EnzML: multi-label prediction of enzyme classes using InterPro signatures
- 4.6k Downloads
Manual annotation of enzymatic functions cannot keep up with automatic genome sequencing. In this work we explore the capacity of InterPro sequence signatures to automatically predict enzymatic function.
We present EnzML, a multi-label classification method that can efficiently account also for proteins with multiple enzymatic functions: 50,000 in UniProt. EnzML was evaluated using a standard set of 300,747 proteins for which the manually curated Swiss-Prot and KEGG databases have agreeing Enzyme Commission (EC) annotations. EnzML achieved more than 98% subset accuracy (exact match of all correct Enzyme Commission classes of a protein) for the entire dataset and between 87 and 97% subset accuracy in reannotating eight entire proteomes: human, mouse, rat, mouse-ear cress, fruit fly, the S. pombe yeast, the E. coli bacterium and the M. jannaschii archaebacterium. To understand the role played by the dataset size, we compared the cross-evaluation results of smaller datasets, either constructed at random or from specific taxonomic domains such as archaea, bacteria, fungi, invertebrates, plants and vertebrates. The results were confirmed even when the redundancy in the dataset was reduced using UniRef100, UniRef90 or UniRef50 clusters.
InterPro signatures are a compact and powerful attribute space for the prediction of enzymatic function. This representation makes multi-label machine learning feasible in reasonable time (30 minutes to train on 300,747 instances with 10,852 attributes and 2,201 class values) using the Mulan Binary Relevance Nearest Neighbours algorithm implementation (BR-kNN).
KeywordsEnzyme Commission Enzyme Commission Number InterPro Domain Cross Evaluation Subset Accuracy
Assigning enzymatic function to the proteins in a genome is one of the first essential steps of metabolic reconstruction, important for biology, medicine, industrial production and environmental studies. Without precise annotation of the reactions a protein can perform, the subsequent pathway assembly and verification becomes problematic . Metabolic flux studies that aim to understand diseased states or biomass production become almost impossible.
Unfortunately, at the current rate of genome sequencing and manual annotation, manual curation will never complete the functional annotation of all available proteomes . Hence in this work we propose and evaluate a method to automatically predict the enzymatic functions of a protein. Previously, Tetko et al.  used component analysis to show that the highest contributor to the performance of various protein function prediction methods were InterPro signatures. InterPro is an extensive database of conserved sequence signatures and domains  that can be computed from sequence data alone and for any sequence using the publicly available InterProScan algorithm [4, 5]. Through the use of InterPro signatures, we demonstrate that it is possible to predict Enzyme Commission (EC) numbers  with high accuracy, recall (sensitivity) and precision (specificity), using the information contained in the protein sequence exclusively.
Despite some known limitations, such as some inconsistencies between the rules set by the nomenclature committee and the actual class definitions , we use the NC-IUBMB Enzyme Commission (EC) nomenclature to define enzymatic reactions, as it is the current standard for enzyme function classification. The EC nomenclature uses a four digit code, such as EC 22.214.171.124, to represent an enzymatic class. The first three digits represent an increasingly detailed definition of reaction class, while the last digit represents the accepted substrates.
Our approach is widely applicable as it uses exclusively information contained in the protein sequence, in contrast with methods that also require existing or computationally inferred structural information . Further, our method supports multi-label classification, that is, the direct association of multiple enzymatic functions to each protein. A single enzyme can perform different reactions, either due to the presence of multiple catalytic sites or by regulation of a single site, and can hence be associated with multiple EC numbers. Multi-label learning can take multiple EC numbers, and their hierarchical relation, into account more coherently and effectively than creating an individual classifier for each class. It can also leverage the information contained in proteins annotated with incomplete EC numbers (about 2% of UniProt and 9% of Swiss-Prot annotations), such as EC 1.-.-.-, EC 1.2.-.- or EC 1.2.3.-.
Sequence based methods for the prediction of EC numbers include EFICAz , ModEnzA  and PRIAM . PRIAM uses a set of position-specific scoring matrices (profiles) specific for each EC number to predict the existence of a given EC function somewhere in a fully sequenced genome. EnzML, ModEnzA and EFICAz try to assign EC numbers to individual protein sequences or fragments. ModEnzA builds Hidden Markov model profiles of positive and negative sequences specific for each four digits EC numbers, partial or multiple EC numbers cannot be assigned.
EFICAz can assign multiple EC numbers of exactly three or four digits by weighting information from four sequence based predictions methods using functionally discriminating residues for enzyme families, pairwise sequence comparison, Pfam enzyme families and Prosite patterns (EFICAz2  is enhanced using Support Vector Machine learning). EFICAz, ModEnzA and PRIAM are further discussed and quantitatively compared with EnzML in the Discussion section and Additional file 1: methods_comparison.pdf.
Multi-label learning has been successfully applied to predict FunCat protein functions in yeast , GO functions in yeast , CYGD functions in yeast , FunCat and GO functions in yeast and plants  and other species , but has not yet been extensively applied to the prediction of enzyme functionality. A multi-label support vector machines methodology was used in the past to predict EC numbers but only up to the second EC digit (e.g.. EC 1.2.-.-) and only on 8,291 enzymes . Hierarchical classification was also applied to about 6,000 enzymes from KEGG, obtaining over 85% accuracy in predicting four digits EC numbers . However, here we demonstrate that bigger datasets can cause dramatic improvement in performance. We make use of Mulan [20, 21], an open-source software infrastructure for evaluation and prediction based on the Weka framework , to improve the potential for extension and reuse of this work. In addition to the effect of dataset size, we report on how predictions depend on species content and sequence redundancy. We also obtain very good computational performance over a real-life size set of 1,099,321 protein entries.
The protein sequence and EC annotation data was taken from UniProt Knowledge Base  release 2010_12 (Nov-2010) consisting of Swiss-Prot release 2010_12 and TrEMBL release 2010_12, InterPro release 30.0 (Dec 2010), KEGG  release 57.0 (Jan 2011). The InterPro release used contains 21,591 signatures, 21,178 of which present in UniProt. The complete set of 5,222 EC numbers and their status (active, deleted or transferred) was downloaded from ExPASy ENZYME database (11-Jan-2011 release) . All annotations using deleted EC numbers were removed from the data; transferred EC numbers were substituted with their newly assigned EC number(s). The data was further processed using Ondex [26, 27] and MySQL. The data sources content of EC and InterPro annotation is summarised in Additional file 2: ec_interpro_stats.pdf.
In order to execute the different evaluations presented in the Results section, a number of datasets have been created. The main dataset is indicated from now on as SwissProtKEGG. The join symbol () represents the fact that this set contains only annotation that is equal in the two databases. SwissProtKEGG consists of all EC annotations agreeing in both Swiss-Prot and KEGG, an annotation being a couple in the form [UniProt Accession Number, EC number]. The set includes 300,747 proteins, 55% enzymes and 45% non enzymes (see below for a definition of non enzyme). The SwissProtKEGG dataset has thus been submitted to two manual curations, in which none of the authors were involved. In the same way, the TrEMBLKEGG dataset includes all annotations agreeing between UniProt TrEMBL and KEGG. The TrEMBLKEGG dataset is very extensive and varied, but it has not been manually curated in TrEMBL. This dataset has been included in the analysis not for the purpose of method evaluation, but to review EnzML performance on a large dataset and to judge the internal consistency of TrEMBLKEGG itself. The protein instances have surprisingly few features, having an average of 3.55 InterPro signatures (attribute values) and 3.97 EC numbers (class labels, including incomplete EC numbers) per protein.
The proportion of proteins with no EC annotations ranges from 45% of the SwissProtKEGG dataset to 69% of the TrEMBLKEGG dataset. These sets include proteins that have been extensively studied and do not carry enzymatic activity (especially in the SwissProtKEGG dataset) as well as proteins not yet characterised as enzymes or belonging to still unknown enzymatic classes (more probable in the TrEMBLKEGG dataset). Due to the difficulty of distinguishing between these cases, the non and not yet EC proteins are treated as one class. This allows EnzML to emit a cumulative no EC prediction as an alternative to the prediction of one or more EC numbers. A protein predicted as no EC could thus be either a non-enzyme or a not yet characterised enzyme or belonging to a not yet characterised enzyme class. For simplicity we refer to this class as non enzyme from now on. The EnzML method can accept instances with an empty set of attributes, which account for 0.3% of the SwissProtKEGG dataset and 1.7% of the TrEMBLKEGG dataset. These proteins are processed normally, but they are generally predicted as non enzymes due to the fact that most proteins without InterPro signatures also do not have EC annotations. The datasets used also include (and hence the method predicts) incomplete EC classes, such as EC 1.-.-.- , EC 1.2.-.- or EC 1.2.3.-.
The independence of the UniProt and KEGG curation cannot be determined by the annotations alone due to a lack of provenance meta-data. Curators in both institutions use a variety of primary (experimental data and literature) and secondary (other databases) sources to assign an EC annotation. However, out of the 1.8 million proteins annotated in both Uniprot and KEGG, 31% have a disagreeing annotation (20% for Swiss-Prot vs. KEGG and 33% for TrEMBL vs. KEGG), showing that the two knowledge bases curators have different scientific opinions in many cases.
In order to evaluate the impact of the dataset size and taxonomic content on EnzML performance, the SwissProtKEGG dataset has been partitioned into taxonomic domains: archaea, bacteria and eukaria, further divided into fungi, invertebrates, plants and vertebrates. For each taxonomic domain we have investigated the individual proteome having most proteins in the SwissProtKEGG set: Methanocaldococcus jannaschii for archaea, Escherichia coli (all strains) for bacteria, Schizosaccharomyces pombe for fungi, Drosophila melanogaster for invertebrates, Arabidopsys thaliana for plants, Homo sapiens for vertebrates. We also considered Mus musculus and Rattus norvegicus as second and third most represented species overall (the first is Homo sapiens).
To examine the performance on each EC main class, the Escherichia coli dataset was further divided into seven datasets each containing exclusively either the no enzyme annotation (Ecoli_NoEC) or EC annotations starting with a different main EC class (Ecoli_EC1, Ecoli_EC2, , Ecoli_EC6).
As an alternative to machine learning, EC labels could be directly assigned from InterPro domains: the InterPro2GO file associates individual InterPro signatures with GO terms, which in turn are mapped to EC numbers in the EC2GO file. To understand if EnzML is more accurate than this simple transitive assignment, a dataset was created containing all the SwissProtKEGG entries annotated using the InterPro2GO and EC2GO lists provided by the UniProt FTP website (InterPro2GO2EC).
We have also created a separate set (named SwissProt_2011_2012) for proteins that were added to Swiss-Prot between Jan 2011 and March 2012 (16,938 proteins: 7,507 enzymes and 9,431 non-enzymes). The data was taken from BioMart Central UniProt. Of these proteins, an interesting subset consists of those 503 proteins (491 enzymes and 12 non-enzymes) which already existed in TrEMBLKEGG as of Jan 2011 but acquired a new or different label (or lost their EC label) upon incorporation into Swiss-Prot (named TrEMBL_2011_now_in_SwissProt_2012).
The data format consists of a sparse Weka ARFF (Attribute-Relation File Format) file supplemented by a Mulan XML file containing the class labels hierarchy. Examples of ARFF and XML file formats are available in Additional file 3: arff_and_xml_file_examples.tar.gz. The SwissProtKEGG and TrEMBLKEGG data files used for evaluation are also available (Additional file 4: swiss-join-kegg_trembl-join-kegg_files.tar.gz) and so is the Java code used to format the data files and run the experiments (Additional file 5: enzml_java_code.tar.gz).
To analyse the performance of EnzML at different levels of sequence similarity we generated other datasets using UniRef clusters. UniRef100 is a database of clusters of UniProt proteins that are 100% identical in sequence (UniRef90 90% similar, UniRef50 50% similar in sequence). Each cluster has a representative (reference) protein sequence and a group of other sequences similar to it. To measure the effect of sequence redundancy on the method, the SwissProtKEGG dataset was reduced to only its UniRef representative sequences (UniRef100 from SwissProtKEGG, UniRef90 from and UniRef50 from SwissProtKEGG datasets) and cross-evaluated.
EC numbers distribution
The algorithm used throughout this work is BR-kNN . BR-kNN is a multi-label adaptation of the traditional K-Nearest Neighbour using Binary Relevance. Binary Relevance transforms the original dataset into as many datasets as the existing labels, each example being labelled as label true if the label existed in the original example and label false otherwise (also called one-against-all or one-versus-rest approach). The Mulan version 1.2.0 implementation of BR-kNN  used in EnzML makes sure the (Euclidean) distance between neighbours is calculated only once, with considerable time savings on large datasets.
The best choice for the number of neighbours was k 1 (see Additional file 6: number_of_neighbours.pdf). BR-kNN is fast on the data used: less than 30 minutes per fold of a 10-fold cross-evaluation of 300,747 instances, on a dedicated machine with 2 GHz CPU and 4 GB RAM (14 hours to predict over a million instances). As baseline we used the Zero Rule algorithm, which assigns the majority class (non-enzyme) to every instance.
The evaluation metrics are either based on a single round of evaluation (train-test) or, for cross-evaluation, they are the average of a number of cross-evaluation rounds. After examining the standard deviations, we submitted datasets smaller than 40,000 proteins to two rounds of 10-fold cross evaluation, training on 9/10 of the data and testing on the remaining unseen 1/10 (one round of cross evaluation for bigger samples). We present the average value of subset accuracy, a strict measure of prediction success, as it requires the predicted set of class labels to be an exact match of the true set of labels . For example, if a protein has these four EC class labels: [EC 1.-.-.-, EC 1.2.-.-, EC 1.2.3.- and EC 126.96.36.199], and it is assigned as prediction only the three first labels: [EC 1.-.-.-, EC 1.2.-.-, EC 1.2.3.-], this prediction would be considered as completely incorrect, because it misses the last label.
Where computable, we also report micro and macro metrics. In this context micro averaging (averaging over the entire confusion matrix) favours more frequent EC classes, while macro averaging gives equal relevance to both rare and frequent EC classes. Hence a protein will affect the macro-averaged metrics more if it belongs to a rare EC class. Example-based metrics consider how many correct EC predictions have been given to each individual protein example. The full mathematical form of all metrics is defined in  and . The best achievable value of all these measures is 100% when all instances are correctly classified. Where averaged, the metrics are presented with plus and minus standard deviation marks.
where is the Gamma function and r are the degrees of freedom. If the is lower than 5%, the confidence that the samples come from different underlying distribution is higher than 95% and hence the two samples are declared significantly different.
Whole, taxonomic and random datasets
To understand whether taxonomically related proteins were better at predicting proteins in their own taxa, the SwissProtKEGG dataset has been subdivided into archaea, bacteria and eukarya (further divided into fungi, invertebrates, plants or vertebrates). The average classification accuracy after cross-evaluation of each taxonomic dataset was then compared with sets of the same size as each taxonomic set, but comprising proteins picked at random from SwissProtKEGG.
The results in Figure 4 show that the predictions accuracy generally increases as the dataset size increases. Excluding far-related species does not seem to dramatically improve results: only the archaea and bacteria sets significantly outperform a random set of the same size, but they cover a reduced set of enzymatic functions compared to the full set. The plants, invertebrates, fungi and vertebrates sets are not significantly different from a random set of the same size, while the eukarya dataset accuracy is significantly different but lower.
Sequence redundancy reduction
To gauge the predictive power or a single species, the inverse was also attempted: to re-annotate the entire SwissProtKEGG dataset based on a single proteome. This inverse exercise (Figure 7) shows that up to 88% of proteins, and more than a third of the EC classes, can be reannotated correctly in the SwissProtKEGG dataset (minus E. coli) if the training occurs on possibly the most studied species in Molecular Biology, E. coli. This suggests a high level of evolutionary conservation of core metabolism across species.
Comparison with InterPro2GO2EC and TrEMBL
EC labels could also be directly assigned from InterPro domains using the InterPro2GO and EC2GO lists. As shown in Figure 5, this method has much lower accuracy (80%) than EnzML (97%) on the same SwissProtKEGG dataset. To assess computational performance, EnzML was also trained on SwissProtKEGG (the right semicircle in Figure 1) and tested on the diverse and extensive, but not intensively manually curated, TrEMBLKEGG dataset (the left semicircle in Figure 1). The loss of accuracy on the TrEMBLKEGG dataset is not due to a limitation in EnzML, but more to the sheer variety and low internal consistency of TrEMBLKEGG. The SwissProtKEGG - the training set - only contains half of the InterPro domains existing in the TrEMBLKEGG test set (see Additional file 2).
Figure 5 also shows the number of EC digits for the predictions and the correct EC number annotations. The higher the number of digits, the more specific the prediction, for example: EC 1.-.-.- only provides a generic enzymatic classification (oxidoreductases), while EC 188.8.131.52 defines the catalytic functionality down to the class of substrates (oxalate oxidase, with oxygen as acceptor). The proportion of predicted four digits EC numbers appears to be in line with their proportion in the true dataset.
As the predictions emitted by EnzML trained on SwissProtKEGG for the TrEMBLKEGG set are of interest for scientists working on non-model organisms, they are available as Additional file 8: TrEMBL_join_KEGG_true_and_predicted_EC_numbers.tar.gz.
A more detailed analysis of the prediction errors (using the E. coli dataset as example) is contained in Additional file 9: predictions.pdf. The additional file includes a table with the most common errors and the accuracy for each of the six main EC classes.
Predicting recent Swiss-Prot entries
EnzML trained on SwissProtKEGG (Jan 2011) can correctly predict most of the entries incorporated into Swiss-Prot in the following year (SwissProt_2011_2012 set) and does so with 79% subset accuracy, 89% micro averaged precision and 64% macro averaged recall. EnzML performance is limited by the fact that 13% of the entries are annotated with new EC numbers that did not exist in the SwissProtKEGG set of Jan 2011 and so cannot be predicted by the classifier. For comparison, a 10 fold cross-evaluation over the same SwissProt_2011_2012 set achieves much better results (subset accuracy 92% 0.6%, micro averaged precision 96% 0.6%, macro averaged recall 79% 1.7%) because the probability of a class existing in the test set but not in the training set is low.
Also, EnzML trained on SwissProtKEGG can correctly predict 69% of the new labels given to TrEMBL proteins upon their incorporation into Swiss-Prot (TrEMBL_2011_now_in_SwissProt_2012 set). This suggests that many of the mistakes in the TrEMBLKEGG predictions could actually become correct labels after manual curation. Here as well the performance is limited because 15% of the EC classes used in these new annotations did not exist in the SwissProtKEGG set of Jan 2011 the classifier is trained on.
Effects of EC distribution
The long-tail shape of the EC distribution is conserved even when the data is further categorised, often the case with long-tail distributions, and can be seen in the similarity of distributions for single species and full databases (Figure 3). This could be caused by evolutionary conservation of certain metabolic functions. Individual species, even compact bacterial genomes such as E. coli, have redundancy in certain enzymatic functions, and these functions seem to be common across species, leading to very frequent EC numbers such as Cytochrome-c oxidase (EC 184.108.40.206, mitochondrial respiration pathway) representing alone 12% of all UniProt enzymes.
The rare EC numbers do not impact on most evaluation measures as they affect a small number of proteins, but in Figure 4 we can note that the macro-averaged recall, a measure affected by the misprediction of rare classes is generally the lowest and more unpredictable metric for this method, as shown also by the wider standard deviation in Figures 5 and 6. Also, the macro-averaged recall of SwissProtKEGG cross evaluation is lower than expected at 83%, despite only 20% of its EC numbers being very rare (having less than 3 proteins) versus 63% in invertebrates and 22% in bacteria. However, the measure improves (from 83% to 88%) if 20 fold cross evaluation is used instead of 10 fold, hence raising the probability of having in the training set more examples of rare and very rare EC classes (data not shown).
The proposed method is applicable to any partial or complete protein sequence or metagenomic sample, since any genetic sequence can be scanned in silico for the presence of InterPro signatures using the InterProScan algorithm, also available as web service [4, 5].
The overall success of EnzML is due to the fact that InterPro signatures provide a very compact representation of protein functionality. The 13.5 million proteins in UniProt are described by only 154,583 (unordered) sets of InterPro signatures (attributes). Many of these sets are subsets of other longer signature sets. InterPro subsets in UniProt have an average length of 2.77 signatures, while InterPro super-sets have an average length of 4.78 signatures. 58,697 super-sets completely describe the possible combinations of InterPro signatures found in all UniProt proteins. To give a comparison, 1,582 billion combinations of three unordered elements could be obtained from 21,178 InterPro signatures (8.4 E+15 combinations of four elements).
In relation to the method application and evaluation, it must be noted that the distribution of annotation in metabolic databases tends, by definition, to be more enriched in enzymes than in non-enzymes. Even highly-populated databases such as UniProt are biased, with more accurate annotation (and Swiss-Prot status) going to widely studied biological functions. Using only annotations that agree in two manually curated databases (such as Swiss-Prot and KEGG in this work) increases trust, but decreases the number of EC classes that can be predicted. Swiss-Prot contains 2,850 distinct EC classes, and KEGG contains 2,636 EC classes, but the set of annotations agreeing in both databases only contains 2,051 EC classes. Rare EC classes can easily be lost in case of disagreement among the data sources.
The accuracy of the predictions generally increases as the dataset size increases which, combined with the efficiency of the algorithm, is a good case for using a bigger training set whenever possible. Training the classifier on more data from non-manually curated databases, such as UniProt-TrEMBL, might reduce the bias and increase the number of predictable classes, but will also decrease trust. Alternative biocuration scenarios might call for a different balance between coverage and trust, to increase the probability of recognising rare Enzyme Commission classes in newly sequenced genomes.
Although the highest possible level of accuracy is clearly desirable, the high accuracy of EnzML, combined with the measure of confidence that the method emits for each prediction, enables the curators to focus their work. The majority of erroneous annotations have low confidence (results not shown), so curators could tackle the more error prone annotations first. However, active learning research has shown that simply correcting low-confidence annotations is rarely the best strategy, as the representativeness and informative content of each instance also has an impact. A strength of fast re-training systems such as EnzML is the potential to incrementally improve overall accuracy when incorrect annotations are spotted by curators. The authors are currently researching active learning strategies to improve enzyme annotation accuracy in a mixed human-machine learning curation workflow.
Comparison with other EC prediction methods
PRIAM  was designed predict the overall metabolism for an organism, indicating whether particular enzyme functionalities were encoded in the genome, rather than assign functions to individual genes. A gene-oriented version of PRIAM was introduced in 2006 to address this task. In contrast, EnzML is designed to associate EC numbers to individual genes or gene fragments. EnzML improves on ModEnzA  by supporting the prediction of multiple EC numbers for a protein, and on EFICAz  by being able to assign multiple EC numbers of any number of digits. EFICAz2  improves the precision of EFICAz on test sequences having less than 30% similarity to the training set, and has not been evaluated separately from EFICAz.
However, it is possible to compare EFICAz2 results at MTTSI 50% (maximal test to training sequence identity) in Figure 4-C and 4-D of  with those obtained by EnzML (Figure 6 of this article). In more detail, EFICAz2 reports a maximum recall of 47% 49% of standard deviation (for MTTSI 30%), 78% 33% (for MTTSI 30-40%) and 86% 34% (for MTTSI 40-50%). EFICAz2 precision reaches a maximum of 74% 44% of standard deviation (for MTTSI 30%), 82% 36% (for MTTSI 30-40%) and 91% 27% (for MTTSI 40-50%). In a similar range of protein similarity (MTTSI 50%) EnzML obtains generally more accurate results and within 0.7% of standard deviation, thanks also to its extensive dataset. In particular, EnzML results on SwissProtKEGG UniRef50 are 80-95% recall (micro, macro, example based) and 93-98% precision (micro, macro, example based), all within less than 1% of standard deviation.
A comparison between EnzML on the four genomes used for evaluation in  (see Additional file 1: methods_comparison.pdf) shows that our method achieves greater sensitivity and specificity on a greater number of sequences, as our method uses more recent data. The data used for the comparison is available in Mulan ARFF format as Additional file 10: methods_comparison_arff_data.tar.gz and in comma-separated format as Additional file 11: methods_comparison_csv_data.tar.gz (including all the SwissProtKEGG data).
The EnzML method can be applied to any sequenced protein, without need for existing annotation or protein structures and it can provide quick, accurate and complete results on extensive datasets. EnzML leverages the evolutionary similarity of metabolic function yet without loosing performance when sequences redundancy is reduced. Thanks to the Mulan Binary Relevance Nearest Neighbours implementation (BR-kNN) this is possible in reasonable time even for millions of sequences, showing clear potential for meta-genomic analysis. Our approach demonstrates the potential of InterPro signatures in predicting enzymatic function and easing the backlog of manual curation of enzymatic function.
We plan to couple EnzML with pool-based active learning to further reduce the number of annotated instances needed, saving precious annotators time while further speeding up the method. The goal is to create a virtuous cycle between automatic and manual annotation, that is able to keep up with high-throughput sequencing. In the future, EnzML could also be extended to learning all protein functionalities, for example in the form of Gene Ontology terms.
LDF and SA designed the study, analysed the results and wrote the manuscript. LDF collected the data and wrote the EnzML code. JVH and IG helped conceive the study, participated in its design and coordination and helped to draft the manuscript. All authors read and approved the final manuscript.
LDF is funded by ONDEX DTG, BBSRC TPS Grant BB/F529038/1 of the Centre for Systems Biology at Edinburgh and the University of Newcastle. SA is supported by by a Wellcome Trust Value In People award and, together with IG, the Centre for Systems Biology at Edinburgh, a centre funded by the Biotechnology and Biological Sciences Research Council and the Engineering and Physical Sciences Research Council (BB/D019621/1). JVH was funded by various BBSRC and EPSRC grants. Many thanks to the subscribers of the Mulan mailing list, Paolo Besana, Ross Armstrong, Guido Sanguinetti and John Mitchell for their support and suggestions.
- 4.Hunter S, Apweiler R, Attwood TK, Bairoch A, Bateman A, Binns D, Bork P, Das U, Daugherty L, Duquenne L, Finn RD, Gough J, Haft D, Hulo N, Kahn D, Kelly E, Laugraud A, Letunic I, Lonsdale D, Lopez R, Madera M, Maslen J, McAnulla C, McDowall J, Mistry J, Mitchell A, Mulder N, Natale D, Orengo C, Quinn AF, Selengut JD, Sigrist CJA, Thimma M, Thomas PD, Valentin F, Wilson D, Wu CH, Yeats C: InterPro: the integrative protein signature database. Nucleic Acids Res 2009, 37(Database issue):D211.PubMedCentralCrossRefPubMedGoogle Scholar
- 15.Lanckriet GRG, Deng M, Cristianini N, Jordan MI, Noble WS: Kernel-based data fusion and its application to protein function prediction in yeast. Pac Symp Biocomput 2004,:300.Google Scholar
- 20.Tsoumakas G, Katakis I, Vlahavas I: Mining Multi-label Data. In: Data Mining and Knowledge Discovery Handbook. Springer, US; 2010.Google Scholar
- 21.Tsoumakas G, Spyromitros-Xioufis E, Vilcek J, Vlahavas I: MULAN: A Java Library for Multi-Label Learning. Journal of Machine Learning Research 2011, 12: 2411.Google Scholar
- 22.Witten IH, Frank E: Data Mining - Practical machine learning tools and techniques with Java implementations. Morgan Kaufmann, San Francisco; 2005.Google Scholar
- 29.Tsoumakas G, Vlahavas I: Random k -Labelsets: An Ensemble Method for Multilabel Classification. 2007.Google Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.