Robust statistical modeling improves sensitivity of high-throughput RNA structure probing experiments

Selega, Alina; Sirocchi, Christel; Iosub, Ira Alexandra; Granneman, Sander; Sanguinetti, Guido

doi:10.1038/nmeth.4068

Cited by 27 publications

(43 citation statements)

References 37 publications

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“…Then we predicted secondary structures of yeast 18S and 25S rRNAs for each method and evaluated the predicted structures using commonly accepted measures such as sensitivity and positive predictive value (PPV) (Sloma and Mathews, 2015). In this evaluation (Figure 2C), as well as in a comparison (Table S4) with BUM-HMM (Selega et al, 2017), PROBer’s performance was superior to the alternative approaches. In addition, we have demonstrated that PROBer is robust to priming biases with respect to modification probability estimation (Figure S10 and S11).…”

Section: Resultsmentioning

confidence: 80%

PROBer Provides a General Toolkit for Analyzing Sequencing-Based Toeprinting Assays

Tambe

Aviran

et al. 2017

Cell Systems

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SUMMARY A number of sequencing-based transcriptase drop-off assays have recently been developed to probe post-transcriptional dynamics of RNA-protein interaction, RNA structure, and RNA modification. Although these assays survey a diverse set of ‘epitranscriptomic’ marks, we term them as toeprinting assays since they share methodological similarities. As such, their interpretation is predicated on addressing similar computational challenge: how to learn isoform-specific chemical modification profiles in the face of complex read multi-mapping. We introduce PROBer, a statistical model and associated software, that addresses this challenge for the analysis of toeprinting assays. PROBer takes sequencing data as input and outputs estimated transcript abundances and isoform-specific modification profiles. Results on both simulated and biological data demonstrate that PROBer significantly outperforms individual methods tailored for specific toeprinting assays. Since the space of toeprinting assays is ever expanding and these assays are likely to be performed and analyzed together, we believe PROBer’s unified data analysis solution will be valuable to the RNA community.

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Section: Resultsmentioning

confidence: 80%

PROBer Provides a General Toolkit for Analyzing Sequencing-Based Toeprinting Assays

Tambe

Aviran

et al. 2017

Cell Systems

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“…Of note, robust statistical assessment of reactivity from biological replicates is generally lacking, with only a few data analysis pipelines able to return statistically significant sites based on biological replicates (Choudhary, Ruan, Deng, Shih, & Aviran, 2017;Talkish et al, 2014). Recently, a sophisticated Beta-uniform mixture hidden Markov Model (BUM-HMM) machine learning pipeline was developed to deal with biological variability and biases in the chemical probing datasets obtained from NGS experiments (Selega et al, 2017). Furthermore, a similar statistical model has been used to predict small RNA motifs from noisy structural probing data (Ledda & Aviran, 2018).…”

Section: Discussionmentioning

confidence: 99%

“…Aviran et al, ; Talkish et al, ; Tang et al, ; Busan and Weeks, ; Selega, Sirocchi, Iosub, Granneman, and Sanguinetti, ; Ledda and Aviran, ; Li and Aviran, …”

Section: Challenges and Potential Pitfallsmentioning

confidence: 99%

The evolution of RNA structural probing methods: From gels to next‐generation sequencing

et al. 2018

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RNA molecules are important players in all domains of life and the study of the relationship between their multiple flexible states and the associated biological roles has increased in recent years. For several decades, chemical and enzymatic structural probing experiments have been used to determine RNA structure. During this time, there has been a steady improvement in probing reagents and experimental methods, and today the structural biologist community has a large range of tools at its disposal to probe the secondary structure of RNAs in vitro and in cells. Early experiments used radioactive labeling and polyacrylamide gel electrophoresis as read‐out methods. This was superseded by capillary electrophoresis, and more recently by next‐generation sequencing. Today, powerful structural probing methods can characterize RNA structure on a genome‐wide scale. In this review, we will provide an overview of RNA structural probing methodologies from a historical and technical perspective. This article is categorized under: RNA Structure and Dynamics > RNA Structure, Dynamics, and Chemistry RNA Methods > RNA Analyses in vitro and In Silico RNA Methods > RNA Analyses in Cells

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“…Alternatively, reactivities have been estimated as odds ratio of experiment to control detection rates [35]. To control the range of reactivities, others take the logarithm of the odds ratio [30,31,36,55,81]. Occasionally, detection rates in experiment are found to be less than their counterparts in control.…”

Section: Estimation Of Structural Profilementioning

confidence: 99%

“…Another extension of the said statistical modeling work on SHAPE-Seq has been recently published by Selega et al . [81]. This method scores significance of modification levels from stop counts and nucleotide-level coverages under an assumption that modification states do not randomly switch, i.e., significantly reactive/unreactive nucleotides tend to appear in continuous stretches.…”

Section: Estimation Of Structural Profilementioning

confidence: 99%

Comparative and integrative analysis of RNA structural profiling data: current practices and emerging questions

2017

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Background Structure profiling experiments provide single-nucleotide information on RNA structure. Recent advances in chemistry combined with application of high-throughput sequencing have enabled structure profiling at transcriptome scale and in living cells, creating unprecedented opportunities for RNA biology. Propelled by these experimental advances, massive data with ever-increasing diversity and complexity have been generated, which give rise to new challenges in interpreting and analyzing these data. Results We review current practices in analysis of structure profiling data with emphasis on comparative and integrative analysis as well as highlight emerging questions. Comparative analysis has revealed structural patterns across transcriptomes and has become an integral component of recent profiling studies. Additionally, profiling data can be integrated into traditional structure prediction algorithms to improve prediction accuracy. Conclusions To keep pace with experimental developments, methods to facilitate, enhance and refine such analyses are needed. Parallel advances in analysis methodology will complement profiling technologies and help them reach their full potential.

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Robust statistical modeling improves sensitivity of high-throughput RNA structure probing experiments

Cited by 27 publications

References 37 publications

PROBer Provides a General Toolkit for Analyzing Sequencing-Based Toeprinting Assays

PROBer Provides a General Toolkit for Analyzing Sequencing-Based Toeprinting Assays

The evolution of RNA structural probing methods: From gels to next‐generation sequencing

Comparative and integrative analysis of RNA structural profiling data: current practices and emerging questions

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