Abstract
Gene expression is orchestrated by distinct regulatory regions to ensure a wide variety of cell types and functions. A challenge is to identify which regulatory regions are active, what are their associated features and how they work together in each cell type. Several approaches have tackled this problem by modeling gene expression based on epigenetic marks, with the ultimate goal of identifying driving regions and associated genomic variations that are clinically relevant in particular in precision medicine. However, these models rely on experimental data, which are limited to specific samples (even often to cell lines) and cannot be generated for all regulators and all patients. In addition, we show here that, although these approaches are accurate in predicting gene expression, inference of TF combinations from this type of models is not straightforward. Furthermore these methods are not designed to capture regulation instructions present at the sequence level, before the binding of regulators or the opening of the chromatin. Here, we probe sequence-level instructions for gene expression and develop a method to explain mRNA levels based solely on nucleotide features. Our method positions nucleotide composition as a critical component of gene expression. Moreover, our approach, able to rank regulatory regions according to their contribution, unveils a strong influence of the gene body sequence, in particular introns. We further provide evidence that the contribution of nucleotide content can be linked to co-regulations associated with genome 3D architecture and to associations of genes within topologically associated domains.
Highlights
The diversity of cell types and cellular functions is defined by specific patterns of gene expression
Several large-scale data derived from highthroughput experiments can be used to highlight transcription factors (TFs)/RBP binding preferences and build Position Weight Matrixes (PWMs) [11]
Several methods have recently been proposed to tackle this problem [16,17,18,19]. These models appear very efficient in predicting gene expression and identifying key regulators, they mostly rely on experimental data (ChIP-seq, methylation, DNase hypersensitivity), which are limited to specific samples and which cannot be generated for all TFs/RBPs and all cell types
Summary
The diversity of cell types and cellular functions is defined by specific patterns of gene expression. Several methods have recently been proposed to tackle this problem [16,17,18,19] These models appear very efficient in predicting gene expression and identifying key regulators, they mostly rely on experimental data (ChIP-seq, methylation, DNase hypersensitivity), which are limited to specific samples (often to cell lines) and which cannot be generated for all TFs/RBPs and all cell types. These technological features impede from using this type of approaches in a clinical context in particular in precision medicine. In line with this proposal, Raghava and Han developed a Support Vector Machine (SVM)-based method to predict gene expression from amino acid and dipeptide composition in Saccharomyces cerevisiae [26]
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