The discovery potential of RNA processing profiles.

Small non-coding RNAs (sncRNAs) are highly abundant molecules that regulate essential cellular processes and are classified according to sequence and structure. Here we argue that read profiles from size-selected RNA sequencing capture the post-transcriptional processing specific to each RNA family, thereby providing functional information independently of sequence and structure. We developed SeRPeNT, a new computational method that exploits reproducibility across replicates and uses dynamic time-warping and density-based clustering algorithms to identify, characterize and compare sncRNAs by harnessing the power of read profiles. We applied SeRPeNT to: (i) generate an extended human annotation with 671 new sncRNAs from known classes and 131 from new potential classes, (ii) show pervasive differential processing of sncRNAs between cell compartments and (iii) predict new molecules with miRNA-like behaviour from snoRNA, tRNA and long non-coding RNA precursors, potentially dependent on the miRNA biogenesis pathway. Furthermore, we validated experimentally four predicted novel non-coding RNAs: a miRNA, a snoRNA-derived miRNA, a processed tRNA and a new uncharacterized sncRNA. SeRPeNT facilitates fast and accurate discovery and characterization of sncRNAs at an unprecedented scale. SeRPeNT code is available under the MIT license at https://github.com/comprna/SeRPeNT.

Large-scale analysis of genome and transcriptome alterations in multiple tumors unveils novel cancer-relevant splicing networks.

Alternative splicing is regulated by multiple RNA-binding proteins and influences the expression of most eukaryotic genes. However, the role of this process in human disease, and particularly in cancer, is only starting to be unveiled. We systematically analyzed mutation, copy number, and gene expression patterns of 1348 RNA-binding protein (RBP) genes in 11 solid tumor types, together with alternative splicing changes in these tumors and the enrichment of binding motifs in the alternatively spliced sequences. Our comprehensive study reveals widespread alterations in the expression of RBP genes, as well as novel mutations and copy number variations in association with multiple alternative splicing changes in cancer drivers and oncogenic pathways. Remarkably, the altered splicing patterns in several tumor types recapitulate those of undifferentiated cells. These patterns are predicted to be mainly controlled by MBNL1 and involve multiple cancer drivers, including the mitotic gene NUMA1 We show that NUMA1 alternative splicing induces enhanced cell proliferation and centrosome amplification in nontumorigenic mammary epithelial cells. Our study uncovers novel splicing networks that potentially contribute to cancer development and progression.

Dual function of C/D box small nucleolar RNAs in rRNA modification and alternative pre-mRNA splicing.

C/D box small nucleolar RNAs (SNORDs) are small noncoding RNAs, and their best-understood function is to target the methyltransferase fibrillarin to rRNA (for example, SNORD27 performs 2'-O-methylation of A27 in 18S rRNA). Unexpectedly, we found a subset of SNORDs, including SNORD27, in soluble nuclear extract made under native conditions, where fibrillarin was not detected, indicating that a fraction of the SNORD27 RNA likely forms a protein complex different from canonical snoRNAs found in the insoluble nuclear fraction. As part of this previously unidentified complex,SNORD27 regulates the alternative splicing of the transcription factor E2F7p re-mRNA through direct RNA-RNA interaction without methylating the RNA, likely by competing with U1 small nuclear ribonucleoprotein (snRNP). Furthermore, knockdown of SNORD27 activates previously "silent" exons in several other genes through base complementarity across the entire SNORD27 sequence, not just the antisense boxes. Thus, some SNORDs likely function in both rRNA and pre-mRNA processing, which increases the repertoire of splicing regulators and links both processes.

Leveraging transcript quantification for fast computation of alternative splicing profiles.

Alternative splicing plays an essential role in many cellular processes and bears major relevance in the understanding of multiple diseases, including cancer. High-throughput RNA sequencing allows genome-wide analyses of splicing across multiple conditions. However, the increasing number of available data sets represents a major challenge in terms of computation time and storage requirements. We describe SUPPA, a computational tool to calculate relative inclusion values of alternative splicing events, exploiting fast transcript quantification. SUPPA accuracy is comparable and sometimes superior to standard methods using simulated as well as real RNA-sequencing data compared with experimentally validated events. We assess the variability in terms of the choice of annotation and provide evidence that using complete transcripts rather than more transcripts per gene provides better estimates. Moreover, SUPPA coupled with de novo transcript reconstruction methods does not achieve accuracies as high as using quantification of known transcripts, but remains comparable to existing methods. Finally, we show that SUPPA is more than 1000 times faster than standard methods. Coupled with fast transcript quantification, SUPPA provides inclusion values at a much higher speed than existing methods without compromising accuracy, thereby facilitating the systematic splicing analysis of large data sets with limited computational resources. The software is implemented in Python 2.7 and is available under the MIT license at https://bitbucket.org/regulatorygenomicsupf/suppa.

A chromatin code for alternative splicing involving a putative association between CTCF and HP1α proteins.

BACKGROUND: Alternative splicing is primarily controlled by the activity of splicing factors and by the elongation of the RNA polymerase II (RNAPII). Recent experiments have suggested a new complex network of splicing regulation involving chromatin, transcription and multiple protein factors. In particular, the CCCTC-binding factor (CTCF), the Argonaute protein AGO1, and members of the heterochromatin protein 1 (HP1) family have been implicated in the regulation of splicing associated with chromatin and the elongation of RNAPII. These results raise the question of whether these proteins may associate at the chromatin level to modulate alternative splicing. RESULTS: Using chromatin immunoprecipitation sequencing (ChIP-Seq) data for CTCF, AGO1, HP1α, H3K27me3, H3K9me2, H3K36me3, RNAPII, total H3 and 5metC and alternative splicing arrays from two cell lines, we have analyzed the combinatorial code of their binding to chromatin in relation to the alternative splicing patterns between two cell lines, MCF7 and MCF10. Using Machine Learning techniques, we identified the changes in chromatin signals that are most significantly associated with splicing regulation between these two cell lines. Moreover, we have built a map of the chromatin signals on the pre-mRNA, that is, a chromatin-based RNA-map, which can explain 606 (68.55%) of the regulated events between MCF7 and MCF10. This chromatin code involves the presence of HP1α, CTCF, AGO1, RNAPII and histone marks around regulated exons and can differentiate patterns of skipping and inclusion. Additionally, we found a significant association of HP1α and CTCF activities around the regulated exons and a putative DNA binding site for HP1α. CONCLUSIONS: Our results show that a considerable number of alternative splicing events could have a chromatin-dependent regulation involving the association of HP1α and CTCF near regulated exons. Additionally, we find further evidence for the involvement of HP1α and AGO1 in chromatin-related splicing regulation.

A semi-supervised approach uncovers thousands of intragenic enhancers differentially activated in human cells.

BACKGROUND: Transcriptional enhancers are generally known to regulate gene transcription from afar. Their activation involves a series of changes in chromatin marks and recruitment of protein factors. These enhancers may also occur inside genes, but how many may be active in human cells and their effects on the regulation of the host gene remains unclear. RESULTS: We describe a novel semi-supervised method based on the relative enrichment of chromatin signals between 2 conditions to predict active enhancers. We applied this method to the tumoral K562 and the normal GM12878 cell lines to predict enhancers that are differentially active in one cell type. These predictions show enhancer-like properties according to positional distribution, correlation with gene expression and production of enhancer RNAs. Using this model, we predict 10,365 and 9777 intragenic active enhancers in K562 and GM12878, respectively, and relate the differential activation of these enhancers to expression and splicing differences of the host genes. CONCLUSIONS: We propose that the activation or silencing of intragenic transcriptional enhancers modulate the regulation of the host gene by means of a local change of the chromatin and the recruitment of enhancer-related factors that may interact with the RNA directly or through the interaction with RNA binding proteins. Predicted enhancers are available at http://regulatorygenomics.upf.edu/Projects/enhancers.html .

The 5' untranslated region of the serotonin receptor 2C pre-mRNA generates miRNAs and is expressed in non-neuronal cells.

The serotonin receptor 2C (HTR2C) gene encodes a G protein-coupled receptor that is exclusively expressed in neurons. Here, we report that the 5' untranslated region of the receptor pre-mRNA as well as its hosted miRNAs is widely expressed in non-neuronal cell lines. Alternative splicing of HTR2C is regulated by MBII-52. MBII-52 and the neighboring MBII-85 cluster are absent in people with Prader-Willi syndrome, which likely causes the disease. We show that MBII-52 and MBII-85 increase expression of the HTR2C 5' UTR and influence expression of the hosted miRNAs. The data indicate that the transcriptional unit expressing HTR2C is more complex than previously recognized and likely deregulated in Prader-Willi syndrome.

Predictive models of gene regulation from high-throughput epigenomics data.

The epigenetic regulation of gene expression involves multiple factors. The synergistic or antagonistic action of these factors has suggested the existence of an epigenetic code for gene regulation. Highthroughput sequencing (HTS) provides an opportunity to explore this code and to build quantitative models of gene regulation based on epigenetic differences between specific cellular conditions. We describe a new computational framework that facilitates the systematic integration of HTS epigenetic data. Our method relates epigenetic signals to expression by comparing two conditions. We show its effectiveness by building a model that predicts with high accuracy significant expression differences between two cell lines, using epigenetic data from the ENCODE project. Our analyses provide evidence for a degenerate epigenetic code, which involves multiple genic regions. In particular, signal changes at the 1st exon, 1st intron, and downstream of the polyadenylation site are found to associate strongly with expression regulation. Our analyses also show a different epigenetic code for intron-less and intron-containing genes. Our work provides a general methodology to do integrative analysis of epigenetic differences between cellular conditions that can be applied to other studies, like cell differentiation or carcinogenesis.