Inferring global levels of alternative splicing isoforms using a generative model of microarray data

Non-canonical splice junction processing increases the diversity of RBFOX2 splicing isoforms

The underlying mechanisms of splicing regulation through non-canonical splice junction processing remain largely unknown. Here, we identified two RBFOX2 splicing isoforms by alternative 3' splice site selection of exon 9; the non-canonical splice junction processed RBFOX2 transcript (RBFOX2-N.C.) was expressed by the selection of the 3' splice GG acceptor sequence. The cytoplasmic localization of RBFOX2-C., a canonical splice junction-processed RBFOX2 transcript, was different from that of RBFOX2-N.C., which showed nuclear localization. In addition, we confirmed that RBFOX2-C. showed a significantly stronger localization into stress granules than RBFOX2-N.C. upon sodium arsenite treatment. Next, we investigated the importance of non-canonical 3' splice GG sequence selection of specific cis-regulatory elements using minigene constructs of the RBFOX2 gene. We found that the non-canonical 3' splice GG sequence and suboptimal branch point site adjacent region were critical for RBFOX2-N.C. expression through a non-canonical 3' splice selection. Our results suggest a regulatory mechanism for the non-canonical 3' splice selection in the RBFOX2 gene, providing a basis for studies related to the regulation of alternative pre-mRNA splicing through non-canonical splice junction processing.

Expression of a human cDNA in moss results in spliced mRNAs and fragmentary protein isoforms

Production of biopharmaceuticals relies on the expression of mammalian cDNAs in host organisms. Here we show that the expression of a human cDNA in the moss Physcomitrium patens generates the expected full-length and four additional transcripts due to unexpected splicing. This mRNA splicing results in non-functional protein isoforms, cellular misallocation of the proteins and low product yields. We integrated these results together with the results of our analysis of all 32,926 protein-encoding Physcomitrella genes and their 87,533 annotated transcripts in a web application, physCO, for automatized optimization. A thus optimized cDNA results in about twelve times more protein, which correctly localizes to the ER. An analysis of codon preferences of different production hosts suggests that similar effects occur also in non-plant hosts. We anticipate that the use of our methodology will prevent so far undetected mRNA heterosplicing resulting in maximized functional protein amounts for basic biology and biotechnology.

Experimental and Computational Considerations in the Study of RNA-Binding Protein-RNA Interactions

After an RNA is transcribed, it undergoes a variety of processing steps that can change the encoded protein sequence (through alternative splicing and RNA editing), regulate the stability of the RNA, and control subcellular localization, timing, and rate of translation. The recent explosion in genomics techniques has enabled transcriptome-wide profiling of RNA processing in an unbiased manner. However, it has also brought with it both experimental challenges in developing improved methods to probe distinct processing steps, as well as computational challenges in data storage, processing, and analysis tools to enable large-scale interpretation in the genomics era. In this chapter we review experimental techniques and challenges in profiling various aspects of RNA processing, as well as recent efforts to develop analyses integrating multiple data sources and techniques to infer RNA regulatory networks.

Alternative splicing: An important mechanism in stem cell biology

Alternative splicing (AS) is an essential mechanism in post-transcriptional regulation and leads to protein diversity. It has been shown that AS is prevalent in metazoan genomes, and the splicing pattern is dynamically regulated in different tissues and cell types, including embryonic stem cells. These observations suggest that AS may play critical roles in stem cell biology. Since embryonic stem cells and induced pluripotent stem cells have the ability to give rise to all types of cells and tissues, they hold the promise of future cell-based therapy. Many efforts have been devoted to understanding the mechanisms underlying stem cell self-renewal and differentiation. However, most of the studies focused on the expression of a core set of transcription factors and regulatory RNAs. The role of AS in stem cell differentiation was not clear. Recent advances in high-throughput technologies have allowed the profiling of dynamic splicing patterns and cis-motifs that are responsible for AS at a genome-wide scale, and provided novel insights in a number of studies. In this review, we discuss some recent findings involving AS and stem cells. An emerging picture from these findings is that AS is integrated in the transcriptional and post-transcriptional networks and together they control pluripotency maintenance and differentiation of stem cells.

Splicing code modeling

How do cis and trans elements involved in pre-mRNA splicing come together to form a splicing "code"? This question has been a driver of much of the research involving RNA biogenesis. The variability of splicing outcome across developmental stages and between tissues coupled with association of splicing defects with numerous diseases highlights the importance of such a code. However, the sheer number of elements involved in splicing regulation and the context-specific manner of their operation have made the derivation of such a code challenging. Recently, machine learning-based methods have been developed to infer computational models for a splicing code. These methods use high-throughput experiments measuring mRNA expression at exonic resolution and binding locations of RNA-binding proteins (RBPs) to infer what the regulatory elements that control the inclusion of a given pre-mRNA segment are. The inferred regulatory models can then be applied to genomic sequences or experimental conditions that have not been measured to predict splicing outcome. Moreover, the models themselves can be interrogated to identify new regulatory mechanisms, which can be subsequently tested experimentally. In this chapter, we survey the current state of this technology, and illustrate how it can be applied by non-computational or RNA splicing experts to study regulation of specific exons by using the AVISPA web tool.

MITIE: Simultaneous RNA-Seq-based transcript identification and quantification in multiple samples

MOTIVATION

High-throughput sequencing of mRNA (RNA-Seq) has led to tremendous improvements in the detection of expressed genes and reconstruction of RNA transcripts. However, the extensive dynamic range of gene expression, technical limitations and biases, as well as the observed complexity of the transcriptional landscape, pose profound computational challenges for transcriptome reconstruction.

RESULTS

We present the novel framework MITIE (Mixed Integer Transcript IdEntification) for simultaneous transcript reconstruction and quantification. We define a likelihood function based on the negative binomial distribution, use a regularization approach to select a few transcripts collectively explaining the observed read data and show how to find the optimal solution using Mixed Integer Programming. MITIE can (i) take advantage of known transcripts, (ii) reconstruct and quantify transcripts simultaneously in multiple samples, and (iii) resolve the location of multi-mapping reads. It is designed for genome- and assembly-based transcriptome reconstruction. We present an extensive study based on realistic simulated RNA-Seq data. When compared with state-of-the-art approaches, MITIE proves to be significantly more sensitive and overall more accurate. Moreover, MITIE yields substantial performance gains when used with multiple samples. We applied our system to 38 Drosophila melanogaster modENCODE RNA-Seq libraries and estimated the sensitivity of reconstructing omitted transcript annotations and the specificity with respect to annotated transcripts. Our results corroborate that a well-motivated objective paired with appropriate optimization techniques lead to significant improvements over the state-of-the-art in transcriptome reconstruction.

AVAILABILITY

MITIE is implemented in C++ and is available from http://bioweb.me/mitie under the GPL license.

An exon-centric perspective

During the past ten years, remarkable progress has been made in our understanding of the complexity and regulation of alternative splicing. The generation of large datasets of quantitative alternative splicing profiling information has revealed that transcripts from at least 95% of multi-exon human genes undergo alternative splicing, and that thousands of exons in mammalian transcriptomes are subject to striking regulatory patterns. Together with advanced computational methods, these datasets have enabled the inference of a predictive code for tissue-dependent alternative splicing. This code has further provided new insight into splicing regulatory mechanisms. Collectively, these approaches are revealing the existence of discrete networks of exons that are coordinately regulated in diverse biologically normal and disease contexts. A major challenge ahead is to systematically determine the functions of exons comprising these exon networks as well as the factors and mechanisms responsible for their regulation. This perspective provides an account of progress in these areas and also discusses future avenues of exon-centric exploration.

Support vector machines-based identification of alternative splicing in Arabidopsis thaliana from whole-genome tiling arrays

Background

Alternative splicing (AS) is a process which generates several distinct mRNA isoforms from the same gene by splicing different portions out of the precursor transcript. Due to the (patho-)physiological importance of AS, a complete inventory of AS is of great interest. While this is in reach for human and mammalian model organisms, our knowledge of AS in plants has remained more incomplete. Experimental approaches for monitoring AS are either based on transcript sequencing or rely on hybridization to DNA microarrays. Among the microarray platforms facilitating the discovery of AS events, tiling arrays are well-suited for identifying intron retention, the most prevalent type of AS in plants. However, analyzing tiling array data is challenging, because of high noise levels and limited probe coverage.

Results

In this work, we present a novel method to detect intron retentions (IR) and exon skips (ES) from tiling arrays. While statistical tests have typically been proposed for this purpose, our method instead utilizes support vector machines (SVMs) which are appreciated for their accuracy and robustness to noise. Existing EST and cDNA sequences served for supervised training and evaluation. Analyzing a large collection of publicly available microarray and sequence data for the model plant A. thaliana, we demonstrated that our method is more accurate than existing approaches. The method was applied in a genome-wide screen which resulted in the discovery of 1,355 IR events. A comparison of these IR events to the TAIR annotation and a large set of short-read RNA-seq data showed that 830 of the predicted IR events are novel and that 525 events (39%) overlap with either the TAIR annotation or the IR events inferred from the RNA-seq data.

Conclusions

The method developed in this work expands the scarce repertoire of analysis tools for the identification of alternative mRNA splicing from whole-genome tiling arrays. Our predictions are highly enriched with known AS events and complement the A. thaliana genome annotation with respect to AS. Since all predicted AS events can be precisely attributed to experimental conditions, our work provides a basis for follow-up studies focused on the elucidation of the regulatory mechanisms underlying tissue-specific and stress-dependent AS in plants.

Systems analysis of alternative splicing and its regulation

Alternative splicing (AS) has emerged as a key mechanism that accounts for gene expression diversity in metazoan organisms. Splicing is tightly regulated by a repertoire of RNA and protein factors and RNA sequence elements that function in a cooperative manner. Systems-level experimental and computational approaches have been instrumental in establishing comprehensive profiles of transcript variants generated by AS. In addition, systems biology approaches are starting to define how combinatorial splicing regulation shapes the complex splicing phenotypes observed in different tissue types and developmental stages and under different conditions. Here, we review recent progress in these areas.

LATENT RANK CHANGE DETECTION FOR ANALYSIS OF SPLICE-JUNCTION MICROARRAYS WITH NONLINEAR EFFECTS

Alternative splicing of gene transcripts greatly expands the functional capacity of the genome, and certain splice isoforms may indicate specific disease states such as cancer. Splice junction microarrays interrogate thousands of splice junctions, but data analysis is difficult and error prone because of the increased complexity compared to differential gene expression analysis. We present Rank Change Detection (RCD) as a method to identify differential splicing events based upon a straightforward probabilistic model comparing the over- or underrepresentation of two or more competing isoforms. RCD has advantages over commonly used methods because it is robust to false positive errors due to nonlinear trends in microarray measurements. Further, RCD does not depend on prior knowledge of splice isoforms, yet it takes advantage of the inherent structure of mutually exclusive junctions, and it is conceptually generalizable to other types of splicing arrays or RNA-Seq. RCD specifically identifies the biologically important cases when a splice junction becomes more or less prevalent compared to other mutually exclusive junctions. The example data is from different cell lines of glioblastoma tumors assayed with Agilent microarrays.

Global profiling and molecular characterization of alternative splicing events misregulated in lung cancer

Alternative splicing (AS) is a widespread mechanism underlying the generation of proteomic and regulatory complexity. However, which of the myriad of human AS events play important roles in disease is largely unknown. To identify frequently occurring AS events in lung cancer, we used AS microarray profiling and reverse transcription-PCR (RT-PCR) assays to survey patient-matched normal and adenocarcinoma tumor tissues from the lungs of 29 individuals diagnosed with non-small cell lung cancer (NSCLC). Of 5,183 profiled alternative exons, four displayed tumor-associated changes in the majority of the patients. These events affected transcripts from the VEGFA, MACF1, APP, and NUMB genes. Similar AS changes were detected in NUMB and APP transcripts in primary breast and colon tumors. Tumor-associated increases in NUMB exon 9 inclusion correlated with reduced levels of NUMB protein expression and activation of the Notch signaling pathway, an event that has been linked to tumorigenesis. Moreover, short hairpin RNA (shRNA) knockdown of NUMB followed by isoform-specific rescue revealed that expression of the exon 9-skipped (nontumor) isoform represses Notch target gene activation whereas expression of the exon 9-included (tumor) isoform lacks this activity and is capable of promoting cell proliferation. The results thus reveal widespread AS changes in NSCLC that impact cell signaling in a manner that likely contributes to tumorigenesis.

The nuclear-retained noncoding RNA MALAT1 regulates alternative splicing by modulating SR splicing factor phosphorylation

Alternative splicing (AS) of pre-mRNA is utilized by higher eukaryotes to achieve increased transcriptome and proteomic complexity. The serine/arginine (SR) splicing factors regulate tissue- or cell-type-specific AS in a concentration- and phosphorylation-dependent manner. However, the mechanisms that modulate the cellular levels of active SR proteins remain to be elucidated. In the present study, we provide evidence for a role for the long nuclear-retained regulatory RNA (nrRNA), MALAT1 in AS regulation. MALAT1 interacts with SR proteins and influences the distribution of these and other splicing factors in nuclear speckle domains. Depletion of MALAT1 or overexpression of an SR protein changes the AS of a similar set of endogenous pre-mRNAs. Furthermore, MALAT1 regulates cellular levels of phosphorylated forms of SR proteins. Taken together, our results suggest that MALAT1 regulates AS by modulating the levels of active SR proteins. Our results further highlight the role for an nrRNA in the regulation of gene expression.

Global impact of RNA polymerase II elongation inhibition on alternative splicing regulation

The rate of RNA polymerase II (Pol II) elongation can influence splice site selection in nascent transcripts, yet the extent and physiological relevance of this kinetic coupling between transcription and alternative splicing (AS) is not well understood. We performed experiments to perturb Pol II elongation and then globally compared AS patterns with genome-wide Pol II occupancy. RNA binding and RNA processing functions were significantly enriched among the genes with Pol II elongation inhibition-dependent changes in AS. Under conditions that interfere with Pol II elongation, including cell stress, increased Pol II occupancy was detected in the intronic regions flanking the alternative exons in these genes, and these exons generally became more included. A disproportionately high fraction of these exons introduced premature termination codons that elicited nonsense-mediated mRNA decay (NMD), thereby further reducing transcript levels. Our results provide evidence that kinetic coupling between transcription, AS, and NMD affords a rapid mechanism by which cells can respond to changes in growth conditions, including cell stress, to coordinate the levels of RNA processing factors with mRNA levels.

Model-based detection of alternative splicing signals

Motivation: Transcripts from ∼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.

Contact:yoseph@psi.utoronto.ca; frey@psi.utoronto.ca

Supplementary information:Supplementary data are available at Bioinformatics online.

Verifying expressed transcript variants by detecting and assembling stretches of consecutive exons

We herein describe an integrated system for the high-throughput analysis of splicing events and the identification of transcript variants. The system resolves individual splicing events and elucidates transcript variants via a pipeline that combines aspects such as bioinformatic analysis, high-throughput transcript variant amplification, and high-resolution capillary electrophoresis. For the 14 369 human genes known to have transcript variants, minimal primer sets were designed to amplify all transcript variants and examine all splicing events; these have been archived in the ASprimerDB database, which is newly described herein. A high-throughput thermocycler, dubbed GenTank, was developed to simultaneously perform thousands of PCR amplifications. Following the resolution of the various amplicons by capillary gel electrophoresis, two new computer programs, AmpliconViewer and VariantAssembler, may be used to analyze the splicing events, assemble the consecutive exons embodied by the PCR amplicons, and distinguish expressed versus putative transcript variants. This novel system not only facilitates the validation of putative transcript variants and the detection of novel transcript variants, it also semi-quantitatively measures the transcript variant expression levels of each gene. To demonstrate the system’s capability, we used it to resolve transcript variants yielded by single and multiple splicing events, and to decipher the exon connectivity of long transcripts.

Deciphering the splicing code

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.

MMBGX: a method for estimating expression at the isoform level and detecting differential splicing using whole-transcript Affymetrix arrays

Affymetrix has recently developed whole-transcript GeneChips-'Gene' and 'Exon' arrays-which interrogate exons along the length of each gene. Although each probe on these arrays is intended to hybridize perfectly to only one transcriptional target, many probes match multiple transcripts located in different parts of the genome or alternative isoforms of the same gene. Existing statistical methods for estimating expression do not take this into account and are thus prone to producing inflated estimates. We propose a method, Multi-Mapping Bayesian Gene eXpression (MMBGX), which disaggregates the signal at 'multi-match' probes. When applied to Gene arrays, MMBGX removes the upward bias of gene-level expression estimates. When applied to Exon arrays, it can further disaggregate the signal between alternative transcripts of the same gene, providing expression estimates of individual splice variants. We demonstrate the performance of MMBGX on simulated data and a tissue mixture data set. We then show that MMBGX can estimate the expression of alternative isoforms within one experimental condition, confirming our results by RT-PCR. Finally, we show that our method for detecting differential splicing has a lower error rate than standard exon-level approaches on a previously validated colon cancer data set.

Resolving deconvolution ambiguity in gene alternative splicing

Background

For many gene structures it is impossible to resolve intensity data uniquely to establish abundances of splice variants. This was empirically noted by Wang et al. in which it was called a "degeneracy problem". The ambiguity results from an ill-posed problem where additional information is needed in order to obtain an unique answer in splice variant deconvolution.

Results

In this paper, we analyze the situations under which the problem occurs and perform a rigorous mathematical study which gives necessary and sufficient conditions on how many and what type of constraints are needed to resolve all ambiguity. This analysis is generally applicable to matrix models of splice variants. We explore the proposal that probe sequence information may provide sufficient additional constraints to resolve real-world instances. However, probe behavior cannot be predicted with sufficient accuracy by any existing probe sequence model, and so we present a Bayesian framework for estimating variant abundances by incorporating the prediction uncertainty from the micro-model of probe responsiveness into the macro-model of probe intensities.

Conclusion

The matrix analysis of constraints provides a tool for detecting real-world instances in which additional constraints may be necessary to resolve splice variants. While purely mathematical constraints can be stated without error, real-world constraints may themselves be poorly resolved. Our Bayesian framework provides a generic solution to the problem of uniquely estimating transcript abundances given additional constraints that themselves may be uncertain, such as regression fit to probe sequence models. We demonstrate the efficacy of it by extensive simulations as well as various biological data.

Probe-level estimation improves the detection of differential splicing in Affymetrix exon array studies

A novel statistical procedure is presented that uses probe-level information on Affymetrix exon arrays to detect differential splicing.

The recent advent of exon microarrays has made it possible to reveal differences in alternative splicing events on a global scale. We introduce a novel statistical procedure that takes full advantage of the probe-level information on Affymetrix exon arrays when detecting differential splicing between sample groups. In comparison to existing ranking methods, the procedure shows superior reproducibility and accuracy in distinguishing true biological findings from background noise in high agreement with experimental validations.

Genome-wide identification of alternative splice forms down-regulated by nonsense-mediated mRNA decay in Drosophila

Alternative mRNA splicing adds a layer of regulation to the expression of thousands of genes in Drosophila melanogaster. Not all alternative splicing results in functional protein; it can also yield mRNA isoforms with premature stop codons that are degraded by the nonsense-mediated mRNA decay (NMD) pathway. This coupling of alternative splicing and NMD provides a mechanism for gene regulation that is highly conserved in mammals. NMD is also active in Drosophila, but its effect on the repertoire of alternative splice forms has been unknown, as has the mechanism by which it recognizes targets. Here, we have employed a custom splicing-sensitive microarray to globally measure the effect of alternative mRNA processing and NMD on Drosophila gene expression. We have developed a new algorithm to infer the expression change of each mRNA isoform of a gene based on the microarray measurements. This method is of general utility for interpreting splicing-sensitive microarrays and high-throughput sequence data. Using this approach, we have identified a high-confidence set of 45 genes where NMD has a differential effect on distinct alternative isoforms, including numerous RNA–binding and ribosomal proteins. Coupled alternative splicing and NMD decrease expression of these genes, which may in turn have a downstream effect on expression of other genes. The NMD–affected genes are enriched for roles in translation and mitosis, perhaps underlying the previously observed role of NMD factors in cell cycle progression. Our results have general implications for understanding the NMD mechanism in fly. Most notably, we found that the NMD–target mRNAs had significantly longer 3′ untranslated regions (UTRs) than the nontarget isoforms of the same genes, supporting a role for 3′ UTR length in the recognition of NMD targets in fly.

A gene can be processed into multiple mRNAs through alternative splicing. Alternative splicing increases the number of proteins encoded by the genome, but not all alternative mRNAs produce protein. Instead, some are degraded by nonsense-mediated mRNA decay (NMD), a surveillance system that was originally identified as a means of clearing the cell of mRNAs with nonsense, or stop codon, mutations. Alternative splicing that introduces early stop codons will lead to NMD, offering a way for the cell to down-regulate gene expression after a gene has been transcribed. In this paper, we have developed a new analysis method to study the combined effect of alternative splicing and degradation in the fruit fly Drosophila melanogaster using microarrays. We have found a stringently defined set of 45 genes that can be spliced either into an mRNA that encodes a protein or into an mRNA that is degraded by NMD, down-regulating the overall gene expression. The affected genes include a number that are central to the cell's regulatory processes, including translation, RNA splicing, and cell cycle progression. Our results also help shed light on how NMD determines whether a stop codon is premature, and thus whether to target an mRNA for degradation.

Evolution of alternative splicing regulation: changes in predicted exonic splicing regulators are not associated with changes in alternative splicing levels in primates

Alternative splicing is tightly regulated in a spatio-temporal and quantitative manner. This regulation is achieved by a complex interplay between spliceosomal (trans) factors that bind to different sequence (cis) elements. cis-elements reside in both introns and exons and may either enhance or silence splicing. Differential combinations of cis-elements allows for a huge diversity of overall splicing signals, together comprising a complex ‘splicing code’. Many cis-elements have been identified, and their effects on exon inclusion levels demonstrated in reporter systems. However, the impact of interspecific differences in these elements on the evolution of alternative splicing levels has not yet been investigated at genomic level. Here we study the effect of interspecific differences in predicted exonic splicing regulators (ESRs) on exon inclusion levels in human and chimpanzee. For this purpose, we compiled and studied comprehensive datasets of predicted ESRs, identified by several computational and experimental approaches, as well as microarray data for changes in alternative splicing levels between human and chimpanzee. Surprisingly, we found no association between changes in predicted ESRs and changes in alternative splicing levels. This observation holds across different ESR exon positions, exon lengths, and 5′ splice site strengths. We suggest that this lack of association is mainly due to the great importance of context for ESR functionality: many ESR-like motifs in primates may have little or no effect on splicing, and thus interspecific changes at short-time scales may primarily occur in these effectively neutral ESRs. These results underscore the difficulties of using current computational ESR prediction algorithms to identify truly functionally important motifs, and provide a cautionary tale for studies of the effect of SNPs on splicing in human disease.

A hierarchical Bayesian model for comparing transcriptomes at the individual transcript isoform level

The complexity of mammalian transcriptomes is compounded by alternative splicing which allows one gene to produce multiple transcript isoforms. However, transcriptome comparison has been limited to differential analysis at the gene level instead of the individual transcript isoform level. High-throughput sequencing technologies and high-resolution tiling arrays provide an unprecedented opportunity to compare transcriptomes at the level of individual splice variants. However, sequence read coverage or probe intensity at each position may represent a family of splice variants instead of one single isoform. Here we propose a hierarchical Bayesian model, BASIS (Bayesian Analysis of Splicing IsoformS), to infer the differential expression level of each transcript isoform in response to two conditions. A latent variable was introduced to perform direct statistical selection of differentially expressed isoforms. Model parameters were inferred based on an ergodic Markov chain generated by our Gibbs sampler. BASIS has the ability to borrow information across different probes (or positions) from the same genes and different genes. BASIS can handle the heteroskedasticity of probe intensity or sequence read coverage. We applied BASIS to a human tiling-array data set and a mouse RNA-seq data set. Some of the predictions were validated by quantitative real-time RT-PCR experiments.

REMAS: a new regression model to identify alternative splicing events from exon array data

Background

Alternative splicing (AS) is an important regulatory mechanism for gene expression and protein diversity in eukaryotes. Previous studies have demonstrated that it can be causative for, or specific to splicing-related diseases. Understanding the regulation of AS will be helpful for diagnostic efforts and drug discoveries on those splicing-related diseases. As a novel exon-centric microarray platform, exon array enables a comprehensive analysis of AS by investigating the expression of known and predicted exons. Identifying of AS events from exon array has raised much attention, however, new and powerful algorithms for exon array data analysis are still absent till now.

Results

Here, we considered identifying of AS events in the framework of variable selection and developed a regression method for AS detection (REMAS). Firstly, features of alternatively spliced exons were scaled by reasonably defined variables. Secondly, we designed a hierarchical model which can represent gene structure and transcriptional influence to exons, and the lasso type penalties were introduced in calculation because of huge variable size. Thirdly, an iterative two-step algorithm was developed to select alternatively spliced genes and exons. To avoid negative effects introduced by small sample size, we ranked genes as parameters indicating their AS capabilities in an iterative manner. After that, both simulation and real data evaluation showed that REMAS could efficiently identify potential AS events, some of which had been validated by RT-PCR or supported by literature evidence.

Conclusion

As a new lasso regression algorithm based on hierarchical model, REMAS has been demonstrated as a reliable and effective method to identify AS events from exon array data.

Prediction of alternatively skipped exons and splicing enhancers from exon junction arrays

Background

Alternative splicing of exons in a pre-mRNA transcript is an important mechanism which contributes to protein diversity in human. Arrays for detecting alternative splicing are available using several different probe designs, including those based on exon-junctions. In this work, we introduce a new method for predicting alternatively skipped exons from exon-junction arrays. Predictions based on our method are compared against controls and their sequences are analyzed to identify motifs important for regulating alternative splicing.

Results

Our comparison of several alternative methods shows that an exon-skipping score based on neighboring junctions best discriminates between positive and negative controls. Sequence analysis of our predicted exons confirms the presence of known splicing regulatory sequences. In addition, we also derive a set of development-related alternatively spliced genes based on fetal versus adult tissue comparisons and find that our predictions are consistent with their functional annotations. Ab initio motif finding algorithms are applied to identify several motifs that may be relevant for splicing during development.

Conclusion

This work describes a new method for analyzing exon-junction arrays, identifies sequence motifs that are specific for alternative and constitutive splicing and suggests a role for several known splicing factors and their motifs in developmental regulation.

Efficient algorithms for the computational design of optimal tiling arrays

The representation of a genome by oligonucleotide probes is a prerequisite for the analysis of many of its basic properties, such as transcription factor binding sites, chromosomal breakpoints, gene expression of known genes and detection of novel genes, in particular those coding for small RNAs. An ideal representation would consist of a high density set of oligonucleotides with similar melting temperatures that do not cross-hybridize with other regions of the genome and are equidistantly spaced. The implementation of such design is typically called a tiling array or genome array. We formulate the minimal cost tiling path problem for the selection of oligonucleotides from a set of candidates. Computing the selection of probes requires multi-criterion optimization, which we cast into a shortest path problem. Standard algorithms running in linear time allow us to compute globally optimal tiling paths from millions of candidate oligonucleotides on a standard desktop computer for most problem variants. The solutions to this multi-criterion optimization are spatially adaptive to the problem instance. Our formulation incorporates experimental constraints with respect to specific regions of interest and trade offs between hybridization parameters, probe quality and tiling density easily. A web application is available at http://tileomatic.org.

FIRMA: a method for detection of alternative splicing from exon array data

MOTIVATION

Analyses of EST data show that alternative splicing is much more widespread than once thought. The advent of exon and tiling microarrays means that researchers now have the capacity to experimentally measure alternative splicing on a genome wide level. New methods are needed to analyze the data from these arrays.

RESULTS

We present a method, finding isoforms using robust multichip analysis (FIRMA), for detecting differential alternative splicing in exon array data. FIRMA has been developed for Affymetrix exon arrays, but could in principle be extended to other exon arrays, tiling arrays or splice junction arrays. We have evaluated the method using simulated data, and have also applied it to two datasets: a panel of 11 human tissues and a set of 10 pairs of matched normal and tumor colon tissue. FIRMA is able to detect exons in several genes confirmed by reverse transcriptase PCR.

AVAILABILITY

R code implementing our methods is contributed to the package aroma.affymetrix.

Regulation of multiple core spliceosomal proteins by alternative splicing-coupled nonsense-mediated mRNA decay

Alternative splicing (AS) can regulate gene expression by introducing premature termination codons (PTCs) into spliced mRNA that subsequently elicit transcript degradation by the nonsense-mediated mRNA decay (NMD) pathway. However, the range of cellular functions controlled by this process and the factors required are poorly understood. By quantitative AS microarray profiling, we find that there are significant overlaps among the sets of PTC-introducing AS events affected by individual knockdown of the three core human NMD factors, Up-Frameshift 1 (UPF1), UPF2, and UPF3X/B. However, the levels of some PTC-containing splice variants are less or not detectably affected by the knockdown of UPF2 and/or UPF3X, compared with the knockdown of UPF1. The intron sequences flanking the affected alternative exons are often highly conserved, suggesting important regulatory roles for these AS events. The corresponding genes represent diverse cellular functions, and surprisingly, many encode core spliceosomal proteins and assembly factors. We further show that conserved, PTC-introducing AS events are enriched in genes that encode core spliceosomal proteins. Where tested, altering the expression levels of these core spliceosomal components affects the regulation of PTC-containing splice variants from the corresponding genes. Together, our results show that AS-coupled NMD can have different UPF factor requirements and is likely to regulate many general components of the spliceosome. The results further implicate general spliceosomal components in AS regulation.

SPACE: an algorithm to predict and quantify alternatively spliced isoforms using microarrays

Exon and exon+junction microarrays are promising tools for studying alternative splicing. Current analytical tools applied to these arrays lack two relevant features: the ability to predict unknown spliced forms and the ability to quantify the concentration of known and unknown isoforms. SPACE is an algorithm that has been developed to (1) estimate the number of different transcripts expressed under several conditions, (2) predict the precursor mRNA splicing structure and (3) quantify the transcript concentrations including unknown forms. The results presented here show its robustness and accuracy for real and simulated data.

Functional coordination of alternative splicing in the mammalian central nervous system

A microarray analysis provides new evidence suggesting that specific cellular processes in the mammalian CNS are coordinated at the level of alternative splicing, and that a complex splicing code underlies CNS-specific alternative splicing regulation.

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.

Bioinformatics detection of alternative splicing

In recent years, genome-wide detection of alternative splicing based on Expressed Sequence Tag (EST) sequence alignments with mRNA and genomic sequences has dramatically expanded our understanding of the role of alternative splicing in functional regulation. This chapter reviews the data, methodology, and technical challenges of these genome-wide analyses of alternative splicing, and briefly surveys some of the uses to which such alternative splicing databases have been put. For example, with proper alternative splicing database schema design, it is possible to query genome-wide for alternative splicing patterns that are specific to particular tissues, disease states (e.g., cancer), gender, or developmental stages. EST alignments can be used to estimate exon inclusion or exclusion level of alternatively spliced exons and evolutionary changes for various species can be inferred from exon inclusion level. Such databases can also help automate design of probes for RT-PCR and microarrays, enabling high throughput experimental measurement of alternative splicing.

Global analysis of alternative splicing differences between humans and chimpanzees

Alternative splicing is a powerful mechanism affording extensive proteomic and regulatory diversity from a limited repertoire of genes. However, the extent to which alternative splicing has contributed to the evolution of primate species-specific characteristics has not been assessed previously. Using comparative genomics and quantitative microarray profiling, we performed the first global analysis of alternative splicing differences between humans and chimpanzees. Surprisingly, 6%-8% of profiled orthologous exons display pronounced splicing level differences in the corresponding tissues from the two species. Little overlap is observed between the genes associated with alternative splicing differences and the genes that display steady-state transcript level differences, indicating that these layers of regulation have evolved rapidly to affect distinct subsets of genes in humans and chimpanzees. The alternative splicing differences we detected are predicted to affect diverse functions including gene expression, signal transduction, cell death, immune defense, and susceptibility to diseases. Differences in expression at the protein level of the major splice variant of Glutathione S-transferase omega-2 (GSTO2), which functions in the protection against oxidative stress and is associated with human aging-related diseases, suggests that this enzyme is less active in human cells compared with chimpanzee cells. The results of this study thus support an important role for alternative splicing in establishing differences between humans and chimpanzees.

Alternative splicing in cancer: Noise, functional, or systematic?

Pre-messenger RNA splicing is a fine-tuned process that generates multiple functional variants from individual genes. Various cell types and developmental stages regulate alternative splicing patterns differently in their generation of specific gene functions. In cancers, splicing is significantly altered, and understanding the underlying mechanisms and patterns in cancer will shed new light onto cancer biology. Cancer-specific transcript variants are promising biomarkers and targets for diagnostic, prognostic, and treatment purposes. In this review, we explore how alternative splicing cannot simply be considered as noise or an innocent bystander, but is actively regulated or deregulated in cancers. A special focus will be on aspects of cell biology and biochemistry of alternative splicing in cancer cells, addressing differences in splicing mechanisms between normal and malignant cells. The systems biology of splicing is only now applied to the field of cancer research. We explore functional annotations for some of the most intensely spliced gene classes, and provide a literature mining and clustering that reflects the most intensely investigated genes. A few well-established cancer-specific splice events, such as the CD44 antigen, are used to illustrate the potential behind the exploration of the mechanisms of their regulation. Accordingly, we describe the functional connection between the regulatory machinery (i.e., the spliceosome and its accessory proteins) and their global impact on qualitative transcript variation that are only now emerging from the use of genomic technologies such as microarrays. These studies are expected to open an entirely new level of genetic information that is currently still poorly understood.

High correspondence between Affymetrix exon and standard expression arrays

Exon arrays aim to provide comprehensive gene expression data at the level of individual exons, similar to that provided on a per-gene basis by existing expression arrays. This report describes the performance of Affymetrix GeneChip Human Exon 1.0 ST array by using replicated RNA samples from two human cell lines, MCF7 and MCF10A, hybridized both to Exon 1.0 ST and to HG-U133 Plus2 arrays. Cross-comparison between array types requires an appropriate mapping to be found between individual probe sets. Three possible mappings were considered, reflecting different strategies for dealing with probe sets that target different parts of the same transcript. Irrespective of the mapping used, Exon 1.0 ST and HG-U133 Plus2 arrays show a high degree of correspondence. More than 80% of HG-U133 Plus2 probe sets may be mapped to the Exon chip, and fold changes are found well preserved for over 96% of those probe sets detected present. Since HG-U133 Plus2 arrays have already been extensively validated, these results lend a significant degree of confidence to exon arrays.

Global analysis of alternative splicing during T-cell activation

The role of alternative splicing (AS) in eliciting immune responses is poorly understood. We used quantitative AS microarray profiling to survey changes in AS during activation of Jurkat cells, a leukemia-derived T-cell line. Our results indicate that approximately 10-15% of the profiled alternative exons undergo a >10% change in inclusion level during activation. The majority of the genes displaying differential AS levels are distinct from the set of genes displaying differential transcript levels. These two gene sets also have overlapping yet distinct functional roles. For example, genes that show differential AS patterns during T-cell activation are often closely associated with cell-cycle regulation, whereas genes with differential transcript levels are highly enriched in functions associated more directly with immune defense and cytoskeletal architecture. Previously unknown AS events were detected in genes that have important roles in T-cell activation, and these AS level changes were also observed during the activation of normal human peripheral CD4+ and CD8+ lymphocytes. In summary, by using AS microarray profiling, we have discovered many new AS changes associated with T-cell activation. Our results suggest an extensive role for AS in the regulation of the mammalian immune response.

Microarray analysis of RNA processing and modification

Most RNAs are processed from precursors by mechanisms that include covalent modifications, as well as the removal of flanking and intervening sequences. Traditional methods to detect RNA processing, such as Northern blotting, reverse-transcribed polymerase chain reaction and primer extension assays, are difficult to apply on a large scale. This chapter outlines several methods for analysis of the processing and modification of RNA using microarrays. These encompass protocols for the application of homemade microarrays and custom-designed commercial inkjet microarrays and are tailored for the large-scale analysis of processing of mRNA, including alternative splicing, as well as for the analysis of processing and modification of noncoding RNA. This chapter also describes practical aspects of microarray design, sample preparation, hybridization, and data analysis.

Data analysis of alternative splicing microarrays

The importance of alternative splicing in drug and biomarker discovery is best understood through several example genes. For most genes, the identification, detection and particularly quantification of isoforms in different tissues and conditions remain to be carried out. As a result, the focus in drug and biomarker development is increasingly on high-throughput studies of alternative splicing. Initial strategies for the parallel analysis of alternative splicing by microarrays have been recently published. The design specificities and goals of alternative splicing microarrays, in terms of identification and quantification of multiple mRNAs from one gene, are promoting the development of novel methods of analysis.

Quantitative and evolutionary biology of alternative splicing: how changing the mix of alternative transcripts affects phenotypic plasticity and reaction norms

Alternative splicing (AS) of pre-messenger RNA is a common phenomenon that creates different transcripts from a single gene, and these alternative transcripts affect phenotypes. The majority of AS research has examined tissue and developmental specificity of expression of particular AS transcripts, how this specificity affects cell function, and how aberrant AS is related to disease. Few studies have examined quantitative between-individual variation in AS within a cell or tissue type, or in relation to phenotypes, but the results are compelling: quantitative variation in AS affects plastic traits such as stress, anxiety, fear, egg production, muscle performance, energetics and plant growth. Genomic analyses of AS are also at a nascent stage, but have revealed a number of significant evolutionary patterns. Growing knowledge of upstream genes and kinases that regulate AS provides the as-yet little explored potential to examine how these genes and pathways respond to environmental and genotype variables. Research in this area can provide glimpses of a labyrinth of genetic architectures that have rarely been considered in evolutionary and organismal biology, or in quantitative genetics. The scarcity of contribution to knowledge about AS from these fields is illustrated by the fact that heritability of quantitative variation in AS has not yet been determined for any gene in any organism. New research tactics that incorporate quantitative analyses of AS will allow organismal and evolutionary biologists to attain a fuller mechanistic understanding of many of the traits they study, and may lead to more rapid discovery of functionally important polymorphisms.

Microarray Analysis of Alternative Splicing

Alternative splicing, defined as the generation of multiple RNA transcript species from a common mRNA precursor, is one of the mechanisms for the diversification and expansion of cellular proteins from a smaller set of genes. Current estimates indicate that at least 60% of genes in the human genome exhibit alternative splicing. Over the past decade, alternative splicing has increasingly been recognized as a major regulatory process with a critical role in normal development. Furthermore, the importance of alternative splicing in disease development and treatment is starting to be appreciated. Therefore, an increasing number of high-throughput genomics and proteomics studies are being performed in order to delineate (a) the changes in alternative splicing under various conditions; (b) the properties and functions of protein isoforms; and (c) the splicing and alternative splicing regulation process. Strategies for the parallel analysis of alternative splice forms by microarray experiments have been conceived, and examples have been published. In addition to the differences in microarray probe design, the analysis of microarrays with probes for exons, exon/exon junctions as well as specific splice forms is significantly different from the standard experiment. Several methods are being developed in order to address the particular needs of alternative splicing microarrays. Many reviews have already dealt with alternative splicing. However, high-throughput analysis methods that are becoming increasingly popular have not received much attention. Here, we will provide an overview of the tools and analysis methods that were developed specifically for alternative splicing microarrays described in terms of specific experiments.

Alternative splicing: new insights from global analyses

Recent analyses of sequence and microarray data have suggested that alternative splicing plays a major role in the generation of proteomic and functional diversity in metazoan organisms. Efforts are now being directed at establishing the full repertoire of functionally relevant transcript variants generated by alternative splicing, the specific roles of such variants in normal and disease physiology, and how alternative splicing is coordinated on a global level to achieve cell- and tissue-specific functions. Recent progress in these areas is summarized in this review.

GenRate: a generative model that reveals novel transcripts in genome-tiling microarray data

Genome-wide microarray designs containing millions to hundreds of millions of probes are available for a variety of mammals, including mouse and human. These genome tiling arrays can potentially lead to significant advances in science and medicine, e.g., by indicating new genes and alternative primary and secondary transcripts. While bottom-up pattern matching techniques (e.g., hierarchical clustering) can be used to find gene structures in microarray data, we believe the many interacting hidden variables and complex noise patterns more naturally lead to an analysis based on generative models. We describe a generative model of tiling data and show how the sum-product algorithm can be used to infer hybridization noise, probe sensitivity, new transcripts, and alternative transcripts. The method, called GenRate, maximizes a global scoring function that enables multiple transcripts to compete for ownership of putative probes. We apply GenRate to a new exon tiling dataset from mouse chromosome 4 and show that it makes significantly more predictions than a previously described hierarchical clustering method at the same false positive rate. GenRate correctly predicts many known genes and also predicts new gene structures. As new problems arise, additional hidden variables can be incorporated into the model in a principled fashion, so we believe that GenRate will prove to be a useful tool in the new era of genome-wide tiling microarray analysis.