ArticleNature methods2017
Robust statistical modeling improves sensitivity of high-throughput RNA structure probing experiments.
Article in Nature methods, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed, 33 citations in OpenAlex.
- Deep learning for RNA secondary structure determination: gauging generalizability and broadening the scope of traditional methods.RNA (New York, N.Y.) · 2026Review
- Deep Learning for RNA Secondary Structure Determination: Gauging Generalizability and Broadening the Scope of Traditional Methods.bioRxiv : the preprint server for biology · 2025Article
- Identification of RNA structures and their roles in RNA functions.Nature reviews. Molecular cell biology · 2024Review
- Comprehensive in virio structure probing analysis of the influenza A virus identifies functional RNA structures involved in viral genome replication.Computational and structural biotechnology journal · 2023Article
- RNA Structure Determination by High-Throughput Structural Analysis.Methods in molecular biology (Clifton, N.J.) · 2023Article
- Differential analysis of RNA structure probing experiments at nucleotide resolution: uncovering regulatory functions of RNA structure.Nature communications · 2022Article
- diffBUM-HMM: a robust statistical modeling approach for detecting RNA flexibility changes in high-throughput structure probing data.Genome biology · 2021Article
- Advances in RNA 3D Structure Modeling Using Experimental Data.Frontiers in genetics · 2020Review
- reactIDR: evaluation of the statistical reproducibility of high-throughput structural analyses towards a robust RNA structure prediction.BMC bioinformatics · 2019Article
- dStruct: identifying differentially reactive regions from RNA structurome profiling data.Genome biology · 2019Article
- Detecting RNA G-Quadruplexes (rG4s) in the Transcriptome.Cold Spring Harbor perspectives in biology · 2018Review
- Article
- Maturation of the 90S pre-ribosome requires Mrd1 dependent U3 snoRNA and 35S pre-rRNA structural rearrangements.Nucleic acids research · 2018Article
- PATTERNA: transcriptome-wide search for functional RNA elements via structural data signatures.Genome biology · 2018Article
- RNA Regulations and Functions Decoded by Transcriptome-wide RNA Structure Probing.Genomics, proteomics & bioinformatics · 2017Review
- High-throughput RNA structure probing reveals critical folding events during early 60S ribosome assembly in yeast.Nature communications · 2017Article
- PROBer Provides a General Toolkit for Analyzing Sequencing-Based Toeprinting Assays.Cell systems · 2017Article
- Comparative and integrative analysis of RNA structural profiling data: current practices and emerging questions.Quantitative biology (Beijing, China) · 2017Article
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
Structure probing coupled with high-throughput sequencing could revolutionize our understanding of the role of RNA structure in regulation of gene expression. Despite recent technological advances, intrinsic noise and high sequence coverage requirements greatly limit the applicability of these techniques. Here we describe a probabilistic modeling pipeline that accounts for biological variability and biases in the data, yielding statistically interpretable scores for the probability of nucleotide modification transcriptome wide. Using two yeast data sets, we demonstrate that our method has increased sensitivity, and thus our pipeline identifies modified regions on many more transcripts than do existing pipelines. Our method also provides confident predictions at much lower sequence coverage levels than those recommended for reliable structural probing. Our results show that statistical modeling extends the scope and potential of transcriptome-wide structure probing experiments.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.