Hydroxyl Radical Footprinting (HRF) is a tried-and-tested method for analysis of the tertiary structure of RNA and for identification of protein footprints on RNA. The hydroxyl radical reaction breaks accessible parts of the RNA backbone, thereby allowing ribose accessibility to be determined by detection of reverse transcriptase termination sites. Current methods for HRF rely on reverse transcription of a single primer and detection by fluorescent fragments by capillary electrophoresis. Here, we describe an accurate and efficient massive parallel-sequencing-based method for probing RNA accessibility with hydroxyl radicals, called HRF-Seq. Using random priming and a novel barcoding scheme, we show that HRF-Seq dramatically increases the throughput of HRF experiments and facilitates the parallel analysis of multiple RNAs or experimental conditions. Moreover, we demonstrate that HRF-Seq data for the Escherichia coli 16S rRNA correlates well with the ribose accessible surface area as determined by X-ray crystallography and have a resolution that readily allows the difference in accessibility caused by exposure of one side of RNA helices to be observed.

Selective 2′ Hydroxyl Acylation analyzed by Primer Extension (SHAPE) is an accurate method for probing of RNA secondary structure. In existing SHAPE methods, the SHAPE probing signal is normalized to a no-reagent control to correct for the background caused by premature termination of the reverse transcriptase. Here, we introduce a SHAPE Selection (SHAPES) reagent, N-propanone isatoic anhydride (NPIA), which retains the ability of SHAPE reagents to accurately probe RNA structure, but also allows covalent coupling between the SHAPES reagent and a biotin molecule. We demonstrate that SHAPES-based selection of cDNA-RNA hybrids on streptavidin beads effectively removes the large majority of background signal present in SHAPE probing data and that sequencing-based SHAPES data contain the same amount of RNA structure data as regular sequencing-based SHAPE data obtained through normalization to a no-reagent control. Moreover, the selection efficiently enriches for probed RNAs, suggesting that the SHAPES strategy will be useful for applications with high-background and low-probing signal such as in vivo RNA structure probing.

No abstract

RNase H cleaves RNA in RNA–DNA duplexes. It is present in all domains of life as well as in multiple viruses and is essential for mammalian development and for human immunodeficiency virus replication. Here, we developed a sequencing-based method to measure the cleavage of thousands of different RNA–DNA duplexes and thereby comprehensively characterized the sequence preferences of HIV-1, human and Escherichia coli RNase H enzymes. We find that the catalytic domains of E. coli and human RNase H have nearly identical sequence preferences, which correlate with the efficiency of RNase H-recruiting antisense oligonucleotides. The sequences preferred by HIV-1 RNase H are distributed in the HIV genome in a way suggesting selection for efficient RNA cleavage during replication. Our findings can be used to improve the design of RNase H-recruiting antisense oligonucleotides and show that sequence preferences of HIV-1 RNase H may have shaped evolution of the viral genome and contributed to the use of tRNA-Lys3 as primer during viral replication.

Detection of reverse transcriptase termination sites is important in many different applications, such as structural probing of RNAs, rapid amplification of cDNA 5' ends (5' RACE), cap analysis of gene expression, and detection of RNA modifications and protein-RNA cross-links. The throughput of these methods can be increased by applying massive parallel sequencing technologies.Here, we describe a versatile method for detection of reverse transcriptase termination sites based on ligation of an adapter to the 3' end of cDNA with bacteriophage TS2126 RNA ligase (CircLigase™). In the following PCR amplification, Illumina adapters and index sequences are introduced, thereby allowing amplicons to be pooled and sequenced on the standard Illumina platform for genomic DNA sequencing. Moreover, we demonstrate how to map sequencing reads and perform analysis of the sequencing data with freely available tools that do not require formal bioinformatics training. As an example, we apply the method to detection of transcription start sites in mouse liver cells.