SeATAC: a tool for exploring the chromatin landscape and the role of pioneer factors

Abstract

The position of the nucleosome and chromatin packaging in eukaryotic genomes govern gene regulation and cellular functions. Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq) is an efficient and precise method for revealing chromatin accessibility across the genome. However, there is no method that is specifically designed for detecting differential chromatin accessibility using ATAC-seq datasets. In this study, we developed a bioinformatics tool called SeATAC, that used a conditional variational autoencoder (CVAE) model to learn the latent representation of ATAC-seq V-plots, and to estimate the statistically differential chromatin accessibility. We demonstrated that SeATAC outperformed MACS2 and NucleoATAC on four separate tasks including: (1) detection of differential V-plots; (2) definition of nucleosome positions; (3) detection of nucleosome changes and (4) designation of transcriptional factor binding sites (TFBS) with differential chromatin accessibility. By applying SeATAC to several pioneer factor induced differentiation or reprogramming ATAC-seq datasets, we found that induction of these pioneer factors not only relaxed the closed chromatin but also decreased the chromatin accessibility of 20% - 30% of their target sites. These two groups of TF binding sites were characterized by different genomic distribution and histone marks. Here, we present SeATAC as a novel tool to accurately reveal the genomic regions with differential chromatin accessibility from ATAC-seq data.

Proformer: a hybrid macaron transformer model predicts expression values from promoter sequences

“…Machine learning methods have been developed to identify complex relationships and patterns in large scale DNA sequences (including MPRA data) that may not be apparent through conventional statistical methods. For example, convolutional neural networks (CNN) and recurrent neural networks (RNN) were used to capture the local dependences in DNA sequences and/or genomic features and predict binding affinities 1,31,32 , chromatin features 33,34 , DNA methylation 35,36 , RBP (RNA-binding protein) binding [37][38][39] and gene expression levels 40 . In contrast, Transformers are a type of neural network architecture that has gained popularity in recent years for their ability to process sequential data, such as text and speech, more efficiently and effectively than traditional RNNs and CNNs 41 .…”