DMS‐seq for In Vivo Genome‐Wide Mapping of Protein‐DNA Interactions and Nucleosome Centers

Abstract

The genome exerts its functions through interactions with proteins. Hence, comprehensive identification of protein-occupied sites by genomic footprinting is critical to an in-depth understanding of genome functions. This unit describes the protocol of dimethyl sulfate-sequencing (DMS-seq). DMS is an alkylating reagent that methylates guanine and adenine in double-stranded DNA. DMS added to the culture medium readily enters the cell and methylates its DNA throughout the genome except for the regions bound by proteins, thereby obviating the need for nuclear isolation in genomic footprinting. Polyamine/AP-endonuclease treatment of DNA isolated from DMS-treated cells induces cleavages at the methylated sites. Deep sequencing of these fragments identifies protein-bound sites as peaks of protected fragments or troughs of cleavage sites. Furthermore, DMS displays an unexpected preference to nucleosome centers, enabling their direct detection without genetic manipulation. Therefore, DMS-seq provides a unique method for non-targeted profiling of in vivo protein-DNA interactions. © 2018 by John Wiley & Sons, Inc.

Transcription elongation is finely tuned by dozens of regulatory factors

“…The shuffled control loci were selected to maintain the same number of real and control loci in each gene, controlling for effects of differential gene expression. In total, 51 chromatin and DNA features were compiled for all pause loci ( Supplementary file 5 ; Chiu et al, 2016 ; Oberbeckmann et al, 2019 ; Pelechano et al, 2013 ; Turner and Mathews, 2010 ; Umeyama and Ito, 2018 ; Vinayachandran et al, 2018 ; Weiner et al, 2015 ). Before applying the random forest classifier, we examined the distribution of values for each numeric feature (not discrete sequence) for real Pol II pauses compared to the scrambled control loci; statistical significance in the difference between these distributions was calculated with a Student’s t-test, correcting for multiple hypothesis testing with the Bonferroni correction.…”

Advances and Trends in Omics Technology Development

“…Chemical-based methods utilize small membrane permeable molecules such as nucleobase-specific chemicals, carbodiimide modifying reagents, and ribose-specific probes to interrogate RNA structure, which can be applied both in vitro and in vivo and often capable of achieving single-nucleotide resolution. Chemical-based in vitro methods include structure-seq ( 134 ), dimethyl sulfate sequencing (DMS-seq) ( 135 ), and high-throughput sequencing for chemical probing of RNA structure (Mod-seq) ( 136 ). Chemical-based in vivo methods include chemical inference of RNA structures sequencing (CIRS-seq) ( 137 ), selective 20-hydroxyl acylation analyzed by primer extension and mutational profiling (SHAPE-MaP) ( 138 ), in vivo click selective 2-hydroxyl acylation and profiling experiment (icSHAPE) ( 139 ), and mapping RNA-RNA interactome and RNA structure in vivo (MARIO) ( 140 ).…”

Dynamics of transcription elongation are finely tuned by dozens of regulatory factors

“…The shuffled control loci were selected to maintain the same number of real and control loci in each gene, controlling for effects of differential gene expression. In total, 51 chromatin features were compiled for all pause loci (Table S5) (Chiu et al, 2016;Oberbeckmann et al, 2019;Pelechano et al, 2013;Turner and Mathews, 2010;Umeyama and Ito, 2018;Vinayachandran et al, 2018;Weiner et al, 2015). Before applying the random forest classifier, we examined the distribution of values for each numeric feature (not discrete sequence) for real Pol II pauses compared to the scrambled control loci; statistical significance in the difference between these distributions was calculated with a Student's t test, correcting for multiple hypothesis testing with the Bonferroni correction.…”