maxATAC: Genome-scale transcription-factor binding prediction from ATAC-seq with deep neural networks
Fig 2
maxATAC model architecture, inputs, and standard workflow.
(A) maxATAC inputs are a 1,024bp one-hot encoded DNA-sequence with ATAC-seq signal for the corresponding region, while maxATAC output is an array of 32 TFBS predictions at 32bp resolution, spanning the 1024bp input sequence interval. Inputs go through a total of 5 convolutional blocks. Each convolutional block consists of two layers, each composed of ReLU-activated, 1D convolutional operations and batch normalization. A max pooling layer is interspersed between the convolutional blocks to reduce the spatial dimensions of the input. The kernel width is fixed at 7 across all convolutional blocks. The model uses 15 filters in the first convolutional block, and the number of filters is increased by a factor of 1.5 for every subsequent block. The dilation rate of the convolutional filters increases from one, one, two, four, eight, to sixteen across blocks. Increasing the dilation rate increases the receptive field, so that spatially distant regions share information. In this network, the receptive field grows to +/-512bp, with information sharing proportional to spatial proximity. The final output is produced by a single sigmoid-activated convolutional layer. (B) Schematic overview of a standard maxATAC workflow. maxATAC takes as input a BAM file or scATAC-seq fragments TSV file that is processed to Tn5 cut sites, smoothed and converted to a read-depth-normalized ATAC-seq signal track (robustly min-max normalized between 0–1, see Methods). (C) The maxATAC predict function takes as input the genome reference DNA 2bit sequence file, a trained maxATAC model h5 file and the normalized ATAC-seq signal track to predict TFBS. (D) The outputs of maxATAC are a bigwig file of maxATAC TFBS scores, ranging 0–1, and a BED file of predicted TFBS, thresholded according to a user-selected confidence cutoff (e.g., precision, F1-score, see Methods).