GrafGen

An elaborate molecular evolutionary framework that facilitates straightforward simulation of codon genetic sequences subjected to different degrees and/or patterns of Darwinian selection. The model is built upon the fitness landscape paradigm of Sewall Wright, as popularised by the mutation-selection model of Halpern and Bruno. This enables realistic evolutionary process of living organisms to be reproducible seamlessly. For example, an Ornstein-Uhlenbeck fitness update algorithm is incorporated herein. Consequently, otherwise complex biological processes, such as the effect of the interplay between genetic drift and fitness landscape fluctuations on the inference of diversifying selection, may now be investigated with minimal effort. Frequency-dependent and stochastic fitness landscape update techniques are available.

Biological inferences obtained from molecular data are only as good as the extent of evolutionary signatures retained in the genetic data. Techniques available to quantify these signatures are largely targeted towards phylogeny reconstruction and they often rely on adhoc hypothesis tests of significance. I present a Bayesian function that assesses whether a set of genetic sequences are saturated. That is, it is useful for determining whether the evolutionary information in the sequences has eroded with time. Site specific Bayes factors are generated with respect to codon bases to allow for straightforward applications in extensive computational biology inquiries, including natural selection analyses.

geneXtendeR optimizes the functional annotation of ChIP-seq peaks by exploring relative differences in annotating ChIP-seq peak sets to variable-length gene bodies. In contrast to prior techniques, geneXtendeR considers peak annotations beyond just the closest gene, allowing users to see peak summary statistics for the first-closest gene, second-closest gene, ..., n-closest gene whilst ranking the output according to biologically relevant events and iteratively comparing the fidelity of peak-to-gene overlap across a user-defined range of upstream and downstream extensions on the original boundaries of each gene's coordinates. Since different ChIP-seq peak callers produce different differentially enriched peaks with a large variance in peak length distribution and total peak count, annotating peak lists with their nearest genes can often be a noisy process. As such, the goal of geneXtendeR is to robustly link differentially enriched peaks with their respective genes, thereby aiding experimental follow-up and validation in designing primers for a set of prospective gene candidates during qPCR.

planttfhunter is used to identify plant transcription factors (TFs) from protein sequence data and classify them into families and subfamilies using the classification scheme implemented in PlantTFDB. TFs are identified using pre-built hidden Markov model profiles for DNA-binding domains. Then, auxiliary and forbidden domains are used with DNA-binding domains to classify TFs into families and subfamilies (when applicable). Currently, TFs can be classified in 58 different TF families/subfamilies.

This package provides functions for fitting GPA, a statistical framework to prioritize GWAS results by integrating pleiotropy information and annotation data. In addition, it also includes ShinyGPA, an interactive visualization toolkit to investigate pleiotropic architecture.

Genome-wide association studies (GWAS) is a widely used tool for identification of genetic variants associated with phenotypes and diseases, though complex diseases featuring many genetic variants with small effects present difficulties for traditional these studies. By leveraging pleiotropy, the statistical power of a single GWAS can be increased. This package provides functions for fitting graph-GPA, a statistical framework to prioritize GWAS results by integrating pleiotropy. 'GGPA' package provides user-friendly interface to fit graph-GPA models, implement association mapping, and generate a phenotype graph.