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Register a new userMassively Parallel RNA Chemical Mapping with a Reduced Bias MAP-seq Protocol
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Chemical mapping methods probe RNA structure by revealing and leveraging correlations of a nucleotides structural accessibility or flexibility with its reactivity to various chemical probes. Pioneering work by Lucks and colleagues has expanded this method to probe hundreds of molecules at once on an Illumina sequencing platform, obviating the use of slab gels or capillary electrophoresis on one molecule at a time. Here, we describe optimizations to this method from our lab, resulting in the MAP-seq protocol (Multiplexed Accessibility Probing read out through sequencing), version 1.0. The protocol permits the quantitative probing of thousands of RNAs at once, by several chemical modification reagents, on the time scale of a day using a table-top Illumina machine. This method and a software package MAPseeker (http://simtk.org/home/map_seeker) address several potential sources of bias, by eliminating PCR steps, improving ligation efficiencies of ssDNA adapters, and avoiding problematic heuristics in prior algorithms. We hope that the step-by-step description of MAP-seq 1.0 will help other RNA mapping laboratories to transition from electrophoretic to next-generation sequencing methods and to further reduce the turnaround time and any remaining biases of the protocol.
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Chemical mapping is a widespread technique for structural analysis of nucleic acids in which a molecules reactivity to different probes is quantified at single-nucleotide resolution and used to constrain structural modeling. This experimental framework has been extensively revisited in the past decade with new strategies for high-throughput read-outs, chemical modification, and rapid data analysis. Recently, we have coupled the technique to high-throughput mutagenesis. Point mutations of a base-paired nucleotide can lead to exposure of not only that nucleotide but also its interaction partner. Carrying out the mutation and mapping for the entire system gives an experimental approximation of the molecules contact map. Here, we give our in-house protocol for this mutate-and-map strategy, based on 96-well capillary electrophoresis, and we provide practical tips on interpreting the data to infer nucleic acid structure.
Next-generation RNA sequencing (RNA-seq) technology has been widely used to assess full-length RNA isoform abundance in a high-throughput manner. RNA-seq data offer insight into gene expression levels and transcriptome structures, enabling us to better understand the regulation of gene expression and fundamental biological processes. Accurate isoform quantification from RNA-seq data is challenging due to the information loss in sequencing experiments. A recent accumulation of multiple RNA-seq data sets from the same tissue or cell type provides new opportunities to improve the accuracy of isoform quantification. However, existing statistical or computational methods for multiple RNA-seq samples either pool the samples into one sample or assign equal weights to the samples when estimating isoform abundance. These methods ignore the possible heterogeneity in the quality of different samples and could result in biased and unrobust estimates. In this article, we develop a method, which we call joint modeling of multiple RNA-seq samples for accurate isoform quantification (MSIQ), for more accurate and robust isoform quantification by integrating multiple RNA-seq samples under a Bayesian framework. Our method aims to (1) identify a consistent group of samples with homogeneous quality and (2) improve isoform quantification accuracy by jointly modeling multiple RNA-seq samples by allowing for higher weights on the consistent group. We show that MSIQ provides a consistent estimator of isoform abundance, and we demonstrate the accuracy and effectiveness of MSIQ compared with alternative methods through simulation studies on D. melanogaster genes. We justify MSIQs advantages over existing approaches via application studies on real RNA-seq data from human embryonic stem cells, brain tissues, and the HepG2 immortalized cell line.
Background: High-throughput techniques bring novel tools but also statistical challenges to genomic research. Identifying genes with differential expression between different species is an effective way to discover evolutionarily conserved transcriptional responses. To remove systematic variation between different species for a fair comparison, the normalization procedure serves as a crucial pre-processing step that adjusts for the varying sample sequencing depths and other confounding technical effects. Results: In this paper, we propose a scale based normalization (SCBN) method by taking into account the available knowledge of conserved orthologous genes and hypothesis testing framework. Considering the different gene lengths and unmapped genes between different species, we formulate the problem from the perspective of hypothesis testing and search for the optimal scaling factor that minimizes the deviation between the empirical and nominal type I errors. Conclusions: Simulation studies show that the proposed method performs significantly better than the existing competitor in a wide range of settings. An RNA-seq dataset of different species is also analyzed and it coincides with the conclusion that the proposed method outperforms the existing method. For practical applications, we have also developed an R package named SCBN and the software is available at http://www.bioconductor.org/packages/devel/bioc/html/SCBN.html.
We have established an RNA Mapping Database (RMDB) to enable a new generation of structural, thermodynamic, and kinetic studies from quantitative single-nucleotide-resolution RNA structure mapping (freely available at http://rmdb.stanford.edu). Chemical and enzymatic mapping is a rapid, robust, and widespread approach to RNA characterization. Since its recent coupling with high-throughput sequencing techniques, accelerated software pipelines, and large-scale mutagenesis, the volume of mapping data has greatly increased, and there is a critical need for a database to enable sharing, visualization, and meta-analyses of these data. Through its on-line front-end, the RMDB allows users to explore single-nucleotide-resolution chemical accessibility data in heat-map, bar-graph, and colored secondary structure graphics; to leverage these data to generate secondary structure hypotheses; and to download the data in standardized and computer-friendly files, including the RDAT and community-consensus SNRNASM formats. At the time of writing, the database houses 38 entries, describing 2659 RNA sequences and comprising 355,084 data points, and is growing rapidly.
Chemical purity of RNA samples is critical for high-precision studies of RNA folding and catalytic behavior, but such purity may be compromised by photodamage accrued during ultraviolet (UV) visualization of gel-purified samples. Here, we quantitatively assess the breadth and extent of such damage by using reverse transcription followed by single-nucleotide-resolution capillary electrophoresis. We detected UV-induced lesions across a dozen natural and artificial RNAs including riboswitch domains, other non-coding RNAs, and artificial sequences; across multiple sequence contexts, dominantly at but not limited to pyrimidine doublets; and from multiple lamps that are recommended for UV shadowing in the literature. Most strikingly, irradiation time-courses reveal detectable damage within a few seconds of exposure, and these data can be quantitatively fit to a skin effect model that accounts for the increased exposure of molecules near the top of irradiated gel slices. The results indicate that 200-nucleotide RNAs subjected to 20 seconds or less of UV shadowing can incur damage to 20% of molecules, and the molecule-by-molecule distribution of these lesions is more heterogeneous than a Poisson distribution. Photodamage from UV shadowing is thus likely a widespread but unappreciated cause of artifactual heterogeneity in quantitative and single-molecule-resolution RNA biophysical measurements.
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