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      The Wolbachia Genome of Brugia malayi: Endosymbiont Evolution within a Human Pathogenic Nematode

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          Abstract

          Complete genome DNA sequence and analysis is presented for Wolbachia, the obligate alpha-proteobacterial endosymbiont required for fertility and survival of the human filarial parasitic nematode Brugia malayi. Although, quantitatively, the genome is even more degraded than those of closely related Rickettsia species, Wolbachia has retained more intact metabolic pathways. The ability to provide riboflavin, flavin adenine dinucleotide, heme, and nucleotides is likely to be Wolbachia's principal contribution to the mutualistic relationship, whereas the host nematode likely supplies amino acids required for Wolbachia growth. Genome comparison of the Wolbachia endosymbiont of B. malayi ( wBm) with the Wolbachia endosymbiont of Drosophila melanogaster ( wMel) shows that they share similar metabolic trends, although their genomes show a high degree of genome shuffling. In contrast to wMel, wBm contains no prophage and has a reduced level of repeated DNA. Both Wolbachia have lost a considerable number of membrane biogenesis genes that apparently make them unable to synthesize lipid A, the usual component of proteobacterial membranes. However, differences in their peptidoglycan structures may reflect the mutualistic lifestyle of wBm in contrast to the parasitic lifestyle of wMel. The smaller genome size of wBm, relative to wMel, may reflect the loss of genes required for infecting host cells and avoiding host defense systems. Analysis of this first sequenced endosymbiont genome from a filarial nematode provides insight into endosymbiont evolution and additionally provides new potential targets for elimination of cutaneous and lymphatic human filarial disease.

          Abstract

          Analysis of this Wolbachia genome, which resides within filarial parasites, offers insight into endosymbiont evolution and the promise of new strategies for the elimination of human filarial disease

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          Most cited references190

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          GeneMarkS: a self-training method for prediction of gene starts in microbial genomes. Implications for finding sequence motifs in regulatory regions.

          J Besemer (2001)
          Improving the accuracy of prediction of gene starts is one of a few remaining open problems in computer prediction of prokaryotic genes. Its difficulty is caused by the absence of relatively strong sequence patterns identifying true translation initiation sites. In the current paper we show that the accuracy of gene start prediction can be improved by combining models of protein-coding and non-coding regions and models of regulatory sites near gene start within an iterative Hidden Markov model based algorithm. The new gene prediction method, called GeneMarkS, utilizes a non-supervised training procedure and can be used for a newly sequenced prokaryotic genome with no prior knowledge of any protein or rRNA genes. The GeneMarkS implementation uses an improved version of the gene finding program GeneMark.hmm, heuristic Markov models of coding and non-coding regions and the Gibbs sampling multiple alignment program. GeneMarkS predicted precisely 83.2% of the translation starts of GenBank annotated Bacillus subtilis genes and 94.4% of translation starts in an experimentally validated set of Escherichia coli genes. We have also observed that GeneMarkS detects prokaryotic genes, in terms of identifying open reading frames containing real genes, with an accuracy matching the level of the best currently used gene detection methods. Accurate translation start prediction, in addition to the refinement of protein sequence N-terminal data, provides the benefit of precise positioning of the sequence region situated upstream to a gene start. Therefore, sequence motifs related to transcription and translation regulatory sites can be revealed and analyzed with higher precision. These motifs were shown to possess a significant variability, the functional and evolutionary connections of which are discussed.
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            Consed: a graphical tool for sequence finishing.

            Sequencing of large clones or small genomes is generally done by the shotgun approach (Anderson et al. 1982). This has two phases: (1) a shotgun phase in which a number of reads are generated from random subclones and assembled into contigs, followed by (2) a directed, or finishing phase in which the assembly is inspected for correctness and for various kinds of data anomalies (such as contaminant reads, unremoved vector sequence, and chimeric or deleted reads), additional data are collected to close gaps and resolve low quality regions, and editing is performed to correct assembly or base-calling errors. Finishing is currently a bottleneck in large-scale sequencing efforts, and throughput gains will depend both on reducing the need for human intervention and making it as efficient as possible. We have developed a finishing tool, consed, which attempts to implement these principles. A distinguishing feature relative to other programs is the use of error probabilities from our programs phred and phrap as an objective criterion to guide the entire finishing process. More information is available at http:// www.genome.washington.edu/consed/consed. html.
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              The PSIPRED protein structure prediction server.

              The PSIPRED protein structure prediction server allows users to submit a protein sequence, perform a prediction of their choice and receive the results of the prediction both textually via e-mail and graphically via the web. The user may select one of three prediction methods to apply to their sequence: PSIPRED, a highly accurate secondary structure prediction method; MEMSAT 2, a new version of a widely used transmembrane topology prediction method; or GenTHREADER, a sequence profile based fold recognition method. Freely available to non-commercial users at http://globin.bio.warwick.ac.uk/psipred/
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                Author and article information

                Contributors
                Role: Academic Editor
                Journal
                PLoS Biol
                pbio
                PLoS Biology
                Public Library of Science (San Francisco, USA )
                1544-9173
                1545-7885
                April 2005
                29 March 2005
                : 3
                : 4
                : e121
                Affiliations
                [1] 1Molecular Parasitology Division, New England Biolabs Beverly, MassachusettsUnited States of America
                [2] 2National Center for Biotechnology Information, National Library of Medicine National Institutes of Health, Bethesda, MarylandUnited States of America
                [3] 3Integrated Genomics, Chicago IllinoisUnited States of America
                [4] 4Parasite Genomics, Institute for Genomic Research Rockville, MarylandUnited States of America
                University of Arizona United States of America
                Article
                10.1371/journal.pbio.0030121
                1069646
                15780005
                9ad023f1-d38e-4bc7-aeac-0b8370eb8b0f
                Copyright: © 2005 Foster et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
                History
                : 23 November 2004
                : 2 February 2005
                Categories
                Research Article
                Bioinformatics/Computational Biology
                Evolution
                Genetics/Genomics/Gene Therapy
                Infectious Diseases
                Microbiology
                Eubacteria
                Nematodes
                Homo (Human)

                Life sciences
                Life sciences

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