title: Automated classification of tailed bacteriophages according to their neck organization.
abstract: BACKGROUND
  The genetic diversity observed among bacteriophages remains a major obstacle for the identification of homologs and the comparison of their functional modules. In the structural module, although several classes of homologous proteins contributing to the head and tail structure can be detected, proteins of the head-to-tail connection (or neck) are generally more divergent. Yet, molecular analyses of a few tailed phages belonging to different morphological classes suggested that only a limited number of structural solutions are used in order to produce a functional virion. To challenge this hypothesis and analyze proteins diversity at the virion neck, we developed a specific computational strategy to cope with sequence divergence in phage proteins. We searched for homologs of a set of proteins encoded in the structural module using a phage learning database.
  
  
  RESULTS
  We show that using a combination of iterative profile-profile comparison and gene context analyses, we can identify a set of head, neck and tail proteins in most tailed bacteriophages of our database. Classification of phages based on neck protein sequences delineates 4 Types corresponding to known morphological subfamilies. Further analysis of the most abundant Type 1 yields 10 Clusters characterized by consistent sets of head, neck and tail proteins. We developed Virfam, a webserver that automatically identifies proteins of the phage head-neck-tail module and assign phages to the most closely related cluster of phages. This server was tested against 624 new phages from the NCBI database. 93% of the tailed and unclassified phages could be assigned to our head-neck-tail based categories, thus highlighting the large representativeness of the identified virion architectures. Types and Clusters delineate consistent subgroups of Caudovirales, which correlate with several virion properties.
  
  
  CONCLUSIONS
  Our method and webserver have the capacity to automatically classify most tailed phages, detect their structural module, assign a function to a set of their head, neck and tail genes, provide their morphologic subtype and localize these phages within a "head-neck-tail" based classification. It should enable analysis of large sets of phage genomes. In particular, it should contribute to the classification of the abundant unknown viruses found on assembled contigs of metagenomic samples.
authors.forename: Anne
authors.initials: A
authors.lastname: Lopes
authors.forename: Paulo
authors.initials: P
authors.lastname: Tavares
authors.forename: Marie-Agnès
authors.initials: MA
authors.lastname: Petit
authors.affiliation: CEA, iBiTecS, Gif-sur-Yvette, F-91191 Paris, France. raphael.guerois@cea.fr.
authors.forename: Raphaël
authors.initials: R
authors.lastname: Guérois
authors.forename: Sophie
authors.initials: S
authors.lastname: Zinn-Justin
mesh_terms: D001435:Bacteriophages / Q000145:classification* / Q000235:genetics / Q000378:metabolism; D005075:Biological Evolution; D019295:Computational Biology / Q000379:methods*; D030541:Databases, Genetic; D066264:Datasets as Topic; D005810:Multigene Family; D010802:Phylogeny; D012984:Software; D014764:Viral Proteins / Q000235:genetics / Q000378:metabolism; D064878:Web Browser
chemical_list: D014764:Viral Proteins