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Summary RNA sequencing (RNA-seq) is a powerful technology for studying human transcriptome variation. We introduce PAIRADISE (Paired Replicate Analysis of Allelic Differential Splicing Events), a method for detecting allele-specific alternative splicing (ASAS) from RNA-seq data. Unlike conventional approaches that detect ASAS events one sample at a time, PAIRADISE aggregates ASAS signals across multiple individuals in a population. By treating the two alleles of an individual as paired, and multiple individuals sharing a heterozygous SNP as replicates, we formulate ASAS detection using PAIRADISE as a statistical problem for identifying differential alternative splicing from RNA-seq data with paired replicates. PAIRADISE outperforms alternative statistical models in simulation studies. Applying PAIRADISE to replicate RNA-seq data of a single individual and to population-scale RNA-seq data across many individuals, we detect ASAS events associated with genome-wide association study (GWAS) signals of complex traits or diseases. Additionally, PAIRADISE ASAS analysis detects the effects of rare variants on alternative splicing. PAIRADISE provides a useful computational tool for elucidating the genetic variation and phenotypic association of alternative splicing in populations.

Preeclampsia is a serious condition responsible for much pregnancy-related morbidity and mortality. Diagnosis of preeclampsia is difficult due to the non-specific and subjective nature of symptoms of the disease. To reduce the subjective decision making and management of preeclampsia, we identified a panel of biomarkers representing multiple and different pathogenic pathways implicated in the etiology of preeclampsia, and developed a test referred to as Preecludia™. An algorithm based on eight biomarkers (cluster of differentiation 274 (CD274), decorin, endoglin, fibroblast growth factor-21 (FGF21), soluble fms-related tyrosine kinase 1 (sFlt-1), kidney injury molecule-1 (KIM-1), free placental growth factor (PlGF), and total PlGF) and gestational age at the time of sample collection was constructed to rule out preeclampsia in women presenting with signs and symptoms of preeclampsia. The analytical performance of each of the individual biomarker assays that comprise the Preecludia™ test was evaluated. Herein we report the test's precision, analytical range, analytical sensitivity, parallelism, linearity, interference, analytical specificity, analytical accuracy, and stability. The data indicate that these biomarker assays exhibit a high level of inter-run precision of less than 15%, with minimal interference.

Gene expression is tightly regulated at the post-transcriptional level through splicing, transport, translation, and decay. RNA-binding proteins (RBPs) play key roles in post-transcriptional gene regulation, and genetic variants that alter RBP-RNA interactions can affect gene products and functions. We developed a computational method ASPRIN (Allele-Specific Protein-RNA Interaction), that uses a joint analysis of CLIP-seq (cross-linking and immunoprecipitation followed by high-throughput sequencing) and RNA-seq data to identify genetic variants that alter RBP-RNA interactions by directly observing the allelic preference of RBP from CLIP-seq experiments as compared to RNA-seq. We used ASPRIN to systematically analyze CLIP-seq and RNA-seq data for 166 RBPs in two ENCODE (Encyclopedia of DNA Elements) cell lines. ASPRIN identified genetic variants that alter RBP-RNA interactions by modifying RBP binding motifs within RNA. Moreover, through an integrative ASPRIN analysis with population-scale RNA-seq data, we showed that ASPRIN can help reveal potential causal variants that affect alternative splicing via allele-specific protein-RNA interactions.

While state-of-the-art 3D Convolutional Neural Networks (CNN) achieve very good results on action recognition datasets, they are computationally very expensive and require many GFLOPs. While the GFLOPs of a 3D CNN can be decreased by reducing the temporal feature resolution within the network, there is no setting that is optimal for all input clips. In this work, we therefore introduce a differentiable Similarity Guided Sampling (SGS) module, which can be plugged into any existing 3D CNN architecture. SGS empowers 3D CNNs by learning the similarity of temporal features and grouping similar features together. As a result, the temporal feature resolution is not anymore static but it varies for each input video clip. By integrating SGS as an additional layer within current 3D CNNs, we can convert them into much more efficient 3D CNNs with adaptive temporal feature resolutions (ATFR). Our evaluations show that the proposed module improves the state-of-the-art by reducing the computational cost (GFLOPs) by half while preserving or even improving the accuracy. We evaluate our module by adding it to multiple state-of-the-art 3D CNNs on various datasets such as Kinetics-600, Kinetics-400, mini-Kinetics, Something-Something V2, UCF101, and HMDB51., CVPR 2021

Rbfox proteins control alternative splicing and posttranscriptional regulation in mammalian brain and are implicated in neurological disease. These proteins recognize the RNA sequence (U)GCAUG, but their structures and diverse roles imply a variety of protein-protein interactions. We find that nuclear Rbfox proteins are bound within a large assembly of splicing regulators (LASR), a multimeric complex containing the proteins hnRNP M, hnRNP H, hnRNP C, Matrin3, NF110/NFAR-2, NF45, and DDX5, all approximately equimolar to Rbfox. We show that splicing repression mediated by hnRNP M is stimulated by Rbfox. Virtually all the intron-bound Rbfox is associated with LASR, and hnRNP M motifs are enriched adjacent toRbfox crosslinking sites invivo. These findings demonstrate that Rbfox proteins bind RNA with a defined set of cofactors and affect a broader set ofexons than previously recognized. The function of this multimeric LASR complex has implications for deciphering the regulatory codes controlling splicing networks.

RNA-seq has become a popular technology for studying genetic variation of pre-mRNA alternative splicing. Commonly used RNA-seq aligners rely on the consensus splice site dinucleotide motifs to map reads across splice junctions. Consequently, genomic variants that create novel splice site dinucleotides may produce splice junction RNA-seq reads that cannot be mapped to the reference genome. We developed and evaluated an approach to identify 'hidden' splicing variations in personal transcriptomes, by mapping personal RNA-seq data to personal genomes. Computational analysis and experimental validation indicate that this approach identifies personal specific splice junctions at a low false positive rate. Applying this approach to an RNA-seq data set of 75 individuals, we identified 506 personal specific splice junctions, among which 437 were novel splice junctions not documented in current human transcript annotations. 94 splice junctions had splice site SNPs associated with GWAS signals of human traits and diseases. These involve genes whose splicing variations have been implicated in diseases (such as OAS1), as well as novel associations between alternative splicing and diseases (such as ICA1). Collectively, our work demonstrates that the personal genome approach to RNA-seq read alignment enables the discovery of a large but previously unknown catalog of splicing variations in human populations.

The conserved RNA-binding protein Musashi1 (MSI1) has emerged as a key oncogenic factor in numerous solid tumors, including glioblastoma. However, its mechanism of action has not yet been established comprehensively. To identify its target genes comprehensively and determine the main routes by which it influences glioblastoma phenotypes, we conducted individual-nucleotide resolution cross-linking and immunoprecipitation (iCLIP) experiments. We confirmed that MSI1 has a preference for UAG sequences contained in a particular structural context, especially in 3′ untranslated regions. Although numerous binding sites were also identified in intronic sequences, our RNA transcriptome sequencing analysis does not favor the idea that MSI1 is a major regulator of splicing in glioblastoma cells. MSI1 target mRNAs encode proteins that function in multiple pathways of cell proliferation and cell adhesion. Since these associations indicate potentially new roles for MSI1, we investigated its impact on glioblastoma cell adhesion, morphology, migration, and invasion. These processes are known to underpin the spread and relapse of glioblastoma, in contrast to other tumors where metastasis is the main driver of recurrence and progression.

Co- and post-transcriptional regulation of gene expression is complex and multifaceted, spanning the complete RNA lifecycle from genesis to decay. High-throughput profiling of the constituent events and processes is achieved through a range of technologies that continue to expand and evolve. Fully leveraging the resulting data is nontrivial, and requires the use of computational methods and tools carefully crafted for specific data sources and often intended to probe particular biological processes. Drawing upon databases of information pre-compiled by other researchers can further elevate analyses. Within this review, we describe the major co- and post-transcriptional events in the RNA lifecycle that are amenable to high-throughput profiling. We place specific emphasis on the analysis of the resulting data, in particular the computational tools and resources available, as well as looking toward future challenges that remain to be addressed.

High-throughput protein-RNA interaction data generated by CLIP-seq has provided an unprecedented depth of access to the activities of RNA-binding proteins (RBPs), the key players in co- and post-transcriptional regulation of gene expression. Motif discovery forms part of the necessary follow-up data analysis for CLIP-seq, both to refine the exact locations of RBP binding sites, and to characterize them. The specific properties of RBP binding sites, and the CLIP-seq methods, provide additional information not usually present in the classic motif discovery problem: the binding site structure, and cross-linking induced events in reads. We show that CLIP-seq data contains clear secondary structure signals, as well as technology- and RBP-specific cross-link signals. We introduce Zagros, a motif discovery algorithm specifically designed to leverage this information and explore its impact on the quality of recovered motifs. Our results indicate that using both secondary structure and cross-link modifications can greatly improve motif discovery on CLIP-seq data. Further, the motifs we recover provide insight into the balance between sequence- and structure-specificity struck by RBP binding.

RNA-seq is a powerful and popular technology for studying posttranscriptional regulation of gene expression, such as alternative splicing. The first step in analyzing RNA-seq data is to map the sequenced reads back to the genome. However, commonly used RNA-seq aligners use the consensus splice site dinucleotide motifs to map reads across splice junctions. This can be deceiving due to genomic variants that create novel splice site dinucleotides, leaving the personal splice junction reads un-mapped to the reference genome. We developed and evaluated a method called RNA Personal Genome Alignment Analyzer (rPGA) to identify "hidden" splicing variations in personal transcriptomes, by mapping personal RNA-seq data to personal genomes. Our work demonstrates that the personal genome approach to RNA-seq read alignment enables the discovery of a large but previously unknown catalog of splicing variations in human populations.

Nephron progenitor number determines nephron endowment; a reduced nephron count is linked to the onset of kidney disease. Several transcriptional regulators including Six2, Wt1, Osr1, Sall1, Eya1, Pax2, and Hox11 paralogues are required for specification and/or maintenance of nephron progenitors. However, little is known about the regulatory intersection of these players. Here, we have mapped nephron progenitor-specific transcriptional networks of Six2, Hoxd11, Osr1, and Wt1. We identified 373 multi-factor associated ‘regulatory hotspots’ around genes closely associated with progenitor programs. To examine their functional significance, we deleted ‘hotspot’ enhancer elements for Six2 and Wnt4. Removal of the distal enhancer for Six2 leads to a ~40% reduction in Six2 expression. When combined with a Six2 null allele, progeny display a premature depletion of nephron progenitors. Loss of the Wnt4 enhancer led to a significant reduction of Wnt4 expression in renal vesicles and a mildly hypoplastic kidney, a phenotype also enhanced in combination with a Wnt4 null mutation. To explore the regulatory landscape that supports proper target gene expression, we performed CTCF ChIP-seq to identify insulator-boundary regions. One such putative boundary lies between the Six2 and Six3 loci. Evidence for the functional significance of this boundary was obtained by deep sequencing of the radiation-induced Brachyrrhine (Br) mutant allele. We identified an inversion of the Six2/Six3 locus around the CTCF-bound boundary, removing Six2 from its distal enhancer regulation, but placed next to Six3 enhancer elements which support ectopic Six2 expression in the lens where Six3 is normally expressed. Six3 is now predicted to fall under control of the Six2 distal enhancer. Consistent with this view, we observed ectopic Six3 in nephron progenitors. 4C-seq supports the model for Six2 distal enhancer interactions in wild-type and Br/+ mouse kidneys. Together, these data expand our view of the regulatory genome and regulatory landscape underpinning mammalian nephrogenesis., Author summary Nephrons, the filtering units of the kidney, derive from nephron progenitors. Deficiencies in nephron number increases the risk of kidney disease. An understanding of the regulatory programs governing progenitor actions has important translational potential. Several transcription factors regulate the nephron progenitor population. However, their target interactions are largely unknown. Here, we mapped and intersected the genome-wide binding sites for four such factors in mouse nephron progenitor cells in the developing kidney: Six2, Hoxd11, Osr1, and Wt1. The intersectional data highlight a high-value set of putative enhancer elements linked to genes regulating nephron progenitor properties. We validate the function of two such enhancer elements regulating the levels of Six2, a key transcriptional regulatory factor in nephron progenitor maintenance, and Wnt4, a critical signaling factor controlling the mesenchyme to epithelial transition of induced nephron progenitors. Further characterization of the Six2 regulatory landscape identified higher order regulatory interactions that ensure appropriate enhancer-promoter specificity. CTCF-bound sites between Six2 and the adjacent Six3 locus likely act as boundary elements to define topological interactions domains separating enhancer elements thereby providing distinct tissue specificity to each gene’s expression. An inversion of this region in the Brachyrrhine (Br) mutant mouse reverses Six2 and Six3 expression domains, placing Six3 under control of the Six2 enhancer element above resulting in kidney-specific expression, while Six2 expression shifts to the lens, a normal expression domain for Six3. Together, these data expand our view of the regulatory genome and regulatory landscape underpinning mammalian nephrogenesis.

RNA-seq is a powerful and popular technology for studying posttranscriptional regulation of gene expression, such as alternative splicing. The first step in analyzing RNA-seq data is to map the sequenced reads back to the genome. However, commonly used RNA-seq aligners use the consensus splice site dinucleotide motifs to map reads across splice junctions. This can be deceiving due to genomic variants that create novel splice site dinucleotides, leaving the personal splice junction reads un-mapped to the reference genome. We developed and evaluated a method called RNA Personal Genome Alignment Analyzer (rPGA) to identify "hidden" splicing variations in personal transcriptomes, by mapping personal RNA-seq data to personal genomes. Our work demonstrates that the personal genome approach to RNA-seq read alignment enables the discovery of a large but previously unknown catalog of splicing variations in human populations.

Alternative splicing (AS) is a robust generator of mammalian transcriptome complexity. Splice site specification is controlled by interactions of cis-acting determinants on a transcript with specific RNA binding proteins. These interactions are frequently localized to the intronic U-rich polypyrimidine tracts (PPT) located 5' to the majority of splice acceptor junctions. αCPs (also referred to as polyC-binding proteins (PCBPs) and hnRNPEs) comprise a subset of KH-domain proteins with high affinity and specificity for C-rich polypyrimidine motifs. Here, we demonstrate that αCPs promote the splicing of a defined subset of cassette exons via binding to a C-rich subset of polypyrimidine tracts located 5' to the αCP-enhanced exonic segments. This enhancement of splice acceptor activity is linked to interactions of αCPs with the U2 snRNP complex and may be mediated by cooperative interactions with the canonical polypyrimidine tract binding protein, U2AF65. Analysis of αCP-targeted exons predicts a substantial impact on fundamental cell functions. These findings lead us to conclude that the αCPs play a direct and global role in modulating the splicing activity and inclusion of an array of cassette exons, thus driving a novel pathway of splice site regulation within the mammalian transcriptome.

hnRNPs are polyvalent RNA binding proteins that have been implicated in a range of regulatory roles including splicing, mRNA decay, translation, and miRNA metabolism. A variety of genome wide studies have taken advantage of methods like CLIP and RIP to identify the targets and binding sites of RNA binding proteins. However, due to the complex nature of RNA-binding proteins, these studies are incomplete without assays that characterize the impact of RBP binding on mRNA target expression. Here we used a suite of high-throughput approaches (RIP-Seq, iCLIP, RNA-Seq and shotgun proteomics) to provide a comprehensive view of hnRNP H1s ensemble of targets and its role in splicing, mRNA decay, and translation. The combination of RIP-Seq and iCLIP allowed us to identify a set of 1,086 high confidence target transcripts. Binding site motif analysis of these targets suggests the TGGG tetramer as a prevalent component of hnRNP H1 binding motif, with particular enrichment around intronic hnRNP H1 sites. Our analysis of the target transcripts and binding sites indicates that hnRNP H1s involvement in splicing is 2-fold: it directly affects a substantial number of splicing events, but also regulates the expression of major components of the splicing machinery and other RBPs with known roles in splicing regulation. The identified mRNA targets displayed function enrichment in MAPK signaling and ubiquitin mediated proteolysis, which might be main routes by which hnRNP H1 promotes tumorigenesis.

Co- and post-transcriptional regulation of gene expression is complex and multi-faceted, spanning the complete RNA lifecycle from genesis to decay. High-throughput profiling of the constituent events and processes is achieved through a range of technologies that continue to expand and evolve. Fully leveraging the resulting data is non-trivial, and requires the use of computational methods and tools carefully crafted for specific data sources and often intended to probe particular biological processes. Drawing upon databases of information pre-compiled by other researchers can further elevate analyses. Within this review, we describe the major co- and post-transcriptional events in the RNA lifecycle that are amenable to high-throughput profiling. We place specific emphasis on the analysis of the resulting data, in particular the computational tools and resources available, as well as looking towards future challenges that remain to be addressed.

Motivation: Post-transcriptional and co-transcriptional regulation is a crucial link between genotype and phenotype. The central players are the RNA-binding proteins, and experimental technologies [such as cross-linking with immunoprecipitation-(CLIP-) and RIP-seq] for probing their activities have advanced rapidly over the course of the past decade. Statistically robust, flexible computational methods for binding site identification from high-throughput immunoprecipitation assays are largely lacking however. Results: We introduce a method for site identification which provides four key advantages over previous methods: (i) it can be applied on all variations of CLIP and RIP-seq technologies, (ii) it accurately models the underlying read-count distributions, (iii) it allows external covariates, such as transcript abundance (which we demonstrate is highly correlated with read count) to inform the site identification process and (iv) it allows for direct comparison of site usage across cell types or conditions. Availability and implementation: We have implemented our method in a software tool called Piranha. Source code and binaries, licensed under the GNU General Public License (version 3) are freely available for download from http://smithlab.usc.edu. Contact: [email protected] Supplementary information:Supplementary data available at Bioinformatics online.