.. below role allows to use the html syntax, for example :raw-html:`<br />`
.. role:: raw-html(raw)
    :format: html


========================================
Transcription Factor Footprinting
========================================


**Learning outcomes**


- detect transcription factor binding signatures in ATAC-seq data


.. list-table:: Figure 1. Overview of TF footprinting in ATAC-seq data.
   :widths: 60
   :header-rows: 0

   * - .. image:: figures-ftprnt/TFfootprinting_tobias.png
   			:width: 600px



:raw-html:`<br />`




.. contents:: Contents
    :depth: 1
    :local:




:raw-html:`<br />`


Introduction
=============

Transcription factor (TF) footprinting allows for the prediction of binding of a TF at a particular locus. This is because the DNA bases that are directly bound by the TF are actually protected from transposition while the DNA bases immediately adjacent to TF binding are accessible (Figure 1.). This results in **footprints**: defined regions of decreased signal strength within larger regions of high signal. 

While ATAC-seq can uncover accessible regions where transcription factors (TFs) *might* bind, reliable identification of specific TF binding sites (TFBS) still relies on chromatin immunoprecipitation methods such as ChIP-seq.
ChIP-seq methods require high input cell numbers, are limited to one TF per assay, and are further restricted to TFs for which antibodies are available.

Another caveat of TF footprinting is that not all TFs leave footprints detectable in ATAC-seq data, and not all footprints for given TF can be detected.

In this tutorial we use `TOBIAS <https://github.com/loosolab/TOBIAS/wiki/>`_ to detect TF binding signatures in ATAC-seq data (Bentsen et al 2020).



Data
=====

We will use data that come from publication `Batf-mediated epigenetic control of effector CD8+
T cell differentiation` (Tsao et al 2022). These are **ATAC-seq** libraries (in duplicates) prepared to analyse chromatin accessibility status in murine CD8+ T lymphocytes prior to and upon Batf knockout.

The response of naive CD8+ T cells to their cognate antigen involves rapid and broad changes to gene expression that are coupled with extensive chromatin remodeling. Basic leucine zipper ATF-like transcription
factor **Batf** is essential for the early phases of the process.

We will use data from *in vivo* experiment.


SRA sample accession numbers are listed in Table 1.


.. list-table:: Table 1. Samples used in this tutorial.
   :widths: 10 25 25 50
   :header-rows: 1

   * - No
     - Accession
     - Sample Name
     - Description
   * - 1
     - SRR17296554
     - B1_WT_Batf-floxed_Cre_P14
     - WT Batf
   * - 2
     - SRR17296555
     - B2_WT_Batf-floxed_Cre_P14
     - WT Batf
   * - 3
     - SRR17296556
     - A1_Batf_cKO_P14
     - KO Batf
   * - 4
     - SRR17296557
     - A2_Batf_cKO_P14
     - KO Batf


We will use precomputed tracks and perform the last step: footprints detection.

We will use motifs of selected TF, to shorten the computation time.

We will inspect the results of footprinting of a comprehensive TF landscape of non-redundant vertebrate TF motif collection from `JASPAR <https://jaspar.elixir.no/>`_ .

:raw-html:`<br />`

The starting point for this analyses are bam files with alignments **merged across replicates** i.e. one bam file per condition. This is done to increase read depth.


:raw-html:`<br />`
:raw-html:`<br />`


Setting-up
===========

Starting at ``atacseq/analysis`` we will create a dedicated directory and copy necessary files.


.. code-block:: bash

	mkdir TF_footprinting
	cd TF_footprinting

	cp /sw/courses/epigenomics/2025/lab-prep/cp_TFftprnt.sh .
	bash cp_TFftprnt.sh



Detection of TF Binding Signatures
======================================

``TOBIAS`` workflow consists of three stages:


1. Correction for the Tn5 transposase insertion sequence bias using ``ATACorrect``;

2. Identify regions of protein binding in open chromatin (within peaks) using ``ScoreBigwig``;

3. Calculate TF binding by combining footprint scores and TF binding motif information using ``BINDetect``.


ATACorrect and ScoreBigwig
----------------------------


We have precomputed these tracks, as it takes time and CPU resources.


.. code-block:: bash

	TOBIAS ATACorrect --bam B_WT_merged_replicates.sorted.bam --genome GRCm39/Mus_musculus.GRCm39.dna.primary_assembly.fa --peaks genrich_joint_peaks_merged.Allpeaks_annot.Ensembl.bed --outdir TF_footprinting/tracks/B_WT/Footprint/

	TOBIAS ScoreBigwig --signal Footprint/B_WT_merged_replicates.sorted_corrected.bw --regions genrich_joint_peaks_merged.Allpeaks_annot.Ensembl.bed --output B_WT_footprints.bw



TF Binding Detection
-----------------------

We will run ``TOBIAS`` ``BINDetect`` in **comparative mode** where we compare TF footprints in **BATF KO vs WT**.


We need to set some paths first:


.. code-block:: bash
	
    container_pth="/sw/courses/epigenomics/2025/software/singularity/agatasm-tobias-uropa.img"

    file_fa="/sw/courses/epigenomics/2025/reference/GRCm39/Mus_musculus.GRCm39.dna.primary_assembly.fa"
    
    motifs_file="JASPAR2024_selTFs_pfms_meme.txt"

    peaks="genrich_joint_peaks_merged.Allpeaks_annot.Ensembl.bed"

    footprnt_1="/sw/courses/epigenomics/2025/atacseq/tsao2022/proc_6ix2025/TF_footprinting/tracks/A_Batf_KO/Footprint/A_Batf_KO_footprints.bw"
    
    footprnt_2="/sw/courses/epigenomics/2025/atacseq/tsao2022/proc_6ix2025/TF_footprinting/tracks/B_WT/Footprint/B_WT_footprints.bw"

    smpl="Batf_KO_vs_WT_selTFs"


We will execute ``TOBIAS`` in a software container, so the command looks a bit different.


.. code-block:: bash

	apptainer exec ${container_pth} TOBIAS  BINDetect --motifs ${motifs_file} --signals ${footprnt_1} ${footprnt_2} --genome ${file_fa} --peaks ${peaks} --cores 6 --outdir ${smpl}/BINDdetect


Output description can be found at `BINDetect documentation <https://github.com/loosolab/TOBIAS/wiki/BINDetect#output>`_ 

You can view summary of the results obtained for all TFs in directory ``all_TFs``. In particular, file ``bindetect_A_Batf_KO_footprints_B_WT_footprints.html`` contains an interactive volcano plot highlighting the TFs with most extreme differences in their footprint scores uopn Batf knock-out.


.. list-table:: Figure 2. Results of TF footprinting in Batf KO vs WT.
   :widths: 60
   :header-rows: 0

   * - .. image:: figures-ftprnt/BINDetect_allTFs.png
   			:width: 600px

**Batf** motif is part of the JUN / FOS points cluster at the top left arm of the volcano. The results for one of its motifs are::

	output_prefix	name	motif_id	cluster	total_tfbs	A_Batf_KO_footprints_mean_score	A_Batf_KO_footprints_bound	B_WT_footprints_mean_score	B_WT_footprints_bound	A_Batf_KO_footprints_B_WT_footprints_change	A_Batf_KO_footprints_B_WT_footprints_pvalue	A_Batf_KO_footprints_B_WT_footprints_highlighted

	BATF_MA1634.2	BATF	MA1634.2	C_FOS::JUND	17254	0.33875	2304	0.38674	2747	-0.39584	1.07531E-169	True




References
==========

.. container:: references csl-bib-body hanging-indent
   :name: refs

   .. container:: csl-entry
      :name: ref-Tsao2022

      Tsao, Hsiao-Wei, James Kaminski, Makoto Kurachi, R. Anthony
      Barnitz, Michael A. DiIorio, Martin W. LaFleur, Wataru Ise, et al.
      2022. “Batf-Mediated Epigenetic Control of Effector CD8 + t Cell
      Differentiation.” *Science Immunology* 7 (68).
      https://doi.org/10.1126/sciimmunol.abi4919.


   .. container:: csl-entry
      :name: ref-Bentsen2020

      Bentsen, Mette, Goymann Philipp, Schultheis Hendrik, Klee Kathrin, Petrova Anastasiia, Wiegandt René, Fust Annika, Preussner Jens, Kuenne Carsten, Braun Thomas, Kim Johnny, Looso Mario
      2020. "ATAC-seq footprinting unravels kinetics of transcription factor binding during zygotic genome activation" *Nature Communications*  Vol. 11, No. 1 
      https://doi.org/10.1038/s41467-020-18035-1