Genome-wide Analysis of PTB-RNA Interactions Reveals a Strategy Used by the General Splicing Repressor to Modulate Exon Inclusion or Skipping

Xue, Yuanchao; Zhou, Yu; Wu, Taihu; Zhu, Tuo Fu; Xiong, Ji; Kwon, Young‐Soo; Zhang, Chao; Yeo, G; Black, Douglas L.; Sun, Hui; Fu, Xiang‐Dong; Zhang, Yi

doi:10.1016/j.molcel.2009.12.003

Cited by 422 publications

(567 citation statements)

References 34 publications

(82 reference statements)

Supporting

Mentioning

508

Contrasting

Unclassified

Order By: Relevance

“…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

View full text Add to dashboard Cite

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...

show abstract

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

View full text Add to dashboard Cite

show abstract

“…The splicing-repressive functions of PTB are best studied, although it is also clear that it can promote inclusion of some exons (Xue et al 2009). Like hnRNP A/B proteins, PTB is expressed throughout development, then downregulated in many adult tissues, most notably brain and muscle (Boutz et al 2007a;Makeyev et al 2007).…”

Section: Hnrnp and Sr Proteins In Proliferation And Cancermentioning

confidence: 99%

Alternative pre-mRNA splicing regulation in cancer: pathways and programs unhinged

David

Manley²

2010

Genes Dev.

720

667

View full text Add to dashboard Cite

Alternative splicing of mRNA precursors is a nearly ubiquitous and extremely flexible point of gene control in humans. It provides cells with the opportunity to create protein isoforms of differing, even opposing, functions from a single gene. Cancer cells often take advantage of this flexibility to produce proteins that promote growth and survival. Many of the isoforms produced in this manner are developmentally regulated and are preferentially re-expressed in tumors. Emerging insights into this process indicate that pathways that are frequently deregulated in cancer often play important roles in promoting aberrant splicing, which in turn contributes to all aspects of tumor biology.

show abstract

“…However, more recent work suggested that the splicing regulatory activity can vary for different binding sites as well as for sets of combinatorially acting factors. One example for positiondependent splicing is constituted by the hnRNP protein PTB (Xue et al, 2009;Llorian et al, 2010), an in animals, wellcharacterized regulator of AS that binds to pyrimidine-rich motifs within pre-mRNAs (Sawicka et al, 2008;Wachter et al, 2012). Evidence has been provided that PTB exploits various mechanisms for AS control, including competing with U2 auxiliary factor 65 in binding to the pre-mRNA (Saulière et al, 2006), looping of RNA regions (Spellman and Smith, 2006), and interference with splicing factor interactions required for exon or intron definition (Izquierdo et al, 2005;Sharma et al, 2005).…”

Section: Introductionmentioning

confidence: 99%

Polypyrimidine Tract Binding Protein Homologs from Arabidopsis Are Key Regulators of Alternative Splicing with Implications in Fundamental Developmental Processes

et al. 2012

View full text Add to dashboard Cite

Alternative splicing (AS) generates transcript variants by variable exon/intron definition and massively expands transcriptome diversity. Changes in AS patterns have been found to be linked to manifold biological processes, yet fundamental aspects, such as the regulation of AS and its functional implications, largely remain to be addressed. In this work, widespread AS regulation by Arabidopsis thaliana Polypyrimidine tract binding protein homologs (PTBs) was revealed. In total, 452 AS events derived from 307 distinct genes were found to be responsive to the levels of the splicing factors PTB1 and PTB2, which predominantly triggered splicing of regulated introns, inclusion of cassette exons, and usage of upstream 59 splice sites. By contrast, no major AS regulatory function of the distantly related PTB3 was found. Dependent on their position within the mRNA, PTB-regulated events can both modify the untranslated regions and give rise to alternative protein products. We find that PTB-mediated AS events are connected to diverse biological processes, and the functional implications of selected instances were further elucidated. Specifically, PTB misexpression changes AS of PHYTOCHROME INTERACTING FACTOR6, coinciding with altered rates of abscisic acid-dependent seed germination. Furthermore, AS patterns as well as the expression of key flowering regulators were massively changed in a PTB1/2 level-dependent manner.

show abstract

Genome-wide Analysis of PTB-RNA Interactions Reveals a Strategy Used by the General Splicing Repressor to Modulate Exon Inclusion or Skipping

Cited by 422 publications

References 34 publications

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

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

Alternative pre-mRNA splicing regulation in cancer: pathways and programs unhinged

Polypyrimidine Tract Binding Protein Homologs from Arabidopsis Are Key Regulators of Alternative Splicing with Implications in Fundamental Developmental Processes

Contact Info

Product

Resources

About