A hybrid clustering approach to recognition of protein families in 114 microbial genomes
© Harlow et al; licensee BioMed Central Ltd. 2004
Received: 23 October 2003
Accepted: 29 April 2004
Published: 29 April 2004
Grouping proteins into sequence-based clusters is a fundamental step in many bioinformatic analyses (e.g., homology-based prediction of structure or function). Standard clustering methods such as single-linkage clustering capture a history of cluster topologies as a function of threshold, but in practice their usefulness is limited because unrelated sequences join clusters before biologically meaningful families are fully constituted, e.g. as the result of matches to so-called promiscuous domains. Use of the Markov Cluster algorithm avoids this non-specificity, but does not preserve topological or threshold information about protein families.
We describe a hybrid approach to sequence-based clustering of proteins that combines the advantages of standard and Markov clustering. We have implemented this hybrid approach over a relational database environment, and describe its application to clustering a large subset of PDB, and to 328577 proteins from 114 fully sequenced microbial genomes. To demonstrate utility with difficult problems, we show that hybrid clustering allows us to constitute the paralogous family of ATP synthase F1 rotary motor subunits into a single, biologically interpretable hierarchical grouping that was not accessible using either single-linkage or Markov clustering alone. We describe validation of this method by hybrid clustering of PDB and mapping SCOP families and domains onto the resulting clusters.
Hybrid (Markov followed by single-linkage) clustering combines the advantages of the Markov Cluster algorithm (avoidance of non-specific clusters resulting from matches to promiscuous domains) and single-linkage clustering (preservation of topological information as a function of threshold). Within the individual Markov clusters, single-linkage clustering is a more-precise instrument, discerning sub-clusters of biological relevance. Our hybrid approach thus provides a computationally efficient approach to the automated recognition of protein families for phylogenomic analysis.
The comprehensive classification of proteins into similarity groups is an important but difficult challenge in post-genomic bioinformatics. These similarity groups might be based on e.g. common sequence, structure, or function. We are studying the evolutionary diversification of microbial genomes [1, 2] and therefore wish to group proteins into families based on sequence similarity for subsequent multiple alignment and inference of phylogenetic trees. The ongoing rapid appearance of new microbial genome sequences makes it imperative that this clustering be rapid, scalable, and automated to the extent possible.
Sets of protein sequences linked by pairwise matches can be clustered, and the resulting clusters interpreted as families [3, 4]. This has proven successful for protein domains (ADDA , DIVCLUS , PRODOM ) and for complete protein sequences (ProtoMap , SYSTERS ). Sequence similarity is first assessed pairwise, typically using BLAST , following which single-linkage clustering  is used to generate a hierarchy of internal nodes or subtrees. Sometimes FASTA  or another statistically based comparison tool is utilised in place of BLAST, or another criterion in place of single-linkage (SL) clustering; although our discussion below focuses on BLAST and SL clustering, the argument applies more generally to other pairwise comparison tools and clustering criteria.
This approach to recognition of clusters offers several important advantages. Protein family groups built up in this way are not simply unstructured lists, but natively possess an internal structure (topology) readily interpretable in the formalism of graphs, with vertices representing protein sequences, and edges representing pairwise matches. Each edge can be assigned a length that is inversely proportional to the strength of the corresponding pairwise BLAST match. Membership in these protein families can be made more, or less, stringent by adjusting a single threshold that defines when an edge is recognised to be present. This threshold can be based in a straightforward, intuitive way on the BLAST output (e.g. bit score or e-value). As the stringency of this threshold is (for example) increased, a given paralogous family will often be resolved into its constituent orthologous families.
There is, however, one major drawback to this naïve approach. At threshold values that are sometimes more stringent than required to constitute all orthologous (much less all paralogous) protein families, xenologous (evolutionarily unrelated) proteins begin to be drawn into these clusters, undermining their biological interpretability. This occurs for several reasons. At higher stringencies, BLAST may recognise so-called "promiscuous domains" common to otherwise unrelated proteins . At lower stringencies, BLAST may report weakly convergent motifs  or chance similarities. These non-specific clusters grow explosively in size with decreasing stringency, thereby preventing the useful extension of standard clustering down to the threshold values required to fully constitute most evolutionarily related protein families.
Alternative approaches are of course available, but are not necessarily appropriate for the problem at hand. Approaches based on machine learning, e.g. [15, 16], identify putative homologs even at low levels of sequence identify, but can be computationally very expensive. Putative remote homologs can likewise be recognised based on similarities in folded structure. However, inclusion of remote homologs is likely to be counterproductive for us, for three reasons: rigorous multiple alignment and inference of phylogenetic trees scale exponentially with number of sequences; alignment of weakly similar sequences is problematic; and weakly supported branches contribute little or no biological information to our analyses. For this and similar applications, it is therefore far better that remote homologs be excluded from the analysis pipeline at the clustering stage, rather than later.
Recently, an alternative approach based on the Markov Cluster algorithm (MCL: ) has been introduced to comparative genomics . The MCL algorithm simulates random walks through a graph (e.g. of sequences as vertices, and edges as pairwise matches). By iteratively re-computing random walks and favouring those with higher probability (which tend to be intra-cluster walks) over those with low probability (which tend to be inter-cluster walks), the algorithm partitions the graph into segments that can be interpreted as clusters [4, 17]. Computation is rapid and, in application to molecular sequences, the Markov Cluster algorithm produces clusters that resist contamination by promiscuous domains. However, Markov clusters are unstructured lists without internal topology, and as such do not yield information useful to many biologists, e.g. a hierarchical ordering of orthologs. Information about edge lengths (strength of BLAST matches) is lost (transformed into stochastic Markov probabilities), making it very difficult to conceptualise these clusters in terms familiar to biologists. How aggressively the Markov clusters find membership (i.e., the resulting granularity) can be adjusted only via operators that may have limited useful dynamic range, and are not intuitive to most biologists.
Here we present a hybrid approach to recognizing protein families among very large (multi-genome) datasets. Our hybrid approach preserves the advantages of single-linkage (SL) clustering identified above, but captures the power of the Markov Cluster algorithm to avoid indiscriminate cluster membership. As quality control, we apply our approach to a manually curated database, Protein Data Bank , and report how the Structural Classification of Proteins (SCOP) database families and domains  map onto the Markov clusters. We demonstrate the application of hybrid clustering to a problem that cannot be usefully addressed by either SL or the Markov Cluster algorithm alone, recognition of orthologs and paralogs of rotary motor ATP synthase F1 subunit proteins , and to a 114-genome dataset of protein sequences. Finally, we describe how clusters with non-redundant genome coverage ("maximally representative clusters", or MRCs) can be selected automatically from the output of our hybrid method, for subsequent analysis e.g. in a phylogenomic pipeline.
Results and discussion
Markov clustering of PDB (as of 25 February 2003) at selected inflation (I) values. This version of PDB contains 2147 SCOP families and 4526 SCOP domains. There are 1340 SCOP families among the 6435 entries/IDs annotated with one SCOP family each, and 2621 SCOP domains among the 6430 entries/IDs annotated with one SCOP domain each.
Number of clusters (N ≥ 2)
pure clusters by SCOP family
with 1 SCOP family
with 2 SCOP families
with ≥ 2 SCOP families
most families in 1 cluster
Number of clusters (N ≥ 2)
pure clusters by SCOP domain
with 1 SCOP domain
with 2 SCOP domains
with ≥ 2 SCOP domains
most domains in 1 cluster
Number of families in all clusters (N ≥ 2)
with all members in 1 cluster
with all members in 2 clusters
with all members in 3 clusters
with all members in ≥ 3 clusters
most clusters 1 family occurs in
Number of domains in all clusters (N ≥ 2)
with all instances in 1 cluster
with all instances in 2 clusters
with all instances in ≥ 2 clusters
most clusters 1 domain occurs in
The MCL inflation parameter I, which alters the relative probabilities of within-cluster and between-cluster random walks, is the main parameter by which users can adjust cluster granularity [4, 17]. At inflation value I = 1.00, 89.5% of all n ≥ 2 clusters are "pure" by SCOP domain, i.e. contain all instances of any SCOP domain represented in that cluster. Markov clusters found at I = 1.10 are slightly more pure by SCOP domain (89.7%) but purity diminishes somewhat thereafter with increasing graininess, to 79.5% at I = 5.00 (Table 1). Looking instead at SCOP families, the Markov clusters are less pure, ranging from 56.1% at I = 1.00 to 38.5% at I = 5.00. These percentages reflect the relative granularity SCOP families and SCOP domains within this subset of PDB: at I = 1.00, for example, the 761 Markov clusters of N ≥ 2 contain 1852 SCOP domains but only 831 SCOP families.
Among the n ≥ 2 Markov clusters that are pure by SCOP family, 88–92% (depending on granularity) contain only a single SCOP family, and >97% contain either 1 or 2 SCOP families. 51–60% of these clusters contain a single SCOP domain, and 77–85% either 1 or 2 SCOP domains. Cluster purity tends to increase slightly with increasing granularity: higher inflation values yield more and cleaner clusters (Table 1).
As clusters become finer-grained, individual SCOP families tend to become distributed among more clusters: the proportion fully contained within a single cluster drops from 69% (I = 1.00) to 56% (I = 5.00). Nevertheless, most (87–93%) families have all their members in ≤ 3 clusters throughout this range of inflation values. SCOP domains show a similar but less-pronounced trend, decreasing from 94% (I = 1.00) to 88% (I = 5.00) within a single Markov cluster. Most (97–98%) domains have all their members in ≤ 2 clusters.
We carried out single-linkage clustering (i.e. completed our hybrid method) on the pure Markov clusters that have ≥ 2 SCOP families each. At I = 1.00, for instance, there are 427 - 380 = 47 such clusters. By raising the clustering threshold S'norm (see Methods) we cleanly resolve 30 of these into constituent families (each in its own pure cluster); in the other 17 clusters, at least one family fragments (is not resolved into a pure cluster). Similarly, among 323 pure Markov clusters with ≥ 2 SCOP domains each, hybrid clustering resolves 292 cleanly, while 31 exhibit domain fragmentation (results not shown). The numbers of fragmenting families and domains decrease at higher inflation values (results not shown).
Characterisation of hybrid clusters from multi-genome protein data
In the context of our research on the evolutionary diversification of microbial genomes [1, 2], the purpose of protein clustering is to generate sets of orthologs  that can be taken forward into subsequent analysis steps (multiple sequence alignment, phylogenetic inference, and topological comparison of subtrees). Protein families represented exactly once in each genome are promising candidates for being both ancient and orthologous. Over the entire hybrid cluster space (i.e. all clusters at all thresholds examined, from 1.01 through 0.01) for the 328577 proteins in these 114 microbial genomes, there are 18 clusters (n ≥ 4) of size 114 in which all 114 genomes are represented; 5 clusters of size 113 in which 113 genomes are represented; and 3 of size 112 representing 112 genomes. Twenty-four of these 26 clusters are ribosomal proteins (the other two are phenylalanyl-tRNA synthetase α chain, and an O-sialoglycoprotein endopeptidase).
Because our cluster data are stored under a relational data model (in our case, in Oracle), these and even more-complex queries – for example, involving successive relaxation of the admittedly strict criterion used here for recognizing an MRC – can easily be made using very short SQL scripts.
Example: ATP synthase paralogous protein families
Under our hybrid (Markov Clustering plus SL) approach at I = 1.10, the cluster history is identical to that described immediately above for the SL approach down through S'norm 0.24. The 96 α subunits and (separately) the Methanosarcina acetivorans C2A predicted protein gi|20090748 join them at 0.21, the 38 A subunits at 0.18, and the 80 ρ subunits at 0.13. The Bradyrhizobium japonicum unannotated protein gi|27377965 joins at S'norm 0.06, and the Agrobacterium tumefaciens C58 hypothetical protein gi|17938963 at 0.04. This 433-member paralogous cluster is thus fully constituted at S'norm 0.04 (or at 0.13, if the two outliers are spurious matches), and remains cohesive until at least S'norm 0.01, the lowest threshold we examined. The size of the paralogous cluster depends on granularity (i.e. on parameterisation of I), and the exact threshold value at which a sequence or group of sequences joins a cluster is to some extent data-dependent; but we observed little or no non-specificity within the range of inflation parameter settings we examined for these data.
The results reported above demonstrate that our hybrid (Markov followed by single-linkage) clustering approach efficiently sorts protein sequences into biologically meaningful clusters that are not accessible by SL clustering alone. The hybrid clusters retain intuitively meaningful topological and ordered edge-length information not immediately available using only MCL, as illustrated specifically by our recovery of all six ATP synthase F1 rotary motor subunit paralogous families, and more globally by the extent of SCOP-to-PDB mapping for protein sequences encoded by 114 microbial genomes.
For the well-behaved subset of PDB we examined, MCL preserves most SCOP domains intact, although the (intrinsically larger) SCOP families can be distributed among several Markov clusters within this range of granularities. Most pure clusters (as defined above) contain a single SCOP family. A very substantial majority of domains within multi-domain clusters, and most families within multi-family clusters, are cleanly resolved during the SL stage of our hybrid method. As the SCOP classification of proteins is based neither on evolutionary principles nor on primary-sequence similarity, it is unrealistic to expect perfect concordance between our hybrid clusters and SCOP families or domains.
Enright et al.  used a somewhat different approach to test the effectiveness of MCL in clustering protein sequences. Their analysis of the full PDB yielded 1167 families at I = 1.10 and 1761 families at I = 5.00, i.e. 50.9% increase, whereas for our subset of PDB we found 831 and 812 n ≥ 2 families at these inflation values respectively; this suggests that the extra graininess observed by Enright et al. reflects the formation of many single-member clusters, which although important in some contexts would not be useful for phylogenetic analysis. These authors reported that 79–87% (depending on the MCL inflation value) of proteins were clustered consistently, as assessed against annotation by domain or domain combination. This validation statistic is not easily commensurate with the (in our opinion) more transparent statistics we present herein (above and Table 1), but was interpreted by Enright et al.  as indicating that MCL "accurately and consistently assign(s) proteins into families, despite the fact that this classification relies on structural similarities, which are not all readily detectable at the sequence level". We argue similarly for our results.
For both the 114-genome protein data set in general, and ATP synthase F1 subunits in particular, SL clustering alone fails progressively at thresholds below about S'norm 0.50. This failure is due to non-specific attraction into a large, non-specific cluster. Depending on the data, granularity and probably other factors, more than one such large non-specific cluster can exist fleetingly, but all are soon attracted into a single large "blob" that grows extremely rapidly and eventually takes in most proteins in these genomes. By not allowing edges to be recognised between proteins in different Markov clusters, we prevent the formation of this blob.
Individual Markov clusters may, of course, contain paralogous or, possibly, even non-related sequences; these are resolved into families during the SL step. With the MCL software, the inclusiveness of Markov clusters is determined by the value of the inflation parameter I. The range of I values accepted by MCL  produces only a limited dynamic range of cluster sizes (Figure 3). We hypothesise (based on e.g. the ATP synthase example) that as the mean cluster size increases, at least the larger clusters are more likely to contain paralogous proteins. This will be tested by iterative clustering, alignment and tree inference when our pipeline is in place. We also intend to examine the extent to which the hierarchical course of cluster formation with decreasing S'norm threshold approximates the inferred phylogeny, and whether MRCs tend to be orthologous sets.
At a more algorithmic level, our results illustrate the complementarity between the Markov Clustering algorithm and hierarchical linkage-based clustering in application to these data. MCL operates not so much on the absolute differences among edge lengths, but rather on the overall density structure of the edge-length data . The result is a partitioning of edge space that avoids the formation of non-specific (hence biologically meaningless) groupings, but does not produce the degree of resolution needed to resolve most orthologous protein families. Single-linkage clustering imposes an absolute view of edge-length differences that works with precision locally, but fails globally. MCL thus provides SL with the local environments to which it is best suited, and restricts it from the global context in which it can fail.
We have demonstrated that our hybrid approach can be implemented efficiently in conjunction with a relational database structure, with results saved automatically and queries conducted using generic SQL commands. The hybrid method is fast, and is appropriate for problems where remote homologs are not needed or wanted. It has already proven valuable as part of an automated inference pipeline for studying patterns of vertical and lateral gene transfer among microbial genomes.
PDB, SCOP families and SCOP domains
Protein Data Bank (25 February 2003) contains 17187 PDB IDs, of which 14548 have only one sequence entry and 2639 have ≥ 2 entries (e.g. the ribosome). PDB contains 13764 sequence entries, of which 11353 have only 1 ID each and 2411 have ≥ 2 IDs (e.g. lysozyme under different crystallisation conditions). There are 8180 entries (and IDs) in the intersection of the 14548-entry and 11353-ID sets above, and 7555 of these have a SCOP annotation. Of these 7555, there are 6435 annotated with exactly one SCOP family each, and 6430 with exactly one SCOP domain each; the 6430 are a fully contained subset of the 6435. When using SCOP families (clusters) to interpret the clustering of PDB, we consider only these 6435 (or 6430) to ensure one-to-one mappings among PDB entries, PDB IDs, and SCOP families (or domains). Family and domain information was obtained from the parseable file dir.cla.scop.txt version 1.63 available at http://scop.mrc-lmb.cam.ac.uk/scop/parse/index.html.
Our genomic dataset consisted of 328577 proteins from the 114 fully sequenced microbial genomes publicly available (NCBI) in May 2003 [see Additional file 2]. Sequences were stored in an Oracle 9i database environment on a Sun E450 cluster, and indexed by gi number. Queries (including those returning all statistics presented herein) were presented to the databases using generic SQL.
All-versus-all BLASTp  used NHGRI blastall with default settings, including low-complexity filtering, except that word size was set to 2. All BLASTp output was saved, but only output (bit scores) corresponding to matches for which expectation e ≤ 10-3 was used throughout this work (i.e. for analyses of PDB, multi-genome data and individual protein datasets by single-linkage, Markov and hybrid clustering).
Relatedness between each protein pair (a, b) was calculated as follows: with a as query and b as target, we normalised  the BLASTp bit score S' ab by dividing by S' aa ; then for b as query and a target, we normalised S' ba by dividing by S' bb . The relatedness of a and b is defined as max(S' ab /S' aa , S' ba /S' bb ). Thus each edge recognised as present (i.e. having e ≤10-3 in at least one direction) is assigned a single scalar value (S'norm) between 0 and 1. Due to rounding, scores can slightly exceed 1. Edge values were also stored in the Oracle database at three decimal places precision.
ATP synthase subset
We analysed the clustering of ATP synthase F1 subunit paralogs as a function of threshold [see Additional file 1] using the 114-genome dataset (Results, and Figure 7). Orthology and paralogy were assessed manually based on annotations available in public genome databases (i.e. as supplied by the various genome projects); a more rigorous approach would use phylogenetic analysis (the goal of our automated pipeline project).
We also used the ATP synthase F1 dataset to establish the default setting of the MCL inflation parameter I. Working with an earlier, 84-genome (235970 protein) version of this dataset, by string search of annotation lines we found the largest SL cluster that contained only proteins annotated as homologs of ATP synthase rotary motor subunits. This contained F1 ATPase α (64 proteins) and β subunits (66); archaeal/vacuolar ATPase A (32) and B (24); bacterial flagellar assembly ATPase subunits (53); and termination factor ρ (56) (total, 295 proteins). We added to this cluster all 11083 proteins that match one or more of these 295 at S'norm 0.20, and carried out MCL at values of I between 1.00 and 5.00. Based on these results, we selected a default value (I = 1.10) small enough to group all the paralogous subunits of ATP synthase F1 within a single cluster, but large enough to avoid the extremely long computation times required for inflation values nearer 1.00.
Single-linkage clustering in a relational environment
Single-linkage clustering was initiated with the most-similar matches (those with S'norm ≥ 1.01, as the maximum S'norm observed was 1.012) and proceeded in 0.01 stepwise intervals to S'norm 0.01. Clusters were recognised, and allowed to grow and/or merge with others, as relaxation of S'norm threshold progressively caused additional proteins (vertices) to join through valid matches (edges). The S'norm threshold values we refer to must be understood in context. Recall that we store normalised edge length data to three decimal places precision, but step through these data in increments of 0.01. If two proteins (or groups of proteins) are in the same cluster at (for example) S'norm 0.465 but in different clusters at S'norm 0.466, we could say that they "join at S'norm 0.46" or that they "split at S'norm 0.47". The sets of dual vertical lines in Figure 7 are intended to convey this nuance.
Our automated phylogeny inference pipeline is being implemented over a relational environment for reasons well beyond the scope of work presented in this paper. We therefore implemented SL clustering over Oracle 9i to facilitate data coordination with the larger project. We processed (at each threshold) the ordered S'norm edge data, writing a series of new cluster-state information (membership) tables. We do not store topology tables per se, but reconstruct topologies from membership plus edge (S'norm) data. After the list is processed, clusters (graphs) are labelled in ascending order of S'norm. Graphs present at S'norm 0.01 are arbitrarily numbered 1, 2, etc. and as these graphs fragment with stepwise increase in S'norm, the labels are extended. Thus if the graph labelled 1 fragments at a higher value of S'norm, the fragments (having n ≥ 2 members) are arbitrarily labelled 1.1, 1.2 etc. Similarly, if 1.1 fragments further at a higher S'norm, its daughters (n ≥ 2) are labelled 1.1.1, 1.1.2, etc.
The Markov Cluster algorithm was implemented using MCL (available from http://micans.org/mcl) and was applied with inflation parameter typically set to I = 1.10, and with other parameters at default values. Cluster membership was stored in ordered Oracle tables as described above. With PDB we clustered all sequences, but subsequently used only the 6435 (or 6430) entries/IDs identified above.
Our hybrid clustering method was carried out in two stages, as follows. First, we processed the entire dataset (valid edges with S'norm ≥ 0.01) with MCL, yielding Markov clusters (ordered tables). We then conducted SL clustering as above, but on only those edges that have both ends (proteins) within the same Markov cluster; edges that span Markov clusters (i.e. link proteins in different Markov clusters) were ignored. We again wrote tables of clustered proteins at each threshold, and backtracked to label graphs.
Cluster purity, family/domain distribution, and family/domain splitting
We define a Markov cluster to be pure if it contains only SCOP families (or domains) in their entirety. A pure cluster can contain multiple SCOP families (or domains) so long as each family (or domain) is contained in its entirety. If any member of any of the families (or domains) is external to that cluster, the cluster is not pure. SCOP families (or domains) are distributed over multiple clusters if members of that family (or domain) are found in more than one cluster (n ≥ 2). A SCOP family (or domain) is split if one or more, but not all, of its members occurs in a cluster (n ≥ 2) together with at least one member of at least one different family (or domain). The non-included members of the split family (or domain) do not need to be included in a cluster.
We thank the anonymous referees for constructive comments. We acknowledge support of ARC grants DP0342987 and CE0348221. JPG thanks The University of Queensland for a travel grant.
- Ragan MA, Charlebois RL: Distributional profiles of homologous open reading frames among bacterial phyla: implications for vertical and lateral transmission. Intl J Syst Evol Microbiol 2002, 52: 777–787. 10.1099/ijs.0.02026-0Google Scholar
- Raymond J, Zhaxybayeva O, Gogarten JP, Gerdes SY, Blankenship RE: Whole-genome analysis of photosynthetic prokaryotes. Science 2002, 298: 1616–1620. 10.1126/science.1075558View ArticlePubMedGoogle Scholar
- Tatusov RL, Natale DA, Garkavtsev IV, Tatusova TA, Shankavaram UT, Rao BS, Kiryutin B, Galperin MY, Fedorova ND, Koonin EV: The COG database: new developments in phylogenetic classification of proteins from complete genomes. Nucl Acids Res 2001, 29: 22–28. 10.1093/nar/29.1.22PubMed CentralView ArticlePubMedGoogle Scholar
- Enright AJ, Van Dongen S, Ouzounis CA: An efficient algorithm for large-scale detection of protein families. Nucl Acids Res 2002, 30: 1575–1584. 10.1093/nar/30.7.1575PubMed CentralView ArticlePubMedGoogle Scholar
- Heger A, Holm L: Exhaustive enumeration of protein domain families. J Mol Biol 2003, 328: 749–767. 10.1016/S0022-2836(03)00269-9View ArticlePubMedGoogle Scholar
- Park J, Teichmann SA: DIVCLUS: an automatic method in the GEANFAMMER package that finds homologous domains in single- and multi-domain proteins. Bioinformatics 1998, 14: 144–150. 10.1093/bioinformatics/14.2.144View ArticlePubMedGoogle Scholar
- Servant F, Bru C, Carrere S, Courcelle E, Gouzy J, Peyruc D, Kahn D: ProDom: automated clustering of homologous domains. Brief Bioinform 2002, 3: 246–251.View ArticlePubMedGoogle Scholar
- Yona G, Linial N, Linial M: ProtoMap: automatic classification of protein sequences and hierarchy of protein families. Nucl Acids Res 2000, 28: 49–55. 10.1093/nar/28.1.49PubMed CentralView ArticlePubMedGoogle Scholar
- Krause A, Stoye J, Vingron M: The SYSTERS protein sequence cluster set. Nucl Acids Res 2000, 28: 270–272. 10.1093/nar/28.1.270PubMed CentralView ArticlePubMedGoogle Scholar
- Altschul SF, Madden TL, Schäffer AA, Zhang J, Zhang Z, Miller W, Lipman DJ: Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucl Acids Res 1997, 25: 3389–3402. 10.1093/nar/25.17.3389PubMed CentralView ArticlePubMedGoogle Scholar
- Sokal RR, Sneath PHA: Principles of Numerical Taxonomy. London: Freeman 1963.Google Scholar
- Pearson WR, Lipman DJ: Improved tools for biological sequence analysis. Proc Natl Acad Sci USA 1988, 85: 2444–2448.PubMed CentralView ArticlePubMedGoogle Scholar
- Marcotte EM, Pellegrini M, Ng HL, Rice DW, Yeates TO, Eisenberg D: Detecting protein function and protein-protein interactions from genome sequences. Science 1999, 285: 751–753. 10.1126/science.285.5428.751View ArticlePubMedGoogle Scholar
- Smith TF, Zhang X: The challenges of genome sequence annotation or "The devil is in the details". Nat Biotechnol 1997, 15: 1222–1223.View ArticlePubMedGoogle Scholar
- Karplus K, Barrett C, Hughey R: Hidden Markov models for detecting remote protein homologies. Bioinformatics 1998, 14: 846–856. 10.1093/bioinformatics/14.10.846View ArticlePubMedGoogle Scholar
- Bateman A, Birney E, Cerruti L, Durbin R, Etwiller L, Eddy SR, Griffiths-Jones S, Howe KL, Marshall M, Sonnhammer ELL: The Pfam Protein Families Database. Nucl Acids Res 2002, 30: 276–280. 10.1093/nar/30.1.276PubMed CentralView ArticlePubMedGoogle Scholar
- van Dongen S: Graph clustering by flow simulation. PhD thesis. University of Utrecht 2000. [http://micans.org/mcl/lit/svdthesis.pdf.gz]Google Scholar
- Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE: The Protein Data Bank. Nucl Acids Res 2000, 28: 235–242. 10.1093/nar/28.1.235PubMed CentralView ArticlePubMedGoogle Scholar
- Lo Conte L, Brenner SE, Hubbard TJP, Chothia C, Murzin AG: SCOP database in 2002: refinements accommodate structural genomes. Nucl Acids Res 2002, 30: 264–267. 10.1093/nar/30.1.264PubMed CentralView ArticlePubMedGoogle Scholar
- Stock D, Leslie AGW, Walker JE: Molecular architecture of the rotary motor in ATP synthase. Science 1999, 286: 1700–1705. 10.1126/science.286.5445.1700View ArticlePubMedGoogle Scholar
- Fitch WM: Aspects of molecular evolution. Annu Rev Genet 1973, 7: 343–380. 10.1146/annurev.ge.07.120173.002015View ArticlePubMedGoogle Scholar
- Bansal AK, Bork P, Stuckey PJ: Automated pair-wise comparisons of microbial genomes. Math Modelling Sci Comput 1998, 9: 1–23.View ArticleGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article: verbatim copying and redistribution of this article are permitted in all media for any purpose, provided this notice is preserved along with the article's original URL.