Abstract
While footprinting analysis of ATAC-seq data can theoretically enable investigation of transcription factor (TF) binding, the lack of a computational tool able to conduct different levels of footprinting analysis has so-far hindered the widespread application of this method. Here we present TOBIAS, a comprehensive, accurate, and fast footprinting framework enabling genome-wide investigation of TF binding dynamics for hundreds of TFs simultaneously. We validate TOBIAS using paired ATAC-seq and ChIP-seq data, and find that TOBIAS outperforms existing methods for bias correction and footprinting. As a proof-of-concept, we illustrate how TOBIAS can unveil complex TF dynamics during zygotic genome activation in both humans and mice, and propose how zygotic Dux activates cascades of TFs, binds to repeat elements and induces expression of novel genetic elements.
Highlights
ObservedScores control / condition TF1 TF2 TF3Change in binding fDownstream analysis Local footprints Gene[1]Target predictionAggregate footprintsTranscription factor binding similarity
In contrast to the expression values, TOBIAS predicted SALL4 to have the highest activity in 2C, with decreasing activity in 8C, which is in line with the presence of maternal SALL4 in the zygote. Comparing this change to all transcription factor (TF) changes between 2C and 8C, we find that SALL4 is at the 7th percentile of all changes ranked from decreasing to increasing, which is consistent with the degradation of the protein after the 2C stage
To the best of our knowledge, this is the first application of a digital genomic footprinting (DGF) approach to visualize gain and loss of individual TF footprints in the context of time series, TF overexpression, and TF-DNA binding for a wide-range of TFs in parallel
Summary
Impact of bias correction on footprint visibility. To validate the results of the TOBIAS method, we utilized 217 paired ChIP-seq/ ATAC-seq datasets across four different cell types (GM12878, A549, HepG2, and K562). The TOBIAS bias correction module (named ATACorrect) utilizes a dinucleotide weight matrix (DWM)[13] to estimate the background bias of the Tn5 transposase (Fig. 1c) This DWM is used to calculate an expected Tn5 signal for each genomic region, representing the influence of the Tn5 bias (Fig. 1c; expected cutsites). We wanted to investigate this effect in more detail and found that there is a high correlation between the footprint depths of uncorrected and expected Tn5 signals across all TFs, which vanishes after TOBIAS correction (Supplementary Fig. 2e). This observation demonstrates that bias correction effectively uncovers TF footprints, which were otherwise superimposed by Tn5 bias. In contrast, we observed a measurable footprint for 59% of the TFs a Binding event
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