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      Evolutionary genomics of anthroponosis in Cryptosporidium.

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          Abstract

          Human cryptosporidiosis is the leading protozoan cause of diarrhoeal mortality worldwide, and a preponderance of infections is caused by Cryptosporidium hominis and C. parvum. Both species consist of several subtypes with distinct geographical distributions and host preferences (that is, generalist zoonotic and specialist anthroponotic subtypes). The evolutionary processes that drive the adaptation to the human host and the population structures of Cryptosporidium remain unknown. In this study, we analyse 21 whole-genome sequences to elucidate the evolution of anthroponosis. We show that Cryptosporidium parvum splits into two subclades and that the specialist anthroponotic subtype IIc-a shares a subset of loci with C. hominis that is undergoing rapid convergent evolution driven by positive selection. C. parvum subtype IIc-a also has an elevated level of insertion and deletion mutations in the peri-telomeric genes, which is also a characteristic of other specialist subtypes. Genetic exchange between Cryptosporidium subtypes plays a prominent role throughout the evolution of the genus. Interestingly, recombinant regions are enriched for positively selected genes and potential virulence factors, which indicates adaptive introgression. Analysis of 467 gp60 sequences collected from locations across the world shows that the population genetic structure differs markedly between the main zoonotic subtype (isolation-by-distance) and the anthroponotic subtype (admixed population structure). We also show that introgression between the four anthroponotic Cryptosporidium subtypes and species included in this study has occurred recently, probably within the past millennium.

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          Most cited references39

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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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            Is Open Access

            KaKs_Calculator 2.0: A Toolkit Incorporating Gamma-Series Methods and Sliding Window Strategies

            We present an integrated stand-alone software package named KaKs_Calculator 2.0 as an updated version. It incorporates 17 methods for the calculation of nonsynonymous and synonymous substitution rates; among them, we added our modified versions of several widely used methods as the gamma series including γ-NG, γ-LWL, γ-MLWL, γ-LPB, γ-MLPB, γ-YN and γ-MYN, which have been demonstrated to perform better under certain conditions than their original forms and are not implemented in the previous version. The package is readily used for the identification of positively selected sites based on a sliding window across the sequences of interests in 5’ to 3’ direction of protein-coding sequences, and have improved the overall performance on sequence analysis for evolution studies. A toolbox, including C++ and Java source code and executable files on both Windows and Linux platforms together with a user instruction, is downloadable from the website for academic purpose at https://sourceforge.net/projects/kakscalculator2/.
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              Database resources of the National Center for Biotechnology.

              D Wheeler (2003)
              In addition to maintaining the GenBank(R) nucleic acid sequence database, the National Center for Biotechnology Information (NCBI) provides data analysis and retrieval resources for the data in GenBank and other biological data made available through NCBI's Web site. NCBI resources include Entrez, PubMed, PubMed Central (PMC), LocusLink, the NCBITaxonomy Browser, BLAST, BLAST Link (BLink), Electronic PCR (e-PCR), Open Reading Frame (ORF) Finder, References Sequence (RefSeq), UniGene, HomoloGene, ProtEST, Database of Single Nucleotide Polymorphisms (dbSNP), Human/Mouse Homology Map, Cancer Chromosome Aberration Project (CCAP), Entrez Genomes and related tools, the Map Viewer, Model Maker (MM), Evidence Viewer (EV), Clusters of Orthologous Groups (COGs) database, Retroviral Genotyping Tools, SAGEmap, Gene Expression Omnibus (GEO), Online Mendelian Inheritance in Man (OMIM), the Molecular Modeling Database (MMDB), the Conserved Domain Database (CDD), and the Conserved Domain Architecture Retrieval Tool (CDART). Augmenting many of the Web applications are custom implementations of the BLAST program optimized to search specialized data sets. All of the resources can be accessed through the NCBI home page at: http://www.ncbi.nlm.nih.gov.
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                Author and article information

                Journal
                Nat Microbiol
                Nature microbiology
                Springer Science and Business Media LLC
                2058-5276
                2058-5276
                May 2019
                : 4
                : 5
                Affiliations
                [1 ] Biomedical Research Centre, Norwich Medical School, University of East Anglia, Norwich Research Park, Norwich, UK. johanna.nader@fhi.no.
                [2 ] Department of Genetics and Bioinformatics, Division of Health Data and Digitalisation, Norwegian Institute of Public Health, Oslo, Norway. johanna.nader@fhi.no.
                [3 ] Earlham Institute, Norwich Research Park, Norwich, UK.
                [4 ] Department of Crop Genetics, John Innes Centre, Norwich Research Park, Norwich, UK.
                [5 ] School of Environmental Sciences, University of East Anglia, Norwich Research Park, Norwich, UK.
                [6 ] Institute of Biological, Environmental & Rural Sciences, Aberystwyth University, Aberystwyth, UK.
                [7 ] Cryptosporidium Reference Unit, Public Health Wales Microbiology, Singleton Hospital, Swansea, UK.
                [8 ] Swansea University Medical School, Swansea, UK.
                [9 ] Biomedical Research Centre, Norwich Medical School, University of East Anglia, Norwich Research Park, Norwich, UK.
                [10 ] School of Environmental Sciences, University of East Anglia, Norwich Research Park, Norwich, UK. c.van-oosterhout@uea.ac.uk.
                Article
                10.1038/s41564-019-0377-x
                10.1038/s41564-019-0377-x
                30833731
                e026b4e8-b813-46d0-88cb-cc85add29ad9
                History

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