GOPET: A tool for automated predictions of Gene Ontology terms
BMC Bioinformatics volume 7, Article number: 161 (2006)
Vast progress in sequencing projects has called for annotation on a large scale. A Number of methods have been developed to address this challenging task. These methods, however, either apply to specific subsets, or their predictions are not formalised, or they do not provide precise confidence values for their predictions.
We recently established a learning system for automated annotation, trained with a broad variety of different organisms to predict the standardised annotation terms from Gene Ontology (GO). Now, this method has been made available to the public via our web-service GOPET (Gene Ontology term Prediction and Evaluation Tool). It supplies annotation for sequences of any organism. For each predicted term an appropriate confidence value is provided. The basic method had been developed for predicting molecular function GO-terms. It is now expanded to predict biological process terms. This web service is available via http://genius.embnet.dkfz-heidelberg.de/menu/biounit/open-husar
Our web service gives experimental researchers as well as the bioinformatics community a valuable sequence annotation device. Additionally, GOPET also provides less significant annotation data which may serve as an extended discovery platform for the user.
The expanding amount of sequence data generated from genome and cDNA sequencing projects creates an ever extending demand for automated annotation. The annotation represented in standardised formats like the ones designed by ontologies benefits from its straightforward operability across different analysis platforms. The Gene Ontology (GO) project is a collaborative initiative and provides consistent descriptions of gene products across different species [1, 2]. This gives Gene Ontology the potential of becoming a major basis for automatic annotation.
Gene product prediction is confronted with a variety of challenges coming from ambiguities concerning the underlying input databases, e.g. sequence errors, erroneous and incomplete annotation, and inconsistent annotation across databases or consistent but erroneous annotation across databases. A broad variety of excellent annotation systems have been developed to tackle these problems, e.g. RiceGAAS , GAIA , Genotator , Magpie , GeneQuiz , GeneAtlas , PEDANT , cDNA2Genome , GenDB , GOFigure  and GOtcha . However, little has been done to quantify the annotation accuracy by defined benchmarks and establishing a method to provide a confidence value for each annotation. We developed an automated system for large-scale cDNA function assignment, designed and optimised to achieve a high-level of prediction accuracy without any manual refinement. With our system, Gene Ontology molecular function terms are predicted for uncharacterised cDNA sequences and a defined confidence value is calculated for each prediction. The performance of the system was benchmarked with 36,771 GO annotated cDNA sequences derived from 13 organisms .
We have now extended our approach to predict biological process terms and implemented our method as an online sequence annotation tool (GOPET, Gene Ontology term Prediction and Evaluation Tool). From a user-friendly front-end, the user can upload query protein- and nucleotide-sequences for which the tool assigns Gene Ontology molecular function and biological process terms. It is implemented under the W3H-Task-System which provides a flexible way to configure program and data flow between different biocomputational methods . The W3H-Task-System uses the Heidelberg Unix Sequence Analysis Resources (HUSAR ) which is a sequence analysis software package operating at the German Cancer Research Center.
Construction and content
Nucleotide or protein query sequences are blasted  against GO-mapped protein databases. Currently, we apply 16 GO-annotated protein databases from the following model organisms (downloaded from the data sources given in brackets): Saccharomyces cerevisiae (Stanford University), Drosophila melanogaster (Berkeley Drosophila Genome Project), Mus musculus (Ensembl), Arabidopsis thaliana (MIPS), Caenorhabditis elegans (Sanger Center), Rattus norvegicus (NCBI), Danio rerio (SwissProt), Leishmania major (Sanger Center), Bacillus anthracis Ame (TIGR), Coxiella burnetii RSA 493 (NCBI), Shewanella oneidensis MR-1 (TIGR), Vibrio cholerae (TIGR), Plasmodium falciparum (Plasmodium Genome Research), Oryza sativa (SwissProt, Trembl), Trypanosoma brucei (Sanger Center), Homo sapiens (GOA annotated sequences of SwissProt, Trembl and Enseml), as well as the protein database SwissProt (the SwissProt part of the UniProt family of databases). These databases are constantly updated to keep track of the latest information. The corresponding GO annotations were taken from Gene Ontology . The Sequence Retrieval System SRS  is used to retrieve GO annotation from GO-mapping relations. Blast hits with a relaxed e-value threshold (e-value < 0.01) are considered and all GO-terms are extracted from these hits. Each extracted GO-term is attached to a broad variety of elaborated attributes, including sequence similarity measures, such as e-value, bitscore, identity, coverage score and alignment length. Further attributes use GO-term frequency, GO-term relationships between homologues, the level of annotation within the GO hierarchy and annotation quality of the homologues which are calculated using the evidence codes provided by the gene association tables of the GO mapped sequence databases. Nine commonly used evidence codes are taken into acoount: TAS, NAS, ISS, IPI, IMP, IGI, IEP, IEA, IDA. The entries of theses attributes for each GO term are calculated by summing over the occurrences of the corresponding evidence codes of all blast hits (for more details, see ). In the following, the term "instance" is used for a GO-term together with its corresponding attribute values.
For training and testing the SVM, we selected 39,740 GO-annotatedcDNA sequences from the following organisms: Saccharomyces cerevisiae, Drosophila melanogaster, Mus musculus, Arabidopsis thaliana, Caenorhabditis elegans, Rattus norvegicus, Danio rerio, Leishmania major, Bacillus anthracis Ame, Coxiella burnetii RSA 493, Shewanella oneidensis MR-1, Vibrio cholerae and Plasmodium falciparum (same database sources as for the protein sequences). During the training phase, each instance is compared to the GO annotation of the (known) query sequence. It is classified as "correct" if the GO-term of the instance corresponds to one of the GO-terms from the query sequences, and labelled as "false" otherwise. Support Vector Machines (SVM) are applied to determine the separation between "correct" and "false" instances. Support Vector Machines were chosen due to their ability to learn any decision function . Furthermore, Support Vector Machines have shown a very good generalisation performance, both from a theoretical  and empirical point of view .
After training, the classifier is able to select GO-terms for an unknown query sequence by the same procedure: the query sequence is blasted against the annotated protein sequences of the database, GO-terms from the hits are extracted together with their corresponding attribute values. This instance is transferred to the SVM and classified in accordance to its attribute values.
Note, that we yielded a high amount of instances for training (856,632 instances). Therefore, we could apply a voting scheme. This consists of an assembly of 99 classifiers corresponding to ≈8,600 training instances each. So, multiple classifiers are employed for the classification. The predicted results are combined by a committee approach  in which each classifier contributes a vote that predicts a particular instance as correct. All classifiers got the same weight. Note, that our voting scheme compares to bagging methods in which all classifiers are given the same weight . The number of votes are summed up for each instance. A confidence value is calculated for each GO-term by comparing the number of votes with the corresponding number of true positives divided by all positives during the testing phase. The system was benchmarked by an organism-wise cross-validation, i.e. a set of (known) sequences with GO annotations was chosen to train the classifier. This set contained sequences for all except one organism. Then the performance was tested with the sequences of the remaining organism. This was done for all selected organisms and the overall prediction performance was calculated (for details, see ).
Biological process prediction
We applied the same approach to predict biological process terms and trained 99 new SVM classifiers specifically on GO-terms for biological process. GO-terms for each blast hit were extracted by considering GO-terms corresponding to biological process and by discarding GO-terms that were prefixed with NOT (annotators state that a particular gene product is NOT associated with a particular GO term), or corresponding to "biological process unknown". We were able to select 27,109 sequences from 13 model organisms for training and validation and yielded 1,342,270 instances. Therefore, each classifier was trained with ≈13,558 instances. Table 1 shows the prediction performances for molecular function and biological process. We got only 12% of positive instances for training and testing. When compared to molecular function (31% positive instances), inferring biological process from the sequence was less often possible. This shows that our tested nucleotide sequences encode more information on their molecular function than their biological process. However, the validation result showed a high recall (76%) for biological process terms at 80% precision.
Comparison with GOtcha and GOFigure
We compared our system with the well established annotation tools GoFigure  and GOtcha . GoFigure performs a homology search and constructs the minimum covering graph from the extracted GO terms. The terms are scored based on Blast e-values and terms above a defined threshold are given out. GOtcha predictions are supplied with confidence values (P-score). Basically, these P-scores are calculated from three different scores which are derived from sequence similarity and the GO structure. We took eight Xenopus laevis contig sequences, which we annotated with our system previously  to compare our results for molecular function to GoFigure and GOtcha (using default parameters). The results are shown in Table 2. For GOPET and GOtcha the same confidence threshold of 80% was applied. In general, all three methods show very similar predictions. However, for TC190605 GoFigure yielded a conflicting result (nuclease activity) when compared to GOPET (serine hydrolase activity), while GOtcha supported a more general term (Enzyme activity). GOPET and GOtcha predictions were highly comparable, though the specificity of the annotation varied for some cases. For example, GOtcha provided more specific terms for TC212171, TC187949 and TC199713. For TC196381, TC209487, and TC190605 GOPET annotated more specific terms. For TTC194305 GOTcha didn't get any results with the defined threshold. Interestingly, both system predicted TC210151 as frizzled receptor with similar confidence values (GOPET 82% and GOtcha 80%).
Furthermore, we compared GOPET and GOTCHA in more detail. We selected manually 100 random sequences (excluding IEA annotated ones) from DictyBase (Version 1.68, DictyBase, database for Dictyostelium discoideum). This database was used for the comparison as it was not used for the training of both systems. From GOPET and GOTcha, we selected again only annotations with ≥ 80% confidence. Of the 100 sequences, 72 and 77 were annotated with 332 (true positive: 296) and 535 (true positive: 430) terms by GOPET and GOTcha, respectively. Hence, GOPET showed a better precision (89%) compared to GOTcha (80%) (p-value of a two-sided Fisher exact test: 0.0006) and GOTcha showed a better recall. Note that, in the most cases, GOPET provided more specific annotation terms (all data is provided in supplementary Tables S1 and S2, see Additional file 1 and Additional file 2, respectively).
The W3H task framework has been developed in the HUSAR environment at the German Cancer Research Center . HUSAR includes the tools of the GCG , EMBOSS  packages and the Phylip suite  as well as many other applications. Common sequence databases are also available in the HUSAR environment. The W3H task framework allows the integration of applications and methods to create tailor-made task flows, which can be used in high throughput analyses without the usual need for customised programming. By specifying the program flow and dependency rules between the used applications in the meta-data, tasks of high complexity can be designed. The integration of W3H into W2H (graphical web interface to HUSAR [29, 30]) allows tasks like GOPET to be easily made available on the web without additional programming. The GO database and all sequence to GO-mapping information are implemented in SRS and accessed through an SRS-API. The LIBSVM package (, version 2.4) is installed in HUSAR and was used for our classifications. Figure 1 illustrates the general workflow of the system.
Utility and discussion
The GOPET web-server is accessible via the web-page . The starting page shows the user interface to upload query sequences. More than one sequence can be queried if uploaded in FastA format. GOPET accepts nucleotide and protein sequences as input. The running time of the task depends on the size and complexity of the input sequences and varies between 2 to 4 minutes per sequence on our Sun Multiprocessor machine (Sun E4500). For example, annotating a cDNA sequence with 1173 base pairs (TC291942 from TIGR XGI, ) takes about 2 min 30 seconds. Figure 2 shows the prediction results for this sequence. The output displays every predicted GO-term having at least one vote. The results are sorted by their confidence values. By default, only the top 20 predictions are displayed if more hits have been found.
Confidence values may serve in several ways. Predictions with confidence values ≥ 80% can be used straight away for annotation. In contrast, predictions with low confidence values may serve as a basis for new hypotheses and research, e.g. to infer further relationships to the original function. Automated annotation fails for sequences without any annotated and known homologues and the only alternative remains to analyse the sequence manually and in depth. We included IEA annotated sequences (automated annotated sequences) to improve the annotation coverage. To compare the performance with and without IEA annotated sequences, we calculated the respective prediction accuracies for yeast (non-IEA) based on the worm data-set (IEA) and fly data-set (non-IEA). The results were quite similar (test yeast and training worm: 82%, test yeast and training fly: 81% accuracy, see  for details). However, restricting to non-IEA terms may improve the precision. We consider to provide an option for the user to exclude IEA annotations based predictions for the next release of GOPET.
GOPET serves as a valuable tool for experimental researchers as well as for the bioinformatics community. The underlying methodology shows numerous advantages. The prediction performance is organism-independent, since the applied annotation databases cover a broad variety of different organisms and the attributes selected for classification are by definition not specific to any organism. The prediction quality can be assessed by assigned confidence values. It could be shown recently, that the prediction quality for confidence values ≥ 80% is comparable to high-quality manual annotation . Our confidence values give the user a concrete evaluation of the results and a distinct further processing capability when inspecting the annotation at different levels of certainty. The GOPET server predicts both molecular function and biological GO-terms. In future, we plan to predict cellular component terms and to improve the quality of biological process predictions by including protein-protein interaction data.
GOPET is available via Open-HUSAR, (Heidelberg Unix Sequence Analysis Resources) .
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. 10.1038/75556
Creating the gene ontology resource: design and implementation Genome Res 2001, 11: 1425–1433. 10.1101/gr.180801
Sakata K, Nagamura Y, Numa H, Antonio BA, Nagasaki H, Idonuma A, Watanabe W, Shimizu Y, Horiuchi I, Matsumoto T, Sasaki T, Higo K: RiceGAAS: an automated annotation system and database for rice genome sequence. Nucleic Acids Res 2002, 30: 98–102. 10.1093/nar/30.1.98
Bailey LCJ, Fischer S, Schug J, Crabtree J, Gibson M, Overton GC: GAIA: framework annotation of genomic sequence. Genome Res 1998, 8: 234–250.
Harris NL: Genotator: a workbench for sequence annotation. Genome Res 1997, 7: 754–762.
Gaasterland T, Sensen CW: MAGPIE: automated genome interpretation. Trends Genet 1996, 12: 76–78. 10.1016/0168-9525(96)81406-5
Andrade MA, Brown NP, Leroy C, Hoersch S, de Daruvar A, Reich C, Franchini A, Tamames J, Valencia A, Ouzounis C, Sander C: Automated genome sequence analysis and annotation. Bioinformatics 1999, 15: 391–412. 10.1093/bioinformatics/15.5.391
Kitson DH, Badretdinov A, Zhu ZY, Velikanov M, Edwards DJ, Olszewski K, Szalma S, Yan L: Functional annotation of proteomic sequences based on consensus of sequence and structural analysis. Brief Bioinform 2002, 3: 32–44. 10.1093/bib/3.1.32
Frishman D, Albermann K, Hani J, Heumann K, Metanomski A, Zollner A, Mewes HW: Functional and structural genomics using PEDANT. Bioinformatics 2001, 17: 44–57. 10.1093/bioinformatics/17.1.44
Del Val C, Glatting KH, Suhai S: cDNA2Genome: a tool for mapping and annotating cDNAs. BMC Bioinformatics 2003, 4: 39. 10.1186/1471-2105-4-39
Meyer F, Goesmann A, McHardy AC, Bartels D, Bekel T, Clausen J, Kalinowski J, Linke B, Rupp O, Giegerich R, Puhler A: GenDB--an open source genome annotation system for prokaryote genomes. Nucleic Acids Res 2003, 31: 2187–2195. 10.1093/nar/gkg312
Khan S, Situ G, Decker K, Schmidt CJ: GoFigure: automated Gene Ontology annotation. Bioinformatics 2003, 19: 2484–2485. 10.1093/bioinformatics/btg338
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. 10.1186/1471-2105-5-178
Vinayagam A, König R, Moormann J, Schubert F, Eils R, Glatting KH, Suhai S: Applying Support Vector Machines for Gene Ontology based gene function prediction. BMC Bioinformatics 2004, 5: 116. 10.1186/1471-2105-5-116
Ernst P, Glatting KH, Suhai S: A task framework for the web interface W2H. Bioinformatics 2003, 19: 278–282. 10.1093/bioinformatics/19.2.278
Altschul SF, Madden TL, Schaffer AA, Zhang J, Zhang Z, Miller W, Lipman DJ: Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res 1997, 25: 3389–3402. 10.1093/nar/25.17.3389
The Gene Ontology Consortium[http://www.geneontology.org]
Etzold T, Ulyanov A, Argos P: SRS: information retrieval system for molecular biology data banks. Methods Enzymol 1996, 266: 114–128.
Hammer B, Gersmann K: A Note on the Universal Approximation Capability of Support Vector Machines. Neural Process Lett 2003, 17: 43–53. 10.1023/A:1022936519097
Vapnik VN: The Nature of Statistical Learning Theory. New York, Springer-Verlag; 1995.
Meyer D, Leisch F, Hornik K: The support vector machine under test. Neurocomputing 2003, 55: 169–186. 10.1016/S0925-2312(03)00431-4
Bauer E, Kohavi R: An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants. Mach Learn 1999, 36: 105–139. 10.1023/A:1007515423169
Efron B, Tibshirani RJ: An Introduction to the Bootstrap. London, Chapman & Hall; 1993.
Devereux J, Haeberli P, Smithies O: A comprehensive set of sequence analysis programs for the VAX. Nucleic Acids Res 1984, 12: 387–395.
Rice P, Longden I, Bleasby A: EMBOSS: the European Molecular Biology Open Software Suite. Trends Genet 2000, 16: 276–277. 10.1016/S0168-9525(00)02024-2
Felsenstein J: PHYLIP Phylogeny Inference Package.5.57th edition. [http://evolution.genetics.washington.edu/phylip/doc/main.html]
Senger M, Flores T, Glatting K, Ernst P, Hotz-Wagenblatt A, Suhai S: W2H: WWW interface to the GCG sequence analysis package. Bioinformatics 1998, 14: 452–457. 10.1093/bioinformatics/14.5.452
Shi DL, Boucaut JC: Xenopus frizzled 4 is a maternal mRNA and its zygotic expression is localized to the neuroectoderm and trunk lateral plate mesoderm. Mech Dev 2000, 94: 243–245. 10.1016/S0925-4773(00)00294-X
Umbhauer M, Djiane A, Goisset C, Penzo-Mendez A, Riou JF, Boucaut JC, Shi DL: The C-terminal cytoplasmic Lys-thr-X-X-X-Trp motif in frizzled receptors mediates Wnt/beta-catenin signalling. Embo J 2000, 19: 4944–4954. 10.1093/emboj/19.18.4944
We thank Andrea Mclntosh-Suhr for stylistic corrections. This work was funded by the German Cancer Research Center (DKFZ) and the Nationales Genom-Forschungs-Netz (NGFN). We also thank the GO consortium and all groups involved in GO annotation for making their data available through the web.
All conceived the idea. AV carried out the work and drafted the manuscript. CD implemented the program into the W3H system. RK and FS contributed in developing the methodology and drafting the manuscript. KG implemented the databases in SRS. RK, SS, KG and RE supervised the work. All authors participated in reading, approving and revising the manuscript.
Electronic supplementary material
Additional File 1: Table S2. Comparison of GOPET with the annotation of 100 random selected protein sequences of Dictyostelium discoideum (DOC 170 KB)
Additional File 2: Table S2. Comparison of GOTcha with the annotation of 100 random selected sequences of Dictyostelium discoideum (DOC 252 KB)
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Vinayagam, A., del Val, C., Schubert, F. et al. GOPET: A tool for automated predictions of Gene Ontology terms. BMC Bioinformatics 7, 161 (2006). https://doi.org/10.1186/1471-2105-7-161