Dataset Information

Classification of prokaryotic genetic replicators: between selfishness and altruism.

ABSTRACT: Prokaryotes harbor a variety of genetic replicators, including plasmids, viruses, and chromosomes, each having different effects on the phenotype of the hosting cell. Here, we propose a classification for replicators of bacteria and archaea on the basis of their horizontal-transfer potential and the type of relationships (mutualistic, symbiotic, commensal, or parasitic) that they have with the host cell vehicle. Horizontal movement of replicators can be either active or passive, reflecting whether or not the replicator encodes the means to mediate its own transfer from one cell to another. Some replicators also have an infectious extracellular state, thus separating viruses from other mobile elements. From the perspective of the cell vehicle, the different types of replicators form a continuum from genuinely mutualistic to completely parasitic replicators. This classification provides a general framework for dissecting prokaryotic systems into evolutionarily meaningful components.

SUBMITTER: Jalasvuori M

PROVIDER: S-EPMC4390439 | biostudies-literature | 2015 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Classification of prokaryotic genetic replicators: between selfishness and altruism.

Jalasvuori Matti M Koonin Eugene V EV

Annals of the New York Academy of Sciences 20150219

Prokaryotes harbor a variety of genetic replicators, including plasmids, viruses, and chromosomes, each having different effects on the phenotype of the hosting cell. Here, we propose a classification for replicators of bacteria and archaea on the basis of their horizontal-transfer potential and the type of relationships (mutualistic, symbiotic, commensal, or parasitic) that they have with the host cell vehicle. Horizontal movement of replicators can be either active or passive, reflecting wheth ...[more]

PMID: 25703428

Similar Datasets

Project description:BackgroundOverwhelming majority of the Serine/Threonine protein kinases identified by gleaning archaeal and eubacterial genomes could not be classified into any of the well known Hanks and Hunter subfamilies of protein kinases. This is owing to the development of Hanks and Hunter classification scheme based on eukaryotic protein kinases which are highly divergent from their prokaryotic homologues. A large dataset of prokaryotic Serine/Threonine protein kinases recognized from genomes of prokaryotes have been used to develop a classification framework for prokaryotic Ser/Thr protein kinases.Methodology/principal findingsWe have used traditional sequence alignment and phylogenetic approaches and clustered the prokaryotic kinases which represent 72 subfamilies with at least 4 members in each. Such a clustering enables classification of prokaryotic Ser/Thr kinases and it can be used as a framework to classify newly identified prokaryotic Ser/Thr kinases. After series of searches in a comprehensive sequence database we recognized that 38 subfamilies of prokaryotic protein kinases are associated to a specific taxonomic level. For example 4, 6 and 3 subfamilies have been identified that are currently specific to phylum proteobacteria, cyanobacteria and actinobacteria respectively. Similarly subfamilies which are specific to an order, sub-order, class, family and genus have also been identified. In addition to these, we also identify organism-diverse subfamilies. Members of these clusters are from organisms of different taxonomic levels, such as archaea, bacteria, eukaryotes and viruses.Conclusion/significanceInterestingly, occurrence of several taxonomic level specific subfamilies of prokaryotic kinases contrasts with classification of eukaryotic protein kinases in which most of the popular subfamilies of eukaryotic protein kinases occur diversely in several eukaryotes. Many prokaryotic Ser/Thr kinases exhibit a wide variety of modular organization which indicates a degree of complexity and protein-protein interactions in the signaling pathways in these microbes.

Project description:BACKGROUND: Although it is not difficult for state-of-the-art gene finders to identify coding regions in prokaryotic genomes, exact prediction of the corresponding translation initiation sites (TIS) is still a challenging problem. Recently a number of post-processing tools have been proposed for improving the annotation of prokaryotic TIS. However, inherent difficulties of these approaches arise from the considerable variation of TIS characteristics across different species. Therefore prior assumptions about the properties of prokaryotic gene starts may cause suboptimal predictions for newly sequenced genomes with TIS signals differing from those of well-investigated genomes. RESULTS: We introduce a clustering algorithm for completely unsupervised scoring of potential TIS, based on positionally smoothed probability matrices. The algorithm requires an initial gene prediction and the genomic sequence of the organism to perform the reannotation. As compared with other methods for improving predictions of gene starts in bacterial genomes, our approach is not based on any specific assumptions about prokaryotic TIS. Despite the generality of the underlying algorithm, the prediction rate of our method is competitive on experimentally verified test data from E. coli and B. subtilis. Regarding genomes with high G+C content, in contrast to some previously proposed methods, our algorithm also provides good performance on P. aeruginosa, B. pseudomallei and R. solanacearum. CONCLUSION: On reliable test data we showed that our method provides good results in post-processing the predictions of the widely-used program GLIMMER. The underlying clustering algorithm is robust with respect to variations in the initial TIS annotation and does not require specific assumptions about prokaryotic gene starts. These features are particularly useful on genomes with high G+C content. The algorithm has been implemented in the tool "TICO" (TIs COrrector) which is publicly available from our web site.

Dataset Information

Classification of prokaryotic genetic replicators: between selfishness and altruism.

Publications

Classification of prokaryotic genetic replicators: between selfishness and altruism.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets