- Open Access
Functional inference by ProtoNet family tree: the uncharacterized proteome of Daphnia pulex
© Rappoport and Linial; licensee BioMed Central Ltd. 2013
- Published: 28 February 2013
Daphnia pulex (Water flea) is the first fully sequenced crustacean genome. The crustaceans and insects have diverged from a common ancestor. It is a model organism for studying the molecular makeup for coping with the environmental challenges. In the complete proteome, there are 30,550 putative proteins. However, about 10,000 of them have no known homologues. Currently, the UniProtoKB reports on 95% of the Daphnia's proteins as putative and uncharacterized proteins.
We have applied ProtoNet, an unsupervised hierarchical protein clustering method that covers about 10 million sequences, for automatic annotation of the Daphnia's proteome. 98.7% (26,625) of the Daphnia full-length proteins were successfully mapped to 13,880 ProtoNet stable clusters, and only 1.3% remained unmapped. We compared the properties of the Daphnia's protein families with those of the mouse and the fruitfly proteomes. Functional annotations were successfully assigned for 86% of the proteins. Most proteins (61%) were mapped to only 2953 clusters that contain Daphnia's duplicated genes. We focused on the functionality of maximally amplified paralogs. Cuticle structure components and a variety of ion channels protein families were associated with a maximal level of gene amplification. We focused on gene amplification as a leading strategy of the Daphnia in coping with environmental toxicity.
Automatic inference is achieved through mapping of sequences to the protein family tree of ProtoNet 6.0. Applying a careful inference protocol resulted in functional assignments for over 86% of the complete proteome. We conclude that the scaffold of ProtoNet can be used as an alignment-free protocol for large-scale annotation task of uncharacterized proteomes.
- Gene Ontology
- Ionotropic Glutamate Receptor
- Automatic Annotation
- Stable Cluster
- Root Cluster
Daphnia pulex is a key player in the aquatic ecosystems and an important component in the food web. It is a model organism for studying environmental challenges including toxic conditions . D. pulex is the first crustacean whose genome was sequenced . The crustaceans and insects have diverged from a common ancestor. Nevertheless, they exhibit extraordinary levels of phenotypic diversity. There are 30,550 model proteins, 95% of them are named 'putative uncharacterized'. Over a third of the sequences lack homologues , and thus are considered novel genes. A detailed analysis on the evolutionary trends of Daphnia genome indicates that extensive gene duplication events occurred. Importantly, many of these duplicated genes are under purifying selection . It was proposed that the amount of duplicated genes reflects the harsh living environments of the family Daphniidae. Specifically, genes that appear in tandem duplicated clusters are significantly over-represented in transcriptomes from extreme ecological conditions .
Comparative genomics approaches are useful for the discovery of functional elements from newly sequenced genomes . Such methods were successfully used for complete sequenced Drosophilae (12 species) , and genomes from various yeast strains . Daphnia is the only available crustacean sequenced genome. Thus, the value of a comparative genomics research from its related proteomes (i.e., insects) might be somewhat limited.
ProtoNet is a global automatic classification scheme for the entire protein space [6, 7]. ProtoNet 6.0 provides a hierarchical organization of 10 million protein sequences . The hierarchy results from an unsupervised clustering method that groups proteins according to their mutual similarity. The resulting hierarchy consists of protein clusters that are arranged into several trees. Each such tree represents a protein family at a different granularity - from a broad superfamily to a specialized subfamily . Following pruning of the ProtoNet 6.0 family tree, the system reports on ~162,000 high quality stable clusters (for definitions, see Methods). ProtoNet was applied successfully as a complementary methodology for annotating newly sequenced genomes . The incorporation of external annotation sources that cover structure, function, domain and taxonomy perspectives leads to impartial biological knowledge and functional inference [11, 12].
In this study, we claim that the scaffold of ProtoNet can be successfully used for annotating the Daphnia full-length proteome. We show that by applying strict filters on the ProtoNet tree and adding a number of constrains for functional inference, we could safely map to preexisting clusters 98.7% of the Daphnia's proteome. For 87% of the mapped proteome, functional annotations were securely assigned. We show that the Daphnia proteins are clustered into ~8800 clusters, but only 40% of these clusters include insects' representatives. Most (61%) of the proteins are mapped to ~3000 clusters that contain at least 2 Daphnia's paralogs. We consider the function of the clusters that are exceptionally amplified relative to the fruitfly proteome and those that are maximally enriched in the Daphnia's proteome. We focus on ion channels and cuticle structural families that dominate the amplified duplicated genes. We discuss the relevance of gene expansions and the potential of the organisms to cope with the changing environment.
Automatic mapping of the Daphnia proteome
In order to achieve a global taxonomic view of the Daphnia proteome, we took two perspectives: (a) A protein-based view: Each of the 26,625 Daphnia sequences belongs to one of the ProRoot70 roots. Proteins assigned to the same root belong to the same functional family. For each protein, we check whether it has homologues from the mouse and the fruitfly (Drosophila melanogaster). (b) A root-based view: In ProRoot70, 8838 clusters contain at least one Daphnia's mapped protein. Among the ProRoot70 trees, 2953 clusters contain at least 2 Daphnia's proteins. For each ProRoot70, we check whether it contains proteins from the mouse, fruitfly or other organisms, in addition to the Daphnia proteins. The mouse and the fruitfly were selected as representatives for complex, 'complete proteomes'. In addition, these organisms differ considerably in their evolution history, mutation rate, generation time and other parameters that govern their protein families (see discussion in ).
We repeated the mapping protocol and thresholds as used for the Daphnia proteome for mapping the 17,438 and 39,386 full-length proteins from the fruitfly and the mouse, respectively. Figure 2 shows the results in a Venn diagram. As expected, a large majority (57%) of the proteins have homologues in the mouse and the fruitfly. Interestingly, a substantial fewer roots associate with the D. melanogaster proteome (5894 relative to 8838 ProRoot70 trees). About 40% of Daphnia's clusters include also proteins from the fruitfly. Notably, the fraction of proteins for [Daphnia+/Fruitfly+/Mouse-] or [Daphnia+/Fruitfly-/Mouse+] is identical, with 6% of the Daphnia proteome in each cross-taxa groups (Figure 2).
The proteome of the Daphnia includes many previously unseen proteins that have no homology to mouse or to the fruitfly (30%). Importantly, these 8235 proteins (Figure 2) are mapped to ProRoot70 that include other organisms. The number of proteins that are unique to the fruitfly or the mouse comprises 17% of their analyzed proteome (Figure 2). An interesting subset of proteins is the group of proteins that failed mapping (343). These proteins are potentially Daphnia specific proteins. However, these are prone to mistakes in genome annotations, and therefore, will not be further discussed.
Automatic annotations of the Daphnia proteome
The principle underlying the assignment of annotations to the uncharacterized Daphnia proteome relies of the functional coherence in the ProRoot70 set. Previous quality assessment showed that the clusters of ProtoNet are of high quality in view of their annotations . The sources for the automatic functional annotation task cover the standardized vocabulary of Gene Ontology (GO) (Camon et al. 2004, Harris et al. 2004), UniProt Keywords (, Pfam , Pfam, InterPro  and additional structural and functional classifications [18, 19].
We tested the quality of the ProRoot70 clusters that include Daphnia's proteins, using the specificity score (Figure 3B). The average specificity score for all InterPro terms (families and domains together) is 0.84 (the specificity median score is 0.9). This high specificity is a strong support for the quality of our automatic inference procedure.
The largest trees for [Daphnia+/Mouse+/Fly-] and [Daphnia+/Mouse-/Fly+].
# Daphnia-Mouse proteinsa
NACHT nucleoside triphosphatase
S100/CaBP, calcium binding
Hyaluronic acid binding
2'-5'-oligoadenylate synthetase 1
Fibronectin type III
Proteinase inhibitor I25, cystatin
# Daphnia-Fruitfly proteins a
Insect cuticle protein
Olfactory receptor, Drosophila
Insect cuticle protein
MADF domain/DNA binding
Protein of unknown function DUF243
Odorant binding protein
Protein of unknown function DUF229
Insect pheromone/Odorant binding
Most Daphnia's proteins have paralogs
Using the TS, we indirectly estimated the conservation relative to the size of the cluster subtree that contains all of the Daphnia's proteins within. We identified 305 clusters of TS = 1.0. High TS is indicative of the 'isolation' of the Daphnia's proteins from the other members in the cluster. 54% of the Daphnia's paralogs are associated with high divergence (TS < 0.2, Figure 5B). We examine the Map10 clusters that contain a large number of Daphnia's proteins (≥ 10). Such clusters are spread at all ranges of the TSs (Figure 5C). When the same analysis was performed on Drosophila melanogaster Map10 clusters, the dominating TSs are typically < 0.2, and no cases of high TSs were noted (Figure 5D). The results suggest that in Daphnia (but not the fruitfly), paralogs having low divergence in view of other proteins in the clusters are prevalent. A quantitative comparison of the paralogs in Drosophila and Daphnia was performed. The number of ProRoot70 roots that contain paralogs is 3029 and 2306 in Daphnia and Drosophila, respectively. The relation of the TS and the Tree size (i.e. number of leaves in the analyzed cluster) is shown for Daphnia (Additional file 1).
Functional view on Daphnia's families with amplified paralogs
We inspected the annotations that are associated with clusters having a high number of duplicated genes (≥ 60 paralogs, Additional file 3). The results show that these clusters are rich with viral origin, apparently as relics of transposition events (e.g., integrase) . Other such families include structural proteins of the cuticle and the cytoskeleton, large families of enzymes (e.g., protein kinase), and various signaling receptors (e.g., GPCR).
Functional annotations for Daphnia's proteome at ProRoot70 (> 100 paralogs)
# Daphnia proteins (ProRoot70)
Classic Zinc Finger
Insect cuticle protein
RNA-dep. DNA polymerase
7TM GPCR, rhodopsin-like
Structural molecule activity
Insect cuticle protein
HpI Integrase; Chain A
MULE transposase, domain
RNA recognition motif, RNP-1
A taxonomical imbalance of Daphnia paralogs
Based on the completeness of the Daphnia's genomes, we could focus on protein families that are characterized by a taxonomically imbalanced. Specifically, ProRoot70 trees that contain a high proportion of Daphnia:fly proteins may suggest gene amplifications that support essential function in Daphnia. In order to highlight taxonomically imbalanced clusters, we defined a taxonomical balance score (TB score, see Methods).
Figure 6B shows the TB for the 50 protein families with a maximal (or minimal) TB values. There are 31 clusters with TB ratio ≥ 10 and only 13 clusters that have a TB ratio ≤ 0.1 (i.e. > 10 folds the number of Drosophila relative to Daphnia paralogs) (Figure 6B, dashed line). The functions associated with TB ratio ≥ 10 include nucleic acids regulation (Zn-fingers, HAT dimerization, ATPases), proteins of the stress response (Heat Shock, Clp1), Oxidative phosphorylation (Oxidoreductase, Cytochrome C) and transporters (Major facilitator, Lipid transport, ABC transporter). Drosophila paralogs with high TB ratio (≥ 10) confined to clusters of unknown functions, pheromone and olfactory receptors (Figure 6B).
The TB test indicates the relevance of this measure to the behavior and the environmental difference between the fruitfly and the Daphnia. For example, the essential requirements for stress response elements in Daphnia are exposed through the Dapnia:fly TB score.
Manual evaluation: plasma membrane receptors and ion channels
Inspecting the ProRoot70 trees that contain a large number of Daphnia's proteins revealed families that are particularly enriched with receptors and signaling proteins. We consider three such families that are characterized by a high ratio of the number of paralogs (in the ProRoot70) relative to Map10 clusters (Table 1) and a high TB value relative to the fly (Figure 6). We focus on the amplifications of ion channels and receptors.
The assignment of a large group of Daphnia's paralogs to the ionotropic glutamate receptors is intriguing. Daphnia's representatives were found for each of the three subclasses of glutamate receptor (ProRoot70, ID 4491232): (i) The NMDA (N-methyl-D-aspartate) receptors are highly permeable for Ca2+ ions. NMDA receptors play a key role in the plasticity of the nervous system. (ii) The AMPA (alpha-amino-3-hydroxy-5-methyl-4-isoxazole-4-propionic acid) receptors that are the most commonly found receptors in the nervous system, and (iii) the Kainate receptors.
ProRoot70 tree with a ProtoName of 'Ionotropic glutamate receptor' (InterPro) includes 160 of the Daphnia's paralogs. The InterPro term covers 140/160 instances. The surprisingly high prevalence of glutamate receptors (AMPA, Kainate and NMDA) is most likely to control the excitatory synaptic transmission in the crustacean brain. Specifically, transient activation of NMDA receptors leads to a modification in the strength of synaptic transmission mediated by AMPA and kainate receptors. The ionotropic glutamate receptors are ancient in evolution. Events of genes loss and gain were reported for these genes along the phylogenetic tree . A collection of 160 related gene products in Daphnia has the potential for a rich combinatorial array of ion channels and sensors.
Another functional group includes the Daphnia's paralogs identified as Bestrophin. The Bestrophin is a family of plasma membrane proteins that express in the retinal pigment epithelial cells. Mutations in the homologous human gene cause 'BEST Macular Dystrophy' disease. Bestrophins compose a new class of chloride channels that are restricted to multicellular metazoa. Daphnia's paralogs mapped to the largest Bestrophin subfamily (54 proteins, based on PANTHER ). In this subfamily, the other proteins are from the fruitfly (4 proteins) and Caenorhabditis (C. briggsae and C. elegans with 21 and 25 proteins, respectively).
A remarkable amplification is detected for the 51 Daphnia's proteins that are mapped to Ryanodine receptors (RyR) and inositol 1,4,5-trisphosphate receptors (IP3R) ProtoNet family. These proteins belong to the superfamily of ligand-gated intracellular Ca2+ channels. The RyR and IP3R control the Ca2+ homeostasis of the cells and are essential in neurons, muscle and other secreting cells. The IP3 receptor acts as a Ca2+ release channel from internal stores in smooth muscle and non-muscle tissues. However, at high Ca2+ concentrations in the cytosol, IP3 receptors are inhibited. Such inhibition is an essential mechanism for terminating the channel activity and thus preventing pathological Ca2+ rises.
Figure 7B illustrates the collection of the Daphnia proteins (length > 350 amino acids) according to their domains and descriptors of InterPro as Ryanodine receptors and Ca2+ release channel. The domains according to Pfam are listed (Figure 7B). Interestingly, despite the short Daphnia's proteins in the cluster, only 8 of the 51 Daphnia paralogs failed to meet InterPro definition of 'Ryanodine related receptors'. Notably, the ProRoot70 ID 4478501 (65 proteins) contains proteins from a broad collection of species including human, fruitfly, unicellular ciliate protozoa and Paramecium.
Most methods for functional inference are biased towards the detection of the 'known space' and fail in detecting novel families. A unique aspect of the ProtoNet method is the fact that it is unsupervised. We mapped the Daphnia uncharacterized proteome to ProtoNet 6.0. Once a new genome is sequenced, there are several tasks that may be performed with the goal of functional assignment. These approaches include (i) alignment-based comparative genomics; (ii) matching to predetermined statistical models (e.g. InterProScan). Domain and family-based resources provide an excellent coverage of the 'known space' using HMMs (12,000 in Pfam , 37,000 in EVEREST ). Iterative search using PSSM and HMM Profiles are often used for a comprehensive functional inference. However, all these methods consider each protein as a separate entity. Thus, a global perspective of the analyzed proteome is lost.
A growing number of proteomes, many of them are isolated in the species tree, become available. In the current study, intrinsic features of the data (e.g., PL and LT, Figure 1) guide the functional assignment. Specifically, the composed ProtoName captures the most significant annotations (Figure 3). ProtoName is linked to the majority of the stable clusters . We suggest that our annotation process, in conjunction with supervised methods will provide a maximal coverage. ProtoNet 6.0 serves as the scaffold for the Daphnia annotation. The DB including all the external expert annotations (e.g., SCOP, Pfam, GO) will be updated each year. It will be beneficial to retest the performance sensitivity of inference following an update for all these resources. It will serve to assess the functional inference quality in view of the gradual improvement in external knowledge.
A similar approach, called ProtoBee, was applied for annotating the honey bee proteome . ProtoBee tree was constructed from about 200,000 proteins including 10,000 proteins from the honey bee. About 70% of the bee's proteins were successfully annotated in this task . Our current strategy for annotation assignment is based on mapping the 30,000 Daphnia's proteins on a scaffold of ProtoNet 6.0 tree-like structure. Almost 10 million proteins are included in such a family tree. The success in annotating the Daphnia proteome covers 86% of the full-length proteome, despite the high percentage of proteins that lack known homologues. The enhanced performance in annotating the Daphnia proteome stems from the use of 10 millions sequences from all domains of life. Furthermore, the number of external annotations such as InterPro and GO terms was almost doubled in the 5 years from the ProtoBee project . We conclude that the drastic increase in data improved the performance of genome size automatic annotations.
In this study, we applied a taxonomical view to identify the unique clusters of crustaceans. In this view, [fly+/Daphnia-] and [fly-/Daphnia+] clusters are of a special interest (Figure 2). These sets account for functions that were lost/gain after the separation of crustaceans from insects. The taxonomical view provides an insight on genes that fulfill the Daphnia's unique needs. Evidence from other related genomes will be needed to substantiate the trends of gene loss and gain in crustaceans.
A large fraction of the Daphnis's proteome includes amplified genes. Instead of searching the proteins that meet an artificial predetermined threshold (e.g., Blast E-score < e-20), we mapped proteins to their most reliable cluster (Map10, Figure 1A) and followed their merges along the tree hierarchy. We identified that a fraction of the Daphnia's paralogs is characterized by a low divergence (Figures 5C, high TS). These paralogs are not mixed with other proteins in the cluster. However, such property was not detected among Drosophila's paralogs (Figures 5D). We assume that the Daphnia's paralogs that have high TB score reflect the dynamics of the Daphnia genome. The prevalence of proteins related to viral infection and transposition supports our hypothesis.
We determine hundreds of Daphnia's paralogs (Figure 4). It was noted that Daphnia pulex's genome appears to have twice as many gene duplication events with respect to the duplicate-rich C. elegans genome . Gene duplication in C. elegans occurred more frequently than in Drosophila or yeast. Analysis for gene duplications in Ryanodine receptors (RyR) and IP3R (Figure 7) indicates that RyR and IP3R are spread in small groups of 2-5 genes at a chromosomal proximity. Such organization applies to many of the Daphnia's paralogs .
The TB score is designed to track the extreme instances of imbalance in the number of Daphnia's paralogs. We used the D. melanogaster as a reference for a model organism whose annotation is supported by experimental evidence. The striking enrichment in Daphnia's proteins, using the TB measure, includes cuticle structural elements (Additional file 3), transposon proteins and various ion channels (e.g., glutamate and RyR and IP3 receptors, Figure 7). Analysis of the chemoreceptors [22, 27] suggests that the ionic glutamate receptors belong to a fast evolving superfamily. Similar observations for expanded gene families were reported for Daphnia ABC transporters , transposon proteins  and the Cytochrome P450 . It is anticipated that a network of sensing and signaling molecules is essential for Daphnia's environmental response and acclimation against environmental toxicity.
In this paper, we present a novel method that combines both the tasks of comparative analysis and automatic annotation. One unique aspect of the clustering method used is the fact that it is an unsupervised method. The protocol presented is useful in the annotation task of further genomes, especially in the case that there are no other related genomes in the public domain.
The uncharacterized Daphnia's proteome was mapped successfully to thousands of protein families. For 81% of these families, the functional inference from various external resources was successful.
An unbalanced taxonomical outlook for Daphnia proteome in view of the fruitfly as a model organism was instrumental to identify genes' amplification in Daphnia. These expanded protein families may underlie the capacity of Daphnia to cope with the environmental toxicity, oxygen availability, wide temperature range and other harsh conditions.
All Daphnia pulex proteins that are not assigned as 'fragments' were extracted from UniProtKB (release of April 2011). All Drosophila and Mouse proteins were downloaded from UniProtKB and restricted to 'Complete Proteome' set. The organization of the proteins into a set of families is based on the scaffold of the ProtoNet 6.0 hierarchical tree  that includes 10 million proteins from UniProtKB .
The ProtoNet tree construction is described in [7, 8]. The main steps in the hierarchical tree are (i) All-against-all BLAST. NCBI BLAST is run on all pairs of proteins, using BLOSUM62. All E-values lower than 100 are kept in a matrix. The E-values which are less significant than the value 100 are considered 100; (ii) Hierarchical clustering. An agglomerative clustering procedure is applied in which all clusters start as singletons, and at each step the two clusters that have the lowest score are merged into a new cluster. The score between two clusters is defined as the arithmetic mean of the E-values from all inter-cluster pairs of proteins. An efficient clustering algorithm was implemented ; (iii) Stable cluster and pruning. We only consider clusters that are stable. To this end, we chose Life Time (LT) = 10 for mapping the Daphnia proteins to a subset of robust clusters (Map10); (iv) ProtoLevel 70 was selected for defining the root clusters. The proteins of each of the Map10s are contained in its root cluster of ProRoot70. Therefore, the terms 'tree' and 'root' will be used interchangeably.
ProtoNet scaffold tree is used for classifying each one of the Daphnia's proteins according to the match with the best stable cluster. The Daphnia's clusters from the initial mapping are named Map10 clusters. The depth of the tress (ProtoLevel, PL) is used for estimating the relatedness of the sequences and the clusters' quality. ProtoNet has been shown to produce hierarchies for thousands of highly coherent clusters at high quality at PL that is > 90. We restricted the analysis to clusters' size that are limited by the PL = 70 to ensure the high confidence annotation inference. The collection referred to as ProRoot70 composed of 251,403 roots.
We focused only on the following dominating annotations: UniProt Keywords, EC, GO, InterPro and the structural classifications from CATH  and SCOP  (see database description in ). For each one of these keywords we looked for the one with the highest Correspondence Score (CS) index that reflects the size of the intersection (number of proteins with a specific annotation in the cluster) divided by the size of the union (number of proteins with the specific annotation in the tree). We eliminate annotations that are based on uninformative terms such as 'complete proteome', 'taxonomy' and 'hypothetical protein'.
Each mapped Daphnia protein is assigned the annotations that were given to the cluster to which it belongs and the annotations that were assigned to all the cluster's parents in the ProRoot70. Validated annotations were restricted to clusters that have at least 5 proteins and the cluster specificity is ≥ 0.2. The additional filtrations ensure the safe inference for 86% of the mapped Daphnia's proteome.
We marked Daphnia pulex proteins as paralogs for proteins that were mapped to the same Map10 clusters. Clusters that include at least two proteins from the subjected organism are called paralogs. There are 3395 clusters that contain paralogs (16,134 proteins). At the level of ProRoot70, there are 3029 such clusters. About half of them (1464) include more than one Map10 cluster.
Tree Score's range from 0 to 1.0. The relation between the TB and the size of the cluster is shown (Additional file 1).
The Taxonomy Balance (TB) index measures the imbalance between proteomes. It is measured as the ratio of the Daphnia proteins to any selected reference proteome (Drosophila, mouse) in a ProRoot70 cluster. Only ProRoot70 trees that contain at least one protein from each of the discussed proteomes are considered.
This work is supported in part by the Prospects EU Framework VII. NR is a fellow of the Sudarsky Center for Computational Biology of the Hebrew University.
This article has been published as part of BMC Bioinformatics Volume 14 Supplement 3, 2013: Proceedings of Automated Function Prediction SIG 2011 featuring the CAFA Challenge: Critical Assessment of Function Annotations. The full contents of the supplement are available online at http://www.biomedcentral.com/bmcbioinformatics/supplements/14/S3.
- Schwerin S, Zeis B, Lamkemeyer T, Paul RJ, Koch M, Madlung J, Fladerer C, Pirow R: Acclimatory responses of the Daphnia pulex proteome to environmental changes. II. Chronic exposure to different temperatures (10 and 20 degrees C) mainly affects protein metabolism. BMC Physiol. 2009, 9: 8-10.1186/1472-6793-9-8.PubMed CentralView ArticlePubMedGoogle Scholar
- Colbourne JK, Pfrender ME, Gilbert D, Thomas WK, Tucker A, Oakley TH, Tokishita S, Aerts A, Arnold GJ, Basu MK: The ecoresponsive genome of Daphnia pulex. Science. 2011, 331 (6017): 555-561. 10.1126/science.1197761.PubMed CentralView ArticlePubMedGoogle Scholar
- Rubin GM, Yandell MD, Wortman JR, Gabor Miklos GL, Nelson CR, Hariharan IK, Fortini ME, Li PW, Apweiler R, Fleischmann W: Comparative genomics of the eukaryotes. Science. 2000, 287 (5461): 2204-2215. 10.1126/science.287.5461.2204.PubMed CentralView ArticlePubMedGoogle Scholar
- Stark A, Lin MF, Kheradpour P, Pedersen JS, Parts L, Carlson JW, Crosby MA, Rasmussen MD, Roy S, Deoras AN: Discovery of functional elements in 12 Drosophila genomes using evolutionary signatures. Nature. 2007, 450 (7167): 219-232. 10.1038/nature06340.PubMed CentralView ArticlePubMedGoogle Scholar
- Liti G, Louis EJ: Yeast evolution and comparative genomics. Annu Rev Microbiol. 2005, 59: 135-153. 10.1146/annurev.micro.59.030804.121400.View ArticlePubMedGoogle Scholar
- Kaplan N, Sasson O, Inbar U, Friedlich M, Fromer M, Fleischer H, Portugaly E, Linial N, Linial M: ProtoNet 4.0: a hierarchical classification of one million protein sequences. Nucleic Acids Res. 2005, 33 (Database): D216-218.PubMed CentralPubMedGoogle Scholar
- Sasson O, Vaaknin A, Fleischer H, Portugaly E, Bilu Y, Linial N, Linial M: ProtoNet: hierarchical classification of the protein space. Nucleic Acids Res. 2003, 31 (1): 348-352. 10.1093/nar/gkg096.PubMed CentralView ArticlePubMedGoogle Scholar
- Rappoport N, Karsenty S, Stern A, Linial M, Linial M: ProtoNet 6.0: organizing 10 million protein sequences in a compact hierarchical family tree. Nucleic Acids Research. 2011Google Scholar
- Kifer I, Sasson O, Linial M: Predicting fold novelty based on ProtoNet hierarchical classification. Bioinformatics. 2005, 21 (7): 1020-1027. 10.1093/bioinformatics/bti135.View ArticlePubMedGoogle Scholar
- Kaplan N, Linial M: ProtoBee: hierarchical classification and annotation of the honey bee proteome. Genome Res. 2006, 16 (11): 1431-1438. 10.1101/gr.4916306.PubMed CentralView ArticlePubMedGoogle Scholar
- Kaplan N, Vaaknin A, Linial M: PANDORA: keyword-based analysis of protein sets by integration of annotation sources. Nucleic Acids Res. 2003, 31 (19): 5617-5626. 10.1093/nar/gkg769.PubMed CentralView ArticlePubMedGoogle Scholar
- Rappoport N, Fromer M, Schweiger R, Linial M: PANDORA: analysis of protein and peptide sets through the hierarchical integration of annotations. Nucleic Acids Res. 2010, 38 (Web Server): W84-89. 10.1093/nar/gkq320.PubMed CentralView ArticlePubMedGoogle Scholar
- Kaplan N, Friedlich M, Fromer M, Linial M: A functional hierarchical organization of the protein sequence space. BMC Bioinformatics. 2004, 5: 196-10.1186/1471-2105-5-196.PubMed CentralView ArticlePubMedGoogle Scholar
- Baer CF, Miyamoto MM, Denver DR: Mutation rate variation in multicellular eukaryotes: causes and consequences. Nat Rev Genet. 2007, 8 (8): 619-631. 10.1038/nrg2158.View ArticlePubMedGoogle Scholar
- Wu CH, Apweiler R, Bairoch A, Natale DA, Barker WC, Boeckmann B, Ferro S, Gasteiger E, Huang H, Lopez R: The Universal Protein Resource (UniProt): an expanding universe of protein information. Nucleic Acids Res. 2006, 34 (Database): D187-191.PubMed CentralView ArticlePubMedGoogle Scholar
- Finn RD, Tate J, Mistry J, Coggill PC, Sammut SJ, Hotz HR, Ceric G, Forslund K, Eddy SR, Sonnhammer EL: The Pfam protein families database. Nucleic Acids Res. 2008, 36 (Database): D281-288.PubMed CentralView ArticlePubMedGoogle Scholar
- Hunter S, Apweiler R, Attwood TK, Bairoch A, Bateman A, Binns D, Bork P, Das U, Daugherty L, Duquenne L: InterPro: the integrative protein signature database. Nucleic Acids Res. 2009, 37 (Database): D211-215. 10.1093/nar/gkn785.PubMed CentralView ArticlePubMedGoogle Scholar
- Andreeva A, Howorth D, Brenner SE, Hubbard TJ, Chothia C, Murzin AG: SCOP database in 2004: refinements integrate structure and sequence family data. Nucleic Acids Res. 2004, 32 (Database): D226-229.PubMed CentralView ArticlePubMedGoogle Scholar
- Cuff AL, Sillitoe I, Lewis T, Redfern OC, Garratt R, Thornton J, Orengo CA: The CATH classification revisited--architectures reviewed and new ways to characterize structural divergence in superfamilies. Nucleic Acids Res. 2009, 37 (Database): D310-314. 10.1093/nar/gkn877.PubMed CentralView ArticlePubMedGoogle Scholar
- Larkin MA, Blackshields G, Brown NP, Chenna R, McGettigan PA, McWilliam H, Valentin F, Wallace IM, Wilm A, Lopez R: Clustal W and Clustal × version 2.0. Bioinformatics. 2007, 23 (21): 2947-2948. 10.1093/bioinformatics/btm404.View ArticlePubMedGoogle Scholar
- Schaack S, Choi E, Lynch M, Pritham EJ: DNA transposons and the role of recombination in mutation accumulation in Daphnia pulex. Genome Biol. 2010, 11 (4): R46-10.1186/gb-2010-11-4-r46.PubMed CentralView ArticlePubMedGoogle Scholar
- Croset V, Rytz R, Cummins SF, Budd A, Brawand D, Kaessmann H, Gibson TJ, Benton R: Ancient protostome origin of chemosensory ionotropic glutamate receptors and the evolution of insect taste and olfaction. PLoS Genet. 2010, 6 (8): e1001064-10.1371/journal.pgen.1001064.PubMed CentralView ArticlePubMedGoogle Scholar
- Mi H, Lazareva-Ulitsky B, Loo R, Kejariwal A, Vandergriff J, Rabkin S, Guo N, Muruganujan A, Doremieux O, Campbell MJ: The PANTHER database of protein families, subfamilies, functions and pathways. Nucleic Acids Res. 2005, 33 (Database): D284-288.PubMed CentralPubMedGoogle Scholar
- Finn RD, Mistry J, Tate J, Coggill P, Heger A, Pollington JE, Gavin OL, Gunasekaran P, Ceric G, Forslund K: The Pfam protein families database. Nucleic Acids Res. 2010, 38 (Database): D211-222. 10.1093/nar/gkp985.PubMed CentralView ArticlePubMedGoogle Scholar
- Portugaly E, Harel A, Linial N, Linial M: EVEREST: automatic identification and classification of protein domains in all protein sequences. BMC Bioinformatics. 2006, 7: 277-10.1186/1471-2105-7-277.PubMed CentralView ArticlePubMedGoogle Scholar
- Woollard A: Gene duplications and genetic redundancy in C. elegans. WormBook. 2005, 1-6.Google Scholar
- Penalva-Arana DC, Lynch M, Robertson HM: The chemoreceptor genes of the waterflea Daphnia pulex: many Grs but no Ors. BMC Evol Biol. 2009, 9: 79-10.1186/1471-2148-9-79.PubMed CentralView ArticlePubMedGoogle Scholar
- Sturm A, Cunningham P, Dean M: The ABC transporter gene family of Daphnia pulex. BMC Genomics. 2009, 10: 170-10.1186/1471-2164-10-170.PubMed CentralView ArticlePubMedGoogle Scholar
- Baldwin WS, Marko PB, Nelson DR: The cytochrome P450 (CYP) gene superfamily in Daphnia pulex. BMC Genomics. 2009, 10: 169-10.1186/1471-2164-10-169.PubMed CentralView ArticlePubMedGoogle Scholar
- O'Donovan C, Apweiler R: A guide to UniProt for protein scientists. Methods Mol Biol. 2011, 294: 25-35. 10.1007/978-1-60761-977-2_2.View ArticleGoogle Scholar
- Loewenstein Y, Portugaly E, Fromer M, Linial M: Efficient algorithms for accurate hierarchical clustering of huge datasets: tackling the entire protein space. Bioinformatics. 2008, 24 (13): i41-49. 10.1093/bioinformatics/btn174.PubMed CentralView ArticlePubMedGoogle Scholar
- Pearl F, Todd A, Sillitoe I, Dibley M, Redfern O, Lewis T, Bennett C, Marsden R, Grant A, Lee D: The CATH Domain Structure Database and related resources Gene3D and DHS provide comprehensive domain family information for genome analysis. Nucleic Acids Res. 2005, 33 (Database): D247-251.PubMed CentralPubMedGoogle Scholar
- Lo Conte L, Ailey B, Hubbard TJ, Brenner SE, Murzin AG, Chothia C: SCOP: a structural classification of proteins database. Nucleic Acids Res. 2000, 28 (1): 257-259. 10.1093/nar/28.1.257.PubMed CentralView ArticlePubMedGoogle Scholar
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