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      Plasmodium cynomolgi genome sequences provide insight into Plasmodium vivax and the monkey malaria clade

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          Abstract

          Plasmodium cynomolgi, a malaria parasite of Asian Old World monkeys, is the sister taxon of Plasmodium vivax, the most prevalent human malaria species outside Africa. Since P. cynomolgi shares many phenotypic, biologic and genetic characteristics of P. vivax, we generated draft genome sequences of three P. cynomolgi strains and performed comparative genomic analysis between them and P. vivax, as well as a third previously sequenced simian parasite, Plasmodium knowlesi. Here we show that genomes of the monkey malaria clade can be characterized by CNVs in multigene families involved in evasion of the human immune system and invasion of host erythrocytes. We identify genome-wide SNPs, microsatellites, and CNVs in the P. cynomolgi genome, providing a map of genetic variation for mapping parasite traits and studying parasite populations. The P. cynomolgi genome is a critical step in developing a model system for P. vivax research, and to counteract the neglect of P. vivax.

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          Most cited references28

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          A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data.

          Heng Li (2011)
          Most existing methods for DNA sequence analysis rely on accurate sequences or genotypes. However, in applications of the next-generation sequencing (NGS), accurate genotypes may not be easily obtained (e.g. multi-sample low-coverage sequencing or somatic mutation discovery). These applications press for the development of new methods for analyzing sequence data with uncertainty. We present a statistical framework for calling SNPs, discovering somatic mutations, inferring population genetical parameters and performing association tests directly based on sequencing data without explicit genotyping or linkage-based imputation. On real data, we demonstrate that our method achieves comparable accuracy to alternative methods for estimating site allele count, for inferring allele frequency spectrum and for association mapping. We also highlight the necessity of using symmetric datasets for finding somatic mutations and confirm that for discovering rare events, mismapping is frequently the leading source of errors. http://samtools.sourceforge.net. hengli@broadinstitute.org.
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            Evolutionary and biomedical insights from the rhesus macaque genome.

            The rhesus macaque (Macaca mulatta) is an abundant primate species that diverged from the ancestors of Homo sapiens about 25 million years ago. Because they are genetically and physiologically similar to humans, rhesus monkeys are the most widely used nonhuman primate in basic and applied biomedical research. We determined the genome sequence of an Indian-origin Macaca mulatta female and compared the data with chimpanzees and humans to reveal the structure of ancestral primate genomes and to identify evidence for positive selection and lineage-specific expansions and contractions of gene families. A comparison of sequences from individual animals was used to investigate their underlying genetic diversity. The complete description of the macaque genome blueprint enhances the utility of this animal model for biomedical research and improves our understanding of the basic biology of the species.
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              SSAHA: a fast search method for large DNA databases.

              We describe an algorithm, SSAHA (Sequence Search and Alignment by Hashing Algorithm), for performing fast searches on databases containing multiple gigabases of DNA. Sequences in the database are preprocessed by breaking them into consecutive k-tuples of k contiguous bases and then using a hash table to store the position of each occurrence of each k-tuple. Searching for a query sequence in the database is done by obtaining from the hash table the "hits" for each k-tuple in the query sequence and then performing a sort on the results. We discuss the effect of the tuple length k on the search speed, memory usage, and sensitivity of the algorithm and present the results of computational experiments which show that SSAHA can be three to four orders of magnitude faster than BLAST or FASTA, while requiring less memory than suffix tree methods. The SSAHA algorithm is used for high-throughput single nucleotide polymorphism (SNP) detection and very large scale sequence assembly. Also, it provides Web-based sequence search facilities for Ensembl projects.
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                Author and article information

                Journal
                9216904
                2419
                Nat Genet
                Nat. Genet.
                Nature genetics
                1061-4036
                1546-1718
                24 July 2013
                05 August 2012
                September 2012
                02 September 2013
                : 44
                : 9
                : 1051-1055
                Affiliations
                [1 ]Laboratory of Malariology, Research Institute for Microbial Diseases, Osaka University, Suita, Osaka 565-0871, Japan
                [2 ]Center for Genomics and Systems Biology, Department of Biology, New York University, New York, NY 10003, United States of America
                [3 ]Laboratory of Tropical Medicine and Parasitology, Institute of International Education and Research, Dokkyo Medical University, Shimotsuga, Tochigi 321-0293, Japan
                [4 ]Genome Information Research Center, Research Institute for Microbial Diseases, Osaka University, Suita, Osaka 565-0871, Japan
                [5 ]Department of Molecular Protozoology, Research Institute for Microbial Diseases, Osaka University, Suita, Osaka 565-0871, Japan
                [6 ]The Corporation for Production and Research of Laboratory Primates, Tsukuba, Ibaraki 305-0843, Japan
                [7 ]Department of Protozoology, Institute of Tropical Medicine (NEKKEN) and GCOE, Nagasaki University, Nagasaki 852-8523, Japan
                [8 ]Department of Molecular and Cellular Parasitology, Juntendo University, School of Medicine, Bunkyo-ku, Tokyo, 113-8421, Japan
                [9 ]Department of Biomedical Chemistry, Graduate School of Medicine, The University of Tokyo, Hongo, Tokyo 113-0033, Japan
                [10 ]Tsukuba Primate Research Center, National Institute of Biomedical Innovation, Tsukuba, Ibaraki 305-0843, Japan
                [11 ]Divison of Parasitic Diseases and Malaria, Center for Global Health, Centers for Disease Control and Prevention, Atlanta, GA 30329, United States of America
                [12 ]Center for Evolutionary Medicine and Informatics, The Biodesign Institute, Arizona State University, PO Box 874501, Tempe AZ 85287-4501, United States of America
                Author notes
                Correspondence should be addressed to K. Tanabe ( kztanabe@ 123456biken.osaka-u.ac.jp ) or J. Carlton ( jane.carlton@ 123456nyu.edu )
                [13]

                These authors jointly directed this work

                [†]

                Present address: Career-Path Promotion Unit for Young Life Scientists, Kyoto University, Sakyo-ku, Kyoto 606-8501, Japan

                Article
                NIHMS392994
                10.1038/ng.2375
                3759362
                22863735
                89c61323-f167-4b99-a954-12c8a1821150

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                History
                Funding
                Funded by: National Institute of Allergy and Infectious Diseases Extramural Activities : NIAID
                Award ID: U19 AI089676 || AI
                Funded by: National Center for Research Resources : NCRR
                Award ID: S10 RR026950 || RR
                Funded by: National Institute of General Medical Sciences : NIGMS
                Award ID: R01 GM070793 || GM
                Categories
                Article

                Genetics
                Genetics

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