Visualization of data from any stage of genetic and genomic research is one of the most useful approaches for detecting potential errors, ensuring accuracy and reproducibility, and presentation of the resulting data. Currently software such as Circos, ClicO FS, and RCircos, among others, provide tools for plotting a variety of genetic data types in a concise manner for data exploration and presentation. However, each of the programs have one or more disadvantages that limit their usability in data exploration or construction of publication quality figures, such as inflexibility in formatting and configuration, reduced image quality, lack of potential for automation, or requirements of high-level computational expertise. Therefore, we developed the R package SOFIA, which leverages the capabilities of Circos by manipulating data, preparing configuration files, and running the Perl-native Circos directly from the R environment with minimal user intervention. The advantages of integrating both R and Circos into SOFIA are numerous. R is a very powerful, mid-level programming language widely used among the genetic and genomic research community, while Circos has proven to be a novel software for arranging genomic data to create aesthetical publication quality circular figures. Producing Circos figures in R with SOFIA is simple, requires minimal coding experience, even for complex figures that incorporate high-dimensional genetic information, and allows simultaneous analysis and visual exploration of genomic and genetic data in a single programming environment.
The sommer package has been developed to provide R users with free code to understand how most common algorithms in mixed model analysis work related to genetic analysis and other general experiments, but at the same time allowing to perform their real analysis in diploid and polyploid organisms. This package allows the user to estimate variance components for a mixed model with the advantage of specifying the variance-covariance structure of the random effects and obtain other parameters such as BLUPs, BLUEs, residuals, fitted values, variances for fixed and random effects, etc. The package is focused on genomic prediction (or genomic selection) and GWAS analysis although general mixed models can be fitted as well. The package provides kernels to estimate additive (A.mat), dominant (D.mat), and epistatic (E.mat) relationship matrices that have been shown to increase prediction accuracy. The package provides flexibility to fit other genetic models such as full and half diallele models as well.
GiNA is a free software for measuring horticultural traits related with the shape and color of fruits, vegetables, and seeds. The software was written in both R and MATLAB programming languages. It uses conventional images from digital cameras and processes up to 11 different morphological traits such as length, width, two dimensional area, volume, projected skin, surface area, RGB color, among other parameters.
Fragman is a package designed for Fragment analysis and automatic scoring of biparental populations (such as F1, F2, BC types) and populations for diversity studies. The program is designed to read files with FSA extension (which stands for FASTA-type file and contains lectures for DNA fragments) and extract the DNA intensities from the channels/colors where they are located, based on ABi machine plattforms to perform sizing and allele scoring. The core of the package relays in 4 functions; 1) `storing.inds` is the function in charge of reading the FSA files and storing them with a list structure, 2) `ladder.info.attach` uses the information read from the FSA files and a vector containing the ladder information (DNA size of the fragments) and matches the peaks from the channel where the ladder was run with the DNA sizes for all samples. Then loads such information in the R environment for the use of posterior functions, 3) `overview` & `overview2` create friendly plots for any number of individuals specified and can be used to design panels (overview2) for posterior automatic scoring, or make manual scoring (overview) of individuals such as parents of biparental populations or diversity panels, 4) The `score.easy` function score the alleles by finding all regions where the first derivative of the intensity vector iz zero and reduces the search of peaks using a panel (if provided) otherwise returns all peaks present. This function can be automatized if several markers are located in the same channel by creating lists of panels taking advantage of R capabilities and data structures (see vignettes below).