Evaluation of Repair Activity by Quantification of Ribonucleotides in the Genome

Ribonucleotides incorporated in the genome are a source of endogenous DNA damage, and also serve as signals for repair. Although recent advances of ribonucleotide detection by sequencing, the balance between incorporation and repair of ribonucleotides has not been elucidated. Here, we describe a competitive sequencing method, Ribonucleotide Scanning Quantification sequencing (RiSQ-seq), which enables absolute quantification of misincorporated ribonucleotides throughout the genome by background normalization and standard adjustment within a single sample. RiSQ-seq analysis of cells harboring wild-type DNA polymerases revealed that ribonucleotides were incorporated non-uniformly in the genome with a 3’-shifted distribution and preference for GC sequences. Although ribonucleotide profiles in wild-type and repair-deficient mutant strains showed a similar pattern, direct comparison of distinct ribonucleotide levels in the strains by RiSQ-seq enabled evaluation of ribonucleotide excision repair activity at base resolution and revealed the strand bias of repair. The distinct preferences of ribonucleotide incorporation and repair create vulnerable regions associated with indel hotspots, suggesting that repair at sites of ribonucleotide misincorporation serves to maintain genome integrity and that RiSQ-seq can provide an estimate of indel risk. A detailed description of each sample, experiment design and the corresponding processed data file is provided in the sample description field. DRA Study accession (DRPnnnns have not been assinged): DRA04327, DRA004328, DRA004329, DRA004330, DRA004331, DRA004332, DRA004917 RiSQ_seq_reference.fa = sequence data used for bedgraph files. From original EF4.68 assembly, chromosome numbers were changed from Roman to Arabic numerals and add Ty1 consensus sequence and rDNA repeat unit sequence. RiSQ_seq_genes.gtf = annotation data of genomic elements used in this study. STD_target.fa = reference sequences of standard fragments used in this study. STD_*.tsv = fragment composition from standard fragment (STD) data analysis Mapping_bias.bed = information of mapping biased region