- Research article
- Open Access
Comparative genomics of four closely related Clostridium perfringens bacteriophages reveals variable evolution among core genes with therapeutic potential
© Oakley et al; licensee BioMed Central Ltd. 2011
- Received: 21 January 2011
- Accepted: 1 June 2011
- Published: 1 June 2011
Because biotechnological uses of bacteriophage gene products as alternatives to conventional antibiotics will require a thorough understanding of their genomic context, we sequenced and analyzed the genomes of four closely related phages isolated from Clostridium perfringens, an important agricultural and human pathogen.
Phage whole-genome tetra-nucleotide signatures and proteomic tree topologies correlated closely with host phylogeny. Comparisons of our phage genomes to 26 others revealed three shared COGs; of particular interest within this core genome was an endolysin (PF01520, an N-acetylmuramoyl-L-alanine amidase) and a holin (PF04531). Comparative analyses of the evolutionary history and genomic context of these common phage proteins revealed two important results: 1) strongly significant host-specific sequence variation within the endolysin, and 2) a protein domain architecture apparently unique to our phage genomes in which the endolysin is located upstream of its associated holin. Endolysin sequences from our phages were one of two very distinct genotypes distinguished by variability within the putative enzymatically-active domain. The shared or core genome was comprised of genes with multiple sequence types belonging to five pfam families, and genes belonging to 12 pfam families, including the holin genes, which were nearly identical.
Significant genomic diversity exists even among closely-related bacteriophages. Holins and endolysins represent conserved functions across divergent phage genomes and, as we demonstrate here, endolysins can have significant variability and host-specificity even among closely-related genomes. Endolysins in our phage genomes may be subject to different selective pressures than the rest of the genome. These findings may have important implications for potential biotechnological applications of phage gene products.
- Core Genome
- Domain Architecture
- Phage Genome
- Pfam Family
Concerns over the spread of antibiotic resistances among bacteria have led to a ban on antimicrobial additives to animal feeds in the European Union (EU) [1, 2]. Since its enactment in 2006, the EU-wide ban on the use of antibiotics in animal feed (Regulation 1831/2003/EC) has stimulated a renewed interest in bacteriophage biology and the use of phages and/or phage gene products as alternative antibacterial agents [3, 4]. Prior to the discovery and widespread use of antibiotics, bacterial infections were commonly treated by administering bacteriophages which were marketed and sold commercially for human use up until the 1940's. Bacteriophages continue to be sold in the Russian Federation and Eastern Europe as treatments for bacterial infections .
Recently our laboratory reported the genomic and molecular biological characteristics of two phages isolated from poultry intestinal material and poultry processing drainage water by screening for virulent Clostridium perfringens bacteriophages [6, 7] and demonstrated efficacy of the lytic proteins encoded by the bacteriophage endolysins as a C. perfringens antimicrobial . These phages belonged to the Siphoviridae, a family within the tailed phages. The tailed bacteriophages belong to the order Caudovirales, have icosohedral heads, contain a linear, double-stranded DNA genome that can vary from 17 to 500 kb, and represent ca. 95% of all the bacteriophages examined by electron microscope . Caudovirales are further divided into three families based on tail morphology: phages with contractile tails are placed in the Myoviridae, those with short tails are members of the Podoviridae, and phages with a long non-contractile tail belong to the Siphoviridae[10, 11].
Clostridium perfringens is a Gram-positive, spore forming, anaerobic bacterium that is the 2nd leading bacterial cause of foodborne illness in the U.S., accounting for 10% of foodborne illnesses . C. perfringens can cause food poisoning, gas gangrene (clostridial myonecrosis), enteritis necroticans, and non-foodborne gastrointestinal infections in humans and is a veterinary pathogen causing enteric diseases in both domestic and wild animals [13, 14]. C. perfringens is considered the cause of necrotic enteritis among chickens, and although this does not generally present a threat to humans, it could potentially become a far greater problem for the poultry industry and consumers if antibiotics are withdrawn from animal feeds [13, 14].
Bacteriophages have evolved a wide variety of anti-microbial compounds that can control C. perfringens and other pathogens and are of potential biotechnological importance. To realize this potential, it is essential to have a blueprint of the genomic machinery underlying phage-mediated bacterial lysis. Here we report the results of comparative analyses based on genome sequences of four newly isolated C. perfringens phages and focus on the genomic context and evolution of the phage endolysin genes.
Core and accessory genomes of Clostridial phages
To determine if our phages contain a common set of genes shared with other Clostridial phages, we compared predicted ORFs based on classifications of clusters of orthologous groups (COGs) among the three host groups shown in Figure 1. COGs represent individual proteins or groups of paralogs from at least three lineages corresponding to ancient conserved domains (http://www.ncbi.nlm.nih.gov/COG/) and thus provide an informative means to compare conserved functions across genomes .
Statistical associations between domain architecture and phylogeny
To compare our phage sequences and domain architecture to others, we retrieved amidase sequences belonging to the pfam protein family PF01520 from 26 publicly available bacteriophage genomes (Additional file 1, Table S1) and analysed these as fully described in the methods. Bacteriophage endolysins typically contain two domains: an enzymatically active domain and a cell wall binding domain, some of which have been elucidated with crystal structures . We constructed an alignment of both putative domains after building a Hidden Markov Model from representative sequences in the Conserved Domain Database belonging to PF01520 and considering only columns with >10% sequence conservation to eliminate highly variable positions and control for sequence length heterogeneity.
Second, to better understand the genomic context of the amidase protein and associated holin genes, we used the same statistical approaches to formally compare the association between the domain architecture and phylogeny of the amidase protein. The five phages sequenced by our group belong to their own clade within the amidase tree and were the only genomes in which the holin is immediately downstream of the amidase protein in the presumed direction of transcription, a reversed arrangement of the typical domain architecture (Figure 3). Interestingly, though Φ3626, ΦC2, and ΦCD27 belonged to a sister clade, this domain architecture was unique even among these other Clostridial phages (Figure 3). To confirm this domain architecture for our phages, we re-sequenced the appropriate regions of Φ9O, Φ13O, Φ26F, Φ34O, and several other phage isolates, all of which shared the amidase-holin arrangement. Holin genes were identified using multiple sequence-similarity approaches as described in detail in the methods, and included identifications of transmembrane domains. The association between gene phylogeny and domain architecture was strongly significant as determined by UniFrac (p < 0.001) and P tests (p < 0.001).
Because lysis of bacterial cells generally requires both an endolysin and a holin - membrane disruption (the function of the holin) is considered to be requisite for the endolysin to attack the peptidoglycan  - understanding the phylogenetics and genomic context of these genes are important milestones to develop biotechnological applications. The unusual domain architecture we observed suggests that either the typical gene order or the reverse is a successful evolutionary strategy. The transcriptional regulation of these genes in our phages remains unknown, but searches for transcriptional promoters and terminators using BPROM (Softberry, Inc., Mount Kisco, NY, USA; http://linux1.softberry.com/berry.phtml) and TransTerm (http://nbc3.biologie.uni-kl.de) did not find either within the regions of our endolysin and holin genes; these genes may be co-transcribed. Efficacy of the endolysin as recently demonstrated for phages ΦCP26F and ΦCP39O  could potentially be improved by successful holin purification.
Genomic arrangement and context of orthologs
Conservation and variability of core genome
In the conserved core genome, genes within each of the 12 pfam families were very similar to each other, with a maximum pairwise sequence difference of 8% based on amino acid alignments with bl2seq (Figure 5b). Genes belonging to these 12 pfam families were involved in the following functions: tail protein, phage anti-repressor, ssDNA binding, portal protein, minor structural protein GP20, hydrolase, CHC2 zinc finger, terminase large subunit, virulence-associated protein E, and the holin (Figure 5b).
The holin genes were among the most conserved, with 100% identity among all sequences, and the amidase genes were the most variable (Figure 5b), suggesting these two genes are subject to very different rates of evolution despite their colocation in the genome and paired function in the lytic cycle. Holins target the relatively invariable cytoplasmic membrane, while phage endolysins recognize and degrade the cell wall, which is highly variable. It has been suggested that holins may function as a type of lysis clock, governing the timing of lysis of the host . As the primary determinant of the length of the infective cycle, holins can be considered to experience stabilizing selection as there are opposing fitness advantages to extending the vegetative cycle and allowing phage replication versus lysing the host to release progeny phage to infect new host cells . In contrast, the phage endolysins generally contain an enzymatically active domain and a cell-wall binding domain which recognizes highly-specific ligands on the host cell surface , and thus each domain is under strong directional selective pressures. Our data clearly show strong sequence conservation of the holin protein, and very distinct sequence types within the associated amidase for a group of closely related phages.
Detailed sequence comparisons of variable core genome and association with host genotype
For the four pfam families with known functions in the variable core genome, multiple-sequence alignments of the four genomes presented here and ΦCP39O sequenced previously by our group  revealed some striking differences in amino acid length and content. For all four proteins, two very distinct sequence types were represented.
The tape measure proteins (PF10145/COG5412) of ΦCP26F, ΦCP9O, and ΦCP39O were all 780AA long and 96% similar to each other and quite different from those of ΦCP13O and ΦCP34O. The tape measure proteins of ΦCP34O and ΦCP13O were 95% similar to each other, but only 473AA residues in length with a 225 AA N-terminal portion of the protein encoded by another ORF immediately upstream in the genome. For the portion of the protein encoded by a single reading frame, alignments of these five sequences revealed a deletion of 89 residues in the tape measure proteins of ΦCP34O and ΦCP13O (Figure 6b). Whether these represent gene fissions or fusions, or insertions or deletions relative to the ancestral state remains unknown, as do the consequences for the structure and function of the protein, but clearly these questions warrant further study.
For the thymidylate synthase (PF02511/COG1351), the phage relatedness patterns were the same as for the tape measure protein, with ΦCP34O and ΦCP13O containing a similar genotype distinct from that of ΦCP9O and ΦCP39O, largely defined by a variable region from residues 93-139 (Figure 6c). Similarly, the P22 coat proteins (PF11651) of ΦCP13O and ΦCP34O were distinct from those shared by ΦCP9O, ΦCP26F, and ΦCP39O (Figure 6d).
In contrast to these groupings, genomic fingerprints of the C. perfringens host based on rep-PCR defined three main host groups: 1) Cp34O and Cp9O, 2) Cp13O and Cp39O, and 3) Cp26F as a more distantly related group (Figure 6e). Interestingly, the single gene phage similarities based on the tape measure protein, the thymidylate synthase, and the coat protein reflected the whole-genome groupings shown in Figure 1 with ΦCP13O and ΦCP34O most similar to each other and ΦCP9O, ΦCP26F, and ΦCP39O forming a separate group. In contrast, sequence similarities based on the amidase protein were not concordant with the other genes in the core genome or the whole-genome clustering. Based on these data, we concluded that the selective pressures on the amidase genes for these phages are somehow unique from the rest of the genome. This result may have important implications for potential biotechnological applications in which amidase proteins are used separately or together with other gene products such as holins for bacterial control.
Endolysin protein structure
Comparisons of genome sequences from four newly isolated C. perfringens phages and related sequences previously published has provided new insights into genomic conservation and variability. Sequence and structural variability of the endolysin EAD may have important implications for the potential to target specific strains of pathogenic bacteria. Sequence and structural conservation of the CBD suggests the potential to tailor specificity for detection and differentiation of target cell populations, extending previous work . Holins and endolysins represent conserved functions across divergent phage genomes and, as we demonstrate here, endolysins can have significant variability and host-specificity even among closely-related genomes. Endolysins in our phage genomes may be subject to different selective pressures than the rest of the genome, with important implications for potential biotechnological applications of these phages and their gene products.
Bacteriophage Genome Sequencing
Purification and propagation of bacteriophages and subsequent genomic DNA purification was carried out as previously described in detail . Sequencing of the bacteriophage genomes was completed by MWG Biotech, Inc High Point, NC by Sanger and pyrosequencing to 14-fold redundancy that included primer-walking to fill gaps.
Genome Annotations and comparisons
Gene predictions and genome annotations were performed with the IMG pipeline , which uses a combination of Hidden Markov Models and sequence similarity searches. Briefly, gene predictions were performed with GeneMark  and then compared to COG PSSMs obtained from the CDD database , searched against the KEGG genes database  with BLASTp, and then searched against the Pfam  and TIGRfam  databases using BLAST prefiltering and subsequent comparison to HMMs using hmmsearch . To compare the phylogeny and protein domain architecture of phage-encoded endolysin and holin genes, genomes of 26 bacteriophage were retrieved from IMG (Additional file 1, Table S1) based on top ortholog hits to COG0860. Genome accession numbers and basic summary statistics are shown in Additional file 1, Table S2. Gene predictions, annotations, and genome coordinates are listed for each genome in Additional file 2, Table S3.
Tetra-nucleotide distributions for Clostridial phage genomes and correlation coefficients between genomes were calculated with TETRA . Correlation coefficients were transformed to a dissimilarity matrix for tree construction using the hierarchical clustering algorithm hclust in R , which was also used to generate dendrograms and visualize tetra-nucleotide distributions. Proteomic comparisons of Clostridial phage genomes was performed with a custom analysis pipeline we constructed using CD-HIT  for clustering of predicted ORFs. Output was parsed with a series of perl scripts, and dendrograms constructed in mothur  using the Jaccard similarity index. COG and pfam designations from IMG for each genome were used to determine shared and accessory functions across the 12 Clostridial phage genomes. To construct genome maps, annotated genome files were transferred to Artemis  and genome maps constructed with DNA Plotter . rep-PCR of host genomes was performed as previously described .
Bacteriophage endolysin sequences belonging to COG0860 and/or PF01520 were retrieved from IMG and Genbank genomes using BioPerl. A seed alignment of 100 representative sequences belonging to conserved domain cd0269 in the CDD (10) was used to build a Hidden-Markov profile and the phage sequences shown in Figure 3 were aligned to this HMM model using Hmmer 3.0 (14). Aligned sequences were imported into ARB  where trees were constructed with neighbor-joining and maximum-likelihood methods restricted to columns sharing at least 10% sequence identity. When identical topologies were obtained with both methods, tree files were exported and visualized with ITOL . The significance of associations between phylogeny and host, and phylogeny and protein domain architecture was assessed with UniFrac  and Parsimony tests , which use a Monte Carlo approach to compare observed phylogenies with a null model derived from random permutations.
Designation and comparisons of core versus accessory genomes
Shared and unique genes, COGs, and pfams were determined by two methods. First, the same analysis pipeline described above was used to group predicted ORFs on the basis of sequence similarity as determined by CD-HIT . Second, classifications from IMG were used to determine shared and unique COGs and pfam families. The similarity of genes belonging to each pfam family in the core genome was determined by pairwise blastp implemented with the bl2seq algorithm in a perl script.
The 3D structure of the endolysin from ΦCP26F (ORF22, pfam01520) was modeled using the HHpred server with default settings . Briefly, the HHpred method is specialized in remote homology detection using hidden Markow models (HMMs) built from PSI-BLAST profiles and secondary structures. The crystal structure of Listeria PlyPSA (Protein Data Bank code 1XOV chain A, ) was used as a template since it had the highest sequence and secondary structure scores. Lastly, a 3D model was generated using MODELLER  and visualized using the UCSF Chimera molecular analysis program . Sequence conservation among our five phages was calculated using the mavPercentConservation method based on the AL2CO algorithm  which performs calculations in two steps. First, amino acid frequencies at each position are estimated and then the conservation index is calculated from these frequencies. The results were then mapped to the predicted protein structure of ΦCP26F using the following color parameters: lowest (60%) and highest (100%) sequence conservation.
This work was supported by ARS-USDA project number 6612-32000-046 Interventions and Methodologies to Reduce Human Food-Borne Bacterial Pathogens in Chickens and project number 6612-32000-055 Molecular Characterization and Gastrointestinal Tract Ecology of Commensal Human Food-Borne Bacterial Pathogens in the Chicken. We thank Johnna Garrish for technical assistance and Susan Brooks for assistance with manuscript preparation.
- Bedford M: Removal of antibiotic growth promoters from poultry diets: implications and strategies to minimize subsequent problems. World Poultry Science Journal. 2000, 56: 347-365. 10.1079/WPS20000024.View ArticleGoogle Scholar
- Castanon JI: History of the Use of Antibiotic as Growth Promoters in European Poultry Feeds. Poultry Science. 2007, 86: 2466-2471. 10.3382/ps.2007-00249.View ArticlePubMedGoogle Scholar
- Merril CR, Biswas B, Carlton R, Jensen NC, Creed GJ, Zullo S, Adhya S: Long-circulating bacteriophage as antibacterial agents. Proc Natl Acad Sci USA. 1996, 93: 3188-3192. 10.1073/pnas.93.8.3188.View ArticlePubMedPubMed CentralGoogle Scholar
- Liu J, Dehbi M, Moeck G, Arhin F, Bauda P, Bergeron D, Callejo M, Ferretti V, Ha N, Kwan T, et al: Antimicrobial drug discovery through bacteriophage genomics. Nat Biotechnol. 2004, 22: 185-191. 10.1038/nbt932.View ArticlePubMedGoogle Scholar
- Sulakvelidze A, Alavidze Z, Morris J: Antimicrobial Agents and Chemotherapy. Bacteriophage therapy. 2001, 45: 649-659.Google Scholar
- Seal BS, Fouts DE, Simmons M, Garrish JK, Kuntz RL, Woolsey R, Schegg KM, Kropinski AM, Ackermann HW, Siragusa GR: Clostridium perfringens bacteriophages PhiCP39O and PhiCP26F: genomic organization and proteomic analysis of the virions. Arch Virol. 2010, 21: 21-Google Scholar
- Volozhantsev NV, Verevkin VV, Bannov VA, Krasilnikova VM, Myakinina VP, Zhilenkov EL, Svetoch EA, Stern NJ, Oakley BB, Seal BS: The genome sequence and proteome of bacteriophage PhiCPV1 virulent for Clostridium perfringens. Virus Res. 2010Google Scholar
- Simmons M, Donovan DM, Siragusa GR, Seal BS: Recombinant expression of two bacteriophage proteins that lyse clostridium perfringens and share identical sequences in the C-terminal cell wall binding domain of the molecules but are dissimilar in their N-terminal active domains. J Agric Food Chem. 2010, 58: 10330-10337. 10.1021/jf101387v.View ArticlePubMedPubMed CentralGoogle Scholar
- Ackermann H: 5500 Phages examined in the electron microscope. Archives of Virology. 2007, 152: 227-243. 10.1007/s00705-006-0849-1.View ArticlePubMedGoogle Scholar
- Ackermann H: Bacteriophage observations and evolution. Research in Microbiology. 2003, 154: 245-251. 10.1016/S0923-2508(03)00067-6.View ArticlePubMedGoogle Scholar
- Ackermann H: Classification of Bacteriophages. The Bacteriophages. Edited by: Calender R. 2006, Oxford: Oxford University Press, 8-16.Google Scholar
- Scallan E, Hoekstra RM, Angulo FJ, Tauxe RV, Widdowson MA, Roy SL, Jones JL, Griffin PM: Foodborne illness acquired in the United States-major pathogens. Emerg Infect Dis. 2011, 17: 7-15.View ArticlePubMedPubMed CentralGoogle Scholar
- Sawires YS, Songer JG: Clostridium perfringens: insight into virulence evolution and population structure. Anaerobe. 2006, 12: 23-43. 10.1016/j.anaerobe.2005.10.002.View ArticlePubMedGoogle Scholar
- Van Immerseel F, De Buck J, Pasmans F, Huyghebaert G, Haesebrouck F, Ducatelle R: Clostridium perfringens in poultry: an emerging threat for animal and public health. Avian Pathol. 2004, 33: 537-549. 10.1080/03079450400013162.View ArticlePubMedGoogle Scholar
- Pride DT, Meinersmann RJ, Wassenaar TM, Blaser MJ: Evolutionary implications of microbial genome tetranucleotide frequency biases. Genome Res. 2003, 13: 145-158. 10.1101/gr.335003.View ArticlePubMedPubMed CentralGoogle Scholar
- Teeling H, Meyerdierks A, Bauer M, Amann R, Glockner FO: Application of tetranucleotide frequencies for the assignment of genomic fragments. Environ Microbiol. 2004, 6: 938-947. 10.1111/j.1462-2920.2004.00624.x.View ArticlePubMedGoogle Scholar
- Pride DT, Wassenaar TM, Ghose C, Blaser MJ: Evidence of host-virus co-evolution in tetranucleotide usage patterns of bacteriophages and eukaryotic viruses. BMC Genomics. 2006, 7: 8-10.1186/1471-2164-7-8.View ArticlePubMedPubMed CentralGoogle Scholar
- Tatusov RL, Koonin EV, Lipman DJ: A genomic perspective on protein families. Science. 1997, 278: 631-637. 10.1126/science.278.5338.631.View ArticlePubMedGoogle Scholar
- Wang IN, Smith DL, Young R: Holins: the protein clocks of bacteriophage infections. Annu Rev Microbiol. 2000, 54: 799-825. 10.1146/annurev.micro.54.1.799.View ArticlePubMedGoogle Scholar
- Rigden DJ, Jedrzejas MJ, Galperin MY: Amidase domains from bacterial and phage autolysins define a family of gamma-D,L-glutamate-specific amidohydrolases. Trends Biochem Sci. 2003, 28: 230-234. 10.1016/S0968-0004(03)00062-8.View ArticlePubMedGoogle Scholar
- Loessner MJ: Bacteriophage endolysins--current state of research and applications. Curr Opin Microbiol. 2005, 8: 480-487. 10.1016/j.mib.2005.06.002.View ArticlePubMedGoogle Scholar
- Korndorfer IP, Danzer J, Schmelcher M, Zimmer M, Skerra A, Loessner MJ: The crystal structure of the bacteriophage PSA endolysin reveals a unique fold responsible for specific recognition of Listeria cell walls. J Mol Biol. 2006, 364: 678-689. 10.1016/j.jmb.2006.08.069.View ArticlePubMedGoogle Scholar
- Lozupone C, Knight R: UniFrac: a new phylogenetic method for comparing microbial communities. Appl Environ Microbiol. 2005, 71: 8228-8235. 10.1128/AEM.71.12.8228-8235.2005.View ArticlePubMedPubMed CentralGoogle Scholar
- Martin AP: Phylogenetic approaches for describing and comparing the diversity of microbial communities. Appl Environ Microbiol. 2002, 68: 3673-3682. 10.1128/AEM.68.8.3673-3682.2002.View ArticlePubMedPubMed CentralGoogle Scholar
- Bernhardt TG, Wang IN, Struck DK, Young R: Breaking free: "protein antibiotics" and phage lysis. Res Microbiol. 2002, 153: 493-501. 10.1016/S0923-2508(02)01330-X.View ArticlePubMedGoogle Scholar
- Grundling A, Manson MD, Young R: Holins kill without warning. Proc Natl Acad Sci USA. 2001, 98: 9348-9352. 10.1073/pnas.151247598.View ArticlePubMedPubMed CentralGoogle Scholar
- Loessner MJ, Kramer K, Ebel F, Scherer S: C-terminal domains of Listeria monocytogenes bacteriophage murein hydrolases determine specific recognition and high-affinity binding to bacterial cell wall carbohydrates. Mol Microbiol. 2002, 44: 335-349. 10.1046/j.1365-2958.2002.02889.x.View ArticlePubMedGoogle Scholar
- Henry M, Coffey A, O'Mahony JM, Sleator RD: Comparative modelling of LysB from the mycobacterial bacteriophage Ardmore. Bioengineered Bugs. 2011, 2: 1-8. 10.4161/bbug.2.1.14315.View ArticleGoogle Scholar
- Schmelcher M, Shabarova T, Eugster MR, Eichenseher F, Tchang VS, Banz M, Loessner MJ: Rapid multiplex detection and differentiation of Listeria cells by use of fluorescent phage endolysin cell wall binding domains. Appl Environ Microbiol. 2010, 76: 5745-5756. 10.1128/AEM.00801-10.View ArticlePubMedPubMed CentralGoogle Scholar
- Markowitz VM, Mavromatis K, Ivanova NN, Chen IM, Chu K, Kyrpides NC: IMG ER: a system for microbial genome annotation expert review and curation. Bioinformatics. 2009, 25: 2271-2278. 10.1093/bioinformatics/btp393.View ArticlePubMedGoogle Scholar
- Besemer J, Lomsadze A, Borodovsky M: GeneMarkS: a self-training method for prediction of gene starts in microbial genomes. Implications for finding sequence motifs in regulatory regions. Nucleic Acids Res. 2001, 29: 2607-2618. 10.1093/nar/29.12.2607.View ArticlePubMedPubMed CentralGoogle Scholar
- Marchler-Bauer A, Lu S, Anderson JB, Chitsaz F, Derbyshire MK, Deweese-Scott C, Fong JH, Geer LY, Geer RC, Gonzales NR, et al: CDD: a Conserved Domain Database for the functional annotation of proteins. Nucleic Acids Res. 2010, 24: 24-Google Scholar
- Ogata H, Goto S, Sato K, Fujibuchi W, Bono H, Kanehisa M: KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 1999, 27: 29-34. 10.1093/nar/27.1.29.View ArticlePubMedPubMed CentralGoogle Scholar
- Bateman A, Birney E, Cerruti L, Durbin R, Etwiller L, Eddy SR, Griffiths-Jones S, Howe KL, Marshall M, Sonnhammer EL: The Pfam protein families database. Nucleic Acids Res. 2002, 30: 276-280. 10.1093/nar/30.1.276.View ArticlePubMedPubMed CentralGoogle Scholar
- Haft DH, Loftus BJ, Richardson DL, Yang F, Eisen JA, Paulsen IT, White O: TIGRFAMs: a protein family resource for the functional identification of proteins. Nucleic Acids Res. 2001, 29: 41-43. 10.1093/nar/29.1.41.View ArticlePubMedPubMed CentralGoogle Scholar
- Eddy SR: Profile hidden Markov models. Bioinformatics. 1998, 14: 755-763. 10.1093/bioinformatics/14.9.755.View ArticlePubMedGoogle Scholar
- Teeling H, Waldmann J, Lombardot T, Bauer M, Glockner F: TETRA: a web-service and a stand-alone program for the analysis and comparison of tetranucleotide usage patterns in DNA sequences. BMC Bioinformatics. 2004, 5: 163-10.1186/1471-2105-5-163.View ArticlePubMedPubMed CentralGoogle Scholar
- Team RDC: R: A language and environment for statistical computing. Book R: A language and environment for statistical computing (Editor ed.^eds.). 2008, City: R Foundation for Statistical ComputingGoogle Scholar
- Li W, Godzik A: Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics. 2006, 22: 1658-1659. 10.1093/bioinformatics/btl158.View ArticlePubMedGoogle Scholar
- Schloss PD, Westcott SL, Ryabin T, Hall JR, Hartmann M, Hollister EB, Lesniewski RA, Oakley BB, Parks DH, Robinson CJ, et al: Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl Environ Microbiol. 2009, 75: 7537-7541. 10.1128/AEM.01541-09.View ArticlePubMedPubMed CentralGoogle Scholar
- Rutherford K, Parkhill J, Crook J, Horsnell T, Rice P, Rajandream MA, Barrell B: Artemis: sequence visualization and annotation. Bioinformatics. 2000, 16: 944-945. 10.1093/bioinformatics/16.10.944.View ArticlePubMedGoogle Scholar
- Carver T, Thomson N, Bleasby A, Berriman M, Parkhill J: DNAPlotter: circular and linear interactive genome visualization. Bioinformatics. 2009, 25: 119-120. 10.1093/bioinformatics/btn578.View ArticlePubMedGoogle Scholar
- Siragusa GR, Danyluk MD, Hiett KL, Wise MG, Craven SE: Molecular subtyping of poultry-associated type A Clostridium perfringens isolates by repetitive-element PCR. J Clin Microbiol. 2006, 44: 1065-1073. 10.1128/JCM.44.3.1065-1073.2006.View ArticlePubMedPubMed CentralGoogle Scholar
- Ludwig W, Strunk O, Westram R, Richter L, Meier H, Yadhukumar , Buchner A, Lai T, Steppi S, Jobb G, et al: ARB: a software environment for sequence data. Nucleic Acids Res. 2004, 32: 1363-1371. 10.1093/nar/gkh293.View ArticlePubMedPubMed CentralGoogle Scholar
- Letunic I, Bork P: Interactive Tree Of Life (iTOL): an online tool for phylogenetic tree display and annotation. Bioinformatics. 2007, 23: 127-128. 10.1093/bioinformatics/btl529.View ArticlePubMedGoogle Scholar
- Soding J, Biegert A, Lupas AN: The HHpred interactive server for protein homology detection and structure prediction. Nucleic Acids Res. 2005, 33: W244-248. 10.1093/nar/gki408.View ArticlePubMedPubMed CentralGoogle Scholar
- Eswar N, Webb B, Marti-Renom MA, Madhusudhan MS, Eramian D, Shen MY, Pieper U, Sali A: Comparative protein structure modeling using Modeller. Curr Protoc Bioinformatics. 2006, Chapter 5: Unit 5 6-PubMedGoogle Scholar
- Pettersen EF, Goddard TD, Huang CC, Couch GS, Greenblatt DM, Meng EC, Ferrin TE: UCSF Chimera--a visualization system for exploratory research and analysis. J Comput Chem. 2004, 25: 1605-1612. 10.1002/jcc.20084.View ArticlePubMedGoogle Scholar
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