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Functional annotation of novel proteins is one of the central problems in bioinformatics. With the ever-increasing development of genome sequencing technologies, more and more sequence information is becoming available to analyze and annotate. To achieve fast and automatic function annotation, many computational automated function prediction AFP methods have been developed. To objectively evaluate the performance of such methods on a large scale, community-wide assessment experiments have been conducted.

Here, we report benchmark results of our methods obtained in the course of preparation for CAFA2 prior to submitting function predictions for CAFA2 targets. For CAFA2, we updated the annotation databases used by our methods, protein function prediction PFP and extended similarity group ESGand benchmarked their function prediction performances using the original older and updated databases. We also developed two ensemble methods that combine function predictions from six independent, sequence-based AFP methods.

We further analyzed the performances of our prediction methods by enriching the predictions with prior distribution of gene ontology GO terms. Examples of predictions by the ensemble methods are discussed. Adding the prior distribution of GO terms did not make much improvement. Both of the ensemble methods we developed improved the average Fmax score over all individual component methods except for ESG. Our benchmark results will not only complement the overall assessment that will be done by the CAFA organizers, but also help elucidate the predictive powers of sequence-based function prediction methods in general.

Advancement in high-throughput genome sequencing technologies in the last decade has posed a challenge in the arena of protein bioinformatics — the exponential growth of new sequence data that awaits functional elucidation.

In addition, there are several methods that thoroughly extract function information from sequence database search results using different strategies. There are other function prediction methods that consider coexpression patterns of genes [ 20 — 24 ], 3D structures of proteins [ 25 — 34 ], and interacting proteins in large-scale protein-protein interaction networks [ 35 — 40 ]. In CAFA, participants submit function annotation using gene ontology GO [ 4243 ] terms for a large number of target proteins.

The organizers evaluate the accuracy of predicted GO terms for a subset of target annotations that are newly revealed after the submission deadline. In the second round of CAFA, i. CAFA2, for which an evaluation meeting was held as a special interest group meeting at the Intelligent Byi in Molecular Biology ISMB conference in Boston, a total ofbbi protein sequences from 27 species were provided. ESG performs iterative sequence database searches and assigns probability scores to GO terms based on their relative similarity scores to multiple-level neighbours in a protein similarity graph [ 16 ].

The annotation databases for PFP and ESG have not been updated sincewhen the two methods were initially developed.

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In this study, we also wanted to examine the improved methods for predicting the current GO annotations of protein sequences by using the updated databases.

Among the six individual methods, ESG with the updated database performed the best.

Successful and unsuccessful cases of the CONS ensemble method are discussed. UniRef provides clustered sets of sequences from the UniProt knowledgebase.

Among these UniRef50 clusters, we selected one representative protein from each of the clusters that satisfied the following two criteria: The framework of both methods consists of three steps: Two different databases are used in the procedure: The latter database is referred to as the annotation database.

We have been using a vgi of Swiss-Prot, but this time it was updated to the version 20 January PFPDB is discussed in detail later in this section. The previous version is fromand the new version used in this work and in CAFA2 was downloaded in Table 1 describes the differences in the number of sequences and GO terms between the old and new databases. The number of sequences in Swiss-Prot-SeqDB is expanded in the new database to more than double the size 2.

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In Table 2we show the effects of combining multiple annotation resources from which annotations are transferred for the updated PFPDB in terms of hgi sequence coverage and the GO coverage.

The sequence coverage is the percentage of sequences in Swiss-Prot that have at least one GO term annotation. The rest of the databases have relatively low coverage, with InterPro being the highest among them; however, its GO coverage is as low as At that time, the sequence coverage jumped bggi Sequence coverage is the percentage 55009 sequences in Swiss-Prot annotated 5090 at least one Bhi term after addition of translated terms from the format in column 1.

The bgj for the small gain in coverage can probably be attributed to the fact that GO annotations in Swiss-Prot have been far better developed since then, and annotations in different databases are now better shared between databases. To simulate a realistic scenario in which close homologs of a query do not exist in the sequence database, sequences similar to the target in the sequence database that have a certain Bgl or smaller i. The E-value cut-off is shown along the x -axis of the figure.

Thus, for example, with an E-value of 0. The y -axis reports the average F max score See Methods Section over all benchmark targets.

For this evaluation, we extend both predicted and true GO terms of each target with parental GO terms in the GO hierarchy. This parental propagation on the true and predicted annotation sets was also adopted in the official CAFA assessments. The performance evaluation without applying the parental propagation is provided in Figures S1 and S2 in Additional file 1. The FAM score is the probability that a GO term f a coexists in the annotation of a protein when another GO term f i already exists in the annotation of the protein.

For example, in Fig. Among five different FAM score threshold values 0. At the first E-value cut-off, 0. We also evaluated predictions when IEA GO terms are excluded from correct GO terms in the benchmark dataset Figure S3 in Additional file 1where a substantial drop in the accuracy was observed.

This is because the IEA GO terms of target proteins, which can be easily identified by sequence similarity, are now considered to be false positives. Before evaluating predictions, both predicted and true GO terms were propagated to the root of the ontology. Figure 1b shows the performance on MF GO terms. Overall, prediction accuracy for MF Fig.

Thus, the findings for the current benchmark with the updated database is consistent with the earlier study [ 14 ]. Each predicted and true GO term was propagated to the root of the ontology before evaluation. In summary, updating the databases contributed to improving the prediction accuracy average Fmax scores substantially for both PFP and ESG. Next we discuss the prediction accuracy of two ensemble methods in comparison with individual component methods Table 3.

The weight of a method is prior knowledge of the accuracy of the method. FPM selects combinations of GO terms that are computed from the predictions of multiple methods with a sufficiently high score see Methods. In Table 3we show results of two variations of FPM. Overall, out of all the individual and ensemble methods, the most successful method was ESG-Updated, which showed the largest average Fmax score of 0.

CONS had the second highest score Fmax score of 0. To further understand performance of the ensemble methods, we next examined the number of wins for each method, i. In this analysis, the confidence cut-off values used for each component method were optimized for each target to give the largest F max score to the target; this was done in order to understand how well ensemble methods can assemble individual predictions for the best-case scenario in which each component method offers its best possible prediction.

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Note, there are queries where multiple methods tied for same Fmax score.

Overall, the two ensemble methods did not show better performance than the best component method, ESG, but as illustrated later, there are many cases in which the ensemble methods successfully selected correct GO terms from different component methods. All true and predicted annotations have been propagated to the root of the ontology. All three GO categories were used in the evaluation.

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In addition, Figure S5 in Additional file 1 provides further information about the fraction of queries where predictions from CONS and FPM had the highest, second highest, third highest, etc. Table 4 illustrates how CONS combines predictions of the individual methods. Bgl first two examples Tables 4 and 5 are cases where CONS improved the prediction over the individual methods.

In its top hits, CONS bgu predicted all five GO annotations of this protein shown in bold in the table together with two parental terms of correct GO terms shown in italics in the table. Fraction of queries where each method showed the largest F max score.

The fraction on the y -axis was computed as the number of queries in which a method had the largest F max score over the total number of queries 2, protein sequences. Examples of predictions by CONS and individual-component methods. Capsid bti UniProt ID: GO terms in bold are correct annotations of the protein. Terms in italic indicate parental terms of correct GO terms. Terms in parentheses are wrong predictions. The query, succinate dehydrogenase iron-sulfur subunit, has bgl GO term annotations.

Out of these eight GO-term annotations, GO: Thus, CONS can successfully select different correct terms from different methods.

Succinate dehydrogenase iron-sulfur subunit UniProt ID: In this example, all five correct GO terms were predicted by ESG, but four of them were with weak scores. PFP predicted only two correct terms, GO: Thus, combining prediction gbi could 500 increase the scores of the correct terms, and rather, introduced over incorrect terms. We performed this experiment because it was shown in CAFA1 [ 4150 ] that the prior distribution itself often has relatively good 5090 performance, particularly when no easily identified homologs with known function are available for a query protein.

The prior GO-term distribution was added to the predicted GO terms for a target as follows: In parallel, the frequency 0. Then, the top 1, most-frequent GO terms in Swiss-Prot were added to the set of predicted GO terms and sorted by the normalized score.

The same 1, most-frequent GO terms were added to all the targets. The same data were plotted in two different ways: For all the prediction methods, adding prior GO distribution did not improve the accuracy, which can be seen from the plots and the Fmax values shown in the symbol legends.

An essential task in bioinformatics is to propose and develop new tools and new ideas. However, to support the biological community, it is equally important to maintain and update previously developed software 500 so that users can continue using them. For a prediction method, it is important that the prediction accuracy be improved over bgii so that it can keep pace with other existing methods of the same type.

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