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  • 1
    In: Bioinformatics, Oxford University Press (OUP), Vol. 30, No. 8 ( 2014-04-15), p. 1187-1189
    Abstract: Motivation: DIYABC is a software package for a comprehensive analysis of population history using approximate Bayesian computation on DNA polymorphism data. Version 2.0 implements a number of new features and analytical methods. It allows (i) the analysis of single nucleotide polymorphism data at large number of loci, apart from microsatellite and DNA sequence data, (ii) efficient Bayesian model choice using linear discriminant analysis on summary statistics and (iii) the serial launching of multiple post-processing analyses. DIYABC v2.0 also includes a user-friendly graphical interface with various new options. It can be run on three operating systems: GNU/Linux, Microsoft Windows and Apple Os X. Availability: Freely available with a detailed notice document and example projects to academic users at http://www1.montpellier.inra.fr/CBGP/diyabc Contact:  estoup@supagro.inra.fr Supplementary information:  Supplementary data are available at Bioinformatics online.
    Type of Medium: Online Resource
    ISSN: 1367-4811 , 1367-4803
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2014
    detail.hit.zdb_id: 1468345-3
    SSG: 12
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  • 2
    Online Resource
    Online Resource
    Oxford University Press (OUP) ; 2016
    In:  Bioinformatics Vol. 32, No. 6 ( 2016-03-15), p. 859-866
    In: Bioinformatics, Oxford University Press (OUP), Vol. 32, No. 6 ( 2016-03-15), p. 859-866
    Abstract: Motivation: Approximate Bayesian computation (ABC) methods provide an elaborate approach to Bayesian inference on complex models, including model choice. Both theoretical arguments and simulation experiments indicate, however, that model posterior probabilities may be poorly evaluated by standard ABC techniques. Results: We propose a novel approach based on a machine learning tool named random forests (RF) to conduct selection among the highly complex models covered by ABC algorithms. We thus modify the way Bayesian model selection is both understood and operated, in that we rephrase the inferential goal as a classification problem, first predicting the model that best fits the data with RF and postponing the approximation of the posterior probability of the selected model for a second stage also relying on RF. Compared with earlier implementations of ABC model choice, the ABC RF approach offers several potential improvements: (i) it often has a larger discriminative power among the competing models, (ii) it is more robust against the number and choice of statistics summarizing the data, (iii) the computing effort is drastically reduced (with a gain in computation efficiency of at least 50) and (iv) it includes an approximation of the posterior probability of the selected model. The call to RF will undoubtedly extend the range of size of datasets and complexity of models that ABC can handle. We illustrate the power of this novel methodology by analyzing controlled experiments as well as genuine population genetics datasets. Availability and implementation: The proposed methodology is implemented in the R package abcrf available on the CRAN. Contact:  jean-michel.marin@umontpellier.fr Supplementary information:  Supplementary data are available at Bioinformatics online.
    Type of Medium: Online Resource
    ISSN: 1367-4811 , 1367-4803
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2016
    detail.hit.zdb_id: 1468345-3
    SSG: 12
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  • 3
    In: Molecular Ecology, Wiley, Vol. 22, No. 11 ( 2013-06), p. 3165-3178
    Abstract: Inexpensive short‐read sequencing technologies applied to reduced representation genomes is revolutionizing genetic research, especially population genetics analysis, by allowing the genotyping of massive numbers of single‐nucleotide polymorphisms ( SNP ) for large numbers of individuals and populations. Restriction site–associated DNA ( RAD ) sequencing is a recent technique based on the characterization of genomic regions flanking restriction sites. One of its potential drawbacks is the presence of polymorphism within the restriction site, which makes it impossible to observe the associated SNP allele (i.e. allele dropout, ADO ). To investigate the effect of ADO on genetic variation estimated from RAD markers, we first mathematically derived measures of the effect of ADO on allele frequencies as a function of different parameters within a single population. We then used RAD data sets simulated using a coalescence model to investigate the magnitude of biases induced by ADO on the estimation of expected heterozygosity and F ST under a simple demographic model of divergence between two populations. We found that ADO tends to overestimate genetic variation both within and between populations. Assuming a mutation rate per nucleotide between 10 −9 and 10 −8 , this bias remained low for most studied combinations of divergence time and effective population size, except for large effective population sizes. Averaging F ST values over multiple SNP s, for example, by sliding window analysis, did not correct ADO biases. We briefly discuss possible solutions to filter the most problematic cases of ADO using read coverage to detect markers with a large excess of null alleles.
    Type of Medium: Online Resource
    ISSN: 0962-1083 , 1365-294X
    URL: Issue
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    Language: English
    Publisher: Wiley
    Publication Date: 2013
    detail.hit.zdb_id: 2020749-9
    detail.hit.zdb_id: 1126687-9
    SSG: 12
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