A brief history about the R package ‘metan’

Post provided by Tiago Olivoto

Este post também pode ser lido em Português

In our recent paper in Methods in Ecology and Evolution, Alessandro Lúcio and I describe a new R package, metan, for multi-environment trial analysis. Multi-environment trials are a kind of trial in plant breeding programs where several genotypes are evaluated in a set of environments. Analyzing such data requires the combination of several approaches including data manipulation, visualization and modelling. The latest stable version of metan (v1.5.1) is now on CRAN. So, I want to share the history about my first foray into using R, creating an R package, and submitting a paper to a journal that I’ve never had submitted before.

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Uma breve história sobre o pacote R ‘metan’

Post ESCRITO POR Tiago Olivoto

This post is also available in English

Em nosso recente artigo na Methods in Ecology and Evolution, Alessandro D. Lúcio e eu descrevemos um novo pacote R para análise de ensaios multi-ambientes chamado metan. Ensaios multi-ambientes são um tipo de ensaio em programas de melhoramento de plantas, onde vários genótipos são avaliados em um conjunto de ambientes. A análise desses dados requer a combinação de várias abordagens, incluindo manipulação, visualização e modelagem de dados. A versão estável mais recente do metan (v1.5.1) está disponível agora no repositório CRAN. Então, pensei em compartilhar a história da minha primeira incursão no uso do R criando um pacote e submetendo um artigo para uma revista que nunca havia submetido antes.

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Gaining Genetic and Epigenetic Data from a Single Established Next-Generation Sequencing Approach

Post provided by Marco Crotti

How organisms adapt to the environment they live in is a key question in evolutionary biology. Genetic variation, i.e. how individuals within populations differ from each other in terms of their DNA, is an essential element in the process of adaptation. It can arise through different mechanisms, including DNA mutations, genetic drift, and recombination.

Example of how genetic drift can occur over generations via random sampling (i.e. random mating) in a population. (Picture credit: Gringer).

Differences in DNA sequences between individuals can results in differences in the expression of genes. This can therefore determine the organism’s capacity to grow, develop, and react to environmental stimuli. However, a growing body of literature reveals that there are other ways organisms can change the way they interact with the world without mutations in the DNA sequence.

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Issue 7.8

Issue 7.8 is now online!

The August issue of Methods is now online!

This month’s issue contains two Applications articles and two Open Access articles, all of which are freely available.

Plant-O-Matic: A free iOS application that combines the species distribution models with the location services built into a mobile device to provide users with a list of all plant species expected to occur in the 100 × 100 km geographic grid cell corresponding to the user’s location.

RClone: An R package built upon genclone software which includes functions to handle clonal data sets, allowing:

  • Checking for data set reliability to discriminate multilocus genotypes (MLGs)
  • Ascertainment of MLG and semi-automatic determination of clonal lineages (MLL)
  • Genotypic richness and evenness indices calculation based on MLGs or MLLs
  • Describing several spatial components of clonality

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