Week 1: ChIPseq

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Project 2 Directions

Now that we have experience with Nextflow from two prior projects, the directions for this project will be much less detailed. I will describe what you should do and you will be expected to implement it yourself. If you are asked to perform a certain task, you should create working nextflow modules, construct the proper channels in your main.nf, and run your workflow.

Please follow all the conventions we’ve established so far in the course.

These conventions include:

Using isolated containers specific for each tool
Write extensible and generalizble nextflow modules for each task
Encoding reference file paths in the nextflow.config
Encoding sample info and sample file paths in a csv that drives your workflow
Requesting appropriate computational resources per job

Week 1 Overview

For many NGS experiments, the initial steps are largely universal. We perform quality control on the sequencing reads, build an index for the reference genome, and align the reads. However, the source of the data will inform what quality metrics are relevant and the particular choice of tools to accomplish these steps. For RNAseq, it is important to use a splice-aware aligner when aligning against a reference genome since our sequences originated from mRNA. For ChIPseq experiments, our reads originated from DNA sequences and we can use a non-splice aware algorithm to map our reads to the reference genome.

Objectives

Assess QC on sequencing reads using FastQC
Trim adapters and low-quality reads using Trimmomatic
Align trimmed reads to the human reference genome
Run samtools flagstat to assess alignment statistics
Use MultiQC to aggregate all of the QC metrics
Use samtools to sort and index your BAM (alignment) files

Containers for Project 2

FastQC: ghcr.io/bf528/fastqc:latest

multiQC: ghcr.io/bf528/multiqc:latest

bowtie2: ghcr.io/bf528/bowtie2:latest

deeptools: ghcr.io/bf528/deeptools:latest

trimmomatic: ghcr.io/bf528/trimmomatic:latest

samtools: ghcr.io/bf528/samtools:latest

macs3: ghcr.io/bf528/macs3:latest

bedtools: ghcr.io/bf528/bedtools:latest

homer: ghcr.io/bf528/homer:latest

homer/samtools: ghcr.io/bf528/homer_samtools:latest

Quality Control, Genome indexing and alignment

Between project 1 and the early labs we did, you should have working modules that perform quality control using FastQC and trimmomatic, build a genome index using bowtie2, and align reads to a genome. We are going to take advantage of the modularity of nextflow by simply copying these previous modules and incorporating them into this workflow.

The samples termed IP are the samples of interest (IP for a factor of interest) and the samples labeled INPUT are the control samples. Remember ChIP-seq experiments are paired (e.g. INPUT_rep1 is the control for IP_rep1)

Use the provided CSV files that point to both the subsampled and full files. When you are developing the beginning of your workflow, use the subsampled files (they will not work once you get to the peak calling step).

Please note that the subsampled_files are named differently than the full files!

The paths to the human reference genome, GTF and a few other critical files are already encoded in your nextflow.config.
Update your workflow main.nf to perform quality control using FastQC and trimmomatic, build a bowtie2 index for the human reference genome and align the reads to the reference genome. Remember that we have worked on code for trimmomatic and bowtie2 in the labs.

N.B. Some of the code from the labs will need to be modified to work for this specific experiment (paired end vs. single end, etc.)

Sorting and indexing the alignments

Many subsequent analyses on our BAM files will require them to be both sorted and indexed. Just like for large sequences in FASTA files, sorting and indexing the alignments will allow us to perform much more efficient computational operations on them.

Create a module(s) that will both sort and index your BAM files using Samtools.

Calculate alignment statistics using samtools flagstat

The samtools flagstat utility will report various statistics regarding the alignment flags found in the BAM.

Create a module that will run samtools flagstat on all of your BAM files

Aggregating QC results with MultiQC

Just like in project 1, we are going to use multiqc to collect the various quality control metrics from our pipeline. Ensure that multiqc collects the outputs from FastQC, Trimmomatic and flagstat.

Make a channel in your workflow main.nf that collects all of the relevant QC outputs needed for multiqc (fastqc zip files, trimmomatic log, and samtools flagstat output)
Copy your previous multiqc module and incorporate it into your workflow to generate a MultiQC report for the listed outputs

Generating bigWig files from our BAM files

Now that we have sorted and indexed our alignments, we are going to generate bigWig files or coverage tracks containing the number of reads per genomic interval or bin for each sample. We will use these coverage tracks for calculating correlation between our samples and visualizing the read coverage in specific regions of interest.

Use the bamCoverage deeptools utility to generate a bigWig file for each of the sample BAM files.
You may use all default parameters. If you wish, you may change the -bs and -p flags as needed.

Week 1 Tasks Summary

Create nextflow modules that run the following tools:
1. FastQC
2. Trimmomatic
3. Bowtie2-index
Sort and index your bams using a single nextflow module and samtools
Calculate alignment statistics using samtools flagstat
Aggregate all of the QC output results from the previous tools using MultiQC
Generate a nextflow module that uses deeptools to create bigWig representations of your BAM files

Week 4: RNAseq

Week 2: ChIPseq