DRC2024 basic bioinformatics

github page | source code

This package will create the docker file for the tools used for RNA-seq and ChIP-seq analysis, the sample code and files to run the pipeline, and the slides for the course.

Pre-requisites

• Basic knowledge of next generation sequence
• Basic knowledge of R
• Basic knowledge of Docker
• A computer with internet connection

To use the resulting image:

docker file for genomic tools

Dockerfile to build bwa, kallisto, MACS2, samtools, picard-tools, fastQC, bedtools, cutadapt, deeptools, R, ucsc genome tools images Based on Ubuntu

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

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

To try with this repository docker image:

docker run -e PASSWORD=123456 -p 8787:8787 ghcr.io/jianhong/genomictools:latest

NOTE: Running docker that uses the password in plain text like above exposes the password to others in a multi-user system (like a shared workstation or compute node). In practice, consider using an environment variable instead of plain text to pass along passwords and other secrets in docker command lines.

pipeline for RNA-seq

The sample RNA-seq data will be analyzed with kallisto or Salmon

• tximport + DESeq2.

pipeline for ChIP-seq

The ChIP-seq data will be analyzed with bwa + MACS2.

Learning goals

1. Gain the basic knowledge of typical workflows for RNA-seq and ChIP-seq

2. Learn how to understand the results of RNA-seq and ChIP-seq data

3. Become aware the experimental approaches and the limitation of the pipeline.

references:

• BioinformaticsTraningWorkshop

• Introduction to ChIP-seq using high performance computing

• Webinar: New and Improved RNA-Seq Workflows

• Alignment-free RNA-seq workflow

• Kallisto and Sleuth

• cornell computational pipeline for ChIP-seq Data Analysis