this leads to a dramatic increase in the number of predicted protein sequences , which serve as a starting point for structural , functional and phylogenomic studies . in such studies , high - throughput comparative analyses are often required to transfer information between organisms , for which the concept of orthology is crucial . the original definition by fitch ( 1 ) describes orthologs as genes that diverged through a speciation event , as opposed to paralogs , which diverged after a duplication event . this has been extended and refined by introducing the concepts of orthologous groups ( ogs ) ( 2 ) , in - paralogs and out - paralogs ( 3,4 ) . in practice , however , the identification and classification of homologous genes remain very difficult and rely on operational definitions . an enormous effort is being put into the development of different approaches to establish orthologous relationships between genes from different genomes . this includes several algorithms using the simple graph - based methods , including reciprocal - best - hit approach ( 5 ) , identification of best - hit triangles ( 2,68 ) and clustering - based approaches ( 911 ) as well as tree - based methods ( 1216 ) . in addition to the quality of the grouping of genes , the practical usability of ogs is determined by the ability to provide a robust functional annotation . thus , newer projects not only aggregate orthology information from various sources to allow comparison between methods but also aim to provide annotation tools ( 17,18 ) . nevertheless , evolutionary genealogy of genes : non - supervised ogs ( eggnog ) ( 19 ) and the cog / kog / arcog resources ( 2,6,7 ) are still the only databases providing explicit functional annotations for the ogs at different hierarchical levels , whereby the cog / kog resource is based on a robust manual expert annotation , which eggnog is using and automatically extending ( 19 ) . here , we describe the new features of the second version of eggnog , a resource that provides ogs from the three domains of life at several levels of resolution . eggnog v2 contains twice as many species and proteins as the previous version , additional hierarchical levels allowing higher resolution for a number of taxonomic groups , new annotation sources and an extended interface for an in - depth analysis of orthologous relationships . the automated procedure described previously ( 19 ) has been used to assemble proteins into ogs from 630 complete genomes ( 529 bacteria , 46 archaea and 55 eukaryotes ) . complete proteomes were downloaded from the refseq ( 20 ) , ensembl ( 21 ) , giardiadb ( 22 ) or tair ( 23 ) databases . this particular data set also forms the basis for string v8 ( 24 ) and stitch v2 ( 25 ) , allowing for easy integration across these databases . altogether , the protein data set covers 2 590 259 proteins of which 2 242 035 ( 87% ) were included in at least one of 224 847 ogs generated by eggnog . the growing number of species and proteins included in this release drastically increased the computational time . all - against - all similarity searches have therefore been performed using basic local alignment search tool ( blast ) ( 26 ) instead of the smith  waterman algorithm ( 27 ) . compared to the 4873 cogs and the 4850 kogs that are constructed across all three domains of life and for all eukaryotes , respectively , this procedure assembles additional proteins into nogs ( 440 359 proteins into 59 497 nogs and 181 427 into 17 845 eunogs ) . these complement the published cogs and kogs built respectively for 66 and seven species ( 6 ) , which are extended in eggnog to cover 630 species encompassing , respectively , 1 547 381 and 483 043 proteins . to provide a higher resolution of ogs in frequently used taxonomic groupings we updated the previously computed more fine - grained nogs at the level of fungi ( funogs ) , metazoans ( menogs ) , insects ( innogs ) , vertebrates ( venogs ) and mammals ( manogs ) and added groups for archaea ( arnogs ) , fishes ( finogs ) , rodents ( ronogs ) and primates ( prnogs ) . an important feature of eggnog is the functional annotations of the ogs . our original pipeline , providing functional descriptions for the nogs , is now complemented by an automatic inference of functional categories ( fcs ) which were taken from the cog database ( 2 ) . the 25 fcs available from the cog resource have been widely used to assess comparative genomics studies and will enable higher - order analyses of ogs identified in any data set . we use two complementary methods to infer fcs of ogs based on the 4617 cogs ( used for nogs and arnogs ) and 4381 kogs ( used for all other ogs ) . the first method uses support vector machines ( svm ) trained on the cogs and kogs to classify nogs into the 25 fcs based on feature vectors . two feature vectors were created for each og . one was built from functional information mapped onto the eggnog protein data set , including kegg pathways and modules ( 28 ) , go terms ( 29 ) , smart domains ( 30 ) , pfam domains ( 31 ) , uniprot keywords ( 32 ) and words from uniprot / refseq ( 20 ) description lines . the second feature vector includes also words from medline abstracts referring to a particular protein ( 24 ) . each attribute in the feature vector encodes the fraction of proteins in the group having the feature in question . the second method for assigning fcs makes use of the hierarchical structure of eggnog , namely that the same proteins can be assigned to ogs at several levels in the tree of life ( e.g. a kog and a menog ) . in case an fc could not be assigned to a nog by the svm method , we check if most of the proteins in the nog belong to a common functionally annotated cog or kog , in which case we transfer the fcs from the coarse - grained level ( cogs or kogs ) to the more fine - grained one ( e.g. arnogs or menogs ) . the assignment of an fc to a single nog is achieved on the basis of a coverage value determined by the occurrence of that fc ( via the proteins shared with the reference level ) in respect to the total number of proteins in that nog . an important feature of eggnog is the functional annotations of the ogs . our original pipeline , providing functional descriptions for the nogs , is now complemented by an automatic inference of functional categories ( fcs ) which were taken from the cog database ( 2 ) . the 25 fcs available from the cog resource have been widely used to assess comparative genomics studies and will enable higher - order analyses of ogs identified in any data set . we use two complementary methods to infer fcs of ogs based on the 4617 cogs ( used for nogs and arnogs ) and 4381 kogs ( used for all other ogs ) . the first method uses support vector machines ( svm ) trained on the cogs and kogs to classify nogs into the 25 fcs based on feature vectors . two feature vectors were created for each og . one was built from functional information mapped onto the eggnog protein data set , including kegg pathways and modules ( 28 ) , go terms ( 29 ) , smart domains ( 30 ) , pfam domains ( 31 ) , uniprot keywords ( 32 ) and words from uniprot / refseq ( 20 ) description lines . the second feature vector includes also words from medline abstracts referring to a particular protein ( 24 ) . each attribute in the feature vector the second method for assigning fcs makes use of the hierarchical structure of eggnog , namely that the same proteins can be assigned to ogs at several levels in the tree of life ( e.g. a kog and a menog ) . in case an fc could not be assigned to a nog by the svm method , we check if most of the proteins in the nog belong to a common functionally annotated cog or kog , in which case we transfer the fcs from the coarse - grained level ( cogs or kogs ) to the more fine - grained one ( e.g. arnogs or menogs ) . the assignment of an fc to a single nog is achieved on the basis of a coverage value determined by the occurrence of that fc ( via the proteins shared with the reference level ) in respect to the total number of proteins in that nog . in addition to providing functional annotations via description lines for many nogs ( 19 ) , we are now able to predict functional categories as well . at the universal level , our function annotation pipeline provides description lines for 14 956 ( 25% ) and an fc for 6262 ( 11% ) of the 59 497 coarse - grained nogs . at the eukaryotic level , 7566 eunogs ( 52% ) in addition , eggnog contains 137 782 more fine - grained ogs of which 100 750 ( 73% ) and 89 232 ( 65% ) have been annotated with a description line and an fc , respectively ( table 1 ) . table 1.annotation statistics at different taxonomic levelslevelog countdescription linefunctional categoriesannotated(%)annotated(%)cog + nog64 3704474 + 14 95630.22824 + 626214.1arnog9809414442.2454046.3kog + eunog22 6954288 + 756652.23514 + 412033.6funog9976566156.7577557.9menog22 69116 63673.313 49059.5innog8049503462.5581072.2venog21 35716 72278.313 29162.2finog13 674890365.1958070.1manog20 22216 95983.913 07564.7ronog14 03811 91884.910 54775.1prnog17 96614 77382.213 12473.0at the levels for cogs ( universal ) and kogs ( eukaryotes ) the additional automatically generated non - supervised orthologous groups nogs and eunogs , respectively , are separated . annotation statistics at different taxonomic levels at the levels for cogs ( universal ) and kogs ( eukaryotes ) the additional automatically generated non - supervised orthologous groups nogs and eunogs , respectively , are separated . this enables us to assign 2 242 035 of the 2 590 259 genes ( 87% of the genes in the analyzed genomes ) to an og and to provide at least a broad functional description or fc for 1 966 709 of them ( 78% of the genes that could be assigned to an og ) . the corresponding numbers for each set of ogs as well as for each individual genome are summarized in figure 1 . the stacked bar charts outside the tree show the proportion of genes from each genome that can be assigned to a functionally annotated orthologous group ( green ) , an unannotated orthologous group ( orange ) or no orthologous group ( gray ) . the length of each bar is proportional to the logarithm of the number of genes in the respective genome . the pie charts inside the tree show the fractions of orthologous groups at each level in the hierarchy that could be annotated with a functional category ( green for nogs , light green for extended cogs and kogs ) or not ( orange for nogs , light orange for extended cogs and kogs ) . the stacked bar charts outside the tree show the proportion of genes from each genome that can be assigned to a functionally annotated orthologous group ( green ) , an unannotated orthologous group ( orange ) or no orthologous group ( gray ) . the length of each bar is proportional to the logarithm of the number of genes in the respective genome . the pie charts inside the tree show the fractions of orthologous groups at each level in the hierarchy that could be annotated with a functional category ( green for nogs , light green for extended cogs and kogs ) or not ( orange for nogs , light orange for extended cogs and kogs ) . this figure was made using itol . to facilitate the in - depth analysis of the orthologous relationships within the groups of proteins , we now provide precomputed high - quality multiple sequence alignments ( msas ) and maximum - likelihood trees via the web interface ( figure 2 ) . the navigation tree at the top of the page allows the user to change the view to more coarse - grained orthologous groups , for example , the mammalian orthologous groups . the tab menu , shown here , enables several in - depth interactions with the new data ( i.e. msa or phylogenetic trees , here displayed with smart domains ) . the navigation tree at the top of the page allows the user to change the view to more coarse - grained orthologous groups , for example , the mammalian orthologous groups . the tab menu , shown here , enables several in - depth interactions with the new data ( i.e. msa or phylogenetic trees , here displayed with smart domains ) . numerous methods are available to build msas [ e.g. clustalw ( 33 ) , muscle ( 34 ) , mafft ( 35 ) and prank ( 36 ) ] but some programs appear to be more suitable for particular protein families than others ( 37 ) . thus , we applied a new approach , named automated quality improvement for multiple sequence alignments ( aqua ) ( muller et al . , submitted for publication ) , which combines existing tools to deliver high - quality msas . one hundred bootstrap replicates were created from the msa using the seqboot program from the phylip package ( 38 ) . following this , phyml ( 39 ) was used to find the maximum - likelihood tree for each of the 100 bootstrap replicates and for the original alignment using default parameters . finally , a consensus tree was constructed , using the consense program from the phylip package . we used readseq ( 40 ) to convert between the different sequence file formats used by those programs . to facilitate the in - depth analysis of the orthologous relationships within the groups of proteins , we now provide precomputed high - quality multiple sequence alignments ( msas ) and maximum - likelihood trees via the web interface ( figure 2 ) . the navigation tree at the top of the page allows the user to change the view to more coarse - grained orthologous groups , for example , the mammalian orthologous groups . the tab menu , shown here , enables several in - depth interactions with the new data ( i.e. msa or phylogenetic trees , here displayed with smart domains ) . the navigation tree at the top of the page allows the user to change the view to more coarse - grained orthologous groups , for example , the mammalian orthologous groups . the tab menu , shown here , enables several in - depth interactions with the new data ( i.e. msa or phylogenetic trees , here displayed with smart domains ) . numerous methods are available to build msas [ e.g. clustalw ( 33 ) , muscle ( 34 ) , mafft ( 35 ) and prank ( 36 ) ] but some programs appear to be more suitable for particular protein families than others ( 37 ) . thus , we applied a new approach , named automated quality improvement for multiple sequence alignments ( aqua ) ( muller et al . , submitted for publication ) , which combines existing tools to deliver high - quality msas . one hundred bootstrap replicates were created from the msa using the seqboot program from the phylip package ( 38 ) . following this , phyml ( 39 ) was used to find the maximum - likelihood tree for each of the 100 bootstrap replicates and for the original alignment using default parameters . finally , a consensus tree was constructed , using the consense program from the phylip package . we used readseq ( 40 ) to convert between the different sequence file formats used by those programs . the eggnog resource can be queried via a web interface ; data can be downloaded under the creative commons attribution 3.0 license at : http://eggnog.embl.de or via ftp at : ftp://eggnog.embl.de / eggnog/2.0/. gene and protein names , database identifiers , amino acid sequences , or og names can be used to query the database . as a default the user can navigate among the different levels of orthology using an available guide - tree of organisms to find the desired balance between phylogenetic coverage and functional specificity within our hierarchy of ogs . through the new interface , users can access different information panels encompassing the detailed list of proteins belonging to a particular og as well as the corresponding msa and phylogenetic tree . the msa can be interactively displayed using the jalview applet ( 41 ) or downloaded in fasta format . the phylogenetic trees are accessed through a dedicated itol ( 42 ) viewer together with mapped pfam and smart domains , via the atv program applet ( 43 ) , or can be downloaded in newick format . with 630 genomes covered , an increased og hierarchy , and a high coverage of newly categorized functional annotation , the new version of eggnog is one of the most comprehensive and complete resources for deciphering the orthologous relationships between proteins from various species . the changes and improvements in the interface and the availability of the ogs for download will not only facilitate the daily use of the database , but also the integration of eggnog in high - throughput comparative genomics studies . our future plans include the addition of more complete genomes and development of a more scalable and flexible pipeline for generating the groups . embl , the european commission programme , eurasnet eu [ grant lshg - ct-2005 - 518238 ( fp6 ) , impact 213037 ( fp7 ) ] ; the novo nordisk foundation center for protein research , the swiss institute of bioinformatics ; and the university of zurich ( partial , through its research priority program systems biology and functional genomics ) . this work was supported in part by the bundesministerium fuer bildung und forschung ( nationales genomforschungsnetz foerderkennzeichen 01gs08169 ) .