Identifying promoter sequence architectures via a chunking-based algorithm using non-negative matrix factorisation

Core promoters are stretches of DNA at the beginning of genes that contain information that facilitates the binding of transcription initiation complexes. Different functional subsets of genes have core promoters with distinct architectures and characteristic motifs. Some of these motifs inform the selection of transcription start sites (TSS). By discovering motifs with fixed distances from known TSS positions, we could in principle classify promoters into different functional groups. Due to the variability and overlap of architectures, promoter classification is a difficult task that requires new approaches. In this study, we present a new method based on non-negative matrix factorisation (NMF) and the associated software called seqArchR that clusters promoter sequences based on their motifs at near-fixed distances from a reference point, such as TSS. When combined with experimental data from CAGE, seqArchR can efficiently identify TSS-directing motifs, including known ones like TATA, DPE, and nucleosome positioning signal, as well as novel lineage-specific motifs and the function of genes associated with them. By using seqArchR on developmental time courses, we reveal how relative use of promoter architectures changes over time with stage-specific expression. seqArchR is a powerful tool for initial genome-wide classification and functional characterization of promoters. Its use cases are more general: it can also be used to discover any motifs at near-fixed distances from a reference point, even if they are present in only a small subset of sequences. Transcription of genes by RNA polymerase II enzyme is known to begin at specific positions in parts of DNA sequence called promoters. These positions, called transcription start sites, are chosen by protein complexes binding to sequence signals in the nearby DNA, either upstream or downstream of them. These protein complexes then help recruit RNA polymerase II at a specific location from which it will choose a specific transcription site. The set of sequence signals that governs these events at each promoter constitutes its promoter sequence architecture. Different organisms show diversity in promoter sequence architectures. Even within an organism, different promoter architectures are characteristic of different kinds of genes such as those that are tissue-specific vs those that are ubiquitously expressed across tissues. In this paper, we present seqArchR, a method using non-negative matrix factorisation (NMF) for clustering of promoter sequences into their characteristic promoter sequence architectures de novo. We show that seqArchR is faster than state-of-the-art approaches and gives better or similar results on both simulated and real promoter sequences. We apply seqArchR on promoters across stages of embryonic development in fruit fly and zebrafish and show it reveals changes in relative use of promoter architectures over time. We also show that seqArchR can classify human promoter sequences, demonstrating its power to discover promoter architectures across very different genomes.

Automated summary

Core promoters are DNA sequences at the beginning of genes that contain motifs facilitating the binding of transcription initiation complexes. This study introduces a new method, seqArchR, based on non-negative matrix factorisation (NMF), for clustering promoter sequences according to their near-fixed-distance motifs from a reference point, such as transcription start sites (TSS). seqArchR efficiently identifies TSS-directing motifs and reveals changes in the relative usage of promoter architectures over time. It is a powerful tool for genome-wide classification and functional characterisation of promoters.