In this tutorial, we will re-analyze the ATAC-seq data published in Yu, 2017.

The analysis requires following additional software:

fastq-dump
samtools
MACS2
bwa
bedGraphToBigWig

All you need to do in this tutorial is preparing two files (input.tsv and config.yml) and typing a single command. Taiji will do the rest for you.

If you are wondering what Taiji does under the hood, type taiji view taiji.html to generate an overview of the pipeline. You can then use your favorite web browser to open the HTML file.

Now let’s start the tutorial!

Preparing input

We will use the SRA ID as the input and Taiji will download the data for us. To do this, add the following lines to the “input.tsv” file:

type	id	group	rep	path	format
ATAC-seq	TN	TN	1	SRR5305074	SRA
ATAC-seq	TN	TN	2	SRR5305075	SRA
ATAC-seq	TE	TE	1	SRR5305076	SRA
ATAC-seq	TE	TE	2	SRR5305077	SRA
ATAC-seq	MP	MP	1	SRR5305078	SRA
ATAC-seq	MP	MP	2	SRR5305079	SRA
ATAC-seq	memory	memory	1	SRR5305080	SRA
ATAC-seq	memory	memory	2	SRR5305081	SRA

Please note that the columns are TAB-separated. If there are multiple runs, combine them using “+”. For paired-end sequencing, specify that using the “PairedEnd” tag.

type	id	group	rep	path	format	tags
ATAC-seq	memory	memory	1	SXXXX80+SXXXX81	SRA	
ATAC-seq	memory	memory	1	SXXXX80	SRA	PairedEnd

Taiji also supports ENCODE data. You can input the file ID for any fastq or bam files and associate them with the “ENCODE” tag. For pairend-end fastqs, separate them using “,”.

type	id	group	rep	path	tags
ATAC-seq	control	control	1	ENCFF893KQZ,ENCFF443HVZ	ENCODE

Preparing configuration file

This is how the configuration file looks like:

input: "input.tsv"
output_dir: "output/"
assembly: "mm10"

Running the ATAC-seq analysis

Use the following command to run the pipeline using ten concurrent threads:

taiji run --config config.yml -n 10 +RTS -N10

The arguments -n 10 +RTS -N10 tell the taiji to use 10 cores/threads.

taiji run --config config.yml -n 10 --cloud

if you are using a job scheduling system like slurm or PBS. (Click here to learn more about distributed computing)

Taiji is able to pick up where you left off! To demonstrate this, after finishing a few steps, press CTRL+C to STOP the program and then re-run the program using the exact same command. You shall see the program continues from the last checkpoint.

Results

It will take several hours for the program to finish. Once it finishes, all results will be in the output directory, including BAM files, BED files, peaks, etc.

Usually the first thing we want to look at is the quality of the data. The QC metrics can be visualized by the qc.html file in the output/ATACSeq/QC directory.

Here are some of the QC metrics:

Taiji combines motif scanning, network inference and the PageRank algorithm to rank TFs. This result will be saved in the GeneRank.tsv file. There is also a GeneRank.html file that you can visualize.

The figure above shows different T cell population has its own unique transcriptional profile. We can zoom in to see the list of TFs that show high ranking scores in naive CD8 T cells.

TCF7 is an important gene for the naive population. As an example, we plotted the gene TCF7 and its regulatees in TN and TE populations. It clearly shows that TCF7 regulates much more genes in the naive population.