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

Abstract

Core promoters are stretches of DNA at the beginning of genes that contain information that facilitates the binding of transcription initiation complex. 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. seqArchR is available athttp://www.bioconductor.org/packages/seqArchR.

Core promoterome of barley embryo

“…Based on previous extensive research on promoters of other species (reviewed in [45] and elsewhere), we posited that regions 50 bp up- and downstream of the dominant TSS likely contained a PIC-binding sequence. We employed seqArchR [46] , a recently developed software that uses unsupervised approach using non-negative matrix factorization, to cluster promoter sequences based on their motifs at near-fixed distances from a reference point, such as TSS. These clusters are characterized by de novo-identified sequence elements, such as position-specific motifs and the nucleotide composition of the input sequences.…”