A functional comparative genome analysis is essential to understand the mechanisms underlying bacterial evolution and adaptation. Detection of functional orthologs using standard global sequence similarity methods faces several problems; the need for defining arbitrary acceptance thresholds for similarity and alignment length, lateral gene acquisition and the high computational cost for finding bi-directional best matches at a large scale. We investigated the use of protein domain architectures for large scale functional comparative analysis as an alternative method. The performance of both approaches was assessed through functional comparison of 446 bacterial genomes sampled at different taxonomic levels. We show that protein domain architectures provide a fast and efficient alternative to methods based on sequence similarity to identify groups of functionally equivalent proteins within and across taxonomic boundaries, and it is suitable for large scale comparative analysis. Running both methods in parallel pinpoints potential functional adaptations that may add to bacterial fitness. SB methods may fail to group them hampering a functional comparison. This limitation becomes even more critical when comparing either phylogenetically distant genomes or gene sequences that were acquired with horizontal gene transfer events. Recent technological advancements are resulting in thousands of organisms and billions of proteins being sequenced7 which increases the need of methods able to perform comparisons at the larger scales.Comparative analysis relies on the identification of sets of orthologous and paralogous genes and subsequent transfer of function to the encoding proteins. Technically orthologs are defined as best bi-directional hits (BBH) obtained via pairwise sequence comparison among multiple species and thus exploits sequence similarity for functional grouping. Sequence similarity-based (SB) methods present a number of shortcomings. First, a generalized minimal alignment length and similarity cut-off need to be arbitrarily selected for all, which may hamper proper functional grouping. Second, sequence and function might differ across evolutionary scales. Protein sequences change faster than protein structure and proteins with same function but with low sequence similarity have been identified10 and have been used to perform comparative analyses ofEscherichia coli9,Pseudomonas10,Streptococcus11 and for protein functional annotation13. A protein domain architecture describes the arrangement of domains contained in a protein and is exemplified in14. The concept of using the domain architecture to precisely describe the extent of functional equivalence is exemplified in16.To overcome these bottlenecks, protein domains have been suggested as an alternative for defining groups of functionally equivalent proteins18. This lead to the use of protein domain architectures to classify and identify evolutionarily related proteins and to detect homologs even across evolutionarily distant species22. Structural information encoded in domain architectures has also been deployed to accelerate sequence search methods and to provide better homology detection. Examples are CDART23 which finds homologous proteins across significant evolutionary distances using domain profiles rather than direct sequence similarity, or DeltaBlast24 where a database of pre-constructed position-specific score matrix is queried before searching a protein-sequence database. Considering protein domain content, order, recurrence and position has been shown to increase the accuracy of protein function prediction25 and has led to the development of tools for protein functional annotation, such as UniProt-DAAC26 which uses domain architecture comparison and classification for the automatic functional annotation of large protein sets. The systematic assessment and use of domain architectures is enabled by databases containing protein domain information such as UniProt27, Pfam28, TIGRFAMs29, InterPro30, SMART31 and PROSITE32, that also provide graphical view of domain architectures.Domain architectures have been shown to be preserved at large phylogenetic distances both in prokaryotes and eukaryotesi) the retrieved number of singletons (i.e. clusters containing only one protein) andii) the characteristics of the inferred pan- and core-genome size considering a selection of bacterial genomes (both gram positive and negative) sampled at different taxonomic levels . We show that the DAB approach provides a fast and efficient alternative to SB methods to identify groups of functionally equivalent/related proteins for comparative genome analysis and that the functional pan-genome is more closed in comparison to the sequence based pan-genome. DAB approaches can complement standardly applied sequence similarity methods and can pinpoint potential functional adaptations.Building on these observations we aim at exploring the potential of domain architecture-based (DAB) methods for large scale functional comparative analysis by comparing functionally equivalent sets of proteins, defined using domain architectures, with standard clusters of orthogonal proteins obtained with SB methods. We compared the SB and DAB approach by analysingListeria monocytogenes andHelicobacter pylori), three genera , one family (Enterobacteriaceae), one order , and one phylum (Cyanobacteria) were selected. For each, 60 genome sequences were considered, except forL. monocytogenes for which only 26 complete genome sequences were available. Maximal diversity among genome sequences was ensured by sampling divergent species (when possible) at each taxonomic level. Genome sequences were retrieved from the European Nucleotide Archive database (www.ebi.ac.uk/ena). A full list of genomes analyzed is available in theData availability section.Bacterial species were chosen on the basis of the availability of fully sequenced genomes in the public domain: two species (de novo re-annotated using the SAPP framework (1.0.0)10. In particular, the FASTA2RDF, GeneCaller 33) and InterPro (implementing interproscan-5.17-56.0)34) modules were used to handle, re-annotate the genome sequences and store the results in the RDF data model. This resulted in 446 annotated genomes (7 × 60 genomes + 1 × 26 genomes). For each annotation step the provenance information  was stored together with annotation information in a graph database (RDF-model) and can be reproduced through the SAPP framework (http://semantics.systemsbiology.nl).To avoid bias due to different algorithms used for the annotation of the original deposited genome sequences, all genomes were28, TIGRFAMs29 and InterPro30 identifiers were extracted through SPARQL querying of the graph database and domain architectures were retrieved for each protein individually. InterPro aggregates protein domain signatures from different databases. Here no pruning for redundancies has been done. Identification of domains was done using the intrinsic InterPro cut-off that represents in each case the e-values and the scoring systems of the member databases30. The domain starting position was used to assess relative position in the case of overlapping domains; alphabetic ordering was used to order domains with the same starting position or when the distance between the starting position of overlapping domains was < 3 amino acids.The positions (start and end on the protein sequence) of domains having PfamLabels indicating N-C terminal order of identified domains were assigned to each protein using the starting position of the domains: the same labels were assigned to proteins sharing the same domain architecture.−5 and -max_target_seqs of 105. OrthaGogue (1.0.3)35 combined with MCL (14-137)36 was used to identify protein clusters on the base of sequence similarity.To make a direct comparison possible only protein sequences containing at least one protein domain signature were considered for analysis. BBH were obtained using Blastp (2.2.28+) with an E-value cutoff of 10Domain architecture based clusters were built by clustering proteins with the same labels using bash terminal commands . The number of proteins sharing a given domain architecture in each genome was stored in a 446 × 21054 (genomes × domain architectures) matrix; from this a binarized presence-absence matrix was obtained and used solely for principal component analysis.3 random genome ordering permutations and themicropan R package37.A Heaps’ law model was fit to the abundance matrices using 5 × 1010, used for genome handling and annotation, is available athttp://semantics.systemsbiology.nl. Matrix manipulations and multivariate analysis were performed using the R software (3.2.2).SAPP, a Semantic Annotation Pipeline with Provenance which stores results in a graph databaseListeria monocytogenes for which only 26 complete genomes were publicly available. To facilitate the comparison between DAB and SB clusters only protein sequences that contained at least one domain were considered. On average, 85% of the protein sequences contain at least one domain from the InterPro database (seeE. coli). Since the overall results were the same for gram negative and gram positive bacteria, we will show and comment only on results for the latter. Results obtained for gram negative bacteria are shown in theData availability section.SB and DAB approaches were compared by considering eight sets of genome sequences sampled at different taxonomic levels, from species to order, preserving phylogenetic diversity see. Each sebase see. Values A standard BBH workflow was used to obtain SB protein clusters for the eight sets. We started by calculating the total number of clusters, corresponding to the pan-genome size, as shown ini.e. present in all genomes) and the number of singletons decreased with evolutionary distance (seeH. pylori andL. monocytogenes respectively) to 0.05 and 0.06 when considering members of the same order  and phylum (Cyanobacteria) respectively. A similar pattern is observed when directly comparing the sizes of the pan- and core- genomes of the sampled genomes. Within the gram negative bacteria this ratio ranges from 0.69 for members of the same species (H. pylori) to 0.05 for members of the same phylum (Cyanobacteria) with intermediate values (0.12) for sequences from the same genus (Pseudomonas).The ratio between the size of the core-genome  the ratio was 5.09 and 4.30, respectively, while for member of the same order  and phylum (Cyanobacteria) it was 0.55 and 0.009 respectively. Similarly, also the ratio between the size of the core- and pan-genome decreases as higher taxonomic levels are considered, ranging from 0.54 forH. pylori to 0.04 for Cyanobacteria.Similar to what has been observed in the SB case we observed a decrease of the ratio between the size of the core genome and the number of singletons when higher taxonomic levels are considered. For organisms of the same species (H. pylori) to 1680 (inPseudomonas) corresponding to 50% and 25% of the pan-genome. This indicates that results of SB and DAB clustering tend to be more similar when working at closer phylogenetic distances. However, more complicated cases occur when proteins in a single SB cluster are assigned to various DAB clusters including singletons and vice versa. An overview of the possible mismatches between SB and DAB clusters is inWe compared the clusters obtained using both approaches and the proteins assigned to them. The number of one-to-one relationships (indicating a complete agreement) between SB and DAB clusters is indicated inL. monocytogenes we found 378 1d → 1s DAB cluster mismatches, (Fors →Nd case).Proteins contained in a single DAB cluster but assigned to multiple SB clusters contain mostly ABC transporters-like (PF00005) or Major Facilitator Superfamily  domains. This is not surprising considering that such generic functions are usually associated with a high sequence diversity. Conversely, ABC transporters are found in multiple DAB clusters. However, many of them are grouped into a single SB cluster with ATPase domain containing proteins (1family domain (PF00657 and PF13472 respectively) and those architectures were often seen clustered using a SB approach. However, architectures containing both domains were also identified, pointing to a degree of functional difference as a result of convergent or divergent evolution. Still, the corresponding sequences remain similar enough as to be indistinguishable when a SB approach is used. For SB clustering we also observed the case of identical protein sequences not clustered together, probably because of the tie breaking implementation when BBH are scored.We observed distinct architectures with one of two very similar domains, the GDSL-like Lipase/Acylhydrolase and the GDSL-like Lipase/AcylhydrolaseIn all cases we found the size of both the pan- and the core-genome to be larger when a SB approach is used to identify gene clusters and SB approaches lead to a larger number of singletons than DAB ones. This indicates that DAB clusters are assigned to several SB clusters, many of them consisting of just one protein.H. pylori andL. monocytogenes respectively) to 0.19 and 0.40 when considering organisms of a higher taxonomic level .When going from species to phylum level, the ratio between the number of DAB and SB singletons changes from 0.48 and 0.41 , whileα > 1 indicates a closed one; theα values are given inα values obtained using DAB clusters (αDAB) are systematically closer to one than theαSB obtained with the standard sequence similarity approach.We investigated the predicted size of the pan-genome upon addition of new sequences. Heaps’ law regression can be used to estimate whether the pan-genome is open or closedDABα value retrieved forL. monocytogenes is strikingly low. Heaps law regression relies on the selected genomes providing a uniform sampling of selected taxon, here species. Analysis of the domain content of the selected genomes shows a divergent behaviour of strain LA111 (genome id GCA_000382925-1). This behaviour is clear inL. monocytogenes group. Removal of these outlier leads toDABα = 1.04 andSBα = 0.64, which emphasizes the need for uniform sampling prior to Heaps regression analysis.TheH. pylori andL. monocytogenes) is reflected by the compact clustering, while phylogenetically more distant genomes appear scattered in the functional space defined by the principal components.DAB clusters can be labelled by their domain architecture and since this is a formal description of functional equivalence, results of independently obtained analyses can be combined.We have shown that domain architecture-based methods can be used as an effective approach to identify clusters of functionally equivalent proteins, leading to results similar to those obtained by classical methods based on sequence similarity.39 and MultiPARANOID40 are available that implement different algorithm solutions to identify homologous sequences; however, despite different implementations, they all rely on sequence similarity as a proxy for functional equivalence. Here we considered SB methods as a golden standard for functional comparative genomics, especially when organisms within close evolutionary proximity are considered. Our aim was to investigate whether using domain architectures instead of sequence similarity alone would yield similar results, thereby justifying their use for large scale functional genome comparisons. Regarding domain architectures, we have explored different alternatives, as we have seen that the chosen database or set of reference domains plays a critical role; for example, the low coverage of TIGRFAM prevents the obtaining of reasonable clusters. The DAB approach takes advantage of the large computational effort that has already been devoted to the identification and definition of protein domains in dedicated databases such as Pfam. Protein domain models are built using large scale sequence comparisons which is an extremely computationally intensive task. However, once the domain models are defined, mining a sequence for domain occurrences is much less demanding task. Indeed, the task with the higher computational load (the definition of the domains) is performed only once and results can be stored and re-used for further analysis. This provides an effective scalable approach for large scale functional comparisons which by and large is independent of phylogenetic distances between species.To assess whether DAB results were consistent with those of SB methods we chosen OrthaGogue as a representative of the latter class. Several tools such as COGNITOR30. Using Inter-Pro for DAB clustering would require taking into account the hierarchy of protein families and domains: however, this would pose challenges of its own and would require discrimination of the functional equivalence of different signatures within the same hierarchy.The chosen set of domain models and the database used as a reference greatly impact the results. InterPro aggregates protein domain signatures from different databases, which leads to redundancy of the domain models. This redundancy causes overlaps between the entries and an increase of the granularity of the clusters retrieved: this can bias downwards the size of the pan-genome and upwards the size of the core- genome, as shown in41.Another source of redundancy are functionally equivalent domains from distantly related sequences. Pfam represents this through related families, termed clans, where relationships may be defined by similarity of sequence, structure or profile-HMM. Clans might contain functionally equivalent domains, however it is not clear whether this is always the case as the criteria for clan definition includes functional similarity but not functional equivalenceMembers of a clan have diverging sequences and very often SB approaches would recognize the evolutionary distance between the sequences and group them in different clusters. If we were to assume that members of a clan are functionally equivalent and collect them in the same DA cluster, we will have a higher number of cases where a single DA cluster is split in multiple sequence clusters 1d→Ns. Also there would be higher number of cases of sequence clusters with the same DA but no exactly matching the DA clusters (1s→1d cases).In many cases a one-to-one correspondence could be established between DAB and SB clusters indicating that often the sequence can be used as a proxy for function. At first this may seem a trivial result but it has a profound implication: domain model databases (in this case Pfam) contain enough information, encoded by known domain models, to represent the quasi totality of biological function encoded in the bacterial sequences analyzed here. However, it is important to stress that the comparisons have been performed considering sequences with known domains, representing currently around 85% of the genome coding content, a number that will only increase in the future.42. With the DAB method they are formally included and often semantically linked to one or more domains of known function.A significant advantage of the DAB method over the SB method is that the domain architecture captured within a cluster can be used as a formal description of the function. Currently, more than 20% of all separable domains in the Pfam database, are so-called domains of unknown function (DUFs). Despite this, in bacterial species they are often essentialThe starting position of the domains was used to generate labels indicating N-C terminal order of identified domains. The labels were used only for clustering as proteins sharing the same labels were assigned to the same clusters. Choosing instead the mid-point or the C-terminal position could affect the labeling but not the obtained clusters.O(n), withn the number of genomes to be analyzed. SB methods that create orthologous groups require more memory and time as they come at anO(2n) computational cost. Other SB approaches, such as COGNITOR, reduce the computational costs toO(n) by using pre-computed databases. In this respect, the DAB approach is similar to the approach implemented in COGNITOR, by searching against existing databases of domains architectures. In this way the DAB approach leverages the extensive amount of work already put into defining domain families.A content-wise formal labeling of DAB clusters makes a seamless integration of multiple independently performed DAB analysis possible. This allows for a comparison of potential functionomes across taxonomic boundaries, as presented inThe bimodal shape of the distributions presented inA measure of the impact of vertical descent and horizontal gene transfer is provided by the ratio between the core- and pan- genome sizes. The number of singletons provides a measure of the number of genes horizontally acquired from species outside the considered group.43. This is likely to occur given that they would have similar physiological characteristics, hence they would tend to occupy a similar niche or, at least, more similar than when comparing species from different taxa. As the origin of the horizontally acquired genes may vary for each organism, an SB approach will correctly recognize the heterologous origin of the corresponding sequences and those will be assigned to singletons. However, the probabilistic hidden Markov models used for domain recognition are better at recognizing the functional similarity of the considered sequences and clusters them together.Two of the most prominent differences between the two approaches are the number of retrieved singletons and the core- to pan-genome size ratio. Multiple members of the same taxon might acquire the same function through horizontal gene transferα inαDAB >αSB, meaning that the sequence diversity is larger than the functional diversity: upon addition of new genomes to the sample the rate of addition of new sequence clusters appears higher than the rate of addition of new functions.Another indication of the relative impact of horizontal and vertical gene acquisition events is provided by the openness or closedness of the genome. Values for the decay parameter45. Partial domain hits might arise as a result of alignment, annotation and sequence assembly artefacts. To reduce the number of partial domain hits additional pruning could be implemented to distinguish these cases. However, this is an open problem that requires caution as it could influence the functional capacity of an organism and clustering approaches using DA.We have shown that domain architecture-based methods can be used as an effective approach to identify clusters of functionally equivalent proteins, leading to results similar to those obtained by classical methods based on sequence similarity. However, whether DAB methods are more accurate than SB methods to assess functional equivalence will require further analysis. In this light, results of functional conservation for both approaches could be compared in terms of GO similarity and/or EC number45.The performance of DAB methods may be sub-optimal when dealing with newly sequenced genomes that are not yet well-characterized enough to have all of their domains present in domain databases, since DAB methods will be unable to handle unknown architectural types. Around 15% of the genome coding content corresponds to sequences with no identified protein domains. DAB approaches can be complemented with SB methods to consider these sequences or even protein sequences with low domain coverage, possible indicating the location of protein domains yet to be identified. Since DAB methods rely on the constant upgrading of public resources like UniProt and Pfam databases, an initial assessment of domain coverage appears as a sine qua non condition for application of these methods. DAB approaches could be used to assess the consistency of existing orthologous groups in terms of their domain architectures, at least when domain architectures are expected to be completely known in advance . For other purposes, such as the discovery of a new phyla of cellular life that contains radically different domain architectures, global similarity methods may be preferredAs protein domain databases have evolved to the point where DAB and SB approaches produce similar results in closely related organisms, the DAB approach provides a fast and efficient alternative to SB methods to identify groups of functionally equivalent/related proteins for comparative genome analysis. The lower computational cost of DAB approaches makes them the better choice for large scale comparisons involving hundreds of genomes.Highly redundant databases, such as InterPro, are best suited for domain based protein annotation, but are not effective for DAB clustering if the goal is to identify clusters of functionally equivalent proteins. To enable DAB approaches for highly structured databases, such as Inter-Pro, the hierarchy of protein families and domains within has to be explicitly considered. Currently Pfam is for this task a better alternative.Differences between DAB and SB approaches increase when the goal is to study bacterial groups spanning wider evolutionary distances. The functional pan-genome is more closed in comparison to the sequence based pan-genome. Both methods have a distinct approach towards horizontally transferred genes, and the DAB approach has the potential to detect functional equivalence even when sequence similarities are low.Complementing the standardly applied sequence similarity methods with a DAB approach pinpoints potential functional protein adaptations that may add to the overall fitness.The data referenced by this article are under copyright with the following copyright statement: Copyright: © 2017 Koehorst JJ et al.Data associated with the article are available under the terms of the Creative Commons Zero "No rights reserved" data waiver (CC0 1.0 Public domain dedication).List of genomes used for the analysis at different phylogenetic levels. The genomes are grouped per taxonomic lineage used in this study.BacillusGCA_000523045 Bacillus subtilis BEST7003GCA_000782835 Bacillus subtilisGCA_000832885 Bacillus thuringiensis str. Al HakamGCA_000473245 Bacillus infantis NRRL B-14911GCA_000832585 Bacillus anthracisGCA_000590455 Bacillus pumilusGCA_000831065 Bacillus bombysepticusGCA_000833275 Bacillus anthracis str. Turkey32GCA_000952895 Bacillus sp.GCA_000259365 Bacillus sp. JSGCA_000143605 Bacillus cereus biovar anthracis str. CIGCA_000186745 Bacillus subtilis BSn5GCA_000987825 Bacillus methylotrophicusGCA_000706725 Bacillus lehensis G1GCA_000815145 Bacillus sp. Pc3GCA_000496285 Bacillus toyonensis BCT-7112GCA_000742855 Bacillus mycoidesGCA_000169195 Bacillus coagulans 36D1GCA_000835145 Bacillus amyloliquefaciens KHG19GCA_000321395 Bacillus subtilis subsp. subtilis str. BSP1GCA_000009045 Bacillus subtilis subsp. subtilis str. 168GCA_000293765 Bacillus subtilis QB928GCA_000025805 Bacillus megaterium DSM 319GCA_000747345 Bacillus sp. X1(2014)GCA_000833005 Bacillus amyloliquefaciensGCA_000408885 Bacillus paralicheniformis ATCC 9945aGCA_000742895 Bacillus anthracis str. VollumGCA_000829195 Bacillus sp. OxB-1GCA_000800825 Bacillus sp. WP8GCA_000706705 Bacillus subtilis subsp. subtilis str. OH 131.1GCA_000338735 Bacillus subtilis XF-1GCA_000832445 Bacillus anthracisGCA_000747335 Bacillus anthracisGCA_000008505 Bacillus thuringiensis serovar konkukian str. 97-27GCA_000195515 Bacillus amyloliquefaciens TA208GCA_000209795 Bacillus subtilis subsp. natto BEST195GCA_000017425 Bacillus cytotoxicus NVH 391-98GCA_000877815 Bacillus sp. YP1GCA_000177235 Bacillus cellulosilyticus DSM 2522GCA_000344745 Bacillus subtilis subsp. subtilis 6051-HGWGCA_000227485 Bacillus subtilis subsp. subtilis str. RO-NN-1GCA_000494835 Bacillus amyloliquefaciens CC178GCA_000011145 Bacillus halodurans C-125GCA_000724485 Bacillus methanolicus MGA3GCA_000018825 Bacillus weihenstephanensis KBAB4GCA_000005825 Bacillus pseudofirmus OF4GCA_000017885 Bacillus pumilus SAFR-032GCA_000583065 Bacillus methylotrophicus Trigo-Cor1448GCA_000349795 Bacillus subtilis subsp. subtilis str. BAB-1GCA_000306745 Bacillus thuringiensis Bt407GCA_000011645 Bacillus licheniformis DSM 13 = ATCC 14580GCA_000497485 Bacillus subtilis PY79GCA_000009825 Bacillus clausii KSM-K16GCA_000227465 Bacillus subtilis subsp. spizizenii TU-B-10GCA_000971925 Bacillus subtilis KCTC 1028GCA_000972245 Bacillus endophyticusGCA_000242895 Bacillus sp. 1NLA3EGCA_000832485 Bacillus thuringiensisGCA_000830075 Bacillus atrophaeusGCA_000146565 Bacillus subtilis subsp. spizizenii str. W23CorynebacterialesGCA_000016005 Mycobacterium sp. JLSGCA_000758405 Mycobacterium abscessus subsp. bolletiiGCA_000283295 Mycobacterium smegmatis str. MC2 155GCA_001021045 Corynebacterium testudinorisGCA_000341345 Corynebacterium halotolerans YIM 70093 = DSM 44683GCA_000525655 Corynebacterium falsenii DSM 44353GCA_000255195 Corynebacterium diphtheriae HC04GCA_000523235 Nocardia nova SH22aGCA_000026685 Mycobacterium leprae Br4923GCA_000980815 Corynebacterium camporealensisGCA_000328565 Mycobacterium sp. JS623GCA_000015405 Mycobacterium sp. KMSGCA_000987865 [Brevibacterium] flavumGCA_001020985 Corynebacterium mustelaeGCA_001021065 Corynebacterium uterequiGCA_000177535 Corynebacterium resistens DSM 45100GCA_000011305 Corynebacterium efficiens YS-314GCA_000835265 Mycobacterium avium subsp. paratuberculosisGCA_000739455 Corynebacterium imitansGCA_000831265 Mycobacterium kansasii 662GCA_000819445 Corynebacterium humireducens NBRC 106098 = DSM 45392GCA_000770235 Mycobacterium avium subsp. aviumGCA_000980835 Corynebacterium kutscheriGCA_000010225 Corynebacterium glutamicum RGCA_000590555 Corynebacterium argentoratense DSM 44202GCA_000247715 Gordonia polyisoprenivorans VH2GCA_000416365 Mycobacterium sp. VKM Ac-1817DGCA_000418365 Corynebacterium terpenotabidum Y-11GCA_000092225 Tsukamurella paurometabola DSM 20162GCA_000442645 Corynebacterium maris DSM 45190GCA_000277125 Mycobacterium intracellulare ATCC 13950GCA_000196695 Rhodococcus equi 103SGCA_000828995 Mycobacterium tuberculosis str. KuronoGCA_000006605 Corynebacterium jeikeium K411GCA_000022905 Corynebacterium aurimucosumGCA_001021025 Corynebacterium epidermidicanisGCA_000010105 Rhodococcus erythropolis PR4GCA_000092825 Segniliparus rotundus DSM 44985GCA_000758245 Mycobacterium bovisGCA_000184435 Mycobacterium gilvum Spyr1GCA_000829075 Mycobacterium avium subsp. hominissuis TH135GCA_000214175 Amycolicicoccus subflavus DQS3-9A1GCA_000769635 Corynebacterium ulceransGCA_000626675 Corynebacterium glyciniphilum AJ 3170GCA_001026945 Corynebacterium pseudotuberculosisGCA_000026445 Mycobacterium liflandii 128FXTGCA_000013925 Mycobacterium ulcerans Agy99GCA_000954115 Rhodococcus sp. B7740GCA_000143885 Gordonia sp. KTR9GCA_000014565 Rhodococcus jostii RHA1GCA_000179395 Corynebacterium variabile DSM 44702GCA_000732945 Corynebacterium atypicumGCA_000723425 Mycobacterium marinum E11GCA_000230895 Mycobacterium rhodesiae NBB3GCA_000344785 Corynebacterium callunae DSM 20147GCA_000010805 Rhodococcus opacus B4GCA_000982715 Rhodococcus aetherivoransGCA_000298095 Mycobacterium indicus pranii MTCC 9506GCA_000833575 Corynebacterium singulareGCA_000023145 Corynebacterium kroppenstedtii DSM 44385CyanobacteriaGCA_000317085 Synechococcus sp. PCC 7502GCA_000011385 Gloeobacter violaceus PCC 7421GCA_000014585 Synechococcus sp. CC9311GCA_000012465 Prochlorococcus marinus str. NATL2AGCA_000737535 Synechococcus sp. KORDI-100GCA_000013205 Synechococcus sp. JA-3-3AbGCA_000021825 Cyanothece sp. PCC 7424GCA_000063505 Synechococcus sp. WH 7803GCA_000022045 Cyanothece sp. PCC 7425GCA_000316575 Calothrix sp. PCC 7507GCA_000316685 Synechococcus sp. PCC 6312GCA_000012505 Synechococcus sp. CC9902GCA_000317475 Oscillatoria nigro-viridis PCC 7112GCA_000063525 Synechococcus sp. RCC307GCA_000317695 Anabaena cylindrica PCC 7122GCA_000014265 Trichodesmium erythraeum IMS101GCA_000817325 Synechococcus sp. UTEX 2973GCA_000737575 Synechococcus sp. KORDI-49GCA_000317125 Chroococcidiopsis thermalis PCC 7203GCA_000017845 Cyanothece sp. ATCC 51142GCA_000020025 Nostoc punctiforme PCC 73102GCA_000018105 Acaryochloris marina MBIC11017GCA_000757865 Prochlorococcus sp. MIT 0801GCA_000317045 Geitlerinema sp. PCC 7407GCA_000012625 Synechococcus sp. CC9605GCA_000737595 Synechococcus sp. KORDI-52GCA_000317635 Halothece sp. PCC 7418GCA_000025125 Candidatus Atelocyanobacterium thalassa isolate ALOHAGCA_000010625 Microcystis aeruginosa NIES-843GCA_000317065 Pseudanabaena sp. PCC 7367GCA_000312705 Anabaena sp. 90GCA_000316515 Cyanobium gracile PCC 6307GCA_000316605 Leptolyngbya sp. PCC 7376GCA_000317025 Pleurocapsa sp. PCC 7327GCA_000009705 Nostoc sp. PCC 7120GCA_000013225 Synechococcus sp. JA-2-3B’a(2–13)GCA_000757845 Prochlorococcus sp. MIT 0604GCA_000317515 Microcoleus sp. PCC 7113GCA_000734895 Calothrix sp. 336/3GCA_000007925 Prochlorococcus marinus subsp. marinus str. CCMP1375GCA_000021805 Cyanothece sp. PCC 8801GCA_000019485 Synechococcus sp. PCC 7002GCA_000317655 Cyanobacterium stanieri PCC 7202GCA_000316625 Nostoc sp. PCC 7107GCA_000011465 Prochlorococcus marinus subsp. pastoris str. CCMP1986GCA_000316665 Rivularia sp. PCC 7116GCA_000317105 Oscillatoria acuminata PCC 6304GCA_000317435 Calothrix sp. PCC 6303GCA_000317555 Gloeocapsa sp. PCC 7428GCA_000478825 Synechocystis sp. PCC 6714GCA_000204075 Anabaena variabilis ATCC 29413GCA_000317575 Stanieria cyanosphaera PCC 7437GCA_000161795 Synechococcus sp. WH 8109GCA_000011345 Thermosynechococcus elongatus BP-1GCA_000317615 Dactylococcopsis salina PCC 8305GCA_000284135 Synechocystis sp. PCC 6803 substr. GT-IGCA_000024045 Cyanothece sp. PCC 8802GCA_000317495 Crinalium epipsammum PCC 9333GCA_000317675 Cyanobacterium aponinum PCC 10605GCA_000012525 Synechococcus elongatus PCC 7942EnterobacteriaceaeGCA_000259175 Providencia stuartii MRSN 2154GCA_000214805 Serratia sp. AS13GCA_000330865 Serratia marcescens FGI94GCA_001010285 Photorhabdus temperata subsp. thracensisGCA_000364725 Candidatus Moranella endobia PCVALGCA_000521525 Buchnera aphidicola str. USDA (Myzus persicae)GCA_000517405 Candidatus Sodalis pierantonius str. SOPEGCA_000012005 Shigella dysenteriae Sd197GCA_000196475 Photorhabdus asymbioticaGCA_000750295 Salmonella enterica subsp. enterica serovar EnteritidisGCA_000007885 Yersinia pestis biovar Microtus str. 91001GCA_000739495 Klebsiella pneumoniaeGCA_000252995 Salmonella bongori NCTC 12419GCA_000270125 Pantoea ananatis AJ13355GCA_000215745 Enterobacter aerogenes KCTC 2190GCA_000092525 Shigella sonnei Ss046GCA_000020865 Edwardsiella tarda EIB202GCA_000023545 Dickeya dadantii Ech703GCA_000238975 Serratia symbiotica str. ’Cinara cedri’GCA_000975245 Serratia liquefaciensGCA_000006645 Yersinia pestis KIM10+GCA_000224675 Enterobacter asburiae LF7aGCA_000007405 Shigella flexneri 2a str. 2457TGCA_001022275 Citrobacter freundiiGCA_000963575 Klebsiella michiganensisGCA_000504545 Cronobacter sakazakii CMCC 45402GCA_000012025 Shigella boydii Sb227GCA_000814125 Enterobacter cloacaeGCA_000987925 Yersinia enterocoliticaGCA_000011745 Candidatus Blochmannia pennsylvanicus str. BPENGCA_000255535 Rahnella aquatilis HX2GCA_000952955 Escherichia coliGCA_000695995 Serratia sp. FS14GCA_000648515 Citrobacter freundii CFNIH1GCA_001022295 Klebsiella oxytocaGCA_000147055 Dickeya dadantii 3937GCA_000348565 Edwardsiella piscicida C07-087GCA_000742755 Klebsiella pneumoniae subsp. pneumoniaeGCA_000027225 Xenorhabdus bovienii SS-2004GCA_000247565 Wigglesworthia glossinidia endosymbiont of Glossina morsitans morsitans GCA_000828815 Candidatus Tachikawaea gelatinosaGCA_000022805 Yersinia pestis D106004GCA_001006005 Serratia fonticolaGCA_000018625 Salmonella enterica subsp. arizonae serovar 62:z4,z23:-GCA_000478905 Candidatus Pantoea carbekiiGCA_000410515 Enterobacter sp. R4-368GCA_000148935 Pantoea vagans C9-1GCA_000444425 Proteus mirabilis BB2000GCA_000747565 Serratia sp. SCBIGCA_001022135 Kluyvera intermediaGCA_000757825 Cedecea neteriGCA_000294535 Pectobacterium carotovorum subsp. carotovorum PCC21GCA_000834375 Yersinia pseudotuberculosis YPIIIGCA_000043285 Candidatus Blochmannia floridanusGCA_000093065 Candidatus Riesia pediculicola USDAGCA_000834515 Yersinia intermediaGCA_000759475 Pantoea rwandensisGCA_000027065 Siccibacter turicensis z3032GCA_000582515 Yersinia similisGCA_000300455 Kosakonia sacchari SP1Helicobacter pyloriGCA_000148855 Helicobacter pylori SJM180GCA_000021165 Helicobacter pylori G27GCA_000185245 Helicobacter pylori SouthAfrica7GCA_000093185 Helicobacter pylori v225dGCA_000277365 Helicobacter pylori Shi417GCA_000498315 Helicobacter pylori BM012AGCA_000270065 Helicobacter pylori F57GCA_000392455 Helicobacter pylori UM032GCA_000277385 Helicobacter pylori Shi169GCA_000008525 Helicobacter pylori 26695GCA_000270045 Helicobacter pylori F32GCA_000148915 Helicobacter pylori Sat464GCA_000185225 Helicobacter pylori Lithuania75GCA_000600045 Helicobacter pylori oki102GCA_000600205 Helicobacter pylori oki828GCA_000192335 Helicobacter pylori 2018GCA_000827025 Helicobacter pyloriGCA_000590775 Helicobacter pylori SouthAfrica20GCA_000270025 Helicobacter pylori F30GCA_000148665 Helicobacter pylori 908GCA_000392515 Helicobacter pylori UM037GCA_000392475 Helicobacter pylori UM299GCA_000262655 Helicobacter pylori XZ274GCA_000008785 Helicobacter pylori J99GCA_000685745 Helicobacter pyloriGCA_000185205 Helicobacter pylori Gambia94/24GCA_000826985 Helicobacter pylori 26695-1GCA_000315955 Helicobacter pylori Aklavik117GCA_000498335 Helicobacter pylori BM012SGCA_000277405 Helicobacter pylori Shi112GCA_000224535 Helicobacter pylori Puno120GCA_000317875 Helicobacter pylori Aklavik86GCA_000600185 Helicobacter pylori oki673GCA_000196755 Helicobacter pylori B8GCA_000439295 Helicobacter pylori UM298GCA_000348885 Helicobacter pylori OK310GCA_000307795 Helicobacter pylori 26695GCA_000013245 Helicobacter pylori HPAG1GCA_000392535 Helicobacter pylori UM066GCA_000185185 Helicobacter pylori India7GCA_000213135 Helicobacter pylori 83GCA_000685705 Helicobacter pyloriGCA_000224575 Helicobacter pylori SNT49GCA_000600085 Helicobacter pylori oki112GCA_000023805 Helicobacter pylori 52GCA_000348865 Helicobacter pylori OK113GCA_000259235 Helicobacter pylori HUP-B14GCA_000020245 Helicobacter pylori Shi470GCA_000270005 Helicobacter pylori F16GCA_000192315 Helicobacter pylori 2017GCA_000685665 Helicobacter pyloriGCA_000600165 Helicobacter pylori oki422GCA_000255955 Helicobacter pylori ELS37GCA_000021465 Helicobacter pylori P12GCA_000600145 Helicobacter pylori oki154GCA_000224555 Helicobacter pylori Puno135GCA_000011725 Helicobacter pylori 51GCA_000148895 Helicobacter pylori Cuz20GCA_000817025 Helicobacter pyloriGCA_000178935 Helicobacter pylori 35AListeria monocytogenesGCA_000438745 Listeria monocytogenesGCA_000438705 Listeria monocytogenesGCA_001027125 Listeria monocytogenesGCA_000438725 Listeria monocytogenesGCA_000197755 Listeria monocytogenesGCA_001027245 Listeria monocytogenesGCA_001027085 Listeria monocytogenesGCA_001005925 Listeria monocytogenesGCA_000746625 Listeria monocytogenesGCA_000382925 Listeria monocytogenesGCA_000438665 Listeria monocytogenesGCA_000800335 Listeria monocytogenesGCA_001027165 Listeria monocytogenesGCA_000438605 Listeria monocytogenesGCA_000438585 Listeria monocytogenesGCA_000808055 Listeria monocytogenesGCA_000950775 Listeria monocytogenesGCA_001027065 Listeria monocytogenesGCA_000600015 Listeria monocytogenesGCA_001027205 Listeria monocytogenesGCA_000438685 Listeria monocytogenesGCA_001005985 Listeria monocytogenesGCA_000438625 Listeria monocytogenesGCA_000681515 Listeria monocytogenesGCA_000438645 Listeria monocytogenesGCA_000210815 Listeria monocytogenesPseudomonasGCA_000829885 Pseudomonas aeruginosaGCA_000510285 Pseudomonas monteilii SB3078GCA_000988485 Pseudomonas syringae pv. syringae B301DGCA_000013785 Pseudomonas stutzeri A1501GCA_000759535 Pseudomonas cremoricolorataGCA_000953455 Pseudomonas pseudoalcaligenesGCA_000981825 Pseudomonas aeruginosaGCA_000661915 Pseudomonas stutzeriGCA_000508205 Pseudomonas sp. TKPGCA_000014625 Pseudomonas aeruginosa UCBPP-PA14GCA_000019445 Pseudomonas putida W619GCA_000316175 Pseudomonas sp. UW4GCA_000498975 Pseudomonas mosselii SJ10GCA_000473745 Pseudomonas aeruginosa VRFPA04GCA_000691565 Pseudomonas putidaGCA_000730425 Pseudomonas fluorescensGCA_000007805 Pseudomonas syringae pv. tomato str. DC3000GCA_000349845 Pseudomonas denitrificans ATCC 13867GCA_000026105 Pseudomonas entomophila L48GCA_000689415 Pseudomonas knackmussii B13GCA_000325725 Pseudomonas putida HB3267GCA_000412695 Pseudomonas resinovorans NBRC 106553GCA_000831585 Pseudomonas plecoglossicidaGCA_000756775 Pseudomonas sp. 20_BNGCA_000590475 Pseudomonas stutzeriGCA_000829255 Pseudomonas aeruginosaGCA_000761155 Pseudomonas rhizosphaeraeGCA_001038645 Pseudomonas stutzeriGCA_000264665 Pseudomonas putida ND6GCA_000007565 Pseudomonas putida KT2440GCA_000494915 Pseudomonas sp. VLB120GCA_000226155 Pseudomonas aeruginosa M18GCA_000213805 Pseudomonas fulva 12-XGCA_000194805 Pseudomonas brassicacearum subsp. brassicacearum NFM421GCA_000336465 Pseudomonas poae RE*1-1-14GCA_000828695 Pseudomonas protegens Cab57GCA_000800255 Pseudomonas parafulvaGCA_000257545 Pseudomonas mandelii JR-1GCA_000012205 Pseudomonas savastanoi pv. phaseolicola 1448AGCA_000816985 Pseudomonas aeruginosaGCA_000746525 Pseudomonas alkylphenoliaGCA_000496605 Pseudomonas aeruginosa PA1GCA_000204295 Pseudomonas mendocina NK-01GCA_000829415 Pseudomonas sp. StFLB209GCA_000012265 Pseudomonas protegens Pf-5GCA_000412675 Pseudomonas putida NBRC 14164GCA_000397205 Pseudomonas protegens CHA0GCA_000648735 Pseudomonas syringae pv. actinidiae ICMP 18884GCA_000012245 Pseudomonas syringae pv. syringae B728aGCA_000761195 Pseudomonas chlororaphis subsp. aurantiacaGCA_000818015 Pseudomonas balearica DSM 6083GCA_000219605 Pseudomonas stutzeri ATCC 17588 = LMG 11199GCA_000219705 Pseudomonas putida S16GCA_000511325 Pseudomonas sp. FGI182GCA_000508765 Pseudomonas aeruginosa LES431GCA_000297075 Pseudomonas pseudoalcaligenes CECT 5344GCA_000517305 Pseudomonas cichorii JBC1GCA_000963835 Pseudomonas chlororaphisGCA_000327065 Pseudomonas stutzeri RCH2GCA_000271365 Pseudomonas aeruginosa DK2StreptococcusGCA_000211015 Streptococcus pneumoniae SPN034183GCA_000210975 Streptococcus pneumoniae INV104GCA_000203195 Streptococcus gallolyticus subsp. gallolyticus ATCC BAA-2069GCA_001020185 Streptococcus pyogenesGCA_000253155 Streptococcus oralis Uo5GCA_000696505 Streptococcus equi subsp. zooepidemicus CYGCA_000463355 Streptococcus intermedius B196GCA_000698885 Streptococcus thermophilus ASCC 1275GCA_000014205 Streptococcus sanguinis SK36GCA_000007045 Streptococcus pneumoniae R6GCA_000306805 Streptococcus intermedius JTH08GCA_000196595 Streptococcus pneumoniae TCH8431/19AGCA_000262145 Streptococcus parasanguinis FW213GCA_001026925 Streptococcus agalactiaeGCA_000251085 Streptococcus pneumoniae ST556GCA_000019025 Streptococcus pneumoniae Taiwan19F-14GCA_000211055 Streptococcus pneumoniae SPN994039GCA_000688775 Streptococcus sp. VT 162GCA_000231905 Streptococcus suis D12GCA_000026665 Streptococcus pneumoniae ATCC 700669GCA_000283635 Streptococcus macedonicus ACA-DC 198GCA_000014365 Streptococcus pneumoniae D39GCA_000019265 Streptococcus pneumoniae Hungary19A-6GCA_000299015 Streptococcus pneumoniae gamPNI0373GCA_000019985 Streptococcus pneumoniae CGSP14GCA_000463395 Streptococcus constellatus subsp. pharyngis C232GCA_000187935 Streptococcus parauberis NCFD 2020GCA_000253315 Streptococcus salivarius JIM8777GCA_000427055 Streptococcus agalactiae ILRI112GCA_000246835 Streptococcus infantarius subsp. infantarius CJ18GCA_000427075 Streptococcus agalactiae ILRI005GCA_000007465 Streptococcus mutans UA159GCA_000831165 Streptococcus anginosusGCA_000147095 Streptococcus pneumoniae 670-6BGCA_000817005 Streptococcus pneumoniaeGCA_000180515 Streptococcus pneumoniae SPNA45GCA_000441535 Streptococcus lutetiensis 033GCA_000210955 Streptococcus pneumoniae OXC141GCA_000009545 Streptococcus uberis 0140JGCA_000648555 Streptococcus iniaeGCA_000027165 Streptococcus mitis B6GCA_000018985 Streptococcus pneumoniae JJAGCA_000270165 Streptococcus pasteurianus ATCC 43144GCA_000479315 Streptococcus sp. I-P16GCA_000478925 Streptococcus anginosus subsp. whileyi MAS624GCA_000019825 Streptococcus pneumoniae G54GCA_000017005 Streptococcus gordonii str. Challis substr. CH1GCA_000479335 Streptococcus sp. I-G2GCA_000385925 Streptococcus oligofermentans AS 1.3089GCA_000210935 Streptococcus pneumoniae INV200GCA_000211035 Streptococcus pneumoniae SPN994038GCA_000221985 Streptococcus pseudopneumoniae IS7493GCA_000006885 Streptococcus pneumoniae TIGR4GCA_000018965 Streptococcus pneumoniae 70585GCA_000348705 Streptococcus pneumoniae PCS8235GCA_000210995 Streptococcus pneumoniae SPN034156GCA_000231925 Streptococcus suis ST1GCA_000019005 Streptococcus pneumoniae P1031GCA_000188715 Streptococcus dysgalactiae subsp. equisimilis ATCC 12394GCA_000026585 Streptococcus equi subsp. equi 4047 The manuscript is overall of much higher quality than it was previously. However, not all of the comments were addressed fully. The most egregious of these oversights is that it still gives the impression that a “straw man” argument is being set up to easily fall when set up against the authors’ favored approach. Fortunately this is not quite the case, and therefore this is likely an entirely unintentional effect of not clearly explaining the method that is being used or its comparison to other methods, but even so this issue is quite important since it can easily mislead an unwary reader. e.g., statements about the time and memory requirements of the SB approach having to scale quadratically with the number of genomes to be compared are untrue. In fact, while SB methods to construct orthologous groups do often take advantage of a full all-against-all comparison , perhaps the more proper comparison of DAB is not to the set of SB methods that construct orthologous groups, but rather to those that extend existing groups into new genomes . This fact is now acknowledged in the manuscript, but is buried deeply in the middle of the Discussion section, and yet the confusing description of the comparison of the quadratic to linear scales also remains at several places in the manuscript (such as in the abstract and the second paragraph of the introduction). Perhaps this was merely an oversight, but in any case this issue should be made much more clear than it currently is. If I understand things correctly, the overall summary seems to be that: the DAB approach, much like several existing SB approaches, leverages the extensive amount of work  already put into defining domain families, and attempts to extend these families to identify new members of orthologous groups in newly discovered genomes - which it is able to do more accurately than similar SB methods due to taking into account domain architectures. Both of these approaches - DAB and SB - scale linearly with the number of new genomes to be compared. In contrast, there is also a different class of SB methods  that create orthologous groups de novo - these methods require more memory and time since they scale quadratically with the number of new genomes, although this class of method provides the advantage of being able to work even in the absence of domain family information, which DAB is not able to do. Another oversight occurs in the sentence that “Our aim was to investigate whether using HMMs instead of sequence similarity would yield similar results”, where instead of HMM I think the authors meant domain architectures? I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Thanks, we have amended the manuscript as suggested by the reviewer. Specifically:We have deleted from the abstract and introduction the statements about the time and memory requirements of the SB approach having to scale quadratically with the number of genomes. We have modified the paragraph in the discussion to further emphasizes that  DAB is similar to SB methods that extend existing groups into new genomes. We have also rephase the reviewers comment on the extensive use DAB does on the amount of work put on defining domain families as we think it might  further clarify the text. The sentence “Our aim was to investigate whether using HMMs instead of sequence similarity would yield similar results” has been modified as suggested to: “Our aim was to investigate whether using domain architectures instead of sequence similarity alone would yield similar results.” As the first reviewer also mentioned, methods such as CDART and DELTA-BLAST  have been available for many years. The latter even seems to aim to perform the exact same function as DAB, by considering domain architectures. How is DAB different or better? I suspect that DAB may have greater accuracy since it uses HMMs rather than PSSMs, but this remains to be shown, and DELTA-BLAST is far easier for a user to run, since it is available as a webserver.The comparison performed in this manuscript appears to fall prey to a straw man argument. In some cases, but not all, re-writing the relevant sections of the manuscript would help to avoid any misconceptions in this regard. a) The issue of replacing a2)O(n cost with aO(n) one upon addition of a new genome was dealt with over 15 years ago, so the statement "On the other hand, addition of a new genome using an SB approach require a new set of all-against-all sequence comparisons which come at a2)OO work extremely quickly and efficiently to assign genes in newly-sequenced genomes to existing groups. In fact, COGNITOR works in the exact same manner in which DAB uses pre-computed domain databases to achieve the much lowerO(n) cost, although in COGNITOR's case it searches against a pre-computed database of orthologous groups .The limitations of global sequence similarity based methods to identify proteins that perform similar functions are well-known. Thus, the approach described in this manuscript of using domain-based clustering of orthologous groups (DAB) represents an exciting and very welcome addition to the field. Or at least it will when it is fully developed, although this manuscript has not convinced me that it outperforms other methods at its current level of development, and I have several substantial reservations about some of its content: In should be noted that despite DAB's somewhat higher cost, it has the theoretical potential to achieve better accuracy than COGNITOR (at least in some cases) since as a global sequence similarity approach, the latter does not explicitly consider domain architecture. At least not in an automated fashion - doing so would require manual curation of its results, which is often done by careful researchers, but is not a process that is scalable to handle the ever-decreasing cost and ever-increasing amounts of genomic data. Although since a comparison with COGNITOR was not included in the manuscript, either in terms of speed or accuracy, it is unknown how much more useful DAB would be in practice.2)O new comparisons have to be made - for a total accumulated cost of2)O Even the initial2)O(n procedure that it uses). On the other hand, methods like CDART and COGTRIANGLES are all also automated , and some pre-computed databases  even take advantage of further manual validation, and from which pre-computed groups can be identified in newly-sequenced genomes by the fully automated and even easier approaches such as DELTA-BLAST and COGNITOR. Is it at least possible that the poorer performance of SB methods in comparison to DAB as shown in the current manuscript is due to the choice of this particular SB method? I for one would have loved to see a comparison against the new release of the COGs database last year, since due to its being manually curated it acts as a sort of "Gold Standard" that can be tested against, with perhaps the EGGNOG groups being used as a more realistic measure of what a purely automated method can do without human supervision. Likely, DAB would fall somewhere in-between, and which would benefit the community of researchers who want to do comparative genomics of prokaryotic organisms to have a fully automated method that was demonstrated to surpass the existing fully automated methods. As it now stands though, DAB has only been shown to surpass OrthoMCL, which is not hard to do at all. Indeed, as seventh paragraph of the Discussion section (starting "Two of the most prominent...") states, unlike DAB, the SB methods were not able to cluster together the proteins with functional similarity but little sequence identity, especially across wider taxonomic ranges - which of course is what would be expected from a SB method that uses an e-value cutoff of 1e-5. c) Why was only a single SB method chosen to be a representative for this entire class of approaches? Multiple forms of DAP were tested, whereas the only SB method used for comparison was one that uses a strict e-value cutoff of 1e-5, in the form of OrthaGogue and the OrthoMCL method. Also, why was the latter chosen to be this single representative? The latter approach was designed  for eukaryotic organisms, and while it has been applied more recently to bacteria as well, it is by no means the only - or even necessarily the best - approach for prokaryotic genomes. One advantage that it has is that it is completely automated, and thus is "easy" for people to use  Above and beyond the choice of SB method, it also seems that there may have been a bug in its implementation. The statement "For SB clustering we also observed the case of identical protein sequences not clustered together, probably because of the tie breaking implementation when BBH are scored." However, this was not supposed to happen, due to the within-species reciprocal BBH procedure that is used. In contrast, the tie breaking refers to between-species comparisons, but as shown in Figure 1 of the OrthoMCL paper (http://www.ncbi.nlm.nih.gov/pubmed/12952885), these two sources of information were supposed to have been combined together to form the final orthologous groups. If the proteins were highly similar  then perhaps a tie-breaking could be explained, but for 100% identical proteins - e.g., produced by a tanden duplication event - then they should have been collected into the group. One possibility is that this particular SB method simply was not designed to handle the large numbers of extremely closely-related genome assemblies that are available today, since at the time, very few instances of multiple genomic assemblies were available for the same species. If this explanation was demonstrated to be the reason why these identical proteins were not clustered together, that would be another reason for a user to choose to use DAB over this particular SB method. In any case , this event could greatly contribute to explaining some of the results that were observed whereby this single SB method found so many more singletons than DAB with Pfam - i.e., fixing the bug, or using some other SB method, may move many of those singletons into clusters. Although it would not explain why DAB with InterPro found even more singletons than this SB method?DAB has a lot of potential, but its limitations need to be made more clear: a) Why and how is the matrix of domain architecture binarized? Specifically, what if multiple copies of a domain are present? And does order matter - such as the architectures shown in Figure 2 of "A+B" and "B+A"? So, would "B+A+A" be a different architecture? And, as another reviewer also pointed out, what about "complicated" domain topologies where domains are interrupted by the insertion of another domain? Another major aspect of partial topologies is if DAB only recognizes some but not all of a newly-discovered architecture. E.g., a protein with architecture A+B+C+D, where A is known but B, C and D domains are not yet known. How would this be handled by DAB? Would it be reduced to appear merely as a single-domain "A" architecture? If so, how could that be distinguished from an architecture such as A+Z, which would also be reduced to appear just as a single-domain A? It seems like global sequence similarity methods might be more useful in those particular scenarios? i.e., if all the above domains were the same length, and a coverage threshold was used, then A+B+C+D could not be put into the same group as A+Z and A. Therefore, DAB seems primarily useful to either quickly extend known architectural types into a newly sequenced genome, but at the cost of not being able to work with unknown types.  H. pylori and Cornebacteriales genomes are not able to be assigned to domain families. Even these numbers are merely lower-bound estimates, since brand-new architectures are expected to be discovered constantly, and yet these may incorporate at least one element that is known - such as the aforementioned A+B+C+D architecture, where only the A domain is represented in Pfam, but B and C and D are unknown. And yet it seems likely that even the fact that these domains are unknown would go unrecognized by the DAB approach - unless a factor is added to look for large segments of a gene that do not have matches in the databases of known domains. Therefore, the cost of DAB not being able to work with unknown architectural types might be quite high indeed. Worse, the exact value of that cost is also likewise unknown, and yet it would seem to be the single crucial piece of information that is most sorely needed in order to answer the question: does the benefits of DAB outweigh its costs? b) For newly sequenced genomes that are not yet well-characterized enough to have all of their domains present in the domain databases, DAB can be severely handicapped in comparison to global sequence similarity methods that do not have this limitation. In particular, Table 1 shows that up to nearly a fifth of theIf the goal is to bring together groups of proteins that have functional equivalence, then why was the only comparison that was done performed against the presence/absence membership of SB orthology approaches? Would it not have been better to actually measure the functional consistency observed within the SB groups, and within the DAB groups, in order to show that the latter was higher than the former? Many other methods that purport to improve upon the state-of-the-art orthology prediction process do just that - for instance Figure 4 of http://www.ncbi.nlm.nih.gov/pubmed/19148271 shows several comparisons with similarity of GO terms, enzyme nomenclature (EC), gene expression, and syntenic local neighborhood tests, with 12 different methods of orthology prediction. While neighborhood conservation is irrelevant for the issue of functional equivalence, the former three (or at least GO terms) would help to answer whether DAB is truly better than SB at the task of measuring functional equivalence. It would also help to answer whether this improved functional equivalence would be outweighed by the costs of being unable to handle unknown domain architectures, especially for highly divergent new genomes. If not, DAB may still be useful to check the consistency of existing orthologous groups in terms of their architecture, at least when domain architectures are expected to be completely known in advance - e.g., microevolutionary variations within a species where mutational events may disrupt a protein's function - but for other tasks such as the discovery of a new phyla of cellular life that contains radically different domain architectures, global similarity methods may be preferable instead.       the vertical arrows seem to be pointing the wrong direction - a gene sequence undoubtebly contains more information content than a mere functional description. e.g., if I were to give you a GO code for molecular function, or biological process, then I could not tell you whether the original gene sequence is closer to one type of bacteria vs another type; but if I had the original gene sequence, then I could answer that question as well as many more.I did not see a description of how amino acid coordinates are used anywhere else in the manuscript, either in DAB itself or in the comparison? In short, what does "Structure" have to do with anything, other than the general theoretical flow of "sequence begets structure which begets function"? If the purpose of Figure 2 is to describe the flowchart of DAB specifically though, it should focus only on the relevant elements. I suppose Structure could have meant how the sequence alignment was made, but if that were true, then DAB would only work for domain families for which a structure is available, instead of those for which only genomic or individual gene sequence has been provided.The ordering also seems unclear - wouldn't BBHs inform HMM domains, which then in turn inform domain architectures? Or if starting with BBHs, then how could architectures possibly be known prior to knowing the domains themselves? Or if it should be read from top to bottom as shown, how exactly does one start with Function  and then, somehow via Structure, thereby arrive at a Sequence alignment? Specifically, is a Pfam entry a "Function", from which the Sequence alignment is downloaded? Or are Function and the Sequence alignment both part of the starting Pfam entry ? From which domains are found (but aren't Pfam entries domains to begin with?), and then BBHs are made from the domain architectures? . In any case, as pointed out by other reviewers, the methodology used by DAB is not clearly explained in this figure, nor in the manuscript text. Finally, some minor points concerning Figure 2: Also, the last paragraph of the Discussion uses the word "closeness", but I think "closedness" was intended.I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. As the first reviewer also mentioned, methods such as CDART and DELTA-BLAST  have been available for many years. The latter even seems to aim to perform the exact same function as DAB, by considering domain architectures. How is DAB different or better? I suspect that DAB may have greater accuracy since it uses HMMs rather than PSSMs, but this remains to be shown, and DELTA-BLAST is far easier for a user to run, since it is available as a webserver.Following the suggestions made by the other reviewers we have added a paragraph in the Introduction regarding domain architectures, comparison of domain architectures and their use for sequence search. We have also discussed on how these have been included in domain databases and on the preservation of domain architectures at high phylogenetic distances.    We agree that most likely HMMs outperform PSSMs, however as the reviewer says, that is a topic that would required a dedicated investigation. Here our goal was to used domain architectures for functional comparative genomics, and we agree that a similar approach could be implemented using PSSM.  http://semantics.systemsbiology.nl/ Regarding usability, we have used SAPP (semantic annotation platform with provenance) for genome analysis and annotation. SAPP is able to store the results in the RDF data model, that can be then queried using SPARQL. This tool is available with a web interface and is available at The comparison performed in this manuscript appears to fall prey to a straw man argument. In some cases, but not all, re-writing the relevant sections of the manuscript would help to avoid any misconceptions in this regard. The issue of replacing a O(n2) cost with a O(n) one upon addition of a new genome was dealt with over 15 years ago, so the statement "On the other hand, addition of a new genome using an SB approach require a new set of all-against-all sequence comparisons which come at a O(n2) computational cost" is false - at least as it is currently written.We have amended the above mentioned sentence to:O(n2) computational cost. However, approaches has been proposed to overcome this shortcomings of SB methods, such as COGNITOR which reduces the computational toO(n) by using pre-computed databases. On the other hand, addition of a new genome using an SB approach require a new set of all-against-all sequence comparisons which come at aIt is true that building groups of orthologs do require an initial O(n2) computational cost, but once those orthologous groups are formed, methods such as COGNITOR (first published in the year 2000) work extremely quickly and efficiently to assign genes in newly-sequenced genomes to existing groups. In fact, COGNITOR works in the exact same manner in which DAB uses pre-computed domain databases to achieve the much lower O(n) cost, although in COGNITOR's case it searches against a pre-computed database of orthologous groups . In should be noted that despite DAB's somewhat higher cost, it has the theoretical potential to achieve better accuracy than COGNITOR (at least in some cases) since as a global sequence similarity approach, the latter does not explicitly consider domain architecture. At least not in an automated fashion - doing so would require manual curation of its results, which is often done by careful researchers, but is not a process that is scalable to handle the ever-decreasing cost and ever-increasing amounts of genomic data.We have commented on the analogy between DAB and COGNITOR. In this respect, the DAB approach is similar in to the approach implemented in COGNITOR, by searching against existing databases of domains architectures.Although since a comparison with COGNITOR was not included in the manuscript, either in terms of speed or accuracy, it is unknown how much more useful DAB would be in practice.The focus of the paper was not to propose a comparative analysis of different methods but rather to present and contextualize the use of domain architecture for comparative genomics. However, we want to stress that we are not claiming that DA methods are superior to SB but that are an efficient and scalable alternative.Even the initial O(n2) cost does not have to be terribly burdensome, since the SIMAP method pre-computes and stores BLAST results between all pairs of sequenced genomes anyway, and then uses efficient database retrieval methods to report the stored results. When a new genome is added, O(n) new comparisons have to be made - for a total accumulated cost of O(n2), although with the work spread out over many years - and these in turn are useful for many other purposes, thus mitigating the construction costs. For instance, the EGGNOG database uses this method to build groups of orthologs. Why was only a single SB method chosen to be a representative for this entire class of approaches? Multiple forms of DAB were tested, whereas the only SB method used for comparison was one that uses a strict e-value cutoff of 1e-5, in the form of OrthaGogue and the OrthoMCL method. Also, why was the latter chosen to be this single representative?We have added the following to the discussion: To asses whether DAB results were consistent with those of SB methods we chosen. OrthaGogue as a representative of the latter class. Several tools such as COGNITOR and MultiPARANOID are available that implement different algorithm solutions to the task of identifying homologous sequences;  however, despite different implementation, they all rely on sequence similarity as a proxy for functional equivalence. Here we considered SB methods as a golden standard for functional comparative genomics, especially when organisms within close evolutionary proximity are considered. Our aim was to investigate whether using HMMs instead of sequence similarity would yield similar results, thereby justifying their use for large scale functional genome comparisons. Regarding domain architectures, we have explored different alternatives, as we have seen that the chosen database or set of reference domains plays a critical role, an example is the low coverage of TIGRFAM preventing obtention of reasonable clusters.The latter approach was designed  for eukaryotic organisms, and while it has been applied more recently to bacteria as well, it is by no means the only - or even necessarily the best - approach for prokaryotic genomes. One advantage that it has is that it is completely automated, and thus is "easy" for people to use  procedure that it uses). On the other hand, methods like CDART and COGTRIANGLES are all also automated , and some pre-computed databases  even take advantage of further manual validation, and from which pre-computed groups can be identified in newly-sequenced genomes by the fully automated and even easier approaches such as DELTA-BLAST and COGNITOR. Is it at least possible that the poorer performance of SB methods in comparison to DAB as shown in the current manuscript is due to the choice of this particular SB method? I for one would have loved to see a comparison against the new release of the COGs database last year, since due to its being manually curated it acts as a sort of "Gold Standard" that can be tested against, with perhaps the EGGNOG groups being used as a more realistic measure of what a purely automated method can do without human supervision. Likely, DAB would fall somewhere in-between, and which would benefit the community of researchers who want to do comparative genomics of prokaryotic organisms to have a fully automated method that was demonstrated to surpass the existing fully automated methods. As it now stands though, DAB has only been shown to surpass OrthoMCL, which is not hard to do at all. Indeed, as seventh paragraph of the Discussion section (starting "Two of the most prominent...") states, unlike DAB, the SB methods were not able to cluster together the proteins with functional similarity but little sequence identity, especially across wider taxonomic ranges - which of course is what would be expected from a SB method that uses an e-value cutoff of 1e-5. Above and beyond the choice of SB method, it also seems that there may have been a bug in its implementation. The statement "For SB clustering we also observed the case of identical protein sequences not clustered together, probably because of the tie breaking implementation when BBH are scored." However, this was not supposed to happen, due to the within-species reciprocal BBH procedure that is used. In contrast, the tie breaking refers to between-species comparisons, but as shown in Figure 1 of the OrthoMCL paper (http://www.ncbi.nlm.nih.gov/pubmed/12952885), these two sources of information were supposed to have been combined together to form the final orthologous groups. If the proteins were highly similar  then perhaps a tie-breaking could be explained, but for 100% identical proteins - e.g., produced by a tanden duplication event - then they should have been collected into the group. One possibility is that this particular SB method simply was not designed to handle the large numbers of extremely closely-related genome assemblies that are available today, since at the time, very few instances of multiple genomic assemblies were available for the same species. If this explanation was demonstrated to be the reason why these identical proteins were not clustered together, that would be another reason for a user to choose to use DAB over this particular SB method. In any case , this event could greatly contribute to explaining some of the results that were observed whereby this single SB method found so many more singletons than DAB with Pfam - i.e., fixing the bug, or using some other SB method, may move many of those singletons into clusters. Although it would not explain why DAB with InterPro found even more singletons than this SB method?We have added a paragraph in the discussion regarding why  the InterPro hierarchy has to be taken into account, also we mention this in the conclusion section.  The hierarchical structure  produces an increase in the domain multiplicity as many are related to each other. As a results an artificial variability in the DA is introduced leading to a higher number of singletons.  DAB has a lot of potential, but its limitations need to be made more clear.We have added a new section to the Discussion: Limitations of DAB approachesWhy and how is the matrix of domain architecture binarized? Specifically, what if multiple copies of a domain are present?We understand that our phrasing may have caused some confusion and we apologize for unclarity. The matrix of domain architectures is only binarized (presence/absence) to compute the PCA shown in Fig. 8, not  to compare DAB and SB clustering. We have rephrased this in the Materials and Methods section: ...a binarized presence-absence matrix was obtained and used solely for principal component analysis.[..] does order matter - such as the architectures shown in Figure 2 of "A+B" and "B+A"? So, would "B+A+A" be a different architecture? And, as another reviewer also pointed out, what about "complicated" domain topologies where domains are interrupted by the insertion of another domain? Another major aspect of partial topologies is if DAB only recognizes some but not all of a newly-discovered architecture. E.g., a protein with architecture A+B+C+D, where A is known but B, C and D domains are not yet known. How would this be handled by DAB? Would it be reduced to appear merely as a single-domain "A" architecture? If so, how could that be distinguished from an architecture such as A+Z, which would also be reduced to appear just as a single-domain A? It seems like global sequence similarity methods might be more useful in those particular scenarios? i.e., if all the above domains were the same length, and a coverage threshold was used, then A+B+C+D could not be put into the same group as A+Z and A. Therefore, DAB seems primarily useful to either quickly extend known architectural types into a newly sequenced genome, but at the cost of not being able to work with unknown types. For newly sequenced genomes that are not yet well-characterized enough to have all of their domains present in the domain databases, DAB can be severely handicapped in comparison to global sequence similarity methods that do not have this limitation. In particular, Table 1 shows that up to nearly a fifth of the H. pylori and Cornebacteriales genomes are not able to be assigned to domain families. Even these numbers are merely lower-bound estimates, since brand-new architectures are expected to be discovered constantly, and yet these may incorporate at least one element that is known - such as the aforementioned A+B+C+D architecture, where only the A domain is represented in Pfam, but B and C and D are unknown. And yet it seems likely that even the fact that these domains are unknown would go unrecognized by the DAB approach - unless a factor is added to look for large segments of a gene that do not have matches in the databases of known domains. Therefore, the cost of DAB not being able to work with unknown architectural types might be quite high indeed. Worse, the exact value of that cost is also likewise unknown, and yet it would seem to be the single crucial piece of information that is most sorely needed in order to answer the question: does the benefits of DAB outweigh its costs?The reviewer raises a very interesting point regarding how extensive available knowledge on protein domains is. The high agreement between the results of DAB and SB methods is only possible because databases of protein domains have enough information. Still, we believe many domains remain to be identified and in the scenarios the reviewer mentions DAB methods will be limited. We have added the following to the Discussion section, under the “Limitations of DAB approaches” header. Still around 15% of the genome coding content corresponds to sequences with no identified protein domains. DAB approaches can be complemented with SB methods to consider these sequences or even protein sequences with low domain coverage, possible indicating the location of protein domains yet to be identified.  We have extended the paragraph in the Materials and Methods where domain architectures are defined to further emphasize that  N- C- terminal domain order is an inherent part of domain architecture definition.   Labels indicating N-C terminal order of identified domains were assigned to each protein using the starting position of the domains: the same labels were assigned to proteins sharing the same domain architecture. In the Introduction we have added a paragraph regarding the use of protein domain architecture in protein annotations and we have included references to previous works showing that domain order is often key for the function of the protein and that domain duplications/insertions can also alter the function of the protein. Moreover, a similar point on how domain architectures were defined and how the hierarchical relationships between protein domains, families and clans has been raised by R. Finn and a paragraph has been added in the Discussion (see answer to R. Finn’s comments). If the goal is to bring together groups of proteins that have functional equivalence, then why was the only comparison that was done performed against the presence/absence membership of SB orthology approaches? Would it not have been better to actually measure the functional consistency observed within the SB groups, and within the DAB groups, in order to show that the latter was higher than the former? Many other methods that purport to improve upon the state-of-the-art orthology prediction process do just that - for instance Figure 4 of http://www.ncbi.nlm.nih.gov/pubmed/19148271 shows several comparisons with similarity of GO terms, enzyme nomenclature (EC), gene expression, and syntenic local neighborhood tests, with 12 different methods of orthology prediction. While neighborhood conservation is irrelevant for the issue of functional equivalence, the former three (or at least GO terms) would help to answer whether DAB is truly better than SB at the task of measuring functional equivalence. It would also help to answer whether this improved functional equivalence would be outweighed by the costs of being unable to handle unknown domain architectures, especially for highly divergent new genomes. If not, DAB may still be useful to check the consistency of existing orthologous groups in terms of their architecture, at least when domain architectures are expected to be completely known in advance - e.g., microevolutionary variations within a species where mutational events may disrupt a protein's function - but for other tasks such as the discovery of a new phyla of cellular life that contains radically different domain architectures, global similarity methods may be preferable instead.We have added the following section dedicated to limitations of DAB methods:We have shown that domain architecture-based methods can be used as an effective approach to identify clusters of functionally equivalent proteins, leading to results similar to those obtained by classical methods based on sequence similarity. However, whether DAB methods are more accurate than SB methods to assess functional equivalence will require further analysis. In this light, results of functional conservation for both approaches could be compared in terms of GO similarity and/or EC number. The performance of DAB methods may be sub-optimal when dealing with newly sequenced genomes that are not yet well-characterized enough to have all of their domains present in domain databases, since DAB methods will be unable to handle unknown architectural types.Around 15% of the genome coding content corresponds to sequences with no identified protein domains. DAB approaches can be complemented with SB methods to consider these sequences or even protein sequences with low domain coverage, possible indicating the location of protein domains yet to be identified. Since DAB methods rely on the constant upgrading of public resources like UniProt and Pfam databases, an initial assessment of domain coverage appears as a sine qua non condition for application of these methods.DAB approaches could be used to assess the consistency of existing orthologous groups in terms of their domain architectures, at least when domain architectures are expected to be completely known in advance . For other purposes, such as the discovery of a new phyla of cellular life that contains radically different domain architectures, global similarity methods may be preferred.Finally, some minor points concerning Figure 2: The vertical arrows seem to be pointing the wrong direction - a gene sequence undoubtedly contains more information content than a mere functional description. e.g., if I were to give you a GO code for molecular function, or biological process, then I could not tell you whether the original gene sequence is closer to one type of bacteria vs another type; but if I had the original gene sequence, then I could answer that question as well as many more. I did not see a description of how amino acid coordinates are used anywhere else in the manuscript, either in DAB itself or in the comparison? In short, what does "Structure" have to do with anything, other than the general theoretical flow of "sequence begets structure which begets function"? If the purpose of Figure 2 is to describe the flowchart of DAB specifically though, it should focus only on the relevant elements. I suppose Structure could have meant how the sequence alignment was made, but if that were true, then DAB would only work for domain families for which a structure is available, instead of those for which only genomic or individual gene sequence has been provided. The ordering also seems unclear - wouldn't BBHs inform HMM domains, which then in turn inform domain architectures? Or if starting with BBHs, then how could architectures possibly be known prior to knowing the domains themselves? Or if it should be read from top to bottom as shown, how exactly does one start with Function  and then, somehow via Structure, thereby arrive at a Sequence alignment? Specifically, is a Pfam entry a "Function", from which the Sequence alignment is downloaded? Or are Function and the Sequence alignment both part of the starting Pfam entry ? From which domains are found (but aren't Pfam entries domains to begin with?), and then BBHs are made from the domain architectures? . In any case, as pointed out by other reviewers, the methodology used by DAB is not clearly explained in this figure, nor in the manuscript text.We have edited the Figure for clarity incorporating the reviewer’s suggestions.Also, the last paragraph of the Discussion uses the word "closeness", but I think "closedness" was intended.The typo has been amended. et al. describes a comparison of two approaches for clustering genomes sequences for the purpose of performing comparative genomics. The principle behind the two approaches, sequenced based clustering and domain based clustering, is described well in the introduction. The motivation of the article is clear and well founded. However, the details provided about how domain assignments are actually performed and handled throughout the experiment generated so many questions, these have clouded the validity of any conclusions. The article by KoehorstHow was InterPro used to assign a domain architecture? As the database presents a hierarchy of protein families and domains, unlike Pfam and TIGRFAM, there are numerous overlaps between the entries. Some of these are trivial C-terminal to N-terminal overlaps, while others are complex arrangements that cannot be simply represented as described. If three overlapping domains from InterPro are in the same hierarchy, which domain is used? If all member databases are used, this will account for the explosion of clusters in the InterPro based-clustering seen in Table 1. If InterPro accessions are used (e.g. as seen in the condensed view of a sequence on the InterPro website) then numbers are surprising.How were Family vs Domain “types” handled from InterPro or Pfam? In InterPro, type families tend to be near full length protein families. In Pfam, they represent a more heterogeneous bag of entries that are yet to be established as a ‘domain’.Pfam has a notion of related families, termed clans. Here the entries may not be intended to represent functionally distinct domains, but rather can represent a collection of families representing a continuum of evolution. How are entries belonging to a clan handled? How would the results differ if entries in one clan were treated as a single entity, for example, all P-loop NTPases as CL0023?  How does this influence the sequence cluster to domain architecture relationships .Why was the N-terminal starting position used to assess position of the domain? What is the effect of choosing the mid-point?Both Pfam and TIGRFAM use HMMER version 3, which uses local-local alignment algorithm. How are partial hits to an HMM handled? Would two partial domain matches that occur due to an insertion between two halves of a domain be treated differently ?     The use of domain architectures as an approach for accelerating sequence searching is not that novel, for example, CD-ART has been available for many years. Domain architecture views have been present in most domain databases  for over a decade, and used in genomic contexts. A more extensive overview of the use of domain architectures in the field is desirable.The composite graphs presented in Figures 6, 7 and supplementary figures use different scales, so make the graphs hard to compare.When the domain based clusters are compared to the sequence based clusters, it would be interesting to understand whether the number of domains that makes up the domain architecture influences the correlations to the sequence based clusters. Do single domain architectures predominated the 1:1 clusters?Many readers may be unaware of the thresholds employed in InterProScan relate to the individual databases, so greater clarity is required.Why is the versioned InterProScan described as a semantic wrapper?  Other comments:I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Thank you for the review, we have responded to your comments below:How was InterPro used to assign a domain architecture? As the database presents a hierarchy of protein families and domains, unlike Pfam and TIGRFAM, there are numerous overlaps between the entries. Some of these are trivial C-terminal to N-terminal overlaps, while others are complex arrangements that cannot be simply represented as described. If three overlapping domains from InterPro are in the same hierarchy, which domain is used? If all member databases are used, this will account for the explosion of clusters in the InterPro based-clustering seen in Table 1. If InterPro accessions are used (e.g. as seen in the condensed view of a sequence on the InterPro website) then numbers are surprising.All member databases in InterPro were used. We partly took into account trivial N- terminal overlaps by alphabetically ordering the domains when distances between starting position were <3 amino acids. After analysing the results, we agree that this was not enough and this is the most likely cause of the explosion of this of clusters. As the reviewer suggests, taking the the full hierarchy of protein families and domains within InterPro would be required for comparative genome analysis based on domain architectures. We have now better explained the selection criteria in the Materials and Methods section: The positions (start and end on the protein sequence) of domains having Pfam, TIGRFAMs and InterPro identifiers were extracted through SPARQL querying of the graph database and domain architectures were retrieved for each protein individually. InterPro aggregates protein domain signatures from different databases. Here no pruning for redundancies has been done. Identification of domains was done using the intrinsic InterPro cut-off that represents in each case the e-values and the scoring systems of the member databases. The domain starting position was used to assess relative position in the case of overlapping domains; alphabetic ordering was used to order domains with the same starting position or when the distance between the starting position of overlapping domains was <3 amino acids. Labels indicating N-C terminal order of identified domains were assigned to each protein in such a way that the same labels were assigned to proteins sharing the same domain architecture. We have commented on this point  in the discussion, where the use of InterPro is addressed. This paragraph now reads:      The chosen set of domain models and the database used as a reference greatly impact the results. InterPro aggregates protein domain signatures from different databases, which leads to redundancy of the domain models. This redundancy causes overlaps between the entries and an increase of the granularity of the clusters retrieved: this can bias downwards the size of the pan-genome and upwards the size of the core- genome, as shown in Table 1. In InterPro this redundancy is taken into account by implementing a hierarchy of protein families and domains. The entries at the top of these hierarchies correspond to broad families or domains that share higher level structure and/or function; the entries at the bottom correspond to specific functional subfamilies or structural/functional subclasses of domains \cite{mitchell_interpro_2015}. Using InterPro for DAB clustering would require taking into account the hierarchy of protein families and domains: however, this would pose challenges of its own and would require discrimination of the functional equivalence of different signatures within the same hierarchy. We have also added the following to the conclusion To enable DAB approaches for highly structured databases, such as InterPro, the hierarchy of protein families and domains within has to be explicitly considered.How were Family vs Domain “types” handled from InterPro or Pfam? In InterPro, type families tend to be near full length protein families. In Pfam, they represent a more heterogeneous bag of entries that are yet to be established as a ‘domain’.No distinction has been introduced as there don’t seem to be general rules that apply to all cases. In the discussion section a paragraph has been added on the effects of the structure of the databases.Pfam has a notion of related families, termed clans. Here the entries may not be intended to represent functionally distinct domains, but rather can represent a collection of families representing a continuum of evolution. How are entries belonging to a clan handled? How would the results differ if entries in one clan were treated as a single entity, for example, all P-loop NTPases as CL0023?  How does this influence the sequence cluster to domain architecture relationships .The reviewer raises here an interesting point that we have now discussed. The following has been added to the first paragraph of the discussion section. Another source of redundancy are functionally equivalent domains from distantly related sequences. Pfam represents this notion through related families, termed clans, where relationship may be defined by similarity of sequence, structure or profile-HMM. Clans might contain functionally equivalent domains, however it is not clear whether this is always the case as the criteria for clan definition includes functional similarity but not functional equivalence. Members of a clan have diverging sequences and very often SB approaches would recognize the evolutionary distance between the sequences and group them in different clusters. If we were to assume that members of a clan are functionally equivalent and collect them in the same DA cluster, we will have a higher number of cases where a single DA cluster is split in multiple sequence clusters 1d→Ns. Also there would be higher number of cases of sequence clusters with the same DA but no exactly matching the DA clusters (1s→1d cases).Why was the N-terminal starting position used to assess position of the domain?The following line has been rewritten in the Methods section Labels indicating N-C terminal order of identified domains were assigned to each protein using the starting position of the domains: the same labels were assigned to proteins sharing the same domain architecture.What is the effect of choosing the mid-point?We have commented on this in Results and Discussion. The following paragraph has been added: The starting position of the domains was used to generate labels indicating N-C terminal order of identified domains. The labels were used only for clustering as proteins sharing the same labels were assigned to the same clusters. Choosing instead the mid-point or the C-terminal position could affect the labeling but it not the obtained clusters.Both Pfam and TIGRFAM use HMMER version 3, which uses local-local alignment algorithm. How are partial hits to an HMM handled? Would two partial domain matches that occur due to an insertion between two halves of a domain be treated differently ?In the discussion we have added a subsection on the limitations on DAB approaches. There we have added the following:et al.). To reduce the number of partial domain hits additional pruning could be implemented to distinguish these cases. However, this is an open problem that requires caution as it could influence the functional capacity of an organism and clustering approaches using DA. Partial domain hits might arise as a result of alignment, annotation and sequence assembly artifacts  for over a decade, and used in genomic contexts. A more extensive overview of the use of domain architectures in the field is desirable.We have added the paragraph in the introduction regarding domain architectures, comparison of domain architectures and their use for sequence search. We have also discussed on how these have been included in domain databases and, as also suggested by the first reviewer,  on the preservation of domain architectures at high phylogenetic distances. The following paragraph has been added to the introduction:   Domain architectures have been shown to be preserved at large phylogenetic distances both in prokaryotes and eukaryotes . This lead to the use of protein domain architectures to classify and identify evolutionarily related proteins and to detect homologs even across evolutionarily distant species . Structural information encoded in domain architectures has also been deployed to accelerate sequence search methods and to provide better homology detection. Examples are CDART (Geer 2002) which finds homologous proteins across significant evolutionary distances using domain profiles rather than direct sequence similarity, or DeltaBlast (Boratyn 2012) where a database of pre-constructed position-specific score matrix is queried before searching a protein-sequence database. Considering protein domain content, order, recurrence and position has been shown to increase the accuracy of protein function prediction (Messih 2012) and has led to the development of tools for protein functional annotation, such as UniProt-DAAC (DougaFn 2016) which uses domain architecture comparison and classification for the automatic functional annotation of large protein sets. The systematic assessment and use of domain architectures is enabled by databases containing protein domain information such as UniProt (Uniprot Consortium 2015), Pfam (Finn 2016), TIGRFAMs (Haft 2003) and InterPro (Mitchell 2015), SMART (Letunic 2015) and PROSITE (Sigrist 2012), that also provide graphical view of domain architectures.The composite graphs presented in Figures 6, 7 and supplementary figures use different scales, so make the graphs hard to compare.  Figures 6 and 7 have been combined .When the domain based clusters are compared to the sequence based clusters, it would be interesting to understand whether the number of domains that makes up the domain architecture influences the correlations to the sequence based clusters.  Do single domain architectures predominated the 1:1 clusters?We have looked into this and single domain architectures predominated the 1:1 clusters. A table has been added to the text (Table 3).Many readers may be unaware of the thresholds employed in InterProScan relate to the individual databases, so greater clarity is required.This point was also raised by A. Rosato. We have further explained the selected thresholds in the material and methods. Identification of domains was done using the intrinsic InterPro cut-off that represents in each case the e-values and the scoring systems of the member databases.Why is the versioned InterProScan described as a semantic wrapper? This line has been re-written, now it is explained that the versioned InterProScan stores the output in the RDF data model. The authors present a very detailed and insightful analysis of the performance of alignment-based vs domain-based methods for comparative genomics. For the two methods, the proteins encoded by a selection of genomes are clustered based on pairwise sequence alignments or on their domain architectures, respectively. The first method is in principle more accurate and has higher coverage, whereas the second method is significantly faster and thus more suitable to cope with the explosion of genome information. The authors demonstrate that domain-based methods provide results that are well in line with alignment-based methods. Consequently, their speed advantage does not compromise accuracy. In addition, the authors suggest that the Pfam database works better than InterPro for the present clustering purpose.It is not clear to me why the labels within the colored boxes representing domains of Figure 1 differ in the top panel (Domain architectures) and the bottom panel (Domains)The new genome annotations generated by the authors should be made available to allow others to reproduce their calculations. It would be useful to have some data on the overall difference with respect to the original annotationThere are no details on the parameters used for domain identification such as E-value cut-offs. The latter has a strong impact on the number of singletons (1). It would be even more useful if the authors provided VMs with the complete setup for the entire procedure (from reannotation to clustering)The header SB is misaligned in Table 1. Why did the authors report the fraction of proteins containing at least one InterPro domain when the rest of the analysis is based on Pfam domains?I find the section "Comparison of DAB and SB clusters" difficult to read. In part this is due to the fact that the authors in the text describe actual numbers while Figures 6 and 7 report percentages. In particular, why should the " horizontal acquisition of the gene " reduce the sequence similarity score ? Furthermore, preservation of domain architecture at high phylogenetic distances has been extensively analyzed in the literature. References should be addedIt could be useful to combine Figures 6 and 7 to have a synoptic viewH. pylori or Cyanobacteria). How is this possible?Table 1 shows that InterPro domains provide pangenomes that are not only always larger than the pangenomes obtained from Pfam domains but sometimes even larger than SB-derived pangenomes  and the bottom panel (Domains)In the older version the labels in the top referred to the domain names whereas the labels on the bottom contained the PFAM identifiers. The figure has been changed so that only one set of labels is presented.The new genome annotations generated by the authors should be made available to allow others to reproduce their calculations. It would be useful to have some data on the overall difference with respect to the original annotation.de novo annotation in a set of 432 Pseudomonas genomes. In that case, an average difference of 153 genes per genome was detected. Differences in annotations were observed at all functional levels . The magnitude of the differences correlated with the date the original annotation. The manuscript is currently under review and we will include the reference as soon as it is published.The reviewer raises a very interesting topic that has been the focus of a different study. We have performed a detailed analysis of the differences between the original and thehttp://semantics.systemsbiology.nl/. Extensive documentation is available at http://sapp.readthedocs.io. A section (reproducibility) has been added indicating the workflow to reproduce the analysis here presented. We have included how annotations are compared. The SAPP annotation framework used to generate these files can be found atThere are no details on the parameters used for domain identification such as E-value cut-offs. The latter has a strong impact on the number of singletons (1).et al 2015]. This has been mentioned in the Material and Methods section:We agree that the choice of the E-value cut off plays a critical role on domain detection and greatly impacts the size of the core-genome. However, as reported in InterPro: “The signatures contained within InterPro are produced in different ways by different member databases, so their E-values and/or scoring systems cannot be meaningfully compared” therefore we have selected the intrinsic cutoff within InterPro [Mitchel Identification of domains was done using the intrinsic InterPro cut-off that represents in each case the E-values and the scoring systems of the member databases (Mitchel 2015).It would be even more useful if the authors provided VMs with the complete setup for the entire procedure (from reannotation to clustering)http://semantics.systemsbiology.nl/. Extensive documentation is available at:  http://sapp.readthedocs.io.  A section has been added indicating the workflow to reproduce the analysis here presented.The SAPP annotation framework used to generate these files can be found atThe header SB is misaligned in Table 1. Why did the authors report the fraction of proteins containing at least one InterPro domain when the rest of the analysis is based on Pfam domains?We have modified Table 1 and included an additional column with the fraction of proteins containing at least one Pfam domain.I find the section "Comparison of DAB and SB clusters" difficult to read. In part this is due to the fact that the authors in the text describe actual numbers while Figures 6 and 7 report percentages. In particular, why should the " horizontal acquisition of the gene " reduce the sequence similarity score ?We have rephrased the sentence on horizontal gene acquisition, it now reads: Similarly, there are 399 1s → 1d clusters. Each of these cases represent a sequence cluster where all the sequences share the same domain architecture, but other sequences exist with the same architecture that have not been included in the cluster due to a too low similarity score. The low similarity between sequences with the same domain architecture could be due to  a horizontal acquisition of the gene  or to a fast protein evolution at the sequence level. Genes acquired from high phylogenetic distances could greatly vary in sequence while presenting the same domain architecture.  Furthermore, preservation of domain architecture at high phylogenetic distances has been extensively analyzed in the literature. References should be added.The following paragraph has been added to the introduction:   Domain architectures have been shown to be preserved at large phylogenetic distances both in prokaryotes and eukaryotes . This lead to the use of protein domain architectures to classify and identify evolutionarily related proteins and to detect homologs even across evolutionarily distant species . Structural information encoded in domain architectures has also been deployed to accelerate sequence search methods and to provide better homology detection. Examples are CDART (Geer 2002) which finds homologous proteins across significant evolutionary distances using domain profiles rather than direct sequence similarity, or DeltaBlast (Boratyn 2012) where a database of pre-constructed a position-specific score matrix is queried before searching a protein-sequence database. Considering protein domain content, order, recurrence and position has been shown to increase the accuracy of protein function prediction (Messih 2012) and has led to the development of tools for protein functional annotation, such as UniProt-DAAC (Dougan 2016) which uses domain architecture comparison and classification for the automatic functional annotation of large protein sets. The systematic assessment and use of domain architectures is enabled by databases containing protein domain information such as UniProt (Uniprot Consortium 2015), Pfam (Finn 2016), TIGRFAMs (Haft 2003) and InterPro (Mitchell 2015), SMART (Letunic 2015)  and  PROSITE (Sigrist 2012), that also provide graphical view of domain architectures.It could be useful to combine Figures 6 and 7 to have a synoptic viewFigures 6 and 7 (and supplementary figures) have been combined.Table 1 shows that InterPro domains provide pangenomes that are not only always larger than the pangenomes obtained from Pfam domains but sometimes even larger than SB-derived pangenomes (e.g. H. pylori or Cyanobacteria). How is this possible?     InterPro aggregates protein domain signatures from different databases, which leads to redundancy of the domain models. This redundancy causes overlaps between the entries and an increase of the granularity of the clusters retrieved: this can bias downwards the size of the pan-genome and upwards the size of the core- genome, as shown in Table 1.The low value of alpha in the Heaps regression for L. monocytogenes afforded by the DAB is striking and should be analyzed in more detailWe thank the reviewer for this very interesting observation. We have investigated the low value of alpha in this case and the following paragraph has been addedL. monocytogenes  is strikingly low. Heaps law regression relies on the selected genomes providing a uniform sampling of selected taxon, here species. Analysis of the domain content of the selected genomes shows a divergent behaviour of strain LA111 (genome id GCA\_000382925-1). This behaviour is clear in Figure 7 (PCA), where GCA\_000382925-1 appears as an outlier of theL.monocytogenes group. Removal of these outlier leads to alpha DAB=1.04 and alpha SB=0.64, which emphasizes  the need for uniform sampling prior to Heaps regression analysis. The alpha DAB value retrieved forThe line break after "transfer events" in the second paragraph of the introduction is not neededThe line break has been removed.In the Supplementary material, SSB should SBThis typo has been fixed.