N, Kahn D, Kanapin A, Krestyaninova M, Lopez R, Letunic I, Lonsdale D, Silventoinen V, Orchard SE, Pagni M, Peyruc D, Ponting CP, Selengut JD, Servant F, Sigrist CJ, Vaughan R, Zdobnov EM: The InterPro Database, 2003 brings increased coverage and new features. Nucleic Acids Res 2003, 31: 315–318. 10.1093/nar/gkg046PubMed CentralView ArticlePubMedGoogle Scholar
- Khan S, Situ G, Decker K, Schmidt CJ: GoFigure: automated Gene Ontology annotation. Bioinformatics 2003, 19: 2484–2485. 10.1093/bioinformatics/btg338View ArticlePubMedGoogle Scholar
- Groth D, Lehrach H, Hennig S: GOblet: a platform for Gene Ontology annotation of anonymous sequence data. Nucleic Acids Res 2004, 32: W313–7.PubMed CentralView ArticlePubMedGoogle Scholar
- Zehetner G: OntoBlast function: From sequence similarities directly to potential functional annotations by ontology terms. Nucleic Acids Res 2003, 31: 3799–3803. 10.1093/nar/gkg555PubMed CentralView ArticlePubMedGoogle Scholar
- Martin DM, Berriman M, Barton GJ: GOtcha: a new method for prediction of protein function assessed by the annotation of seven genomes. BMC Bioinformatics 2004, 5: 178. 10.1186/1471-2105-5-178PubMed CentralView ArticlePubMedGoogle Scholar
- Conesa A, Gotz S, Garcia-Gomez JM, Terol J, Talon M, Robles M: Blast2GO: a universal tool for annotation, visualization and analysis in functional genomics research. Bioinformatics 2005, 21: 3674–3676. 10.1093/bioinformatics/bti610View ArticlePubMedGoogle Scholar
- Zdobnov EM, Apweiler R: InterProScan--an integration platform for the signature-recognition methods in InterPro. Bioinformatics 2001, 17: 847–848. 10.1093/bioinformatics/17.9.847View ArticlePubMedGoogle Scholar
- Hayete B, Bienkowska JR: Gotrees: predicting go associations from protein domain composition using decision trees. Pac Symp Biocomput 2005, 127–138.Google Scholar
- El-Sayed NM, Ghedin E, Song J, MacLeod A, Bringaud F, Larkin C, Wanless D, Peterson J, Hou L, Taylor S, Tweedie A, Biteau N, Khalak HG, Lin X, Mason T, Hannick L, Caler E, Blandin G, Bartholomeu D, Simpson AJ, Kaul S, Zhao H, Pai G, Van Aken S, Utterback T, Haas B, Koo HL, Umayam L, Suh B, Gerrard C, Leech V, Qi R, Zhou S, Schwartz D, Feldblyum T, Salzberg S, Tait A, Turner CM, Ullu E, White O, Melville S, Adams MD, Fraser CM, Donelson JE: The sequence and analysis of Trypanosoma brucei chromosome II. Nucleic Acids Res 2003, 31: 4856–4863. 10.1093/nar/gkg673PubMed CentralView ArticlePubMedGoogle Scholar
- Buell CR, Joardar V, Lindeberg M, Selengut J, Paulsen IT, Gwinn ML, Dodson RJ, Deboy RT, Durkin AS, Kolonay JF, Madupu R, Daugherty S, Brinkac L, Beanan MJ, Haft DH, Nelson WC, Davidsen T, Zafar N, Zhou L, Liu J, Yuan Q, Khouri H, Fedorova N, Tran B, Russell D, Berry K, Utterback T, Van Aken SE, Feldblyum TV, D'Ascenzo M, Deng WL, Ramos AR, Alfano JR, Cartinhour S, Chatterjee AK, Delaney TP, Lazarowitz SG, Martin GB, Schneider DJ, Tang X, Bender CL, White O, Fraser CM, Collmer A: The complete genome sequence of the Arabidopsis and tomato pathogen Pseudomonas syringae pv. tomato DC3000. Proc Natl Acad Sci U S A 2003, 100: 10181–10186. 10.1073/pnas.1731982100PubMed CentralView ArticlePubMedGoogle Scholar
- Haas BJ, Delcher AL, Mount SM, Wortman JR, Smith RKJ, Hannick LI, Maiti R, Ronning CM, Rusch DB, Town CD, Salzberg SL, White O: Improving the Arabidopsis genome annotation using maximal transcript alignment assemblies. Nucleic Acids Res 2003, 31: 5654–5666. 10.1093/nar/gkg770PubMed CentralView ArticlePubMedGoogle Scholar
- Wortman JR, Haas BJ, Hannick LI, Smith RKJ, Maiti R, Ronning CM, Chan AP, Yu C, Ayele M, Whitelaw CA, White OR, Town CD: Annotation of the Arabidopsis genome. Plant Physiol 2003, 132: 461–468. 10.1104/pp.103.022251PubMed CentralView ArticlePubMedGoogle Scholar
- Jones CE, Baumann U, Brown AL: Automated methods of predicting the function of biological sequences using GO and BLAST. BMC Bioinformatics 2005, 6: 272. 10.1186/1471-2105-6-272PubMed CentralView ArticlePubMedGoogle Scholar
- Waterston RH, Lindblad-Toh K, Birney E, Rogers J, Abril JF, Agarwal P, Agarwala R, Ainscough R, Alexandersson M, An P, Antonarakis SE, Attwood J, Baertsch R, Bailey J, Barlow K, Beck S, Berry E, Birren B, Bloom T, Bork P, Botcherby M, Bray N, Brent MR, Brown DG, Brown SD, Bult C, Burton J, Butler J, Campbell RD, Carninci P, Cawley S, Chiaromonte F, Chinwalla AT, Church DM, Clamp M, Clee C, Collins FS, Cook LL, Copley RR, Coulson A, Couronne O, Cuff J, Curwen V, Cutts T, Daly M, David R, Davies J, Delehaunty KD, Deri J, Dermitzakis ET, Dewey C, Dickens NJ, Diekhans M, Dodge S, Dubchak I, Dunn DM, Eddy SR, Elnitski L, Emes RD, Eswara P, Eyras E, Felsenfeld A, Fewell GA, Flicek P, Foley K, Frankel WN, Fulton LA, Fulton RS, Furey TS, Gage D, Gibbs RA, Glusman G, Gnerre S, Goldman N, Goodstadt L, Grafham D, Graves TA, Green ED, Gregory S, Guigo R, Guyer M, Hardison RC, Haussler D, Hayashizaki Y, Hillier LW, Hinrichs A, Hlavina W, Holzer T, Hsu F, Hua A, Hubbard T, Hunt A, Jackson I, Jaffe DB, Johnson LS, Jones M, Jones TA, Joy A, Kamal M, Karlsson EK, Karolchik D, Kasprzyk A, Kawai J, Keibler E, Kells C, Kent WJ, Kirby A, Kolbe DL, Korf I, Kucherlapati RS, Kulbokas EJ, Kulp D, Landers T, Leger JP, Leonard S, Letunic I, Levine R, Li J, Li M, Lloyd C, Lucas S, Ma B, Maglott DR, Mardis ER, Matthews L, Mauceli E, Mayer JH, McCarthy M, McCombie WR, McLaren S, McLay K, McPherson JD, Meldrim J, Meredith B, Mesirov JP, Miller W, Miner TL, Mongin E, Montgomery KT, Morgan M, Mott R, Mullikin JC, Muzny DM, Nash WE, Nelson JO, Nhan MN, Nicol R, Ning Z, Nusbaum C, O'Connor MJ, Okazaki Y, Oliver K, Overton-Larty E, Pachter L, Parra G, Pepin KH, Peterson J, Pevzner P, Plumb R, Pohl CS, Poliakov A, Ponce TC, Ponting CP, Potter S, Quail M, Reymond A, Roe BA, Roskin KM, Rubin EM, Rust AG, Santos R, Sapojnikov V, Schultz B, Schultz J, Schwartz MS, Schwartz S, Scott C, Seaman S, Searle S, Sharpe T, Sheridan A, Shownkeen R, Sims S, Singer JB, Slater G, Smit A, Smith DR, Spencer B, Stabenau A, Stange-Thomann N, Sugnet C, Suyama M, Tesler G, Thompson J, Torrents D, Trevaskis E, Tromp J, Ucla C, Ureta-Vidal A, Vinson JP, Von Niederhausern AC, Wade CM, Wall M, Weber RJ, Weiss RB, Wendl MC, West AP, Wetterstrand K, Wheeler R, Whelan S, Wierzbowski J, Willey D, Williams S, Wilson RK, Winter E, Worley KC, Wyman D, Yang S, Yang SP, Zdobnov EM, Zody MC, Lander ES: Initial sequencing and comparative analysis of the mouse genome. Nature 2002, 420: 520–562. 10.1038/nature01262View ArticlePubMedGoogle Scholar
- McCarter JP, Mitreva MD, Martin J, Dante M, Wylie T, Rao U, Pape D, Bowers Y, Theising B, Murphy CV, Kloek AP, Chiapelli BJ, Clifton SW, Bird DM, Waterston RH: Analysis and functional classification of transcripts from the nematode Meloidogyne incognita. Genome Biol 2003, 4: R26. 10.1186/gb-2003-4-4-r26PubMed CentralView ArticlePubMedGoogle Scholar
- Mitreva M, McCarter JP, Martin J, Dante M, Wylie T, Chiapelli B, Pape D, Clifton SW, Nutman TB, Waterston RH: Comparative genomics of gene expression in the parasitic and free-living nematodes Strongyloides stercoralis and Caenorhabditis elegans. Genome Res 2004, 14: 209–220. 10.1101/gr.1524804PubMed CentralView ArticlePubMedGoogle Scholar
- Stein LD, Bao Z, Blasiar D, Blumenthal T, Brent MR, Chen N, Chinwalla A, Clarke L, Clee C, Coghlan A, Coulson A, D'Eustachio P, Fitch DH, Fulton LA, Fulton RE, Griffiths-Jones S, Harris TW, Hillier LW, Kamath R, Kuwabara PE, Mardis ER, Marra MA, Miner TL, Minx P, Mullikin JC, Plumb RW, Rogers J, Schein JE, Sohrmann M, Spieth J, Stajich JE, Wei C, Willey D, Wilson RK, Durbin R, Waterston RH: The genome sequence of Caenorhabditis briggsae: a platform for comparative genomics. PLoS Biol 2003, 1: E45. 10.1371/journal.pbio.0000045PubMed CentralView ArticlePubMedGoogle Scholar
- Khatri P, Draghici S, Ostermeier GC, Krawetz SA: Profiling gene expression using onto-express. Genomics 2002, 79: 266–270. 10.1006/geno.2002.6698View ArticlePubMedGoogle Scholar
- Al-Shahrour F, Diaz-Uriarte R, Dopazo J: FatiGO: a web tool for finding significant associations of Gene Ontology terms with groups of genes. Bioinformatics 2004, 20: 578–580. 10.1093/bioinformatics/btg455View ArticlePubMedGoogle Scholar
- Khatri P, Draghici S: Ontological analysis of gene expression data: current tools, limitations, and open problems. Bioinformatics 2005, 21: 3587–3595. 10.1093/bioinformatics/bti565PubMed CentralView ArticlePubMedGoogle Scholar
- Raymond J, Blankenship RE: The evolutionary development of the protein complement of photosystem 2. Biochim Biophys Acta 2004, 1655: 133–139. 10.1016/j.bbabio.2003.10.015View ArticlePubMedGoogle Scholar
- Koksharova OA, Wolk CP: Genetic tools for cyanobacteria. Appl Microbiol Biotechnol 2002, 58: 123–137. 10.1007/s00253-001-0864-9View ArticlePubMedGoogle Scholar
- Stewart WD, Rowell P, Rai AN: Cyanobacteria-eukaryotic plant symbioses. Ann Microbiol (Paris) 1983, 134B: 205–228.Google Scholar
- Berman-Frank I, Lundgren P, Falkowski P: Nitrogen fixation and photosynthetic oxygen evolution in cyanobacteria. Res Microbiol 2003, 154: 157–164. 10.1016/S0923-2508(03)00029-9View ArticlePubMedGoogle Scholar
- McFadden GI: Endosymbiosis and evolution of the plant cell. Curr Opin Plant Biol 1999, 2: 513–519. 10.1016/S1369-5266(99)00025-4View ArticlePubMedGoogle Scholar
- Paerl HW, Pinckney JL, Steppe TF: Cyanobacterial-bacterial mat consortia: examining the functional unit of microbial survival and growth in extreme environments. Environ Microbiol 2000, 2: 11–26. 10.1046/j.1462-2920.2000.00071.xView ArticlePubMedGoogle Scholar
- Thomas DN: Photosynthetic microbes in freezing deserts. Trends Microbiol 2005, 13: 87–88. 10.1016/j.tim.2004.11.002View ArticlePubMedGoogle Scholar
- Bryant D: The Molecular Biology of Cyanobacteria. Netherlands, Kluwer Academic Publishers; 1994.View ArticleGoogle Scholar
- Kaneko T, Sato S, Kotani H, Tanaka A, Asamizu E, Nakamura Y, Miyajima N, Hirosawa M, Sugiura M, Sasamoto S, Kimura T, Hosouchi T, Matsuno A, Muraki A, Nakazaki N, Naruo K, Okumura S, Shimpo S, Takeuchi C, Wada T, Watanabe A, Yamada M, Yasuda M, Tabata S: Sequence analysis of the genome of the unicellular cyanobacterium Synechocystis sp. strain PCC6803. II. Sequence determination of the entire genome and assignment of potential protein-coding regions. DNA Res 1996, 3: 109–136. 10.1093/dnares/3.3.109View ArticlePubMedGoogle Scholar
- Kaneko T, Nakamura Y, Wolk CP, Kuritz T, Sasamoto S, Watanabe A, Iriguchi M, Ishikawa A, Kawashima K, Kimura T, Kishida Y, Kohara M, Matsumoto M, Matsuno A, Muraki A, Nakazaki N, Shimpo S, Sugimoto M, Takazawa M, Yamada M, Yasuda M, Tabata S: Complete genomic sequence of the filamentous nitrogen-fixing cyanobacterium Anabaena sp. strain PCC 7120. DNA Res 2001, 8: 205–13; 227–53. 10.1093/dnares/8.5.205View ArticlePubMedGoogle Scholar
- Zhang CC, Gonzalez L, Phalip V: Survey, analysis and genetic organization of genes encoding eukaryotic-like signaling proteins on a cyanobacterial genome. Nucleic Acids Res 1998, 26: 3619–3625. 10.1093/nar/26.16.3619PubMed CentralView ArticlePubMedGoogle Scholar
- Knowles VL, Plaxton WC: From genome to enzyme: analysis of key glycolytic and oxidative pentose-phosphate pathway enzymes in the cyanobacterium Synechocystis sp. PCC 6803. Plant Cell Physiol 2003, 44: 758–763. 10.1093/pcp/pcg086View ArticlePubMedGoogle Scholar
- Kotani H, Tabata S: Lessons from Sequencing of the Genome of a Unicellular Cyanobacterium, Synechocystis Sp. Pcc6803. Annu Rev Plant Physiol Plant Mol Biol 1998, 49: 151–171. 10.1146/annurev.arplant.49.1.151View ArticlePubMedGoogle Scholar
- Tamagnini P, Axelsson R, Lindberg P, Oxelfelt F, Wunschiers R, Lindblad P: Hydrogenases and hydrogen metabolism of cyanobacteria. Microbiol Mol Biol Rev 2002, 66: 1–20, table of contents. 10.1128/MMBR.66.1.1-20.2002PubMed CentralView ArticlePubMedGoogle Scholar
- Bhaya D, Dufresne A, Vaulot D, Grossman A: Analysis of the hli gene family in marine and freshwater cyanobacteria. FEMS Microbiol Lett 2002, 215: 209–219. 10.1111/j.1574-6968.2002.tb11393.xView ArticlePubMedGoogle Scholar
- Su Z, Olman V, Mao F, Xu Y: Comparative genomics analysis of NtcA regulons in cyanobacteria: regulation of nitrogen assimilation and its coupling to photosynthesis. Nucleic Acids Res 2005, 33: 5156–5171. 10.1093/nar/gki817PubMed CentralView ArticlePubMedGoogle Scholar
- Martin KA, Siefert JL, Yerrapragada S, Lu Y, McNeill TZ, Moreno PA, Weinstock GM, Widger WR, Fox GE: Cyanobacterial signature genes. Photosynth Res 2003, 75: 211–221. 10.1023/A:1023990402346View ArticlePubMedGoogle Scholar
- Man MZ, Wang X, Wang Y: POWER_SAGE: comparing statistical tests for SAGE experiments. Bioinformatics 2000, 16: 953–959. 10.1093/bioinformatics/16.11.953View ArticlePubMedGoogle Scholar
- Benjamini YYH: Controlling the false discovery rate: a practical and powerful approach to multiple testing. J Roy Statist Soc B 1995, 57: 289–300.Google Scholar
- West AH, Stock AM: Histidine kinases and response regulator proteins in two-component signaling systems. Trends Biochem Sci 2001, 26: 369–376. 10.1016/S0968-0004(01)01852-7View ArticlePubMedGoogle Scholar
- Foussard M, Cabantous S, Pedelacq J, Guillet V, Tranier S, Mourey L, Birck C, Samama J: The molecular puzzle of two-component signaling cascades. Microbes Infect 2001, 3: 417–424. 10.1016/S1286-4579(01)01390-9View ArticlePubMedGoogle Scholar
- Wolanin PM, Thomason PA, Stock JB: Histidine protein kinases: key signal transducers outside the animal kingdom. Genome Biol 2002, 3: REVIEWS3013. 10.1186/gb-2002-3-10-reviews3013PubMed CentralView ArticlePubMedGoogle Scholar
- Hennig S, Groth D, Lehrach H: Automated Gene Ontology annotation for anonymous sequence data. Nucleic Acids Res 2003, 31: 3712–3715. 10.1093/nar/gkg582PubMed CentralView ArticlePubMedGoogle Scholar
- Gerlt JA, Babbitt PC: Can sequence determine function? Genome Biol 2000, 1: REVIEWS0005. 10.1186/gb-2000-1-5-reviews0005PubMed CentralView ArticlePubMedGoogle Scholar
- Brenner SE, Chothia C, Hubbard TJ: Assessing sequence comparison methods with reliable structurally identified distant evolutionary relationships. Proc Natl Acad Sci U S A 1998, 95: 6073–6078. 10.1073/pnas.95.11.6073PubMed CentralView ArticlePubMedGoogle Scholar
- Dolan ME, Ni L, Camon E, Blake JA: A procedure for assessing GO annotation consistency. Bioinformatics 2005, 21 Suppl 1: i136-i143. 10.1093/bioinformatics/bti1019View ArticlePubMedGoogle Scholar
- Mao X, Cai T, Olyarchuk JG, Wei L: Automated genome annotation and pathway identification using the KEGG Orthology (KO) as a controlled vocabulary. Bioinformatics 2005, 21: 3787–3793. 10.1093/bioinformatics/bti430View ArticlePubMedGoogle Scholar
- Boyle EI, Weng S, Gollub J, Jin H, Botstein D, Cherry JM, Sherlock G: GO::TermFinder--open source software for accessing Gene Ontology information and finding significantly enriched Gene Ontology terms associated with a list of genes. Bioinformatics 2004, 20: 3710–3715. 10.1093/bioinformatics/bth456PubMed CentralView ArticlePubMedGoogle Scholar
- Nichols T, Hayasaka S: Controlling the familywise error rate in functional neuroimaging: a comparative review. Stat Methods Med Res 2003, 12: 419–446. 10.1191/0962280203sm341raView ArticlePubMedGoogle Scholar
- Reiner A, Yekutieli D, Benjamini Y: Identifying differentially expressed genes using false discovery rate controlling procedures. Bioinformatics 2003, 19: 368–375. 10.1093/bioinformatics/btf877View ArticlePubMedGoogle Scholar
- Qian HR, Huang S: Comparison of false discovery rate methods in identifying genes with differential expression. Genomics 2005, 86: 495–503. 10.1016/j.ygeno.2005.06.007View ArticlePubMedGoogle Scholar
- Slonim DK: From patterns to pathways: gene expression data analysis comes of age. Nat Genet 2002, 32 Suppl: 502–508. 10.1038/ng1033View ArticlePubMedGoogle Scholar
- Storey JD, Tibshirani R: Statistical significance for genomewide studies. Proc Natl Acad Sci U S A 2003, 100: 9440–9445. 10.1073/pnas.1530509100PubMed CentralView ArticlePubMedGoogle Scholar
- GO annotation file format[http://www.geneontology.org/GO.annotation.html#file]
- Statistics::Distributions modules[http://search.cpan.org/~mikek/Statistics-Distributions-1.02/Distributions.pm]
- GO TermFinder package[http://search.cpan.org/~sherlock/GO-TermFinder-0.64/]
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