Attend and Predict: Understanding Gene Regulation by Selective Attention on Chromatin

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Introduction

Gene regulation is the process of controlling which genes in a cell's DNA are turned 'on' (expressed) or 'off' (not expressed). By this process functional product such as a protein is created. Even though all the cells of an organism contain the same DNA, different types of cells in a multicellular organism may express very different sets of genes.


Background

Gene regulation is the process of controlling which genes in a cell's DNA are turned 'on' (expressed) or 'off' (not expressed). By this process functional product such as a protein is created. Even though all the cells of a multicellular organism (e.g., humans) contain the same DNA, different types of cells in that organism may express very different sets of genes. As a result each cell types have distinct functionality. In other words how a cell operates depends upon the genes expressed in that cell. Many factors including ‘Chromatin modification marks’ influence which genes are abundant in that cell.

The function of chromatin is to efficiently wraps DNA around histones into a condensed volume to fit into the nucleus of a cell and protect the DNA structure and sequence during cell division and replication. Different chemical modifications in the histones of the chromatin, known as histone marks, changes spatial arrangement of the condensed DNA structure. Which in turn affects the gene’s expression of the histone mark’s neighboring region. Histone marks can promote (obstruct) the gene to be turned on by making the gene region accessible (restricted). This section of the DNA, where histone marks can potentially have an impact, is known as DNA flanking region or ‘gene region’ which is considered to cover 10k base pair centered at the transcription start site (TSS) (i.e., 5k base pair in each direction). Unlike genetic mutations, histone modifications are reversible [1]. Therefore, understanding influence of histone marks in determining gene regulation can assist in developing drugs for genetic disease.

Revolution in genomic technologies now enables us to profile genome wide chromatin mark signals. So, we know chromatin signals for the ‘gene region’ for different cell types. This paper proposes an attention based deep learning model to find how this chromatin factors/ histone modifications contributes in gene expression of a cell.