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Published online before print
October 25, 2006, 10.1101/gr.4916306 Genome Res. 16:1431-1438, 2006 ©2006 by Cold Spring Harbor Laboratory Press; ISSN 1088-9051/06 $5.00 OPEN ACCESS ARTICLE
Resource ProtoBee: Hierarchical classification and annotation of the honey bee proteomeDepartment of Biological Chemistry, Life Science Institute, The Hebrew University, Jerusalem 91904, Israel
The recently sequenced genome of the honey bee (Apis mellifera) has produced 10,157 predicted protein sequences, calling for a computational effort to extract biological insights from them. We have applied an unsupervised hierarchical protein-clustering method, which was previously used in the ProtoNet system, to nearly 200,000 proteins consisting of the predicted honey bee proteins, the SWISS-PROT protein database, and the complete set of proteins of the mouse (Mus musculus) and the fruit fly (Drosophila melanogaster). The hierarchy produced by this method has been entitled ProtoBee. In ProtoBee, the proteins are hierarchically organized into 18,936 separate tree hierarchies, each representing a protein functional family. By using the mouse and Drosophila complete proteomes as reference, we are able to highlight functional groups of putative gene-loss events, putative novel proteins of unique functionality, and bee-specific paralogs. We have studied some of the ProtoBee findings and suggest their biological relevance. Examples include novel opsin genes and intriguing nuclear matches of mitochondrial genes. The organization of bee sequences into functional clusters suggests a natural way of automatically inferring functional annotation. Following this notion, we were able to assign functional annotation to about 70% of the sequences. ProtoBee is available at http://www.protobee.cs.huji.ac.il
Comparative genomics are heavily based on computational methods. These methods provide not only automation for handling the immense amount of data held within whole genomes, but are also a means of highlighting biologically interesting differences between genomes. The recently sequenced genome of the honey bee Apis mellifera (The Honey Bee Genome Sequencing Consortium 2006  ) poses an excellent instance for comparative computational analysis in order to identify unique bee phenomena at the genomic and proteomic levels.
ProtoNet is a hierarchical organization of over 1,000,000 protein sequences (Kaplan et al. 2005
We have applied the method used in ProtoNet to 199,343 proteins consisting of the GLEAN3 set of predicted bee proteins (The Honey Bee Genome Sequencing Consortium 2006
One key computational task for a newly sequenced genome is the automatic assignment of functional annotation to its predicted coding sequences (see Discussion in Sasson et al. 2006 A Web site that enables downloading, browsing, and analysis of the ProtoBee hierarchy and classification is available at http://www.protobee.cs.huji.ac.il.
ProtoBee hierarchy The resulting hierarchy of the  200,000 protein sequences contains 85,579 clusters that are organized into 18,936 separate trees. Each such tree is conjectured to represent a family of proteins that are functionally related. The proteins of each tree are all contained in its root cluster; therefore, the terms "tree" and "root" will be used interchangeably. Before proceeding, it is crucial to stress that the bee sequences are based on computational prediction. This means that some of the predicted coding sequences may be either partially or even fully incorrect. Furthermore, it is plausible that some proteins could be missing from the predicted set. In addition, the clustering and annotation methods are also expected to possess some degree of error as expected of any automatic computational method. Although in order to properly distinguish between these possibilities each cluster has to be inspected manually, in some instances it is possible to systematically pinpoint clusters that are more likely to possess unique bee features. This is the approach by which we proceed. In order to gain a global taxonomic view of the bee proteome, we look at two different perspectives. (1) Protein-based view. Each one of the 10,157 predicted bee sequences belongs to one of the roots. Other proteins assigned to the same root are considered to be putative homologs, belonging to the same functional family. For each protein, we check whether it has homologs from the mouse, fly, or other organisms. (2) Root-based view. There are 5095 roots that contain at least one of the 10,157 bee proteins. For each such root, we check whether it contains proteins from the mouse, fly, or other organisms in addition to the bee proteins. Figure 1 shows the summary of these results in a Venn diagram. As expected, a large majority (67%) of proteins have putative homologs both in mouse, fly, and other organisms. However, in terms of roots, these proteins are contained in 2539 roots, which represent only 50% of the total amount of roots. This suggests that several of these roots represent families that possess some functional divergence in the form of paralogs. A total of 87% of the proteins have putative fly homologs and 82% of the proteins have putative mouse homologs.
One of the most interesting subset of proteins is the group of 159 proteins that do not have homologs from any organism in our database. Since these proteins appear in 143 roots, most of them consist of only one bee protein. We expect these to be either bee proteins that have a unique functionality, highly diverged bee orthologs, gene prediction mistakes, or sequences that could not be properly classified by ProtoBee. An interesting subset of these 159 proteins is the subset of proteins that belong to nonsingleton clusters (i.e., consisting of more than one protein). The reason that these are especially interesting is that the chance of them being gene-prediction mistakes is significantly reduced. Such clusters are conjectured to consist of unique bee paralogs, created by gene-duplication events that are unique to the bee. Table 1 shows a list of the nine nonsingleton clusters that contain only bee proteins.
Following this comparative overview and the identification of putative bee sequences that possess a unique functionality, we would like to focus on gene-loss events in the bee. A careful testing of individual genes has previously shown cases of possible gene loss in the bee genome (Whitfield et al. 2002 ). The root clusters are used as our starting points. The 199,343 proteins in the database are contained in 18,936 roots. From these, 2598 roots contain fly proteins, but do not contain bee proteins. In the resulting list, it is difficult to separate these into putative bee gene-loss events and unique fly proteins. A third high-quality annotated insect genome would be helpful as a reference for separating these cases, but currently there is no such genome available (the Anopheles genome [Holt et al. 2002] is currently not sufficient). Therefore, there are two possible approaches. The first is to use the mouse genome as a reference and look at the subset of roots that contain both fly and mouse proteins but do not contain bee proteins (marked fly+/mouse+/bee–). There are 1225 such roots (a list is available on the ProtoBee Web site). While this approach would eliminate the cases of mistaking bee gene-loss events for unique Drosophila functionality, it would miss protein families that are unique in insects. Alternatively, a different approach would be to use the other insect proteins that exist in the SWISS-PROT database as a reference (there are 3465 such proteins). In this approach, we focus on clusters that do not contain bee proteins but contain at least one fly protein and at least one additional protein from a different insect (marked fly+/insect+/bee–). While this approach will certainly miss several cases of gene-loss events due to the lack of coverage of the insect proteins, it focuses on insect functionality. Still, this approach can mistake some instances of unique Drosophila functionality for bee gene-loss events (in cases where the other insect species in the cluster are evolutionarily very close to Drosophila). There are 67 such roots that do not contain bee proteins but do contain at least one fly protein and at least one protein from another insect. A list of these fly+/insect+/bee– clusters is shown in Table 2.
So far we have focused on two sets of proteins that are of special interest in a comparative study of the bee genome— proteins whose function is bee specific and proteins that are missing in the bee due to gene-loss events. One other interesting case is that of paralog enrichment. In the case of paralogs, we would like to focus on protein families that are taxonomically imbalanced. Specifically, roots that contain a high ratio of bee:fly and bee:mouse proteins may suggest that there exist several paralogs in the bee that do not exist in the fly and mouse. In order to highlight taxonomically imbalanced clusters, we use a taxonomical balance score (TB score):
where bee(C) is the number of bee proteins in cluster C and fly(C) is the number of fly proteins in C. The score ranges from 1 (only bee proteins, no fly proteins) to 0 (no bee proteins, only fly proteins), 0.5 indicating an equal amount of fly and bee proteins. A score for bee:mouse ratio is derived in a similar manner. The TB score for each cluster is available through the ProtoBee Web site. Following the procedure described in Methods, 7131 of 10,157 (70%) bee sequences were assigned annotation. While in terms of coverage this is comparable with supervised methods, the fact that the annotation sources used (see Methods) are varied in terms of scope provides viewpoints at several levels of functionality. Namely, we are able to assign a wide range of annotations from very general properties (e.g., signal transduction, metabolism) to very specific properties (e.g., glucose-6-phosphate isomerase). However, the main goal of the automatic annotation effort is to complement the view of the individual protein families. For example, suppose that by using the comparative approach previously described we find that a protein cluster of polymerases does not contain bee orthologs. A natural question would be whether bee polymerases can be found in other clusters. This can be easily examined by checking which bee proteins were annotated as polymerases. Figure 2 shows the distribution of annotated proteins into GO functional categories. A list summarizing the amount of proteins per annotation is available on the ProtoBee Web site.
Manual evaluation of the results It is obvious that the full extent of the biological relevance of the results that are produced by this computational approach cannot be assessed without manual inspection of each and every prediction. Therefore, we proceed by providing an in-depth biological analysis of only some of the results. We start by examining the set of fly+/insect+/bee– clusters (Table 2). Fifteen of these clusters contain multiple biological groups that are apparently unrelated (note that according to our annotation-inference method, such clusters will not be used to infer annotations). However, it is apparent that in some instances these predictions are meaningful, considering the fact that they suggest specific functionally related groups of proteins to be missing (such as mitochondrial proteins, chorion proteins, vision proteins, and developmental proteins). Still, it is crucial to note that not all biologically coherent clusters necessarily indicate gene-loss events. For example, in the case of glucose-6-phosphate isomerase (G6PI), the protein seems to be missing, as it does not appear clustered with the Drosophila protein. G6PI is conserved amongst several species and is crucial for glycolysis, so it is highly unlikely that it does not have a bee homolog. Browsing the ProtoBee annotations, we find that ProtoBee annotates one of the bee proteins as G6PI, and this protein indeed seems to show very high similarity to G6PI proteins. Thus, we suggest that in order to determine whether a fly+/insect+/bee– cluster is indicative of a putative gene loss, one should complement the study of each such cluster with an examination of the corresponding annotations.
Mitochondrial proteins In the case of COX1, ProtoBee is able to correctly cluster the COX1 protein family into a unique tree. However, one of the clusters in this tree also contains GB17755, a 30 amino acid bee protein. GB17755 shows a high level of similarity (59% identity spanning all 30 amino acids) to COX1 from various organisms. Returning to the genome, we find that the sequence of GB17755 is not part of a full-length COX1 homolog in the genome. No evidence of expression or mitochondrial targeting signal was found. In the case of ND1, we find that the bee sequence GB12194 was clustered in a cluster of ND1 orthologs. A BLAST search using GB12194 as the query shows that the best matching sequence in UniProt is the sequence of the bee mitochondrial ND1 (84% identity on a region of over 60 amino acids). Searching the genome showed that this sequence is not part of a full-length nuclear homolog of ND1. Therefore, we do not expect this to be an instance in which a high-similarity homolog was missed in the gene prediction process. Once again, no evidence of EST expression or mitochondrial targeting was found. In light of this, the most likely explanation for the appearance of these sequences in the nuclear DNA is the migration of the mitochondrial sequence to the nuclear DNA, creating NUMTs (nuclear mitochondrial DNA).
In order to further investigate whether these sequences are indeed NUMTs (Richly and Leister 2004
In the case of COX3, we find that the bee sequence GB11138 has been clustered in a cluster with bacterial COX3 and ubiquinol oxidase subunit 3 (UOX3) proteins. GB11138 shows the highest level of similarity (54% identity spanning 90% of the protein) to UOX3 from Escherichia coli. Furthermore, the length of the proteins (206 amino acids) matches that of prokaryotic COX3 and UOX3 proteins rather than that of eukaryotic COX3. The high similarity of this protein to prokaryotic COX3 and UOX3 suggested that this sequence may be of bacterial origin. Examining the contig in which this sequence appears, we have identified two adjacent sequences with high similarity to bacterial UOX and ferredoxin proteins. The contig is currently unlocalized within the genome. No evidence of expression or mitochondrial targeting signal was found. We suggest that GB11138 and its contig are either the result of a recent lateral gene-transfer event or of a contamination within the genome sequence. In the cases of all three sequences it is apparent that although these sequences probably do not code for proteins, the classifications made by ProtoBee in each of these instances were justifiable.
Opsins
Pigment Dispersal Hormone Another protein that surprisingly seems to be missing is the Pigment Dispersal Hormone (PDH). PDH has been suggested to be involved both in vision and the circadian rhythm (Park and Hall 1998 ). However, it is also suggested to exist in the bee and be highly conserved amongst insects (Bloch et al. 2003). Running a BLAST search of PDH against the entire 10,157 proteins set using the fly PDH preprotein as query finds no matching sequences. Since the experimental evidence suggested that this protein does exist, we independently searched the bee genome for a homolog of PDH, using the fly PDH preprotein. One matching sequence was found, displaying an extremely high degree of conservation (identical in all but two amino acids), which is restricted to the part of the preprotein that codes for the PDH peptide. Apart from this region of similarity, the rest of the preprotein sequence does not have matches in the genome. In light of this strong evidence, we suggest that a homolog of PDH does exist in the bee, but was missed by the computational gene prediction.
Unique bee paralogs
Cluster 391,502 consists of Apamin and Mast Cell Degranulating Protein (MCDP), both constituents of the bee venom. Apamin and MCDP were previously shown to share their 3' exon (Gmachl and Kreil 1995
Once a new genome is sequenced, there are several computational tasks that may be performed on it in order to learn about its biology. These include gene prediction, automatic annotation, and comparative analyses. For each of these tasks there are several different approaches. In this work we present a novel method that combines both the tasks of comparative analysis and automatic annotation. One unique aspect of the clustering method used by ProtoBee is the fact that it is an unsupervised method. In the supervised approach, the algorithm is typically provided with a training set of proteins known to belong to the same family, and then learns common features in order to detect new members of this family. This is the most commonly used approach for machine learning of protein families. While this approach delivers extremely high performance in terms of sensitivity and specificity, it creates a heavy bias toward the detection of only that which is known and cannot detect novel protein families. In the unsupervised approach, on the other hand, the method looks for intrinsic features of the data in order to organize it, rather than being guided externally. Using an unsupervised clustering method, ProtoBee is expected to be inferior to supervised methods such as InterProScan in terms of sensitivity/specificity. Thus, we suggest using our annotation method in conjunction with supervised methods in order to provide maximal coverage and specificity. However, the method makes up for this inferiority by its ability to detect novel protein families (e.g., nonsingleton clusters that are unique to bee) and provide a hierarchical comparative view. A genomic view that is based on the comparison of a genome to only two other genomes may be somewhat biased. However, since the computation required by this method is demanding (nearly 4 x 1010 sequence comparisons), a three-way comparison seems to be a reasonable compromise between biological accuracy and computational feasibility.
Testing our method, we have discovered that the phenomenon of NUMTs is extensive in the honey bee genome. The significant appearance of NUMTs in the bee genome is quite surprising considering that this phenomenon in nearly absent both in Anopheles gambiae and in Drosophila melanogaster (Richly and Leister 2004 In contrast to the previous application of this method in ProtoNet, the focus in ProtoBee is on a whole-genome comparative view. The ability to divide the proteins into functional groups and view each group in light of three whole proteomes provides a unique view of the functional organization of the bee proteome in light of two other metazoan proteomes. This led us to highlight interesting groups of proteins that may be able to account for unique biological characteristics of the bee. It is important to recognize that the predictions made by this method may be, in some cases, lacking or mistaken. However, our goal in highlighting potentially interesting clusters is not to provide a finalized comprehensive list of gene-loss and function-gain events, but merely to select a subset of clusters that suggest further in-depth examination. By studying a few examples of such clusters it is evident that some of these are genuinely interesting. The purpose of the examples that we provide is to demonstrate the ability of the ProtoBee method to pinpoint interesting and often surprising biology in the genome. Obviously, these biological findings require further research in order to evaluate their significance. We expect the lists of putative gene losses, unique function proteins, and bee-specific paralogs to conceal within them many more exciting biological stories.
Sources and tools The protein database that was clustered consisted of the SWISS-PROT database version 41.21 (133,312 proteins), additional mouse and fly proteins from TrEMBL version 24.8 (20,730 Drosophila proteins and 35,199 mouse proteins), and the GLEAN3 set of predicted proteins (http://www.protobee.cs.huji.il and http://www.hgsc.bcm.tmc.edu/projects/honeybee) from release v3.0 of the Apis mellifera genome (10,157 proteins). Fifty-five previously known bee proteins that appeared in SWISS-PROT were removed from the database in order to avoid duplicate instances of the proteins, leaving our protein database at a total size of 199,343 protein sequences.
For sequence comparison, NCBI BLAST (Altschul et al. 1997
Protein clustering
Protein annotation
where A is the set of all proteins in the database that have annotation a. These relatively strict requirements ensure that clusters that are biologically incoherent do not affect the process of assigning annotations and that uninformative annotations are avoided. The annotations that are assigned to the clusters are taken from the following sources: UniProt keywords, InterPro, GO "molecular function" and "biological process" terms (including the GOA mapping), and E.C. (Enzyme Classification) numbers. Finally, each bee protein is assigned the annotations that were given to the cluster to which it belongs and the annotations that were assigned to all of the cluster's parents in the hierarchy.
We thank Ori Sasson for his effort in preparing the data for ProtoBee and Alex Savenok for development of the ProtoBee Web site. We thank the Baylor College of Medicine Human Genome Sequencing Center for making the Apis mellifera genome sequence publicly available prior to publication. This work is supported in part by the EU Framework VI, NoE BioSapiens consortium for Genome Annotation. N.K. is a fellow of the Sudarsky Center for Computational Biology of the Hebrew University.
1 Corresponding author.
E-mail michall{at}cc.huji.ac.il; fax 972-2-6586448. Article published online before print. Article and publication date are at http://www.genome.org/cgi/doi/10.1101/gr.4916306.
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Received November 11, 2005; accepted in revised format June 1, 2006.  CiteULike  Connotea  Del.icio.us  Digg  Reddit  Technorati What's this?
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