PATTERNA: transcriptome-wide search for functional RNA elements via structural data signatures

Ledda, Mirko; Aviran, Sharon

doi:10.1186/s13059-018-1399-z

Cited by 20 publications

(68 citation statements)

References 99 publications

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“…A distinct advantage of filtering is that, as with the use of the prior distribution, it 547 provides opportunity to incorporate other types of information into the denoising scheme. Consider, as one example, the correlation effects of neighboring nucleotides in 549 SHAPE experiments, which have been noted and modeled [54]. Although in our study 550…”

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confidence: 84%

“…The intimate coupling of noise characterization and signal 556 extraction underscores the importance of this step in data processing. Although 557 structure prediction is the most prominent applications of SHAPE data, there exists a 558 breadth of emerging applications for SP data, such as data-directed sequence alignment 559 and the identification of conserved and functional RNA structures [27,54,59]. SP data 560…”

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confidence: 99%

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Extracting information from RNA SHAPE data: Kalman filtering approach

Vaziri

Koehl

Aviran

2018

Preprint

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RNA SHAPE experiments have become important and successful sources of information for RNA structure prediction. In such experiments, chemical reagents are used to probe RNA backbone flexibility at the nucleotide level, which in turn provides information on base pairing and therefore secondary structure. Little is known, however, about the statistics of such SHAPE data. In this work, we explore different representations of noise in SHAPE data and propose a statistically sound framework for extracting reliable reactivity information from multiple SHAPE replicates. Our analyses of RNA SHAPE experiments underscore that a normal noise model is not adequate to represent their data. We propose instead a log-normal representation of noise and discuss its relevance.Under this assumption, we observe that processing simulated SHAPE data by directly averaging different replicates leads to bias. Such bias can be reduced by analyzing the data following a log transformation, either by log-averaging or Kalman filtering. Application of Kalman filtering has the additional advantage that a prior on the nucleotide reactivities can be introduced. We show that the performance of Kalman filtering is then directly dependent on the quality of that prior. We conclude the paper with guidelines on signal processing of RNA SHAPE data. September 4, 2018 1/31 Introduction 1 Beyond its role in protein synthesis and the transfer of genetic information, RNA exists 2 as a dynamic cellular component at the core of gene regulation [1]. From microRNAs 3 involved in regulating gene expression [2] and long noncoding RNAs similarly regulating 4 gene expression [3] to ribozymes acting as chemical catalysts [4], RNA plays a central 5 role in a multitude of cellular activities. The diverse repertoire of biological functions 6 that RNAs adopt is deeply rooted in their abilities to form complex three-dimensional 7 structures [1]. This interplay between structure and function underscores the need for 8 robust structural analysis as a prerequisite to a full understanding of the physiological 9 role of RNA [5]. Despite its importance, determining the complex 3D structures of RNA 10 remains a challenging problem, particularly for longer RNAs [6, 7]. 11 Considering the hierarchical nature of RNA folding [8], much of the efforts in 12 structure determination have been devoted to its two-dimensional base-pairing pattern, 13 also known as its secondary structure. This secondary structure is generally considered 14 to be more stable than and independent of the final 3D conformation [8]. Though 15 experimental methods such as nuclear magnetic resonance (NMR) [9] and 16 crystallography [10] can be used to accurately resolve 3D RNA structures, they are 17 time-consuming, expensive, and often preclude the analysis of long or flexible 18 molecules [11]. Comparative sequence analysis, the process of inferring base-pairing 19from co-variations observed in the alignment of homologous sequences, is a robust 20 method for defining the secondary structure of RNA [11,12]. Howeve...

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confidence: 84%

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confidence: 99%

Extracting information from RNA SHAPE data: Kalman filtering approach

Vaziri

Koehl

Aviran

2018

Preprint

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“…In this way, large amount of SP data can be obtained. Despite the availability of genomic-wide SP data, its application for transcriptome-wide structure analysis is promising [72] but has remained largely underutilized. Enhanced ncRNA annotation with in vivo SP data.…”

Section: Clustering Arabidopsis Ncrnas With Dms-seq In Vivo Structurementioning

confidence: 99%

Empowering the annotation and discovery of structured RNAs with scalable and accessible integrative clustering

Miladi

Sokhoyan

Houwaart

et al. 2019

Preprint

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RNA plays essential roles in all known forms of life. Clustering RNA sequences with common sequence and structure is an essential step towards studying RNA function. With the advent of high-throughput sequencing (HTS) techniques, experimental and genomic data are expanding to complement the predictive methods. However, the existing methods do not effectively utilize and cope with the immense amount of data becoming available. Hundreds of thousands of non-coding RNAs (ncRNAs) have been detected, however, the annotation of these ncRNAs is lacking behind. Here we present GraphClust2, a comprehensive approach for scalable clustering of RNAs based on sequence and structural similarities. GraphClust2 bridges the gap between HTS and structural RNA analysis, and provides an integrative solution by incorporating diverse experimental and genomic data in an accessible manner via the Galaxy framework. GraphClust2 can efficiently cluster and annotate large datasets of RNAs and supports structure probing data. We demonstrate that the annotation performance of clustering functional RNAs can be considerably improved. Furthermore, an off-the-shelf procedure is introduced for identifying locally conserved structure candidates in long RNAs. We suggest the presence and the sparsity of phylogenetically conserved local structures for a collection of long non-coding RNAs. By clustering data from two CLIP experiments, we demonstrate the benefits of GraphClust2 for motif discovery under the presence of biological and

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“…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%

“…Also, many biologically important RNAs exist as an equilibrium between multiple configurations, meaning that the presentation of a single “consensus” RNA secondary structure may be misleading, or indeed false if the ensemble data has been forced onto a single structural conformation (Cordero & Das, ; Mailler et al, ; Spasic et al, ). Computational modeling may be able to partially reconstruct RNA structural landscapes from ensemble probing data (Ledda & Aviran, ; Li & Aviran, ; Spasic et al, ), especially when combined with alternative RNA structural probing technologies, such as mutate‐and‐map (Kladwang et al, ). In sum, RNA structural probing experiments provide important information that can help resolve RNA secondary structure, but these data should always be interpreted cautiously and conservatively.…”

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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PATTERNA: transcriptome-wide search for functional RNA elements via structural data signatures

Cited by 20 publications

References 99 publications

Extracting information from RNA SHAPE data: Kalman filtering approach

Extracting information from RNA SHAPE data: Kalman filtering approach

Empowering the annotation and discovery of structured RNAs with scalable and accessible integrative clustering

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

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