IPANEMAP: integrative probing analysis of nucleic acids empowered by multiple accessibility profiles

Abstract The manual production of reliable RNA structure models from chemical probing experiments benefits from the integration of information derived from multiple protocols and reagents. However, the interpretation of multiple probing profiles remains a complex task, hindering the quality and reproducibility of modeling efforts. We introduce IPANEMAP, the first automated method for the modeling of RNA structure from multiple probing reactivity profiles. Input profiles can result from experiments based on diverse protocols, reagents, or collection of variants, and are jointly analyzed to predict the dominant conformations of an RNA. IPANEMAP combines sampling, clustering and multi-optimization, to produce secondary structure models that are both stable and well-supported by experimental evidences. The analysis of multiple reactivity profiles, both publicly available and produced in our study, demonstrates the good performances of IPANEMAP, even in a mono probing setting. It confirms the potential of integrating multiple sources of probing data, informing the design of informative probing assays.

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IPANEMAP: Integrative Probing Analysis of Nucleic Acids Empowered by Multiple Accessibility Profiles

10.1101/2020.07.03.186312 ◽

2020 ◽

Author(s):

Afaf Saaidi ◽

Delphine Allouche ◽

Mireille Regnier ◽

Bruno Sargueil ◽

Yann Ponty

Keyword(s):

Secondary Structure ◽

Nucleic Acids ◽

Rna Structure ◽

Complex Task ◽

Multiple Sources ◽

Automated Method ◽

Chemical Probing ◽

Secondary Structure Models

The manual production of reliable RNA structure models from chemical probing experiments benefits from the integration of information derived from multiple protocols and reagents. However, the interpretation of multiple probing profiles remains a complex task, hindering the quality and reproducibility of modeling efforts. We introduce IPANEMAP, the first automated method for the modeling of RNA structure from multiple probing reactivity profiles. Input profiles can result from experiments based on diverse protocols, reagents, or collection of variants, and are jointly analyzed to predict the dominant conformations of an RNA. IPANEMAP combines sampling, clustering, and multi-optimization, to produce secondary structure models that are both stable and well-supported by experimental evidences. The analysis of multiple reactivity profiles, both publicly available and produced in our study, demonstrates the good performances of IPANEMAP, even in a mono probing setting. It confirms the potential of integrating multiple sources of probing data, informing the design of informative probing assays. Availability: IPANEMAP is freely downloadable at https://github.com/afafbioinfo/IPANEMAP Contact: [email protected]

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RNAProbe: a web server for normalization and analysis of RNA structure probing data

Nucleic Acids Research ◽

10.1093/nar/gkaa396 ◽

2020 ◽

Vol 48 (W1) ◽

pp. W292-W299 ◽

Cited By ~ 2

Author(s):

Tomasz K Wirecki ◽

Katarzyna Merdas ◽

Agata Bernat ◽

Michał J Boniecki ◽

Janusz M Bujnicki ◽

...

Keyword(s):

Secondary Structure ◽

Rna Structure ◽

Structure Prediction ◽

Secondary Structure Prediction ◽

Structural Characteristics ◽

Web Server ◽

Rna Molecules ◽

Chemical Probing ◽

Structure Probing ◽

Low Pass

Abstract RNA molecules play key roles in all living cells. Knowledge of the structural characteristics of RNA molecules allows for a better understanding of the mechanisms of their action. RNA chemical probing allows us to study the susceptibility of nucleotides to chemical modification, and the information obtained can be used to guide secondary structure prediction. These experimental results can be analyzed using various computational tools, which, however, requires additional, tedious steps (e.g., further normalization of the reactivities and visualization of the results), for which there are no fully automated methods. Here, we introduce RNAProbe, a web server that facilitates normalization, analysis, and visualization of the low-pass SHAPE, DMS and CMCT probing results with the modification sites detected by capillary electrophoresis. RNAProbe automatically analyzes chemical probing output data and turns tedious manual work into a one-minute assignment. RNAProbe performs normalization based on a well-established protocol, utilizes recognized secondary structure prediction methods, and generates high-quality images with structure representations and reactivity heatmaps. It summarizes the results in the form of a spreadsheet, which can be used for comparative analyses between experiments. Results of predictions with normalized reactivities are also collected in text files, providing interoperability with bioinformatics workflows. RNAProbe is available at https://rnaprobe.genesilico.pl.

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Chemical probing of adenine residues within the secondary structure of rabbit 18S ribosomal RNA

Biochemistry ◽

10.1021/bi00402a013 ◽

1988 ◽

Vol 27 (2) ◽

pp. 582-592 ◽

Cited By ~ 17

Author(s):

Asha Rairkar ◽

Heidi M. Rubino ◽

Raymond E. Lockard

Keyword(s):

Secondary Structure ◽

Ribosomal Rna ◽

18S Ribosomal Rna ◽

Chemical Probing

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Studies on nucleic acids. VIII. Changes in the stability of DNA secondary structure by interaction with divalent metal ions

Biopolymers ◽

10.1002/bip.1966.360040306 ◽

1966 ◽

Vol 4 (3) ◽

pp. 321-335 ◽

Cited By ~ 53

Author(s):

H. Venner ◽

Ch. Zimmer

Keyword(s):

Secondary Structure ◽

Nucleic Acids ◽

Metal Ions ◽

Divalent Metal ◽

Divalent Metal Ions ◽

Dna Secondary Structure ◽

The Stability

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Temperature-based variation of rRNA secondary structure models: a case study in the insect Drosophila simulans, the land snail Isabellaria adriani, and the crustacean Daphnia pulex

Canadian Journal of Zoology ◽

10.1139/z00-205 ◽

2001 ◽

Vol 79 (2) ◽

pp. 334-345 ◽

Cited By ~ 3

Author(s):

Georg FJ Armbruster

Keyword(s):

Secondary Structure ◽

Daphnia Pulex ◽

Land Snail ◽

Small Subunit ◽

Drosophila Simulans ◽

Ssu Rrna ◽

Rrna Molecule ◽

Different Temperatures ◽

Secondary Structure Models ◽

Rrna Secondary Structure

The influence of a temperature default on ribosomal RNA (rRNA) secondary structure models was studied with the "Mfold" energy-optimization program. Folding models of the internal transcribed spacer (ITS) 1 rRNA for both Drosophila simulans (Insecta) and Isabellaria adriani (Gastropoda) were generated at two different temperatures. The folding models are compared with the models previously shown for the ITS-1 of D. melanogaster Oregon R strain and I. adriani. A search for phylogenetically informative ITS-1 folding motifs was conducted for D. simulans. In I. adriani, a new approach for ITS-1 secondary structure analyses is suggested. The paper also elucidates results inferred from three energy-optimizing programs (Mfold, GeneBee, and STAR). These three folding programs give different information on the structure and free energy of a ITS-1 rRNA molecule. Furthermore, secondary-structure models of the small subunit (ssu) rRNA of Daphnia pulex (Crustacea: Cladocera) were investigated. The ssu rRNA molecule is usually folded according to alignment information. Here, ssu folding patterns are computed with Mfold using two temperature conditions. The two Mfold models are compared with the alignment model previously suggested for D. pulex. Three cladoceran-specific motifs and a short stem motif within the ssu rRNA of eukaryotes are discussed with respect to structure and phylogenetic information.

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A method for RNA structure prediction shows evidence for structure in lncRNAs

10.1101/284869 ◽

2018 ◽

Author(s):

Riccardo Delli ponti ◽

Alexandros Armaos ◽

Stefanie Marti ◽

Gian Gaetano Tartaglia

Keyword(s):

Secondary Structure ◽

Rna Structure ◽

Structure Prediction ◽

Sequence Information ◽

Time Warping ◽

Single Nucleotide ◽

Rna Molecules ◽

Rna Structure Prediction ◽

Dynamic Time ◽

Nucleotide Resolution

AbstractTo compare the secondary structures of RNA molecules we developed the CROSSalign method. CROSSalign is based on the combination of the Computational Recognition Of Secondary Structure (CROSS) algorithm to predict the RNA secondary structure at single-nucleotide resolution using sequence information, and the Dynamic Time Warping (DTW) method to align profiles of different lengths. We applied CROSSalign to investigate the structural conservation of long non-coding RNAs such as XIST and HOTAIR as well as ssRNA viruses including HIV. In a pool of sequences with the same secondary structure CROSSalign accurately recognizes repeat A of XIST and domain D2 of HOTAIR and outperforms other methods based on covariance modelling. CROSSalign can be applied to perform pair-wise comparisons and is able to find homologues between thousands of matches identifying the exact regions of similarity between profiles of different lengths. The algorithm is freely available at the webpage http://service.tartaglialab.com//new_submission/CROSSalign.

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