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      RNRdb, a curated database of the universal enzyme family ribonucleotide reductase, reveals a high level of misannotation in sequences deposited to Genbank

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          Abstract

          Background

          Ribonucleotide reductases (RNRs) catalyse the only known de novo pathway for deoxyribonucleotide synthesis, and are therefore essential to DNA-based life. While ribonucleotide reduction has a single evolutionary origin, significant differences between RNRs nevertheless exist, notably in cofactor requirements, subunit composition and allosteric regulation. These differences result in distinct operational constraints (anaerobicity, iron/oxygen dependence and cobalamin dependence), and form the basis for the classification of RNRs into three classes.

          Description

          In RNRdb (Ribonucleotide Reductase database), we have collated and curated all known RNR protein sequences with the aim of providing a resource for exploration of RNR diversity and distribution. By comparing expert manual annotations with annotations stored in Genbank, we find that significant inaccuracies exist in larger databases. To our surprise, only 23% of protein sequences included in RNRdb are correctly annotated across the key attributes of class, role and function, with 17% being incorrectly annotated across all three categories. This illustrates the utility of specialist databases for applications where a high degree of annotation accuracy may be important. The database houses information on annotation, distribution and diversity of RNRs, and links to solved RNR structures, and can be searched through a BLAST interface. RNRdb is accessible through a public web interface at http://rnrdb.molbio.su.se.

          Conclusion

          RNRdb is a specialist database that provides a reliable annotation and classification resource for RNR proteins, as well as a tool to explore distribution patterns of RNR classes. The recent expansion in available genome sequence data have provided us with a picture of RNR distribution that is more complex than believed only a few years ago; our database indicates that RNRs of all three classes are found across all three cellular domains. Moreover, we find a number of organisms that encode all three classes.

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          Most cited references31

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          Profile hidden Markov models.

          S. Eddy (1998)
          The recent literature on profile hidden Markov model (profile HMM) methods and software is reviewed. Profile HMMs turn a multiple sequence alignment into a position-specific scoring system suitable for searching databases for remotely homologous sequences. Profile HMM analyses complement standard pairwise comparison methods for large-scale sequence analysis. Several software implementations and two large libraries of profile HMMs of common protein domains are available. HMM methods performed comparably to threading methods in the CASP2 structure prediction exercise.
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            Genomic sequence of a Lyme disease spirochaete, Borrelia burgdorferi.

            The genome of the bacterium Borrelia burgdorferi B31, the aetiologic agent of Lyme disease, contains a linear chromosome of 910,725 base pairs and at least 17 linear and circular plasmids with a combined size of more than 533,000 base pairs. The chromosome contains 853 genes encoding a basic set of proteins for DNA replication, transcription, translation, solute transport and energy metabolism, but, like Mycoplasma genitalium, it contains no genes for cellular biosynthetic reactions. Because B. burgdorferi and M. genitalium are distantly related eubacteria, we suggest that their limited metabolic capacities reflect convergent evolution by gene loss from more metabolically competent progenitors. Of 430 genes on 11 plasmids, most have no known biological function; 39% of plasmid genes are paralogues that form 47 gene families. The biological significance of the multiple plasmid-encoded genes is not clear, although they may be involved in antigenic variation or immune evasion.
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              The genome of the protist parasite Entamoeba histolytica.

              Entamoeba histolytica is an intestinal parasite and the causative agent of amoebiasis, which is a significant source of morbidity and mortality in developing countries. Here we present the genome of E. histolytica, which reveals a variety of metabolic adaptations shared with two other amitochondrial protist pathogens: Giardia lamblia and Trichomonas vaginalis. These adaptations include reduction or elimination of most mitochondrial metabolic pathways and the use of oxidative stress enzymes generally associated with anaerobic prokaryotes. Phylogenomic analysis identifies evidence for lateral gene transfer of bacterial genes into the E. histolytica genome, the effects of which centre on expanding aspects of E. histolytica's metabolic repertoire. The presence of these genes and the potential for novel metabolic pathways in E. histolytica may allow for the development of new chemotherapeutic agents. The genome encodes a large number of novel receptor kinases and contains expansions of a variety of gene families, including those associated with virulence. Additional genome features include an abundance of tandemly repeated transfer-RNA-containing arrays, which may have a structural function in the genome. Analysis of the genome provides new insights into the workings and genome evolution of a major human pathogen.
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                Author and article information

                Journal
                BMC Genomics
                BMC Genomics
                BioMed Central
                1471-2164
                2009
                8 December 2009
                : 10
                : 589
                Affiliations
                [1 ]Department of Molecular Biology and Functional Genomics, Stockholm University, Stockholm, Sweden
                [2 ]Cellular Biotechnology, Institute for Bioengineering of Catalonia (IBEC), Scientific Park of Barcelona, Barcelona, Spain
                [3 ]School of Biological Sciences, University of Canterbury, Christchurch, New Zealand
                Article
                1471-2164-10-589
                10.1186/1471-2164-10-589
                2795772
                19995434
                602c7040-e4ed-4cd3-ac63-b0f6cff6095e
                Copyright ©2009 Lundin et al; licensee BioMed Central Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 14 July 2009
                : 8 December 2009
                Categories
                Database

                Genetics
                Genetics

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