Applying tidy principles to investigating chromatin composition and architecture

Authors: Jacques Serizay1,
Last modified: 2024-07-23.

Overview

By adhering to the principles of tidy data organization and the elegant syntax of tidyverse packages, researchers can navigate the complexities of genomic datasets with unprecedented ease and efficiency.

In this workshop, we will go over three recent packages:

  • plyranges, developed to manipulate generic genomic ranges within the tidyomics framework;
  • plyinteractions, specifically developed to manipulate chromatin conformation capture (3C, Hi-C micro-C, etc);
  • tidyCoverage, to manipulate and extract coverage tracks within the tidyomics framework.

plyinteractions and tidyCoverage packages introduce novel SummarizedExperiment-derived S4 classes to store genomics data and expand tidy methods, following the principles defined in plyranges and tidySummarizedExperiment.

They synergize the existing functionalities of tidyverse and Bioconductor, to seamlessly intertwine data manipulation, aggregation, visualization, and modeling within a unified framework.

Participation

This 90min-long workshop will include brief overview of some of the state-of-the-art packages following the tidyomics ecosystem recommendations. Most of the workshop will be based on a combination of instructor-led live demo and hands-on guided exercises.

Pre-requisites

  • Knowledge of GenomicRanges and SummarizedExperiment classes of object
  • Familiarity with standard genomic processed data formats (e.g. bed files, bigwig files, …)

The following resources are relevant to this workshop:

R / Bioconductor packages used

  • plyranges
  • plyinteractions
  • tidyCoverage

Time outline

Activity Time
Manipulating genomic ranges data 20m
Manipulating genomic interaction data 35m
Manipulating coverage data 35m

Workshop goals and objectives

Learning goals:

  • Manipulate genomic features, genomic interactions and/or genomic tracks using tidyomics principles;
  • Integrating different levels of genomic information together.

Learning objectives:

  • Import/coerce genomic features and/or interactions into relevant Bioconductor classes;
  • Summarize genomic information using tidy data approaches;
  • Visualize and aggregate genomic coverage data over genomic features of interest in a tidy manner.

Workshop environment

The companion website for this workshop is available here:

https://js2264.github.io/Bioc2024tidyworkshop

To use the workshop image:

docker run -e PASSWORD=<choose_a_password_for_rstudio> -p 8787:8787 ghcr.io/js2264/bioc2024tidyworkshop:latest

Once running, navigate to http://localhost:8787/ and then login with rstudio:yourchosenpassword.

  1. Institut Pasteur, Paris↩︎