Aug 26, 2020 · In this investigation, we apply TOBIAS to ATAC-seq data from both human and mouse PD and show how visible TF footprints correlate with the timings of TF activity throughout development.

If unaccounted for, this impairs the discovery of true TF footprints. In this tutorial we use an R / Bioconductor packages ATACseqQC and MotifDb to detect TF binding signatures in ATAC-seq data.

In this tutorial, we will show how to use HINT-ATAC to compare transcription factor binding activity based on differential footprinting analysis. We will use a public scATAC-seq dataset from human hematopoietic cell.

TOBIAS is a collection of command-line bioinformatics tools for performing footprinting analysis on ATAC-seq data, and includes: Correction of Tn5 insertion bias

ATAC-seq footprints infer factor occupancy genome-wide. The factorFootprints function uses matchPWM to predict the binding sites using the input position weight matrix (PWM).

In this tutorial, we will show how to use HINT-ATAC to compare changes in the activity of transcription factors with differential footprinting. We will use this data presented at HINT-ATAC paper, where we contrast the footprints of two dendritic cells.

Here, we discuss the major steps in ATAC-seq data analysis, including pre-analysis (quality check and alignment), core analysis (peak calling), and advanced analysis (peak differential analysis and annotation, motif enrichment, footprinting, and nucleosome position analysis).

Feb 26, 2019 · Transposase-Accessible Chromatin followed by sequencing (ATAC-seq) is a simple protocol for detection of open chromatin. Computational footprinting, the search for regions with depletion of cleavage events due to transcription factor binding, is …

We describe here how to detect footprints using HINT for ATAC-seq, DNase-seq, and histone modifications data. To perform footprinting, you need at least two files, one with the aligned reads of your chromatin data and another describing the regions to detect footprints.

Using the Tn5 bias corrected signals, this function estimates the footprints (i.e putative TF binding sites) within peak regions by identifying regions with depleted cutsite signal and flanked by high cutsite signal.