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      The genome of Onchocerca volvulus, agent of river blindness

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          Abstract

          Human onchocerciasis is a serious neglected tropical disease caused by the filarial nematode Onchocerca volvulus that can lead to blindness and chronic disability. Control of the disease relies largely on mass administration of a single drug, and the development of new drugs and vaccines depends on a better knowledge of parasite biology. Here, we describe the chromosomes of O. volvulus and its Wolbachia endosymbiont. We provide the highest-quality sequence assembly for any parasitic nematode to date, giving a glimpse into the evolution of filarial parasite chromosomes and proteomes. This resource was used to investigate gene families with key functions that could be potentially exploited as targets for future drugs. Using metabolic reconstruction of the nematode and its endosymbiont, we identified enzymes that are likely to be essential for O. volvulus viability. In addition, we have generated a list of proteins that could be targeted by Federal-Drug-Agency-approved but repurposed drugs, providing starting points for anti-onchocerciasis drug development.

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          Genome sequence of the nematode C. elegans: a platform for investigating biology.

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          The 97-megabase genomic sequence of the nematode Caenorhabditis elegans reveals over 19,000 genes. More than 40 percent of the predicted protein products find significant matches in other organisms. There is a variety of repeated sequences, both local and dispersed. The distinctive distribution of some repeats and highly conserved genes provides evidence for a regional organization of the chromosomes.
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            Gene prediction in novel fungal genomes using an ab initio algorithm with unsupervised training.

            We describe a new ab initio algorithm, GeneMark-ES version 2, that identifies protein-coding genes in fungal genomes. The algorithm does not require a predetermined training set to estimate parameters of the underlying hidden Markov model (HMM). Instead, the anonymous genomic sequence in question is used as an input for iterative unsupervised training. The algorithm extends our previously developed method tested on genomes of Arabidopsis thaliana, Caenorhabditis elegans, and Drosophila melanogaster. To better reflect features of fungal gene organization, we enhanced the intron submodel to accommodate sequences with and without branch point sites. This design enables the algorithm to work equally well for species with the kinds of variations in splicing mechanisms seen in the fungal phyla Ascomycota, Basidiomycota, and Zygomycota. Upon self-training, the intron submodel switches on in several steps to reach its full complexity. We demonstrate that the algorithm accuracy, both at the exon and the whole gene level, is favorably compared to the accuracy of gene finders that employ supervised training. Application of the new method to known fungal genomes indicates substantial improvement over existing annotations. By eliminating the effort necessary to build comprehensive training sets, the new algorithm can streamline and accelerate the process of annotation in a large number of fungal genome sequencing projects.
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              Fast algorithms for large-scale genome alignment and comparison.

              We describe a suffix-tree algorithm that can align the entire genome sequences of eukaryotic and prokaryotic organisms with minimal use of computer time and memory. The new system, MUMmer 2, runs three times faster while using one-third as much memory as the original MUMmer system. It has been used successfully to align the entire human and mouse genomes to each other, and to align numerous smaller eukaryotic and prokaryotic genomes. A new module permits the alignment of multiple DNA sequence fragments, which has proven valuable in the comparison of incomplete genome sequences. We also describe a method to align more distantly related genomes by detecting protein sequence homology. This extension to MUMmer aligns two genomes after translating the sequence in all six reading frames, extracts all matching protein sequences and then clusters together matches. This method has been applied to both incomplete and complete genome sequences in order to detect regions of conserved synteny, in which multiple proteins from one organism are found in the same order and orientation in another. The system code is being made freely available by the authors.
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                Author and article information

                Journal
                101674869
                44774
                Nat Microbiol
                Nat Microbiol
                Nature microbiology
                2058-5276
                7 February 2017
                21 November 2016
                21 November 2016
                15 February 2017
                : 2
                : 16216
                Affiliations
                [1 ]Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SA, UK
                [2 ]Laboratory of Parasitic Diseases, National Institute of Allergy and Infectious Diseases, NIH, Bethesda, Maryland 20892, USA
                [3 ]Center for Genomics and Systems Biology, Department of Biology, New York University, New York, New York 10003, USA
                [4 ]Institute of Parasitology, McGill University, Montreal, Quebec H9X 3V9, Canada
                [5 ]Institute for Genome Sciences, Department of Microbiology and Immunology, University of Maryland School of Medicine, Baltimore, Maryland 21201, USA
                [6 ]Global Health Infectious Disease Research Program, Department of Global Health, College of Public Health, University of South Florida, Tampa, Florida 33612, USA
                [7 ]European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
                [8 ]Department of Computer Science, University of Toronto, Toronto M5S 3G4, Canada
                [9 ]Division of Molecular Structure and Function, Research Institute, Hospital for Sick Children, Toronto, Ontario M5G 1X8, Canada
                [10 ]Laboratory of Malaria and Vector Research, National Institute of Allergy and Infectious Diseases, NIH, Bethesda, Maryland 20892, USA
                [11 ]Children’s Hospital of Pittsburgh, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania 15224, USA
                [12 ]New York Blood Center, New York, New York 10065, USA
                [13 ]Departments of Biochemistry and Molecular Genetics, University of Toronto, M5S 1A8, Canada
                [14 ]College of Global Public Health, New York University, New York, New York 10003, USA
                Author notes
                [* ]Correspondence and requests for materials should be addressed to E.G., M.B. and S.L. slustigman@ 123456nybloodcenter.org ; mb4@ 123456sanger.ac.uk ; elodie.ghedin@ 123456nyu.edu

                Present addresses: Division of Parasitology, Faculty of Medicine, University of Miyazaki, Miyazaki, Japan (T.K.); Eagle Genomics Ltd, Babraham Hall, Babraham Research Campus, Babraham, Cambridgeshire CB22 3AT, UK (E.S.); Biodiversity Research Center, Academia Sinica, Taipei 11529, Taiwan (I.J.T.).

                Article
                NIHMS848855
                10.1038/nmicrobiol.2016.216
                5310847
                27869790
                a5d616bb-dccb-4b13-bb94-9286ddb17118

                Reprints and permissions information is available at www.nature.com/reprints.

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