scholarly journals FragSeq: transcriptome-wide RNA structure probing using high-throughput sequencing

2010 ◽  
Vol 7 (12) ◽  
pp. 995-1001 ◽  
Author(s):  
Jason G Underwood ◽  
Andrew V Uzilov ◽  
Sol Katzman ◽  
Courtney S Onodera ◽  
Jacob E Mainzer ◽  
...  
Keyword(s):  
High Throughput ◽  
Rna Structure ◽  
High Throughput Sequencing ◽  
Structure Probing

scholarly journals Differential BUM-HMM: a robust statistical modelling approach for detecting RNA flexibility changes in high-throughput structure probing data

2020 ◽  
Author(s):  
Paolo Marangio ◽  
Ka Ying Toby Law ◽  
Guido Sanguinetti ◽  
Sander Granneman
Keyword(s):  
Protein Binding ◽  
High Throughput ◽  
Conformational Changes ◽  
Binding Sites ◽  
Rna Structure ◽  
Structural Changes ◽  
High Throughput Sequencing ◽  
Structural Information ◽  
Protein Binding Sites ◽  
Structure Probing

Combining RNA structure probing with high-throughput sequencing technologies has greatly enhanced our ability to analyze RNA structure at transcriptome scale. However, the high level of noise and variability encountered in these data call for the development of computational tools that robustly extract RNA structural information. Here we present diffBUM-HMM, a noise-aware model that enables accurate detection of RNA flexibility and conformational changes from high-throughput RNA structure-probing data. DiffBUM-HMM is compatible with a wide variety of high-throughput RNA structure probing data, taking into consideration biological variation, sequence coverage and sequence representation biases. We demonstrate that, compared to the existing approaches, diffBUM-HMM displays higher sensitivity while calling virtually no false positives. DiffBUM-HMM analysis of ex vivo and in vivo Xist SHAPE-MaP data detected many more RNA structural differences, involving mostly single-stranded nucleotides located at or near protein-binding sites. Collectively, our analyses demonstrate the value of diffBUM-HMM for quantitatively detecting RNA structural changes and reinforce the notion that RNA structure probing is a very powerful tool for identifying protein-binding sites.

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

2016 ◽  
Vol 14 (1) ◽  
pp. 83-89 ◽  
Author(s):  
Alina Selega ◽  
Christel Sirocchi ◽  
Ira Iosub ◽  
Sander Granneman ◽  
Guido Sanguinetti
Keyword(s):  
Statistical Modeling ◽  
High Throughput ◽  
Rna Structure ◽  
Structure Probing

scholarly journals A comprehensive database of high-throughput sequencing-based RNA secondary structure probing data (Structure Surfer)

2016 ◽  
Vol 17 (1) ◽  
Author(s):  
Nathan D. Berkowitz ◽  
Ian M. Silverman ◽  
Daniel M. Childress ◽  
Hilal Kazan ◽  
Li-San Wang ◽  
...  
Keyword(s):  
Data Structure ◽  
Secondary Structure ◽  
High Throughput ◽  
Rna Secondary Structure ◽  
High Throughput Sequencing ◽  
Comprehensive Database ◽  
Structure Probing

Genome-wide mapping of RNA structure using nuclease digestion and high-throughput sequencing

2013 ◽  
Vol 8 (5) ◽  
pp. 849-869 ◽  
Author(s):  
Yue Wan ◽  
Kun Qu ◽  
Zhengqing Ouyang ◽  
Howard Y Chang
Keyword(s):  
High Throughput ◽  
Rna Structure ◽  
High Throughput Sequencing ◽  
Genome Wide ◽  
Nuclease Digestion

scholarly journals Mod-seq: high-throughput sequencing for chemical probing of RNA structure

RNA ◽  
2014 ◽  
Vol 20 (5) ◽  
pp. 713-720 ◽  
Author(s):  
Jason Talkish ◽  
Gemma May ◽  
Yizhu Lin ◽  
John L. Woolford ◽  
C. Joel McManus
Keyword(s):  
High Throughput ◽  
Rna Structure ◽  
High Throughput Sequencing ◽  
Chemical Probing

scholarly journals Corrigendum: Mod-seq: high-throughput sequencing for chemical probing of RNA structure

RNA ◽  
2019 ◽  
Vol 25 (9) ◽  
pp. 1226_1-1226_1
Author(s):  
Jason Talkish ◽  
Gemma May ◽  
Yizhu Lin ◽  
John L. Woolford ◽  
C. Joel Mcmanus
Keyword(s):  
High Throughput ◽  
Rna Structure ◽  
High Throughput Sequencing ◽  
Chemical Probing

scholarly journals diffBUM-HMM: a robust statistical modeling approach for detecting RNA flexibility changes in high-throughput structure probing data

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Paolo Marangio ◽  
Ka Ying Toby Law ◽  
Guido Sanguinetti ◽  
Sander Granneman
Keyword(s):  
High Throughput ◽  
Conformational Changes ◽  
Rna Structure ◽  
Structural Changes ◽  
Rna Binding ◽  
Structural Information ◽  
Sequence Coverage ◽  
Structure Probing ◽  
Generation Sequencing ◽  
Higher Sensitivity

AbstractAdvancing RNA structural probing techniques with next-generation sequencing has generated demands for complementary computational tools to robustly extract RNA structural information amidst sampling noise and variability. We present diffBUM-HMM, a noise-aware model that enables accurate detection of RNA flexibility and conformational changes from high-throughput RNA structure-probing data. diffBUM-HMM is widely compatible, accounting for sampling variation and sequence coverage biases, and displays higher sensitivity than existing methods while robust against false positives. Our analyses of datasets generated with a variety of RNA probing chemistries demonstrate the value of diffBUM-HMM for quantitatively detecting RNA structural changes and RNA-binding protein binding sites.

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