Table of Contents

• Introduction
  • Designed with customization in mind
  • Extensive downstream RNA-seq
  • Extenstive exploration of ChIP-seq peaks
  • Track hubs
  • Unified approach to reference genomes
  • Integration with external data and figure-making
  • Tested automatically
  • All the advantages of Snakemake
    • Run locally or on a cluster with the same code
    • Only run the required jobs
    • Parallelization
    • Software installation
• Getting started
  • Setting up a project
  • 1. Deploying lcdb-wf
    • Option 1: Download and run the deployment script
    • Option 2: Clone repo manually
  • 2. Configure
  • 3. Run
    • Next steps
• Guide to file hierarchy
  • Folder organization
  • Annotated tree
• Overview of workflows
  • Features common to workflows
  • Primary analysis workflows
  • Integrative analysis workflows
  • Next Steps
• Configuration
  • General configuration
    • Config YAML
    • References config
    • Sample tables
    • Patterns and targets
  • Running on a cluster
• References workflow
  • A dictionary of references
• RNA-seq workflow
  • Downstream analysis
    • RNA-Seq downstream analysis
• RNA-Seq downstream analysis
  • How to use this code
  • More details
    • Detailed documentation of RNA-Seq downstream
• ChIP-seq workflow
• Integrative workflows
  • Colocalization workflow
  • “External” workflow
  • “Figures” workflow
• conda and conda envs in lcdb-wf
  • Conda basics
  • Building the environments
  • Troubleshooting environments
  • Conda envs in lcdb-wf
  • Design decisions
• Testing the installation
  • Create conda envs
  • Activate the main env
  • Download example data
  • A note about test settings
  • Run the RNA-seq workflow with example data
  • Run the ChIP-seq workflow with example data
  • Exhaustive tests
• FAQs
  • Can I run multiple workflows at once?
  • How do I handle multiple experiments in the same project?
    • Samples need the same library layout
    • Samples need to use the same parameters
    • Samples must use the same assembly
  • How are low counts handled during differential expression analysis? Should we use a read-count threshold to filter genes?
  • How do I troubleshoot failed jobs?
  • How do I update my deployment?
• Changelog
  • v1.10.3
  • v1.10.2
  • v1.10.1
  • v1.10
    • General
    • RNA-seq
  • v1.9
    • General
    • RNA-seq
    • Tests
    • References
  • v1.8
    • General
    • RNA-seq
    • References
    • ChIP-seq
  • v1.7
    • Setup
    • Testing
    • References
    • RNA-seq
    • Testing
  • v1.6
    • References
    • ChIP-seq workflow
    • RNA-seq workflow
    • RNA-seq downstream
    • General
  • v1.5.3
    • General
    • Bugs
    • RNA-seq
    • Tests
  • v1.5.2
    • Bug fixes
  • v1.5.1
    • Bug fixes
    • General
    • ChIP-seq and RNA-seq
    • RNA-seq
  • v1.5 (Sept 2019)
    • RNA-seq
    • References
    • All workflows
  • v1.4.2 (Jul 2019)
    • Bugfixes
  • v1.4.1 (Jul 2019)
    • RNA-seq
    • References
  • v1.4 (May 2019)
    • RNA-seq
  • v1.3 (May 2019)
    • Bugfixes
    • Infrastructure
    • ChIP-seq and RNA-seq
  • v1.2 (Mar 2019)
    • RNA-seq
    • References
    • ChIP-seq and RNA-seq
    • Infrastructure
    • Colocalization
  • v1.1 (Aug 2018)
    • Infrastructure
    • References
    • Both RNA-seq and ChIP-seq
    • ChIP-seq
    • RNA-seq
  • v1.0.1 (Jun 2018)
    • Infrastructure
    • RNA-seq
    • downstream/rnaseq.Rmd
    • ChIP-seq
    • Both RNA-seq and ChIP-seq
  • v1.0 (May 2018)
• For Developers
  • Creating and updating conda envs
  • Running the full complex datasets
  • Module documentation
    • Module lib.common
    • Module lib.chipseq
    • Module lib.patterns_targets
  • Adding a new aligner
    • Modules
    • Configuration
    • Testing
  • Adding a new peak-caller