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README.md

Welcome to GERP Deleterious Alleles project

This is a research repo for our project entitled "incomplete dominance of deleterious alleles contributes substantially to trait variation and heterosis in maize". The preprint manuscript could be found via bioRxiv.

Introduction

In this study, we take advantage of the genetic and genomic tools available in maize to investigate the contribution of deleterious alleles to phenotypic variation. We use the deleterious annotation to inform our genomic prediction model to improve the prediction accuracy for phenotypic traits and heterosis.

Architecture about this Repo

This project contains ~400 commits. A largedata directory was intentionally ignored by adding to gitignore because of the large size of the files within the folder. To guide the visitors having a better idea about the repo, here we briefly introduce the functions or sepecific purposes of the directory system. The layout of directories is based on the idea from ProjectTemplate.

  1. cache: Here we store intermediate data sets that are generated during a preprocessing step.
  2. data: Here we store our raw data of small size. Data of large size, i.e. > 100M, store in a largedata folder that has been ignored using gitignore.
  3. doc: Documentation codes (i.e. Rmd files) for generating the figures.
  4. graphs: Graphs produced during the study.
  5. lib: Some functions for our work.
  6. profilling: Analysis scripts for the project. It contains some sub-directories.
  7. table: Table produced during the study.

Figures

Rmd code to generate Figures in the paper.

  1. Figure 1: Panels of this figure can be produced with code doc/Fig1.Rmd. And the Figure1 in a pdf version.
  2. Figure 2: Panels of this figure can be produced with code doc/Fig2.Rmd. And the Figure2 in a pdf version.
  3. Figure 3: Panels of this figure can be produced with code doc/Fig3.Rmd. And the Figure3 in a pdf version.

License

This repo is free and open source for research usage, licensed under GPLv2.

About

Research repo for Yang et al., 2017 paper.

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