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While Mcheza tries to minimize usage errors, the user should be aware of potential limits of the underlying method. Potential users are advised to read Caballero et al.

In particular, the Bayesian method of Foll and Gaggiotti , implemented in the program BayeScan is an important alternative, with which results should be compared.

In summary, Mcheza tries to provide an intuitive interface, which includes intelligent suggestions to the user with regards to correct usage of software, while enforcing model constraints and providing necessary corrections e.

It is hoped that this approach will lower barriers to its use, allowing researchers to concentrate more on the biological problems including the theoretical assumptions and limitations of underlying models and less on unnecessary software complexity.

Oxford University Press is a department of the University of Oxford. It furthers the University's objective of excellence in research, scholarship, and education by publishing worldwide.

Sign In or Create an Account. Close mobile search navigation Article navigation. Bioinformatics , Volume 27, Issue 12, 15 June , Pages —, https: Identifying adaptive genetic divergence among populations from genome scans.

Controlling the false discovery rate: Impact of amplified fragment length polymorphism size homoplasy on the estimation of population genetic diversity and the detection of selective loci.

Adaptive divergence and speciation among sexual and pseudoviviparous populations of Festuca. A genome-scan method to identify selected loci appropriate for both dominant and codominant markers: Comparing three different methods to detect selective loci using dominant markers.

R Development Core Team. Using genome scans of DNA polymorphism to infer adaptive population divergence,. Estimating population structure in diploids with multilocus dominant DNA markers.

Published by Oxford University Press. For Permissions, please email: Email alerts New issue alert. Receive exclusive offers and updates from Oxford Academic.

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Moravce — Podbrezova 2 3. The recent prominence of these markers has coincided with renewed interest in detecting the effects of local selection and adaptation at the level of the genome.

We present Mcheza, an application for detecting loci under selection based on a well-evaluated F ST -outlier method.

The application allows robust estimates to be made of model parameters e. Mcheza also provides functionality to mitigate common analytical errors when scanning for loci under selection.

Mcheza is freely available under GPL version 3 from http: Non-specific amplification methods, such as Diversity Arrays Technology DArT markers and amplified fragment length polymorphism AFLP , are commonly used for analysis of within-species variation because they allow the rapid acquisition of substantial genetic information, at relatively low cost.

Although other alternative sequencing techniques have since been developed, DArTs and AFLPs are still widely used in the fields of evolutionary genetics, ecology and conservation.

One of the most important applications of these dominant markers is in detecting the effects of selection and local adaptation at the level of the genome, in areas ranging from parasitology to conservation genetics.

There are two current approaches to detect selection: These markers have two phenotypes detectable at each locus: Heterozygous genotypes cannot be directly distinguished from homozygotes making the estimation of allele frequencies non-trivial.

Briefly, coalescent simulations are used to generate a null sampling distribution of estimates of F ST based upon neutral expectations.

The performance of the method has been examined, using data simulated with known levels of selection, in Caballero et al. For example, the tuning of parameters for the coalescent simulations is non-trivial, and the input dataset has to be formatted in a non-standard way.

We describe Mcheza, a new application based on DFDIST, with a graphical user interface allowing easier configuration of some non-trivial parameters.

The Mcheza architecture is composed of two parts: Estimation of the mean neutral F ST , while taking into account loci that might be under selection.

An improved method for ensuring that the simulated distribution of F ST has a mean that is close to the required value. The Mcheza interface provides a correction that accurately approximates F ST even when the number of demes is very low.

Support for very large datasets is, as expected, computationally more intensive. Mcheza also introduces support for multitest correction based on false discovery rates FDRs Benjamini, , as implemented in Chiurugwi et al.

A multicore aware version of DFDIST with computational performance gains that are near linear with the number of cores.

An easy-to-use interface including the ability to import data in the standard Genepop format Rousset, , generation of charts, export in standard formats including R Development Core Team, and spreadsheets, iterative smoothing of confidence contours, choice of population and loci among other features.

The use of the Jython programming language allows the use of Biopython Cock et al. A fundamental consideration in the design of Mcheza is supporting the user by correctly computing important non-trivial parameters that are needed to properly calculate candidate loci for selection.

Erroneous usage of population genetics applications can easily produce results that seem correct but are, in effect wrong. While Mcheza tries to minimize usage errors, the user should be aware of potential limits of the underlying method.

Potential users are advised to read Caballero et al.

mcheza -

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