Search

RNA sequencing (RNA-seq) is widely adopted for transcriptome analysis but has inherent biases that hinder the comprehensive detection and quantification of alternative splicing. To address this, we present an efficient targeted RNA-seq method that greatly enriches for splicing-informative junction-spanning reads. Local splicing variation sequencing (LSV-seq) utilizes multiplexed reverse transcription from highly scalable pools of primers anchored near splicing events of interest. Primers are designed using Optimal Prime, a novel machine learning algorithm trained on the performance of thousands of primer sequences. In experimental benchmarks, LSV-seq achieves high on-target capture rates and concordance with RNA-seq, while requiring significantly lower sequencing depth. Leveraging deep learning splicing code predictions, we used LSV-seq to target events with low coverage in GTEx RNA-seq data and newly discover hundreds of tissue-specific splicing events. Our results demonstrate the ability of LSV-seq to quantify splicing of events of interest at high-throughput and with exceptional sensitivity., (© The Author(s) 2024. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Relapsed or refractory B-cell acute lymphoblastic leukemia (B-ALL) is a major cause of pediatric cancer-related deaths. Relapse-specific mutations do not account for all chemotherapy failures in B-ALL patients, suggesting additional mechanisms of resistance. By mining RNA sequencing datasets of paired diagnostic/relapse pediatric B-ALL samples, we discovered pervasive alternative splicing (AS) patterns linked to relapse and affecting drivers of resistance to glucocorticoids, antifolates, and thiopurines. Most splicing variations represented cassette exon skipping, "poison" exon inclusion, and intron retention, phenocopying well-documented loss-of-function mutations. In contrast, relapse-associated AS of NT5C2 mRNA yielded an isoform with the functionally uncharacterized in-frame exon 6a. Incorporation of the 8-amino acid sequence SQVAVQKR into this enzyme created a putative phosphorylation site and resulted in elevated nucleosidase activity, which is a known consequence of gain-of-function mutations in NT5C2 and a common determinant of 6-mercaptopurine resistance. Consistent with this finding, NT5C2ex6a and the R238W hotspot variant conferred comparable levels of resistance to 6-mercaptopurine in B-ALL cells both in vitro and in vivo. Furthermore, both NT5C2ex6a and the R238W variant induced collateral sensitivity to the inosine monophosphate dehydrogenase inhibitor mizoribine. These results ascribe to splicing perturbations an important role in chemotherapy resistance in relapsed B-ALL and suggest that inosine monophosphate dehydrogenase inhibitors, including the commonly used immunosuppressive agent mycophenolate mofetil, could be a valuable therapeutic option for treating thiopurine-resistant leukemias. Significance: Alternative splicing is a potent mechanism of acquired drug resistance in relapsed/refractory acute lymphoblastic leukemias that has diagnostic and therapeutic implications for patients who lack mutations in known chemoresistance genes., (©2024 The Authors; Published by the American Association for Cancer Research.)

Quantification of RNA splicing variations based on RNA-Sequencing can reveal tissue- and disease-specific splicing patterns. To study such splicing variations, we introduce MAJIQlopedia, an encyclopedia of splicing variations that encompasses 86 human tissues and 41 cancer datasets. MAJIQlopedia reports annotated and unannotated splicing events for a total of 486 175 alternative splice junctions in normal tissues and 338 317 alternative splice junctions in cancer. This database, available at https://majiq.biociphers.org/majiqlopedia/, includes a user-friendly interface that provides graphical representations of junction usage quantification for each junction across all tissue or cancer types. To demonstrate case usage of MAJIQlopedia, we review splicing variations in genes WT1, MAPT and BIN1, which all have known tissue or cancer-specific splicing variations. We also use MAJIQlopedia to highlight novel splicing variations in FDX1 and MEGF9 in normal tissues, and we uncover a novel exon inclusion event in RPS6KA6 that only occurs in two cancer types. Users can download the database, request the addition of data to the webtool, or install a MAJIQlopedia server to integrate proprietary data. MAJIQlopedia can serve as a reference database for researchers seeking to understand what splicing variations exist in genes of interest, and those looking to understand tissue- or cancer-specific splice isoform usage., (© The Author(s) 2023. Published by Oxford University Press on behalf of Nucleic Acids Research.)

We performed genome-wide association study meta-analysis to identify genetic determinants of skeletal age (SA) deviating in multiple growth disorders. The joint meta-analysis (N = 4557) in two multiethnic cohorts of school-aged children identified one locus, CYP11B1 (expression confined to the adrenal gland), robustly associated with SA (rs6471570-A; β = 0.14; P = 6.2 × 10 -12 ). rs6410 (a synonymous variant in the first exon of CYP11B1 in high LD with rs6471570), was prioritized for functional follow-up being second most significant and the one closest to the first intron-exon boundary. In 208 adrenal RNA-seq samples from GTEx, C-allele of rs6410 was associated with intron 3 retention (P = 8.11 × 10 -40 ), exon 4 inclusion (P = 4.29 × 10 -34 ), and decreased exon 3 and 5 splicing (P = 7.85 × 10 -43 ), replicated using RT-PCR in 15 adrenal samples. As CYP11B1 encodes 11-β-hydroxylase, involved in adrenal glucocorticoid and mineralocorticoid biosynthesis, our findings highlight the role of adrenal steroidogenesis in SA in healthy children, suggesting alternative splicing as a likely underlying mechanism., (© 2021. The Author(s).)

Resistance to CD19-directed immunotherapies in lymphoblastic leukemia has been attributed, among other factors, to several aberrant CD19 pre-mRNA splicing events, including recently reported excision of a cryptic intron embedded within CD19 exon 2. While "exitrons" are known to exist in hundreds of human transcripts, we discovered, using reporter assays and direct long-read RNA sequencing (dRNA-seq), that the CD19 exitron is an artifact of reverse transcription. Extending our analysis to publicly available datasets, we identified dozens of questionable exitrons, dubbed "falsitrons," that appear only in cDNA-seq, but never in dRNA-seq. Our results highlight the importance of dRNA-seq for transcript isoform validation.

Most genes associated with acute myeloid leukemia (AML) are mutated in less than 10% of patients, suggesting that alternative mechanisms of gene disruption contribute to this disease. Here, we find a set of splicing events that alter the expression of a subset of AML-associated genes independent of known somatic mutations. In particular, aberrant splicing triples the number of patients with reduced functional EZH2 compared with that predicted by somatic mutation alone. In addition, we unexpectedly find that the nonsense-mediated decay factor DHX34 exhibits widespread alternative splicing in sporadic AML, resulting in a premature stop codon that phenocopies the loss-of-function germline mutations observed in familial AML. Together, these results demonstrate that classical mutation analysis underestimates the burden of functional gene disruption in AML and highlight the importance of assessing the contribution of alternative splicing to gene dysregulation in human disease., Competing Interests: The authors declare no competing interest.

Purpose: RNA-seq is a promising approach to improve diagnoses by detecting pathogenic aberrations in RNA splicing that are missed by DNA sequencing. RNA-seq is typically performed on clinically accessible tissues (CATs) from blood and skin. RNA tissue specificity makes it difficult to identify aberrations in relevant but nonaccessible tissues (non-CATs). We determined how RNA-seq from CATs represent splicing in and across genes and non-CATs., Methods: We quantified RNA splicing in 801 RNA-seq samples from 56 different adult and fetal tissues from Genotype-Tissue Expression Project (GTEx) and ArrayExpress. We identified genes and splicing events in each non-CAT and determined when RNA-seq in each CAT would inadequately represent them. We developed an online resource, MAJIQ-CAT, for exploring our analysis for specific genes and tissues., Results: In non-CATs, 40.2% of genes have splicing that is inadequately represented by at least one CAT; 6.3% of genes have splicing inadequately represented by all CATs. A majority (52.1%) of inadequately represented genes are lowly expressed in CATs (transcripts per million (TPM) < 1), but 5.8% are inadequately represented despite being well expressed (TPM > 10)., Conclusion: Many splicing events in non-CATs are inadequately evaluated using RNA-seq from CATs. MAJIQ-CAT allows users to explore which accessible tissues, if any, best represent splicing in genes and tissues of interest.

Aberrant splicing is a hallmark of leukemias with mutations in splicing factor (SF)-encoding genes. Here we investigated its prevalence in pediatric B-cell acute lymphoblastic leukemias (B-ALL), where SFs are not mutated. By comparing these samples to normal pro-B cells, we found thousands of aberrant local splice variations (LSVs) per sample, with 279 LSVs in 241 genes present in every comparison. These genes were enriched in RNA processing pathways and encoded ∼100 SFs, e.g. hnRNPA1. HNRNPA1 3'UTR was most pervasively mis-spliced, yielding the transcript subject to nonsense-mediated decay. To mimic this event, we knocked it down in B-lymphoblastoid cells and identified 213 hnRNPA1-regulated exon usage events comprising the hnRNPA1 splicing signature in pediatric leukemia. Some of its elements were LSVs in DICER1 and NT5C2, known cancer drivers. We searched for LSVs in other leukemia and lymphoma drivers and discovered 81 LSVs in 41 additional genes. Seventy-seven LSVs out of 81 were confirmed using two large independent B-ALL RNA-seq datasets, and the twenty most common B-ALL drivers, including NT5C2, showed higher prevalence of aberrant splicing than of somatic mutations. Thus, post-transcriptional deregulation of SF can drive widespread changes in B-ALL splicing and likely contributes to disease pathogenesis.

Motivation: A key component in many RNA-Seq-based studies is contrasting multiple replicates from different experimental conditions. In this setup, replicates play a key role as they allow to capture underlying biological variability inherent to the compared conditions, as well as experimental variability. However, what constitutes a 'bad' replicate is not necessarily well defined. Consequently, researchers might discard valuable data or downstream analysis may be hampered by failed experiments., Results: Here we develop a probability model to weigh a given RNA-Seq sample as a representative of an experimental condition when performing alternative splicing analysis. We demonstrate that this model detects outlier samples which are consistently and significantly different compared with other samples from the same condition. Moreover, we show that instead of discarding such samples the proposed weighting scheme can be used to downweight samples and specific splicing variations suspected as outliers, gaining statistical power. These weights can then be used for differential splicing (DS) analysis, where the resulting algorithm offers a generalization of the MAJIQ algorithm. Using both synthetic and real-life data, we perform an extensive evaluation of the improved MAJIQ algorithm in different scenarios involving perturbed samples, mislabeled samples, same condition groups, and different levels of coverage, showing it compares favorably to other tools. Overall, this work offers an outlier detection algorithm that can be combined with any splicing pipeline, a generalized and improved version of MAJIQ for DS detection, and evaluation metrics with matching code and data for DS algorithms., Availability and Implementation: Software and data are accessible via majiq.biociphers.org/norton&#95;et&#95;al&#95;2017/., Contact: yosephb@upenn.edu., Supplementary Information: Supplementary data are available at Bioinformatics online.

Alternative splicing contributes to gene expression dynamics in many tissues, yet its role in auditory development remains unclear. We performed whole-exome sequencing in individuals with sensorineural hearing loss (SNHL) and identified pathogenic mutations in Epithelial Splicing-Regulatory Protein 1 (ESRP1). Patient-derived induced pluripotent stem cells showed alternative splicing defects that were restored upon repair of an ESRP1 mutant allele. To determine how ESRP1 mutations cause hearing loss, we evaluated Esrp1 -/- mouse embryos and uncovered alterations in cochlear morphogenesis, auditory hair cell differentiation, and cell fate specification. Transcriptome analysis revealed impaired expression and splicing of genes with essential roles in cochlea development and auditory function. Aberrant splicing of Fgfr2 blocked stria vascularis formation due to erroneous ligand usage, which was corrected by reducing Fgf9 gene dosage. These findings implicate mutations in ESRP1 as a cause of SNHL and demonstrate the complex interplay between alternative splicing, inner ear development, and auditory function., (Copyright © 2017 Elsevier Inc. All rights reserved.)

Motivation: Advancements in sequencing technologies have highlighted the role of alternative splicing (AS) in increasing transcriptome complexity. This role of AS, combined with the relation of aberrant splicing to malignant states, motivated two streams of research, experimental and computational. The first involves a myriad of techniques such as RNA-Seq and CLIP-Seq to identify splicing regulators and their putative targets. The second involves probabilistic models, also known as splicing codes, which infer regulatory mechanisms and predict splicing outcome directly from genomic sequence. To date, these models have utilized only expression data. In this work, we address two related challenges: Can we improve on previous models for AS outcome prediction and can we integrate additional sources of data to improve predictions for AS regulatory factors., Results: We perform a detailed comparison of two previous modeling approaches, Bayesian and Deep Neural networks, dissecting the confounding effects of datasets and target functions. We then develop a new target function for AS prediction in exon skipping events and show it significantly improves model accuracy. Next, we develop a modeling framework that leverages transfer learning to incorporate CLIP-Seq, knockdown and over expression experiments, which are inherently noisy and suffer from missing values. Using several datasets involving key splice factors in mouse brain, muscle and heart we demonstrate both the prediction improvements and biological insights offered by our new models. Overall, the framework we propose offers a scalable integrative solution to improve splicing code modeling as vast amounts of relevant genomic data become available., Availability and Implementation: Code and data available at: majiq.biociphers.org/jha&#95;et&#95;al&#95;2017/., Contact: yosephb@upenn.edu., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com)

The brain is made up of trillions of synaptic connections that together form neural networks needed for normal brain function and behavior. SLM2 is a member of a conserved family of RNA binding proteins, including Sam68 and SLM1, that control splicing of Neurexin1-3 pre-mRNAs. Whether SLM2 affects neural network activity is unknown. Here, we find that SLM2 levels are maintained by a homeostatic feedback control pathway that predates the divergence of SLM2 and Sam68. SLM2 also controls the splicing of Tomosyn2, LysoPLD/ATX, Dgkb, Kif21a, and Cask, each of which are important for synapse function. Cortical neural network activity dependent on synaptic connections between SLM2-expressing-pyramidal neurons and interneurons is decreased in Slm2-null mice. Additionally, these mice are anxious and have a decreased ability to recognize novel objects. Our data reveal a pathway of SLM2 homeostatic auto-regulation controlling brain network activity and behavior., (Copyright © 2016 The Author(s). Published by Elsevier Inc. All rights reserved.)

Alternative splicing (AS) can critically affect gene function and disease, yet mapping splicing variations remains a challenge. Here, we propose a new approach to define and quantify mRNA splicing in units of local splicing variations (LSVs). LSVs capture previously defined types of alternative splicing as well as more complex transcript variations. Building the first genome wide map of LSVs from twelve mouse tissues, we find complex LSVs constitute over 30% of tissue dependent transcript variations and affect specific protein families. We show the prevalence of complex LSVs is conserved in humans and identify hundreds of LSVs that are specific to brain subregions or altered in Alzheimer's patients. Amongst those are novel isoforms in the Camk2 family and a novel poison exon in Ptbp1, a key splice factor in neurogenesis. We anticipate the approach presented here will advance the ability to relate tissue-specific splice variation to genetic variation, phenotype, and disease.

Unlabelled: The CD19 antigen, expressed on most B-cell acute lymphoblastic leukemias (B-ALL), can be targeted with chimeric antigen receptor-armed T cells (CART-19), but relapses with epitope loss occur in 10% to 20% of pediatric responders. We detected hemizygous deletions spanning the CD19 locus and de novo frameshift and missense mutations in exon 2 of CD19 in some relapse samples. However, we also discovered alternatively spliced CD19 mRNA species, including one lacking exon 2. Pull-down/siRNA experiments identified SRSF3 as a splicing factor involved in exon 2 retention, and its levels were lower in relapsed B-ALL. Using genome editing, we demonstrated that exon 2 skipping bypasses exon 2 mutations in B-ALL cells and allows expression of the N-terminally truncated CD19 variant, which fails to trigger killing by CART-19 but partly rescues defects associated with CD19 loss. Thus, this mechanism of resistance is based on a combination of deleterious mutations and ensuing selection for alternatively spliced RNA isoforms., Significance: CART-19 yield 70% response rates in patients with B-ALL, but also produce escape variants. We discovered that the underlying mechanism is the selection for preexisting alternatively spliced CD19 isoforms with the compromised CART-19 epitope. This mechanism suggests a possibility of targeting alternative CD19 ectodomains, which could improve survival of patients with B-cell neoplasms., (©2015 American Association for Cancer Research.)

Alternative splicing is prevalent among genes encoding signaling molecules; however, the functional consequence of differential isoform expression remains largely unknown. Here we demonstrate that, in response to T-cell activation, the Jun kinase (JNK) kinase MAP kinase kinase 7 (MKK7) is alternatively spliced to favor an isoform that lacks exon 2. This isoform restores a JNK-docking site within MKK7 that is disrupted in the larger isoform. Consistently, we show that skipping of MKK7 exon 2 enhances JNK pathway activity, as indicated by c-Jun phosphorylation and up-regulation of TNF-α. Moreover, this splicing event is itself dependent on JNK signaling. Thus, MKK7 alternative splicing represents a positive feedback loop through which JNK promotes its own signaling. We further show that repression of MKK7 exon 2 is dependent on the presence of flanking sequences and the JNK-induced expression of the RNA-binding protein CELF2, which binds to these regulatory elements. Finally, we found that ∼25% of T-cell receptor-mediated alternative splicing events are dependent on JNK signaling. Strikingly, these JNK-dependent events are also significantly enriched for responsiveness to CELF2. Together, our data demonstrate a widespread role for the JNK-CELF2 axis in controlling splicing during T-cell activation, including a specific role in propagating JNK signaling., (© 2015 Martinez et al.; Published by Cold Spring Harbor Laboratory Press.)

Alternative splicing of pre-mRNA is a complex process whose outcome depends on elements reviewed in the previous chapters such as the core spliceosome units, how the core spliceosome units interact between themselves and with other splicing enhancers and repressors, primary sequence motifs, and local RNA secondary structure. Connections between RNA splicing, transcription, and other processes have also been reviewed in the previous chapters. Splicing is inherently a stochastic process: Some defective transcripts are produced and handled by mechanisms such as nonsense-mediated decay (NMD), and studies report high variability at the transcript level between cells supposedly in similar states. Nonetheless, splicing is obviously not a random process: Many determinants of splicing regulation have been identified, and experimental measurements detect highly robust and conserved splicing changes between developmental stages and tissues. These observations naturally lead to the following questions: Can we devise a method that predicts given a cellular context and the primary transcript what would be the splicing outcome? What can such a method tell us about the underlying mechanisms that govern alternative splicing?This chapter describes how these questions can be framed and addressed using machine-learning methodology. We describe how to extract putative RNA regulatory features from genomic sequence of exons and proximal introns, how to define target values based on experimental measurements of exon inclusion, how to learn a simple splicing model that optimizes the prediction the observed exon inclusion levels from the identified RNA features, and how to subsequently evaluate the learned model accuracy.

Transcriptome complexity and its relation to numerous diseases underpins the need to predict in silico splice variants and the regulatory elements that affect them. Building upon our recently described splicing code, we developed AVISPA, a Galaxy-based web tool for splicing prediction and analysis. Given an exon and its proximal sequence, the tool predicts whether the exon is alternatively spliced, displays tissue-dependent splicing patterns, and whether it has associated regulatory elements. We assess AVISPA's accuracy on an independent dataset of tissue-dependent exons, and illustrate how the tool can be applied to analyze a gene of interest. AVISPA is available at http://avispa.biociphers.org.

Motivation: Alternative splicing is a major contributor to cellular diversity in mammalian tissues and relates to many human diseases. An important goal in understanding this phenomenon is to infer a 'splicing code' that predicts how splicing is regulated in different cell types by features derived from RNA, DNA and epigenetic modifiers., Methods: We formulate the assembly of a splicing code as a problem of statistical inference and introduce a Bayesian method that uses an adaptively selected number of hidden variables to combine subgroups of features into a network, allows different tissues to share feature subgroups and uses a Gibbs sampler to hedge predictions and ascertain the statistical significance of identified features., Results: Using data for 3665 cassette exons, 1014 RNA features and 4 tissue types derived from 27 mouse tissues (http://genes.toronto.edu/wasp), we benchmarked several methods. Our method outperforms all others, and achieves relative improvements of 52% in splicing code quality and up to 22% in classification error, compared with the state of the art. Novel combinations of regulatory features and novel combinations of tissues that share feature subgroups were identified using our method., Contact: frey@psi.toronto.edu, Supplementary Information: Supplementary data are available at Bioinformatics online.

Motivation: Transcripts from approximately 95% of human multi-exon genes are subject to alternative splicing (AS). The growing interest in AS is propelled by its prominent contribution to transcriptome and proteome complexity and the role of aberrant AS in numerous diseases. Recent technological advances enable thousands of exons to be simultaneously profiled across diverse cell types and cellular conditions, but require accurate identification of condition-specific splicing changes. It is necessary to accurately identify such splicing changes to elucidate the underlying regulatory programs or link the splicing changes to specific diseases., Results: We present a probabilistic model tailored for high-throughput AS data, where observed isoform levels are explained as combinations of condition-specific AS signals. According to our formulation, given an AS dataset our tasks are to detect common signals in the data and identify the exons relevant to each signal. Our model can incorporate prior knowledge about underlying AS signals, measurement quality and gene expression level effects. Using a large-scale multi-tissue AS dataset, we demonstrate the advantage of our method over standard alternative approaches. In addition, we describe newly found tissue-specific AS signals which were verified experimentally, and discuss associated regulatory features., Supplementary Information: Supplementary data are available at Bioinformatics online.

Alternative splicing has a crucial role in the generation of biological complexity, and its misregulation is often involved in human disease. Here we describe the assembly of a 'splicing code', which uses combinations of hundreds of RNA features to predict tissue-dependent changes in alternative splicing for thousands of exons. The code determines new classes of splicing patterns, identifies distinct regulatory programs in different tissues, and identifies mutation-verified regulatory sequences. Widespread regulatory strategies are revealed, including the use of unexpectedly large combinations of features, the establishment of low exon inclusion levels that are overcome by features in specific tissues, the appearance of features deeper into introns than previously appreciated, and the modulation of splice variant levels by transcript structure characteristics. The code detected a class of exons whose inclusion silences expression in adult tissues by activating nonsense-mediated messenger RNA decay, but whose exclusion promotes expression during embryogenesis. The code facilitates the discovery and detailed characterization of regulated alternative splicing events on a genome-wide scale.

To identify human intronic sequences associated with 5' splice site recognition, we performed a systematic search for motifs enriched in introns downstream of both constitutive and alternative cassette exons. Significant enrichment was observed for U-rich motifs within 100 nucleotides downstream of 5' splice sites of both classes of exons, with the highest enrichment between positions +6 and +30. Exons adjacent to U-rich intronic motifs contain lower frequencies of exonic splicing enhancers and higher frequencies of exonic splicing silencers, compared with exons not followed by U-rich intronic motifs. These findings motivated us to explore the possibility of a widespread role for U-rich motifs in promoting exon inclusion. Since cytotoxic granule-associated RNA binding protein (TIA1) and TIA1-like 1 (TIAL1; also known as TIAR) were previously shown in vitro to bind to U-rich motifs downstream of 5' splice sites, and to facilitate 5' splice site recognition in vitro and in vivo, we investigated whether these factors function more generally in the regulation of splicing of exons followed by U-rich intronic motifs. Simultaneous knockdown of TIA1 and TIAL1 resulted in increased skipping of 36/41 (88%) of alternatively spliced exons associated with U-rich motifs, but did not affect 32/33 (97%) alternatively spliced exons that are not associated with U-rich motifs. The increase in exon skipping correlated with the proximity of the first U-rich motif and the overall "U-richness" of the adjacent intronic region. The majority of the alternative splicing events regulated by TIA1/TIAL1 are conserved in mouse, and the corresponding genes are associated with diverse cellular functions. Based on our results, we estimate that approximately 15% of alternative cassette exons are regulated by TIA1/TIAL1 via U-rich intronic elements.

Background: Alternative splicing (AS) functions to expand proteomic complexity and plays numerous important roles in gene regulation. However, the extent to which AS coordinates functions in a cell and tissue type specific manner is not known. Moreover, the sequence code that underlies cell and tissue type specific regulation of AS is poorly understood., Results: Using quantitative AS microarray profiling, we have identified a large number of widely expressed mouse genes that contain single or coordinated pairs of alternative exons that are spliced in a tissue regulated fashion. The majority of these AS events display differential regulation in central nervous system (CNS) tissues. Approximately half of the corresponding genes have neural specific functions and operate in common processes and interconnected pathways. Differential regulation of AS in the CNS tissues correlates strongly with a set of mostly new motifs that are predominantly located in the intron and constitutive exon sequences neighboring CNS-regulated alternative exons. Different subsets of these motifs are correlated with either increased inclusion or increased exclusion of alternative exons in CNS tissues, relative to the other profiled tissues., Conclusion: Our findings provide new evidence that specific cellular processes in the mammalian CNS are coordinated at the level of AS, and that a complex splicing code underlies CNS specific AS regulation. This code appears to comprise many new motifs, some of which are located in the constitutive exons neighboring regulated alternative exons. These data provide a basis for understanding the molecular mechanisms by which the tissue specific functions of widely expressed genes are coordinated at the level of AS.