SHAPE-Seq 2.0: systematic optimization and extension of high-throughput chemical probing of RNA secondary structure with next generation sequencing

Abstract

RNA structure is a primary determinant of its function, and methods that merge chemical probing with next generation sequencing have created breakthroughs in the throughput and scale of RNA structure characterization. However, little work has been done to examine the effects of library preparation and sequencing on the measured chemical probe reactivities that encode RNA structural information. Here, we present the first analysis and optimization of these effects for selective 2′-hydroxyl acylation analyzed by primer extension sequencing (SHAPE-Seq). We first optimize SHAPE-Seq, and show that it provides highly reproducible reactivity data over a wide range of RNA structural contexts with no apparent biases. As part of this optimization, we present SHAPE-Seq v2.0, a ‘universal’ method that can obtain reactivity information for every nucleotide of an RNA without having to use or introduce a specific reverse transcriptase priming site within the RNA. We show that SHAPE-Seq v2.0 is highly reproducible, with reactivity data that can be used as constraints in RNA folding algorithms to predict structures on par with those generated using data from other SHAPE methods. We anticipate SHAPE-Seq v2.0 to be broadly applicable to understanding the RNA sequence–structure relationship at the heart of some of life's most fundamental processes.

Using in-cell SHAPE-Seq and simulations to probe structure–function design principles of RNA transcriptional regulators

“…The resulting cDNAs were prepared for sequencing following the in-cell SHAPE-Seq protocol, sequenced on an Illumina MiSeq and analyzed by the standard SHAPE-Seq computational pipeline ( Fig. 1B; Aviran et al 2011a,b;Lucks et al 2011a;Loughrey et al 2014). The output of an in-cell SHAPE-Seq experiment is a reactivity spectrum for a specific RNA that indicates the scaled probability that each nucleotide in the RNA was modified by 1M7 in the cell.…”

“…Reactivity spectra were calculated using Spats v0.8.0 and a number of utility scripts to prepare the Illumina output for Spats following previous work (Loughrey et al 2014). Illumina adapter sequences were trimmed from each read using the FASTX toolkit (http ://hannonlab.cshl.edu/fastx_toolkit/) and then aligned to the target RNA sequences with Bowtie 0.12.8 (Langmead et al 2009) based on the input sense and antisense RNAs to determine locations of modifications.…”

“…In-cell SHAPE-Seq reactivities (ρ) were used to restrain predictions with the pseudo-freeenergy parameters m (1.1) and b (−0.3) (Loughrey et al 2014) where indicated.…”

Data-directed RNA secondary structure prediction using probabilistic modeling

“…We used this model to relate reactivity values to corresponding variances by scaling a standard Gaussian noise term for each reactivity. Five noise levels were applied to the data with variances monotonically increased from 1 to 10,000 by factors of 10, starting at values previously observed in experimental data (Loughrey et al 2014). Interestingly, all schemes are robust to noise up to a factor of 100 (Supplemental Fig.…”

“…In the absence of biological replicates, we resorted to simulations. Our previous analysis of a different, yet related, probing data set (Loughrey et al 2014) revealed heteroskedasticity, which we modeled to capture this additional complexity (K Choudhary, NP Shih, F Deng, M Ledda, B Li, S Aviran, in prep.). We used this model to relate reactivity values to corresponding variances by scaling a standard Gaussian noise term for each reactivity.…”