SNP Calling, Genotype Calling, and Sample Allele Frequency Estimation from New-Generation Sequencing Data
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{"title"=>"SNP calling, genotype calling, and sample allele frequency estimation from new-generation sequencing data", "type"=>"journal", "authors"=>[{"first_name"=>"Rasmus", "last_name"=>"Nielsen", "scopus_author_id"=>"56008936500"}, {"first_name"=>"Thorfinn", "last_name"=>"Korneliussen", "scopus_author_id"=>"26639439000"}, {"first_name"=>"Anders", "last_name"=>"Albrechtsen", "scopus_author_id"=>"23032972200"}, {"first_name"=>"Yingrui", "last_name"=>"Li", "scopus_author_id"=>"23766780600"}, {"first_name"=>"Jun", "last_name"=>"Wang", "scopus_author_id"=>"8716933500"}], "year"=>2012, "source"=>"PLoS ONE", "identifiers"=>{"pui"=>"365316798", "isbn"=>"1932-6203", "issn"=>"19326203", "doi"=>"10.1371/journal.pone.0037558", "scopus"=>"2-s2.0-84864273210", "pmid"=>"22911679", "sgr"=>"84864273210"}, "id"=>"68739ea9-82d8-3d03-b453-bcfe793003cd", "abstract"=>"We present a statistical framework for estimation and application of sample allele frequency spectra from New-Generation Sequencing (NGS) data. In this method, we first estimate the allele frequency spectrum using maximum likelihood. In contrast to previous methods, the likelihood function is calculated using a dynamic programming algorithm and numerically optimized using analytical derivatives. We then use a bayesian method for estimating the sample allele frequency in a single site, and show how the method can be used for genotype calling and SNP calling. We also show how the method can be extended to various other cases including cases with deviations from Hardy-Weinberg equilibrium. We evaluate the statistical properties of the methods using simulations and by application to a real data set.", "link"=>"http://www.mendeley.com/research/snp-calling-genotype-calling-sample-allele-frequency-estimation-newgeneration-sequencing-data", "reader_count"=>460, "reader_count_by_academic_status"=>{"Unspecified"=>7, "Professor > Associate Professor"=>17, "Researcher"=>137, "Student > Doctoral Student"=>22, "Student > Ph. D. Student"=>134, "Student > Postgraduate"=>21, "Student > Master"=>72, "Other"=>8, "Student > Bachelor"=>26, "Lecturer"=>3, "Lecturer > Senior Lecturer"=>3, "Professor"=>10}, "reader_count_by_user_role"=>{"Unspecified"=>7, "Professor > Associate Professor"=>17, "Researcher"=>137, "Student > Doctoral Student"=>22, "Student > Ph. D. Student"=>134, "Student > Postgraduate"=>21, "Student > Master"=>72, "Other"=>8, "Student > Bachelor"=>26, "Lecturer"=>3, "Lecturer > Senior Lecturer"=>3, "Professor"=>10}, "reader_count_by_subject_area"=>{"Unspecified"=>10, "Agricultural and Biological Sciences"=>336, "Arts and Humanities"=>1, "Chemistry"=>1, "Computer Science"=>29, "Earth and Planetary Sciences"=>1, "Engineering"=>4, "Environmental Science"=>11, "Biochemistry, Genetics and Molecular Biology"=>52, "Mathematics"=>2, "Medicine and Dentistry"=>7, "Physics and Astronomy"=>2, "Social Sciences"=>2, "Immunology and Microbiology"=>1, "Linguistics"=>1}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>7}, "Social Sciences"=>{"Social Sciences"=>2}, "Physics and Astronomy"=>{"Physics and Astronomy"=>2}, "Mathematics"=>{"Mathematics"=>2}, "Unspecified"=>{"Unspecified"=>10}, "Environmental Science"=>{"Environmental Science"=>11}, "Arts and Humanities"=>{"Arts and Humanities"=>1}, "Engineering"=>{"Engineering"=>4}, "Chemistry"=>{"Chemistry"=>1}, "Earth and Planetary Sciences"=>{"Earth and Planetary Sciences"=>1}, "Immunology and Microbiology"=>{"Immunology and Microbiology"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>336}, "Computer Science"=>{"Computer Science"=>29}, "Linguistics"=>{"Linguistics"=>1}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>52}}, "reader_count_by_country"=>{"Colombia"=>2, "Argentina"=>1, "United States"=>23, "United Kingdom"=>5, "Portugal"=>3, "Switzerland"=>5, "Russia"=>1, "Spain"=>4, "New Zealand"=>1, "Canada"=>3, "Netherlands"=>2, "Czech Republic"=>1, "Sweden"=>2, "Turkey"=>1, "Belgium"=>1, "China"=>1, "Brazil"=>3, "Italy"=>3, "France"=>3, "Australia"=>1, "Germany"=>3}, "group_count"=>10}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/603434"], "description"=>"<p>The mean sequencing depths are 1X (a), 3X (b), 5X (c), and 10X (d). The values presented in the figure legend box are the estimates of the proportion of sites that are variable in the sample.</p>", "links"=>[], "tags"=>["unfolded", "sfs", "presented", "genotype", "calling", "choosing", "highest", "posterior", "probability", "bayesian", "50", "mb", "10", "diploid", "snps", "allele", "proportional"], "article_id"=>273900, "categories"=>["Mathematics", "Biological Sciences", "Genetics", "Evolutionary Biology"], "users"=>["Rasmus Nielsen", "Thorfinn Korneliussen", "Anders Albrechtsen", "Yingrui Li", "Jun Wang"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0037558.g001", "stats"=>{"downloads"=>7, "page_views"=>35, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_distribution_of_true_True_and_estimated_unfolded_SFS_using_the_Maximum_Likelihood_method_ML_presented_in_the_paper_genotype_calling_based_on_choosing_the_genotype_with_highest_posterior_probability_GC_and_using_the_Bayesian_procedure_described_in_the/273900", "title"=>"The distribution of true (True) and estimated unfolded SFS using the Maximum Likelihood method (ML) presented in the paper, genotype calling based on choosing the genotype with highest posterior probability (GC), and using the Bayesian procedure described in the text (Bay) in a sample from 50 MB 10 diploid individuals, where 2% of all SNPs are variable in the population and follow a distribution of allele frequencies, <i>p</i>, proportional to 1/<i>p</i>. An error rate of 0.5% is assumed.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-07-24 01:05:00"}
  • {"files"=>["https://ndownloader.figshare.com/files/603715"], "description"=>"<p>The data were previously analyzed in Yi <i>et al.</i> 2010.</p>", "links"=>[], "tags"=>["unfolded", "25", "danish"], "article_id"=>274206, "categories"=>["Mathematics", "Biological Sciences", "Genetics", "Evolutionary Biology"], "users"=>["Rasmus Nielsen", "Thorfinn Korneliussen", "Anders Albrechtsen", "Yingrui Li", "Jun Wang"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0037558.g004", "stats"=>{"downloads"=>9, "page_views"=>416, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_unfolded_site_frequency_spectrum_from_25_Danish_indivuduals_/274206", "title"=>"The unfolded site frequency spectrum from 25 Danish indivuduals.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-07-24 01:10:06"}
  • {"files"=>["https://ndownloader.figshare.com/files/315746", "https://ndownloader.figshare.com/files/315842"], "description"=>"<div><p>We present a statistical framework for estimation and application of sample allele frequency spectra from New-Generation Sequencing (NGS) data. In this method, we first estimate the allele frequency spectrum using maximum likelihood. In contrast to previous methods, the likelihood function is calculated using a dynamic programming algorithm and numerically optimized using analytical derivatives. We then use a Bayesian method for estimating the sample allele frequency in a single site, and show how the method can be used for genotype calling and SNP calling. We also show how the method can be extended to various other cases including cases with deviations from Hardy-Weinberg equilibrium. We evaluate the statistical properties of the methods using simulations and by application to a real data set.</p> </div>", "links"=>[], "tags"=>["snp", "genotype", "allele", "estimation", "new-generation", "sequencing"], "article_id"=>122332, "categories"=>["Mathematics", "Biological Sciences", "Genetics", "Evolutionary Biology"], "users"=>["Rasmus Nielsen", "Thorfinn Korneliussen", "Anders Albrechtsen", "Yingrui Li", "Jun Wang"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0037558.s001", "https://dx.doi.org/10.1371/journal.pone.0037558.s002"], "stats"=>{"downloads"=>8, "page_views"=>39, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/SNP_Calling_Genotype_Calling_and_Sample_Allele_Frequency_Estimation_from_New_Generation_Sequencing_Data/122332", "title"=>"SNP Calling, Genotype Calling, and Sample Allele Frequency Estimation from New-Generation Sequencing Data", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2012-07-24 00:38:52"}
  • {"files"=>["https://ndownloader.figshare.com/files/603610"], "description"=>"<p>The SFS-method is the method described in the main text. The MAF method is based on first obtaining a maximum likelihood estimate of the allele frequency, and then use the estimated allele frequency to define priors for genotype calling. The GC-max method is based on calling genotypes with highest posterior probability. The GC-ratio method is based on calling genotypes depending on the ratio of the likelihood for the most likely to second most likely genotype. The jagged behavior of some of the curves is a consequence of the discrete nature of the data, i.e. an individual contains a discrete number of copies of the minor allele. 10 individuals are simulated for 50,000 variable sites with a distribution of allele frequencies (<i>p</i>), proportional to 1/<i>p</i> with an error rate of 0.5%. Results for other error rates are shown in Figure S2.</p>", "links"=>[], "tags"=>["genotype", "callers"], "article_id"=>274101, "categories"=>["Mathematics", "Biological Sciences", "Genetics", "Evolutionary Biology"], "users"=>["Rasmus Nielsen", "Thorfinn Korneliussen", "Anders Albrechtsen", "Yingrui Li", "Jun Wang"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0037558.g003", "stats"=>{"downloads"=>2, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_error_rate_of_different_genotype_callers_for_different_call_rates_/274101", "title"=>"The error rate of different genotype callers for different call rates.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-07-24 01:08:21"}
  • {"files"=>["https://ndownloader.figshare.com/files/603520"], "description"=>"<p>Data for 10 individuals were simulated assuming a sequencing depth of 2 and a raw sequencing error rate of 1% (A) and (B) a depth of 5 and a raw sequencing error rate of 5%. The SFS method is the main method described in the text. The GC method is based on genotype calling using the genotype with the highest posterior probability. The LR method is based on a likelihood ratio test of the hypothesis that the allele frequency is zero. The SFS based method and the LR method have similar performance except for very high error rates, where the SFS tends to be somewhat better. Both methods in general perform much better than the GC method. The difference would even larger in larger panels of individuals. Simulations under other conditions can be found in Figure S1.</p>", "links"=>[], "tags"=>["curves", "snp"], "article_id"=>274014, "categories"=>["Mathematics", "Biological Sciences", "Genetics", "Evolutionary Biology"], "users"=>["Rasmus Nielsen", "Thorfinn Korneliussen", "Anders Albrechtsen", "Yingrui Li", "Jun Wang"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0037558.g002", "stats"=>{"downloads"=>2, "page_views"=>18, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_ROC_curves_for_different_SNP_callers_/274014", "title"=>"ROC curves for different SNP callers.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-07-24 01:06:54"}

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Relative Metric

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