An RNA map predicting Nova-dependent splicing regulation

Ule, Jernej; Stefani, Giovanni; Mele, Aldo; Ruggiu, Matteo; Wang, Xuning; Taneri, Bahar; Gaasterland, Terry; Blencowe, Benjamin J.; Darnell, Robert B.

doi:10.1038/nature05304

Cited by 489 publications

(647 citation statements)

References 49 publications

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“…A combination of microarray, CLIP-seq, high-throughput RT-PCR, and computational analyses by different laboratories resulted in the identification of many endogenous target genes, and confirmed the so-called ''RNA map,'' meaning that the splicing outcome (activation or repression) depends in a predictable manner on the location of the UGCAUG element (Zhang et al 2008;Venables et al 2009;Yeo et al 2009). Certain other splicing factors, such as Nova and PTB, also have a similar map with position-dependent activities (Ule et al 2006;Llorian et al 2010). The fact that different splicing factors with completely distinct binding sites have RNA maps with similar features suggests a convergence in the underlying molecular mechanisms of splicing regulation.…”

Section: Introductionmentioning

confidence: 89%

Mechanisms of activation and repression by the alternative splicing factors RBFOX1/2

Sun¹,

Zuo²,

Fregoso³

et al. 2011

RNA

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RBFOX1 and RBFOX2 are alternative splicing factors that are predominantly expressed in the brain and skeletal muscle. They specifically bind the RNA element UGCAUG, and regulate alternative splicing positively or negatively in a position-dependent manner. The molecular basis for the position dependence of these and other splicing factors on alternative splicing of their targets is not known. We explored the mechanisms of RBFOX splicing activation and repression using an MS2-tethering assay. We found that the Ala/Tyr/Gly-rich C-terminal domain is sufficient for exon activation when tethered to the downstream intron, whereas both the C-terminal domain and the central RRM are required for exon repression when tethered to the upstream intron. Using immunoprecipitation and mass spectrometry, we identified hnRNP H1, RALY, and TFG as proteins that specifically interact with the C-terminal domain of RBFOX1 and RBFOX2. RNA interference experiments showed that hnRNP H1 and TFG modulate the splicing activity of RBFOX1/2, whereas RALY had no effect. However, TFG is localized in the cytoplasm, and likely modulates alternative splicing indirectly.

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Section: Introductionmentioning

confidence: 89%

Mechanisms of activation and repression by the alternative splicing factors RBFOX1/2

Sun¹,

Zuo²,

Fregoso³

et al. 2011

RNA

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“…8, and Supplementary Table 9). For example, exons known to be downregulated by Nova had higher Nova scores in their upstream introns, and exons known to be upregulated by Nova had higher Nova scores in their downstream intron 48 . Similarly, TIA has been shown to upregulate exons when bound to the downstream intron 49 , and PTBP1 has been shown to suppress exon inclusion when bound to upstream introns of weaker splice sites 50 .…”

Section: Deepbind Models Identify Deleterious Genomic Variantsmentioning

confidence: 99%

Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning

et al. 2015

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Knowing the sequence specificities of DNA-and RNA-binding proteins is essential for developing models of the regulatory processes in biological systems and for identifying causal disease variants. Here we show that sequence specificities can be ascertained from experimental data with 'deep learning' techniques, which offer a scalable, flexible and unified computational approach for pattern discovery. Using a diverse array of experimental data and evaluation metrics, we find that deep learning outperforms other state-of-the-art methods, even when training on in vitro data and testing on in vivo data. We call this approach DeepBind and have built a stand-alone software tool that is fully automatic and handles millions of sequences per experiment. Specificities determined by DeepBind are readily visualized as a weighted ensemble of position weight matrices or as a 'mutation map' that indicates how variations affect binding within a specific sequence.DNA-and RNA-binding proteins play a central role in gene regulation, including transcription and alternative splicing. The sequence specificities of a protein are most commonly characterized using position weight matrices 1 (PWMs), which are easy to interpret and can be scanned over a genomic sequence to detect potential binding sites. However, growing evidence indicates that sequence specificities can be more accurately captured by more complex techniques 2-5 . Recently, 'deep learning' has achieved record-breaking performance in a variety of information technology applications 6,7 . We adapted deep learning methods to the task of predicting sequence specificities and found that they compete favorably with the state of the art. Our approach, called DeepBind, is based on deep convolutional neural networks and can discover new patterns even when the locations of patterns within sequences are unknown-a task for which traditional neural networks require an exorbitant amount of training data.There are several challenging aspects in learning models of sequence specificity using modern high-throughput technologies. First, the data come in qualitatively different forms. Protein binding microarrays (PBMs) 8 and RNAcompete assays 9 provide a specificity coefficient for each probe sequence, whereas chromatin immunoprecipitation (ChIP)-seq 10 provides a ranked list of putatively bound sequences of varying length, and HT-SELEX 11 generates a set of very high affinity sequences. Second, the quantity of data is large. A typical high-throughput experiment measures between 10,000 and 100,000 sequences, and it is computationally demanding to incorporate them all. Third, each data acquisition technology has its own artifacts, biases and limitations, and we must discover the pertinent specificities despite these unwanted effects. For example, ChIP-seq reads often localize to "hyper-ChIPable" regions of the genome near highly expressed genes 12 .DeepBind (Fig. 1) addresses the above challenges. (i) It can be applied to both microarray and sequencing data; (ii) it can learn from millions of...

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“…Thirdly, splicing-sensitive microarrays and whole genome exon arrays [64,78,82,83,[223][224][225], combined with loss-of-functions of splicing factors (such as knockout or RNAi), will allow the identification of a group of exons/genes controlled by a particular factor during Ca ++ -regulation of splicing. Lastly, bioinformatics approaches with highly predictive RNA elements could also help identify a group of exons regulated by Ca ++ signals, as has been demonstrated for the alternative splicing factor Nova-1 [62]. These recent developments will facilitate the study of Ca ++ -regulated splicing and will allow a more accurate assessment of the physiological impact of a particular splicing factor/RNA element through regulating the alternative splicing of a specific functional group of genes.…”

Section: Perspectivesmentioning

confidence: 99%

“…The relative levels of positive and negative regulatory factors determine the generation of variant mRNA isoforms in specific cell types [73][74][75]. Brainenriched splicing factors include Nova-1 and -2 [62][63][64][65][66], FOX-1 and -2 [54-61], PTBP2 (nPTB) and neuronal Hu proteins [76][77][78][79][80][81][82][83].…”

Section: Introductionmentioning

confidence: 99%

Control of alternative pre-mRNA splicing by Ca++ signals

Xie¹

2008

Biochimica et Biophysica Acta (BBA) - Gene Regulatory Mechanism

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Alternative pre-mRNA splicing is a common way of gene expression regulation in metazoans. The selective use of specific exons can be modulated in response to various manipulations that alter Ca ++ signals, particularly in neurons. A number of splicing factors have also been found to be controlled by Ca ++ signals. Moreover, pre-mRNA elements have been identified that are essential and sufficient to mediate Ca ++ -regulated splicing, providing model systems for dissecting the involved molecular components. In neurons, this regulation likely contributes to the fine-tuning of neuronal properties.

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An RNA map predicting Nova-dependent splicing regulation

Cited by 489 publications

References 49 publications

Mechanisms of activation and repression by the alternative splicing factors RBFOX1/2

Mechanisms of activation and repression by the alternative splicing factors RBFOX1/2

Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning

Control of alternative pre-mRNA splicing by Ca++ signals

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