Aura 2

Dassi, Erik; Re, Angela; Leo, Sara; Tebaldi, Toma; Pasini, Luigi; Peroni, Daniele; Quattrone, Alessandro

doi:10.4161/trla.27738

Cited by 69 publications

(32 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Autogenous interactions have been observed for at least 57 human RBPs (as per the AURA2 database; Dassi et al, 2014 ), and are thus increasingly considered to be a mechanism of general significance. Analyzing the human proteome with the catRAPID algorithm highlighted an enrichment of autogenous associations within aggregation-prone disordered proteins (Zanzoni et al, 2013 ).…”

Section: Mutual Rbp Regulationmentioning

confidence: 99%

Handshakes and Fights: The Regulatory Interplay of RNA-Binding Proteins

Dassi

2017

Front. Mol. Biosci.

Self Cite

129

102

View full text Add to dashboard Cite

What drives the flow of signals controlling the outcome of post-transcriptional regulation of gene expression? This regulatory layer, presiding to processes ranging from splicing to mRNA stability and localization, is a key determinant of protein levels and thus cell phenotypes. RNA-binding proteins (RBPs) form a remarkable army of post-transcriptional regulators, strong of more than 1,500 genes implementing this expression fine-tuning plan and implicated in both cell physiology and pathology. RBPs can bind and control a wide array of RNA targets. This sheer amount of interactions form complex regulatory networks (PTRNs) where the action of individual RBPs cannot be easily untangled from each other. While past studies have mostly focused on the action of individual RBPs on their targets, we are now observing an increasing amount of evidence describing the occurrence of interactions between RBPs, defining how common target RNAs are regulated. This suggests that the flow of signals in PTRNs is driven by the intertwined contribution of multiple RBPs, concurrently acting on each of their targets. Understanding how RBPs cooperate and compete is thus of paramount importance to chart the wiring of PTRNs and their impact on cell phenotypes. Here we review the current knowledge about patterns of RBP interaction and attempt at describing their general principles. We also discuss future directions which should be taken to reach a comprehensive understanding of this fundamental aspect of gene expression regulation.

show abstract

Section: Mutual Rbp Regulationmentioning

confidence: 99%

Handshakes and Fights: The Regulatory Interplay of RNA-Binding Proteins

Dassi

2017

Front. Mol. Biosci.

Self Cite

129

102

View full text Add to dashboard Cite

show abstract

“…The AURA database (Dassi et al 2014) contains a curated set of 754 RNA-RBP interactions and miRNA binding sites derived from the literature and various experimental data sets. We used this resource to search for regulatory motifs enriched in each of our three gene groups of interest (see Supplemental Methods for full details).…”

Section: Mechanisms Underlying Rhythmic Transcription and Translationmentioning

confidence: 99%

Ribosome profiling reveals an important role for translational control in circadian gene expression

et al. 2015

View full text Add to dashboard Cite

Physiological and behavioral circadian rhythms are driven by a conserved transcriptional/translational negative feedback loop in mammals. Although most core clock factors are transcription factors, post-transcriptional control introduces delays that are critical for circadian oscillations. Little work has been done on circadian regulation of translation, so to address this deficit we conducted ribosome profiling experiments in a human cell model for an autonomous clock. We found that most rhythmic gene expression occurs with little delay between transcription and translation, suggesting that the lag in the accumulation of some clock proteins relative to their mRNAs does not arise from regulated translation. Nevertheless, we found that translation occurs in a circadian fashion for many genes, sometimes imposing an additional level of control on rhythmically expressed mRNAs and, in other cases, conferring rhythms on noncycling mRNAs. Most cyclically transcribed RNAs are translated at one of two major times in a 24-h day, while rhythmic translation of most noncyclic RNAs is phased to a single time of day. Unexpectedly, we found that the clock also regulates the formation of cytoplasmic processing (P) bodies, which control the fate of mRNAs, suggesting circadian coordination of mRNA metabolism and translation.

show abstract

“…The detailed composition is listed in the Additional file 1 . The second in vivo dataset was the overlap of the in vivo dataset derived by Cirillo et al [ 24 ] from the AURA [ 25 ] database with our InVitro dataset. RNAs here are all long non-coding RNAs (lncRNAs).…”

Section: Methodsmentioning

confidence: 99%

Inferring RNA sequence preferences for poorly studied RNA-binding proteins based on co-evolution

Yang

Wang

2018

BMC Bioinformatics

View full text Add to dashboard Cite

BackgroundCharacterizing the binding preference of RNA-binding proteins (RBP) is essential for us to understand the interaction between an RBP and its RNA targets, and to decipher the mechanism of post-transcriptional regulation. Experimental methods have been used to generate protein-RNA binding data for a number of RBPs in vivo and in vitro. Utilizing the binding data, a couple of computational methods have been developed to detect the RNA sequence or structure preferences of the RBPs. However, the majority of RBPs have not yet been experimentally characterized and lack RNA binding data. For these poorly studied RBPs, the identification of their binding preferences cannot be performed by most existing computational methods because the experimental binding data are prerequisite to these methods.ResultsHere we propose a new method based on co-evolution to predict the sequence preferences for the poorly studied RBPs, waiving the requirement of their binding data. First, we demonstrate the co-evolutionary relationship between RBPs and their RNA partners. We then present a K-nearest neighbors (KNN) based algorithm to infer the sequence preference of an RBP using only the preference information from its homologous RBPs. By benchmarking against several in vitro and in vivo datasets, our proposed method outperforms the existing alternative which uses the closest neighbor’s preference on all the datasets. Moreover, it shows comparable performance with two state-of-the-art methods that require the presence of the experimental binding data. Finally, we demonstrate the usage of this method to infer sequence preferences for novel proteins which have no binding preference information available.ConclusionFor a poorly studied RBP, the current methods used to determine its binding preference need experimental data, which is expensive and time consuming. Therefore, determining RBP’s preference is not practical in many situations. This study provides an economic solution to infer the sequence preference of such protein based on the co-evolution. The source codes and related datasets are available at https://github.com/syang11/KNN.Electronic supplementary materialThe online version of this article (10.1186/s12859-018-2091-8) contains supplementary material, which is available to authorized users.

show abstract

Aura 2

Cited by 69 publications

References 46 publications

Handshakes and Fights: The Regulatory Interplay of RNA-Binding Proteins

Handshakes and Fights: The Regulatory Interplay of RNA-Binding Proteins

Ribosome profiling reveals an important role for translational control in circadian gene expression

Inferring RNA sequence preferences for poorly studied RNA-binding proteins based on co-evolution

Contact Info

Product

Resources

About