MADS: a new and improved method for analysis of differential alternative splicing by exon-tiling microarrays

Capture RIC-seq reveals positional rules of PTBP1-associated RNA loops in splicing regulation

RNA-binding proteins (RBPs) bind at different positions of the pre-mRNA molecules to promote or reduce the usage of a particular exon. Seeking to understand the working principle of these positional effects, we develop a capture RIC-seq (CRIC-seq) method to enrich specific RBP-associated in situ proximal RNA-RNA fragments for deep sequencing. We determine hnRNPA1-, SRSF1-, and PTBP1-associated proximal RNA-RNA contacts and regulatory mechanisms in HeLa cells. Unexpectedly, the 3D RNA map analysis shows that PTBP1-associated loops in individual introns preferentially promote cassette exon splicing by accelerating asymmetric intron removal, whereas the loops spanning across cassette exon primarily repress splicing. These "positional rules" can faithfully predict PTBP1-regulated splicing outcomes. We further demonstrate that cancer-related splicing quantitative trait loci can disrupt RNA loops by reducing PTBP1 binding on pre-mRNAs to cause aberrant splicing in tumors. Our study presents a powerful method for exploring the functions of RBP-associated RNA-RNA proximal contacts in gene regulation and disease.

Multi-Omics Analysis of Multiple Glucose-Sensing Receptor Systems in Yeast

The yeast Saccharomyces cerevisiae has long been used to produce alcohol from glucose and other sugars. While much is known about glucose metabolism, relatively little is known about the receptors and signaling pathways that indicate glucose availability. Here, we compare the two glucose receptor systems in S. cerevisiae. The first is a heterodimer of transporter-like proteins (transceptors), while the second is a seven-transmembrane receptor coupled to a large G protein (Gpa2) that acts in coordination with two small G proteins (Ras1 and Ras2). Through comprehensive measurements of glucose-dependent transcription and metabolism, we demonstrate that the two receptor systems have distinct roles in glucose signaling: the G-protein-coupled receptor directs carbohydrate and energy metabolism, while the transceptors regulate ancillary processes such as ribosome, amino acids, cofactor and vitamin metabolism. The large G-protein transmits the signal from its cognate receptor, while the small G-protein Ras2 (but not Ras1) integrates responses from both receptor pathways. Collectively, our analysis reveals the molecular basis for glucose detection and the earliest events of glucose-dependent signal transduction in yeast.

SRSF1 and PTBP1 Are trans-Acting Factors That Suppress the Formation of a CD33 Splicing Isoform Linked to Alzheimer's Disease Risk

A single nucleotide polymorphism (SNP) in exon 2 of the CD33 gene is associated with reduced susceptibility to late-onset Alzheimer’s disease (AD) and causal for elevated mRNA lacking exon 2. In contrast to full-length CD33, transcripts lacking exon 2 result in CD33 protein unable to suppress activation responses in myeloid cells, including microglia. Currently, little is known about the regulation of CD33 exon 2 splicing.

A single nucleotide polymorphism (SNP) in exon 2 of the CD33 gene is associated with reduced susceptibility to late-onset Alzheimer’s disease (AD) and causal for elevated mRNA lacking exon 2. In contrast to full-length CD33, transcripts lacking exon 2 result in CD33 protein unable to suppress activation responses in myeloid cells, including microglia. Currently, little is known about the regulation of CD33 exon 2 splicing. Using functional genomics and proteomic approaches, we found that SRSF1 and PTBP1 act as splicing enhancers to increase CD33 exon 2 inclusion in mRNA. Binding of PTBP1 to RNA sequences proximal to the intron 1-exon 2 splice junction is altered by the SNP and represents a potential mechanism behind the SNP-genotype dependent alternative splicing. Our studies also reveal that binding of SRSF1 to the CD33 RNA is not altered by the SNP genotype. Instead, a putative SRSF1 binding sequence at the 3′ end of exon 2 directs CD33 exon 2 inclusion into the mRNA, indicating that PTBP1 and SRSF1 promote full-length isoform expression through different mechanisms. Our findings shed light on molecular interactions that regulate CD33 exon 2 splicing, ultimately impacting receptor expression on the cell surface. These data aid in the understanding of CD33’s regulation of microglial signaling underpinning the AD genetic associations.

Minor Isozymes Tailor Yeast Metabolism to Carbon Availability

Gene duplication is one of the main evolutionary paths to new protein function. Typically, duplicated genes either accumulate mutations and degrade into pseudogenes or are retained and diverge in function. Some duplicated genes, however, show long-term persistence without apparently acquiring new function. An important class of isozymes consists of those that catalyze the same reaction in the same compartment, where knockout of one isozyme causes no known functional defect. Here we present an approach to assigning specific functional roles to seemingly redundant isozymes. First, gene expression data are analyzed computationally to identify conditions under which isozyme expression diverges. Then, knockouts are compared under those conditions. This approach revealed that the expression of many yeast isozymes diverges in response to carbon availability and that carbon source manipulations can induce fitness phenotypes for seemingly redundant isozymes. A driver of these fitness phenotypes is differential allosteric enzyme regulation, indicating isozyme divergence to achieve more-optimal control of metabolism.

Isozymes are enzymes that differ in sequence but catalyze the same chemical reactions. Despite their apparent redundancy, isozymes are often retained over evolutionary time, suggesting that they contribute to fitness. We developed an unsupervised computational method for identifying environmental conditions under which isozymes are likely to make fitness contributions. This method analyzes published gene expression data to find specific experimental perturbations that induce differential isozyme expression. In yeast, we found that isozymes are strongly enriched in the pathways of central carbon metabolism and that many isozyme pairs show anticorrelated expression during the respirofermentative shift. Building on these observations, we assigned function to two minor central carbon isozymes, aconitase 2 (ACO2) and pyruvate kinase 2 (PYK2). ACO2 is expressed during fermentation and proves advantageous when glucose is limiting. PYK2 is expressed during respiration and proves advantageous for growth on three-carbon substrates. PYK2’s deletion can be rescued by expressing the major pyruvate kinase only if that enzyme carries mutations mirroring PYK2’s allosteric regulation. Thus, central carbon isozymes help to optimize allosteric metabolic regulation under a broad range of potential nutrient conditions while requiring only a small number of transcriptional states.

IMPORTANCE Gene duplication is one of the main evolutionary paths to new protein function. Typically, duplicated genes either accumulate mutations and degrade into pseudogenes or are retained and diverge in function. Some duplicated genes, however, show long-term persistence without apparently acquiring new function. An important class of isozymes consists of those that catalyze the same reaction in the same compartment, where knockout of one isozyme causes no known functional defect. Here we present an approach to assigning specific functional roles to seemingly redundant isozymes. First, gene expression data are analyzed computationally to identify conditions under which isozyme expression diverges. Then, knockouts are compared under those conditions. This approach revealed that the expression of many yeast isozymes diverges in response to carbon availability and that carbon source manipulations can induce fitness phenotypes for seemingly redundant isozymes. A driver of these fitness phenotypes is differential allosteric enzyme regulation, indicating isozyme divergence to achieve more-optimal control of metabolism.

Mapping of In Vivo RNA-Binding Sites by Ultraviolet (UV)-Cross-Linking Immunoprecipitation (CLIP)

RNA "CLIP" (cross-linking immunoprecipitation), the method by which RNA-protein complexes are covalently cross-linked and purified and the RNA sequenced, has attracted attention as a powerful means of developing genome-wide maps of direct, functional RNA-protein interaction sites. These maps have been used to identify points of regulation, and they hold promise for understanding the dynamics of RNA regulation in normal cell function and its dysregulation in disease.

Exon Array Biomarkers for the Differential Diagnosis of Schizophrenia and Bipolar Disorder

This study developed potential blood-based biomarker tests for diagnosing and differentiating schizophrenia (SZ), bipolar disorder type I (BD), and normal control (NC) subjects using mRNA gene expression signatures. A total of 90 subjects (n = 30 each for the three groups of subjects) provided blood samples at two visits. The Affymetrix exon microarray was used to profile the expression of over 1.4 million probesets. We selected potential biomarker panels using the temporal stability of the probesets and also back-tested them at two different visits for each subject. The 18-gene biomarker panels, using logistic regression modeling, correctly differentiated the three groups of subjects with high accuracy across the two different clinical visits (83-88% accuracy). The results are also consistent with the actual data and the "leave-one-out" analyses, indicating that the models should be predictive when applied to independent data cohorts. Many of the SZ and BD subjects were taking antipsychotic and mood stabilizer medications at the time of blood draw, raising the possibility that these drugs could have affected some of the differential transcription signatures. Using an independent Illumina data set of gene expression data from antipsychotic medication-free SZ subjects, the 18-gene biomarker panels produced a receiver operating characteristic curve accuracy greater than 0.866 in patients that were less than 30 years of age and medication free. We confirmed select transcripts by quantitative PCR and the nCounter® System. The episodic nature of psychiatric disorders might lead to highly variable results depending on when blood is collected in relation to the severity of the disease/symptoms. We have found stable trait gene panel markers for lifelong psychiatric disorders that may have diagnostic utility in younger undiagnosed subjects where there is a critical unmet need. The study requires replication in subjects for ultimate proof of the utility of the differential diagnosis.

Analysis of alternative splicing events for cancer diagnosis using a multiplexing nanophotonic biosensor

Personalized medicine is a promising tool not only for prevention, screening and development of more efficient treatment strategies, but also for diminishing the side effects caused by current therapies. Deciphering gene regulation pathways provides a reliable prognostic analysis to elucidate the origin of grave diseases and facilitate the selection of the most adequate treatment for each individual. Alternative splicing of mRNA precursors is one of these gene regulation pathways and enables cells to generate different protein outputs from the same gene depending on their developmental or homeostatic status. Its deregulation is strongly linked to disease onset and progression constituting a relevant and innovative class of biomarker. Herein we report a highly selective and sensitive nanophotonic biosensor based on the direct monitoring of the aberrant alternative splicing of Fas gene. Unlike conventional methods, the nanobiosensor performs a real-time detection of the specific isoforms in the fM-pM range without any cDNA synthesis or PCR amplification requirements. The nanobiosensor has been proven isoform-specific with no crosshybridization, greatly minimizing detection biases. The demonstrated high sensitivity and specificity make our nanobiosensor ideal for examining significant tumor-associated expression shifts of alternatively spliced isoforms for the early and accurate theranostics of cancer.

A proteogenomic approach to understand splice isoform functions through sequence and expression-based computational modeling

The products of multi-exon genes are a mixture of alternatively spliced isoforms, from which the translated proteins can have similar, different or even opposing functions. It is therefore essential to differentiate and annotate functions for individual isoforms. Computational approaches provide an efficient complement to expensive and time-consuming experimental studies. The input data of these methods range from DNA sequence, to RNA selection pressure, to expressed sequence tags, to full-length complementary DNA, to exon array, to RNA-seq expression, to proteomic data. Notably, RNA-seq technology generates quantitative profiling of transcript expression at the genome scale, with an unprecedented amount of expression data available for developing isoform function prediction methods. Integrative analysis of these data at different molecular levels enables a proteogenomic approach to systematically interrogate isoform functions. Here, we briefly review the state-of-the-art methods according to their input data sources, discuss their advantages and limitations and point out potential ways to improve prediction accuracies.

Quantitative evaluation of alternatively spliced mRNA isoforms by label-free real-time plasmonic sensing

Alternative splicing of mRNA precursors enables cells to generate different protein outputs from the same gene depending on their developmental or homeostatic status. Its deregulation is strongly linked to disease onset and progression. Current methodologies for monitoring alternative splicing demand elaborate procedures and often present difficulties in discerning between closely related isoforms, e.g. due to cross-hybridization during their detection. Herein, we report a general methodology using a Surface Plasmon Resonance (SPR) biosensor for label-free monitoring of alternative splicing events in real-time, without any cDNA synthesis or PCR amplification requirements. We applied this methodology to RNA isolated from HeLa cells for the quantification of alternatively spliced isoforms of the Fas gene, involved in cancer progression through regulation of programmed cell death. We demonstrate that our methodology is isoform-specific, with virtually no cross-hybridization, achieving limits of detection (LODs) in the picoMolar (pM) range. Similar results were obtained for the detection of the BCL-X gene mRNA isoforms. The results were independently validated by RT-qPCR, with excellent concordance in the determination of isoform ratios. The simplicity and robustness of this biosensor technology can greatly facilitate the exploration of alternative splicing biomarkers in disease diagnosis and therapy.

RASA: Robust Alternative Splicing Analysis for Human Transcriptome Arrays

Human transcriptome arrays (HTA) have recently been developed for high-throughput alternative splicing analysis by measuring signals not only from exons but also from exon-exon junctions. Effective use of these rich signals requires the development of computational methods for better gene and alternative splicing analyses. In this work, we introduce a computational method, Robust Alternative Splicing Analysis (RASA), for the analysis of the new transcriptome arrays by effective integration of the exon and junction signals. To increase robustness, RASA calculates the expression of each gene by selecting exons classified as not alternatively spliced. It then identifies alternatively spliced exons that are supported by both exon and junction signals to reduce the false positives. Finally, it detects additional alternative splicing candidates that are supported by only exon signals because the signals from the corresponding junctions are not well detected. RASA was demonstrated with Affymetrix HTAs and its performance was evaluated with mRNA-Seq and RT-PCR. The validation rate is 52.4%, which is a 60% increase when compared with previous methods that do not use selected exons for gene expression calculation and junction signals for splicing detection. These results suggest that RASA significantly improves alternative splicing analyses on HTA platforms.

Algorithms for differential splicing detection using exon arrays: a comparative assessment

Background

The analysis of differential splicing (DS) is crucial for understanding physiological processes in cells and organs. In particular, aberrant transcripts are known to be involved in various diseases including cancer. A widely used technique for studying DS are exon arrays. Over the last decade a variety of algorithms for the detection of DS events from exon arrays has been developed. However, no comprehensive, comparative evaluation including sensitivity to the most important data features has been conducted so far. To this end, we created multiple data sets based on simulated data to assess strengths and weaknesses of seven published methods as well as a newly developed method, KLAS. Additionally, we evaluated all methods on two cancer data sets that comprised RT-PCR validated results.

Results

Our studies indicated ARH as the most robust methods when integrating the results over all scenarios and data sets. Nevertheless, special cases or requirements favor other methods. While FIRMA was highly sensitive according to experimental data, SplicingCompass, MIDAS and ANOSVA showed high specificity throughout the scenarios. On experimental data ARH, FIRMA, MIDAS, and KLAS performed best.

Conclusions

Each method shows different characteristics regarding sensitivity, specificity, interference to certain data settings and robustness over multiple data sets. While some methods can be considered as generally good choices over all data sets and scenarios, other methods show heterogeneous prediction quality on the different data sets. The adequate method has to be chosen carefully and with a defined study aim in mind.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1322-x) contains supplementary material, which is available to authorized users.

A generalized dSpliceType framework to detect differential splicing and differential expression events using RNA-Seq

Transcriptomes are routinely compared in term of a list of differentially expressed genes followed by functional enrichment analysis. Due to the technology limitations of microarray, the molecular mechanisms of differential expression is poorly understood. Using RNA-seq data, we propose a generalized dSpliceType framework to systematically investigate the synergistic and antagonistic effects of differential splicing and differential expression. We applied the method to two public RNA-seq data sets and compared the transcriptomes between treatment and control conditions. The generalized dSpliceType detects and prioritizes a list of genes that are differentially expressed and/or spliced. In particular, the multivariate dSpliceType is among the fist to utilize sequential dependency of normalized base-wise read coverage signals and capture biological variability among replicates using a multivariate statistical model. We compared dSpliceType with two other methods in terms of five most common types of differential splicing events between two conditions using RNA-Seq. dSpliceType is free, available from http://dsplicetype.sourceforge.net/.

Computational extraction of a neural molecular network through alternative splicing

BACKGROUND

Generally, the results of high throughput analyses contain information about gene expressions, and about exon expressions. Approximately 90% of primary protein-coding transcripts undergo alternative splicing in mammals. However, changes induced by alternative exons have not been properly analyzed for their impact on important molecular networks or their biological events. Even when alternative exons are identified, they are usually subjected to bioinformatics analysis in the same way as the gene ignoring the possibility of functionality change because of the alteration of domain caused by alternative exon. Here, we reveal an effective computational approach to explore an important molecular network based on potential changes of functionality induced by alternative exons obtained from our comprehensive analysis of neuronal cell differentiation.

RESULTS

From our previously identified 262 differentially alternatively spliced exons during neuronal cell differentiations, we extracted 241 sets that changed the amino acid sequences between the alternatively spliced sequences. Conserved domain searches indicated that annotated domain(s) were changed in 128 sets. We obtained 49 genes whose terms overlapped between domain description and gene annotation. Thus, these 49 genes have alternatively differentially spliced in exons that affect their main functions. We performed pathway analysis using these 49 genes and identified the EGFR (epidermal growth factor receptor) and mTOR (mammalian target of rapamycin) signaling pathway as being involved frequently. Recent studies reported that the mTOR pathway is associated with neuronal cell differentiation, vindicating that our approach extracted an important molecular network successfully.

CONCLUSIONS

Effective informatics approaches for exons should be more complex than those for genes, because changes in alternative exons affect protein functions via alterations of amino acid sequences and functional domains. Our method extracted alterations of functional domains and identified key alternative splicing events. We identified the EGFR and mTOR signaling pathway as the most affected pathway. The mTOR pathway is important for neuronal differentiation, suggesting that this in silico extraction of alternative splicing networks is useful. This preliminary analysis indicated that automated analysis of the effects of alternative splicing would provide a rich source of biologically relevant information.

A robust estimation of exon expression to identify alternative spliced genes applied to human tissues and cancer samples

BACKGROUND

Accurate analysis of whole-gene expression and individual-exon expression is essential to characterize different transcript isoforms and identify alternative splicing events in human genes. One of the omic technologies widely used in many studies on human samples are the exon-specific expression microarray platforms.

RESULTS

Since there are not many validated comparative analyses to identify specific splicing events using data derived from these types of platforms, we have developed an algorithm (called ESLiM) to detect significant changes in exon use, and applied it to a reference dataset of 270 human genes that show alternative expression in different tissues. We compared the results with three other methodological approaches and provided the R source code to be applied elsewhere. The genes positively detected by these analyses also provide a verified subset of human genes that present tissue-regulated isoforms. Furthermore, we performed a validation analysis on human patient samples comparing two different subtypes of acute myeloid leukemia (AML) and we experimentally validated the splicing in several selected genes that showed exons with highly significant signal change.

CONCLUSIONS

The comparative analyses with other methods using a fair set of human genes that show alternative splicing and the validation on clinical samples demonstrate that the proposed novel algorithm is a reliable tool for detecting differential splicing in exon-level expression data.

The conserved ubiquitin-like protein Hub1 plays a critical role in splicing in human cells

Different from canonical ubiquitin-like proteins, Hub1 does not form covalent conjugates with substrates but binds proteins non-covalently. In Saccharomyces cerevisiae, Hub1 associates with spliceosomes and mediates alternative splicing of SRC1, without affecting pre-mRNA splicing generally. Human Hub1 is highly similar to its yeast homolog, but its cellular function remains largely unexplored. Here, we show that human Hub1 binds to the spliceosomal protein Snu66 as in yeast; however, unlike its S. cerevisiae homolog, human Hub1 is essential for viability. Prolonged in vivo depletion of human Hub1 leads to various cellular defects, including splicing speckle abnormalities, partial nuclear retention of mRNAs, mitotic catastrophe, and consequently cell death by apoptosis. Early consequences of Hub1 depletion are severe splicing defects, however, only for specific splice sites leading to exon skipping and intron retention. Thus, the ubiquitin-like protein Hub1 is not a canonical spliceosomal factor needed generally for splicing, but rather a modulator of spliceosome performance and facilitator of alternative splicing.

De novo prediction of PTBP1 binding and splicing targets reveals unexpected features of its RNA recognition and function

The splicing regulator Polypyrimidine Tract Binding Protein (PTBP1) has four RNA binding domains that each binds a short pyrimidine element, allowing recognition of diverse pyrimidine-rich sequences. This variation makes it difficult to evaluate PTBP1 binding to particular sites based on sequence alone and thus to identify target RNAs. Conversely, transcriptome-wide binding assays such as CLIP identify many in vivo targets, but do not provide a quantitative assessment of binding and are informative only for the cells where the analysis is performed. A general method of predicting PTBP1 binding and possible targets in any cell type is needed. We developed computational models that predict the binding and splicing targets of PTBP1. A Hidden Markov Model (HMM), trained on CLIP-seq data, was used to score probable PTBP1 binding sites. Scores from this model are highly correlated (ρ = −0.9) with experimentally determined dissociation constants. Notably, we find that the protein is not strictly pyrimidine specific, as interspersed Guanosine residues are well tolerated within PTBP1 binding sites. This model identifies many previously unrecognized PTBP1 binding sites, and can score PTBP1 binding across the transcriptome in the absence of CLIP data. Using this model to examine the placement of PTBP1 binding sites in controlling splicing, we trained a multinomial logistic model on sets of PTBP1 regulated and unregulated exons. Applying this model to rank exons across the mouse transcriptome identifies known PTBP1 targets and many new exons that were confirmed as PTBP1-repressed by RT-PCR and RNA-seq after PTBP1 depletion. We find that PTBP1 dependent exons are diverse in structure and do not all fit previous descriptions of the placement of PTBP1 binding sites. Our study uncovers new features of RNA recognition and splicing regulation by PTBP1. This approach can be applied to other multi-RRM domain proteins to assess binding site degeneracy and multifactorial splicing regulation.

A key step in the regulation of mammalian genes is the splicing of the messenger RNA precursor to produce a mature mRNA that can be translated into a particular protein needed by the cell. Through the process of alternative splicing, mRNAs encoding different proteins can be derived from the same primary gene transcript. The regulation of this process plays essential roles in the development of differentiated tissues and is mediated by special pre-mRNA binding proteins. To understand how these proteins control gene expression, one must characterize what they recognize in RNA and identify these binding sites across the genome in order to predict their targets. Models that allow this prediction are essential to understanding developmental regulatory programs and their perturbation by disease causing mutations. In this study, we use statistical methods to build models of RNA recognition by the important splicing regulator PTBP1 and then apply these models to predict PTBP1 regulation of new gene transcripts. We show that PTBP1 has different specificity for RNA than was previously recognized and that its target exons are more diverse than was known before. There are many similar splicing regulators in mammalian cells, and these analyses provide a general framework for the computational analysis of their RNA binding and target identification.

New insights into functional roles of the polypyrimidine tract-binding protein

Polypyrimidine Tract Binding Protein (PTB) is an intensely studied RNA binding protein involved in several post-transcriptional regulatory events of gene expression. Initially described as a pre-mRNA splicing regulator, PTB is now widely accepted as a multifunctional protein shuttling between nucleus and cytoplasm. Accordingly, PTB can interact with selected RNA targets, structural elements and proteins. There is increasing evidence that PTB and its paralog PTBP2 play a major role as repressors of alternatively spliced exons, whose transcription is tissue-regulated. In addition to alternative splicing, PTB is involved in almost all steps of mRNA metabolism, including polyadenylation, mRNA stability and initiation of protein translation. Furthermore, it is well established that PTB recruitment in internal ribosome entry site (IRES) activates the translation of picornaviral and cellular proteins. Detailed studies of the structural properties of PTB have contributed to our understanding of the mechanism of RNA binding by RNA Recognition Motif (RRM) domains. In the present review, we will describe the structural properties of PTB, its paralogs and co-factors, the role in post-transcriptional regulation and actions in cell differentiation and pathogenesis. Defining the multifunctional roles of PTB will contribute to the understanding of key regulatory events in gene expression.

Functional characterization of human genes from exon expression and RNA interference results

Complex biological systems comprise a large number of interacting molecules. The identification and detailed characterization of the functions of the involved genes and proteins are crucial for modeling and understanding such systems. To interrogate the various cellular processes, high-throughput techniques such as the Affymetrix Exon Array or RNA interference (RNAi) screens are powerful experimental approaches for functional genomics. However, they typically yield long gene lists that require computational methods to further analyze and functionally annotate the experimental results and to gain more insight into important molecular interactions. Here, we focus on bioinformatics software tools for the functional interpretation of exon expression data to discover alternative splicing events and their impact on gene and protein architecture, molecular networks, and pathways. We additionally demonstrate how to explore large lists of candidate genes as they also result from RNAi screens. In particular, our exemplary application studies show how to analyze the function of human genes that play a major role in human stem cells or viral infections.

eQTL Mapping Using RNA-seq Data

As RNA-seq is replacing gene expression microarrays to assess genome-wide transcription abundance, gene expression Quantitative Trait Locus (eQTL) studies using RNA-seq have emerged. RNA-seq delivers two novel features that are important for eQTL studies. First, it provides information on allele-specific expression (ASE), which is not available from gene expression microarrays. Second, it generates unprecedentedly rich data to study RNA-isoform expression. In this paper, we review current methods for eQTL mapping using ASE and discuss some future directions. We also review existing works that use RNA-seq data to study RNA-isoform expression and we discuss the gaps between these works and isoform-specific eQTL mapping.

puma 3.0: improved uncertainty propagation methods for gene and transcript expression analysis

Background

Microarrays have been a popular tool for gene expression profiling at genome-scale for over a decade due to the low cost, short turn-around time, excellent quantitative accuracy and ease of data generation. The Bioconductor package puma incorporates a suite of analysis methods for determining uncertainties from Affymetrix GeneChip data and propagating these uncertainties to downstream analysis. As isoform level expression profiling receives more and more interest within genomics in recent years, exon microarray technology offers an important tool to quantify expression level of the majority of exons and enables the possibility of measuring isoform level expression. However, puma does not include methods for the analysis of exon array data. Moreover, the current expression summarisation method for Affymetrix 3’ GeneChip data suffers from instability for low expression genes. For the downstream analysis, the method for differential expression detection is computationally intensive and the original expression clustering method does not consider the variance across the replicated technical and biological measurements. It is therefore necessary to develop improved uncertainty propagation methods for gene and transcript expression analysis.

Results

We extend the previously developed Bioconductor package puma with a new method especially designed for GeneChip Exon arrays and a set of improved downstream approaches. The improvements include: (i) a new gamma model for exon arrays which calculates isoform and gene expression measurements and a level of uncertainty associated with the estimates, using the multi-mappings between probes, isoforms and genes, (ii) a variant of the existing approach for the probe-level analysis of Affymetrix 3’ GeneChip data to produce more stable gene expression estimates, (iii) an improved method for detecting differential expression which is computationally more efficient than the existing approach in the package and (iv) an improved method for robust model-based clustering of gene expression, which takes technical and biological replicate information into consideration.

Conclusions

With the extensions and improvements, the puma package is now applicable to the analysis of both Affymetrix 3’ GeneChips and Exon arrays for gene and isoform expression estimation. It propagates the uncertainty of expression measurements into more efficient and comprehensive downstream analysis at both gene and isoform level. Downstream methods are also applicable to other expression quantification platforms, such as RNA-Seq, when uncertainty information is available from expression measurements. puma is available through Bioconductor and can be found at http://www.bioconductor.org.

Estimation of data-specific constitutive exons with RNA-Seq data

BACKGROUND

RNA-Seq has the potential to answer many diverse and interesting questions about the inner workings of cells. Estimating changes in the overall transcription of a gene is not straightforward. Changes in overall gene transcription can easily be confounded with changes in exon usage which alter the lengths of transcripts produced by a gene. Measuring the expression of constitutive exons--xons which are consistently conserved after splicing--ffers an unbiased estimation of the overall transcription of a gene.

RESULTS

We propose a clustering-based method, exClust, for estimating the exons that are consistently conserved after splicing in a given data set. These are considered as the exons which are "constitutive" in this data. The method utilises information from both annotation and the dataset of interest. The method is implemented in an openly available R function package, sydSeq.

CONCLUSION

When used on two real datasets exClust includes more than three times as many reads as the standard UI method, and improves concordance with qRT-PCR data. When compared to other methods, our method is shown to produce robust estimates of overall gene transcription.

Detection of Alternatively Spliced or Processed RNAs in Cancer Using Oligonucleotide Microarray

Deregulation of gene expression plays a pivotal role in tumorigenesis, so the ability to detect RNA alterations is of great value in cancer diagnosis and management. DNA microarrays have been used to measure changes in mRNA or microRNA level, but less often the change of RNA isoforms. Here we appraise the utilization of microarray in detecting alternatively processed RNAs, which have alternative splice forms, retained introns, or altered 3' untranslated regions. We cover the methodology and focus on cancer studies. Recent development in parallel or deep sequencing used in transcriptome analysis is also discussed.

Pathway analysis of the human brain transcriptome in disease

Pathway analysis is a powerful method for discerning differentially regulated genes and elucidating their biological importance. It allows for the identification of perturbed or aberrantly expressed genes within a biological context from extensive data sets and offers a simplistic approach for interrogating such data sets. With the growing use of microarrays and RNA-Seq, data for genome-wide studies are growing at an alarming rate, and the use of deep sequencing is revealing elements of the genome previously uncharacterised. Through the employment of pathway analysis, mechanisms in complex diseases may be explored and novel causatives found primarily through differentially regulated genes. Further, with the implementation of next generation sequencing, a deeper resolution may be attained, particularly in identification of isoform diversity and SNPs. Here, we look at a broad overview of pathway analysis in the human brain transcriptome and its relevance in teasing out underlying causes of complex diseases. We will outline processes in data gathering and analysis of particular diseases in which these approaches have been successful.

PTBP1-dependent regulation of USP5 alternative RNA splicing plays a role in glioblastoma tumorigenesis

Aberrant RNA splicing is thought to play a key role in tumorigenesis. The assessment of its specific contributions is limited by the complexity of information derived from genome-wide array-based approaches. We describe how performing splicing factor-specific comparisons using both tumor and cell line data sets may more readily identify physiologically relevant tumor-specific splicing events. Affymetrix exon array data derived from glioblastoma (GBM) tumor samples with defined polypyrimidine tract-binding protein 1 (PTBP1) levels were compared with data from U251 GBM cells with and without PTBP1 knockdown. This comparison yielded overlapping gene sets that comprised only a minor fraction of each data set. The identification of a novel GBM-specific splicing event involving the USP5 gene led us to further examine its role in tumorigenesis. In GBM, USP5 generates a shorter isoform 2 through recognition of a 5' splice site within exon 15. Production of the USP5 isoform 2 was strongly correlated with PTBP1 expression in GBM tumor samples and cell lines. Splicing regulation was consistent with the presence of an intronic PTBP1 binding site and could be modulated through antisense targeting of the isoform 2 splice site to force expression of isoform 1 in GBM cells. The forced expression of USP5 isoform 1 in two GBM cell lines inhibited cell growth and migration, implying an important role for USP5 splicing in gliomagenesis. These results support a role for aberrant RNA splicing in tumorigenesis and suggest that changes in relatively few genes may be sufficient to drive the process.

A mutation in the Srrm4 gene causes alternative splicing defects and deafness in the Bronx waltzer mouse

Sensory hair cells are essential for hearing and balance. Their development from epithelial precursors has been extensively characterized with respect to transcriptional regulation, but not in terms of posttranscriptional influences. Here we report on the identification and functional characterization of an alternative-splicing regulator whose inactivation is responsible for defective hair-cell development, deafness, and impaired balance in the spontaneous mutant Bronx waltzer (bv) mouse. We used positional cloning and transgenic rescue to locate the bv mutation to the splicing factor-encoding gene Ser/Arg repetitive matrix 4 (Srrm4). Transcriptome-wide analysis of pre–mRNA splicing in the sensory patches of embryonic inner ears revealed that specific alternative exons were skipped at abnormally high rates in the bv mice. Minigene experiments in a heterologous expression system confirmed that these skipped exons require Srrm4 for inclusion into the mature mRNA. Sequence analysis and mutagenesis experiments showed that the affected transcripts share a novel motif that is necessary for the Srrm4-dependent alternative splicing. Functional annotations and protein–protein interaction data indicated that the encoded proteins cluster in the secretion and neurotransmission pathways. In addition, the splicing of a few transcriptional regulators was found to be Srrm4 dependent, and several of the genes known to be targeted by these regulators were expressed at reduced levels in the bv mice. Although Srrm4 expression was detected in neural tissues as well as hair cells, analyses of the bv mouse cerebellum and neocortex failed to detect splicing defects. Our data suggest that Srrm4 function is critical in the hearing and balance organs, but not in all neural tissues. Srrm4 is the first alternative-splicing regulator to be associated with hearing, and the analysis of bv mice provides exon-level insights into hair-cell development.

The identification of novel deafness-causing mutations has been instrumental in revealing unexpected mechanisms that are required for development of the sound- and gravity-sensing hair cells of the inner ear. The Bronx waltzer (bv) mouse is characterized by defects in hair-cell development, as well as by deafness and impaired balance. Here, we report on our identification of a mutation in the Ser/Arg repetitive matrix 4 (Srrm4) gene as the source of these defects. The encoded protein, Srrm4, belongs to a family of RNA splicing factors that regulate the inclusion of certain genetic information (i.e. alternative exons) into the transcribed RNA. We analyzed the molecular function of Srrm4 by comparing the exon composition of RNAs in the inner ear of bv and control mice. This approach revealed that, in the bv mice, specific alternative exons were omitted from protein-encoding RNAs. The affected transcripts shared two features: they contained a short sequence motif that was required for Srrm4-dependent splicing, and they encoded proteins that were related predominantly to secretion and neurotransmission. In addition, RNAs of a few gene expression regulators contained Srrm4-regulated exons. Our data suggest that Srrm4-dependent alternative splicing has a profound effect on the developmental program of hair cells.

SplicerEX: a tool for the automated detection and classification of mRNA changes from conventional and splice-sensitive microarray expression data

The key postulate that one gene encodes one protein has been overhauled with the discovery that one gene can generate multiple RNA transcripts through alternative mRNA processing. In this study, we describe SplicerEX, a novel and uniquely motivated algorithm designed for experimental biologists that (1) detects widespread changes in mRNA isoforms from both conventional and splice sensitive microarray data, (2) automatically categorizes mechanistic changes in mRNA processing, and (3) mitigates known technological artifacts of exon array-based detection of alternative splicing resulting from 5' and 3' signal attenuation, background detection limits, and saturation of probe set signal intensity. In this study, we used SplicerEX to compare conventional and exon-based Affymetrix microarray data in a model of EBV transformation of primary human B cells. We demonstrated superior detection of 3'-located changes in mRNA processing by the Affymetrix U133 GeneChip relative to the Human Exon Array. SplicerEX-identified exon-level changes in the EBV infection model were confirmed by RT-PCR and revealed a novel set of EBV-regulated mRNA isoform changes in caspases 6, 7, and 8. Finally, SplicerEX as compared with MiDAS analysis of publicly available microarray data provided more efficiently categorized mRNA isoform changes with a significantly higher proportion of hits supported by previously annotated alternative processing events. Therefore, SplicerEX provides an important tool for the biologist interested in studying changes in mRNA isoform usage from conventional or splice-sensitive microarray platforms, especially considering the expansive amount of archival microarray data generated over the past decade. SplicerEX is freely available upon request.

CD28 costimulation regulates genome-wide effects on alternative splicing

CD28 is the major costimulatory receptor required for activation of naïve T cells, yet CD28 costimulation affects the expression level of surprisingly few genes over those altered by TCR stimulation alone. Alternate splicing of genes adds diversity to the proteome and contributes to tissue-specific regulation of genes. Here we demonstrate that CD28 costimulation leads to major changes in alternative splicing during activation of naïve T cells, beyond the effects of TCR alone. CD28 costimulation affected many more genes through modulation of alternate splicing than by modulation of transcription. Different families of biological processes are over-represented among genes alternatively spliced in response to CD28 costimulation compared to those genes whose transcription is altered, suggesting that alternative splicing regulates distinct biological effects. Moreover, genes dependent upon hnRNPLL, a global regulator of splicing in activated T cells, were enriched in T cells activated through TCR plus CD28 as compared to TCR alone. We show that hnRNPLL expression is dependent on CD28 signaling, providing a mechanism by which CD28 can regulate splicing in T cells and insight into how hnRNPLL can influence signal-induced alternative splicing in T cells. The effects of CD28 on alternative splicing provide a newly appreciated means by which CD28 can regulate T cell responses.

Identification of conserved splicing motifs in mutually exclusive exons of 15 insect species

Background

During alternative splicing, the inclusion of an exon in the final mRNA molecule is determined by nuclear proteins that bind cis-regulatory sequences in a target pre-mRNA molecule. A recent study suggested that the regulatory codes of individual RNA-binding proteins may be nearly immutable between very diverse species such as mammals and insects. The model system Drosophila melanogaster therefore presents an excellent opportunity for the study of alternative splicing due to the availability of quality EST annotations in FlyBase.

Methods

In this paper, we describe an in silico analysis pipeline to extract putative exonic splicing regulatory sequences from a multiple alignment of 15 species of insects. Our method, ESTs-to-ESRs (E2E), uses graph analysis of EST splicing graphs to identify mutually exclusive (ME) exons and combines phylogenetic measures, a sliding window approach along the multiple alignment and the Welch's t statistic to extract conserved ESR motifs.

Results

The most frequent 100% conserved word of length 5 bp in different insect exons was "ATGGA". We identified 799 statistically significant "spike" hexamers, 218 motifs with either a left or right FDR corrected spike magnitude p-value < 0.05 and 83 with both left and right uncorrected p < 0.01. 11 genes were identified with highly significant motifs in one ME exon but not in the other, suggesting regulation of ME exon splicing through these highly conserved hexamers. The majority of these genes have been shown to have regulated spatiotemporal expression. 10 elements were found to match three mammalian splicing regulator databases. A putative ESR motif, GATGCAG, was identified in the ME-13b but not in the ME-13a of Drosophila N-Cadherin, a gene that has been shown to have a distinct spatiotemporal expression pattern of spliced isoforms in a recent study.

Conclusions

Analysis of phylogenetic relationships and variability of sequence conservation as implemented in the E2E spikes method may lead to improved identification of ESRs. We found that approximately half of the putative ESRs in common between insects and mammals have a high statistical support (p < 0.01). Several Drosophila genes with spatiotemporal expression patterns were identified to contain putative ESRs located in one exon of the ME exon pairs but not in the other.

BEAT: Bioinformatics Exon Array Tool to store, analyze and visualize Affymetrix GeneChip Human Exon Array data from disease experiments

BACKGROUND

It is known from recent studies that more than 90% of human multi-exon genes are subject to Alternative Splicing (AS), a key molecular mechanism in which multiple transcripts may be generated from a single gene. It is widely recognized that a breakdown in AS mechanisms plays an important role in cellular differentiation and pathologies. Polymerase Chain Reactions, microarrays and sequencing technologies have been applied to the study of transcript diversity arising from alternative expression. Last generation Affymetrix GeneChip Human Exon 1.0 ST Arrays offer a more detailed view of the gene expression profile providing information on the AS patterns. The exon array technology, with more than five million data points, can detect approximately one million exons, and it allows performing analyses at both gene and exon level. In this paper we describe BEAT, an integrated user-friendly bioinformatics framework to store, analyze and visualize exon arrays datasets. It combines a data warehouse approach with some rigorous statistical methods for assessing the AS of genes involved in diseases. Meta statistics are proposed as a novel approach to explore the analysis results. BEAT is available at http://beat.ba.itb.cnr.it.

RESULTS

BEAT is a web tool which allows uploading and analyzing exon array datasets using standard statistical methods and an easy-to-use graphical web front-end. BEAT has been tested on a dataset with 173 samples and tuned using new datasets of exon array experiments from 28 colorectal cancer and 26 renal cell cancer samples produced at the Medical Genetics Unit of IRCCS Casa Sollievo della Sofferenza.To highlight all possible AS events, alternative names, accession Ids, Gene Ontology terms and biochemical pathways annotations are integrated with exon and gene level expression plots. The user can customize the results choosing custom thresholds for the statistical parameters and exploiting the available clinical data of the samples for a multivariate AS analysis.

CONCLUSIONS

Despite exon array chips being widely used for transcriptomics studies, there is a lack of analysis tools offering advanced statistical features and requiring no programming knowledge. BEAT provides a user-friendly platform for a comprehensive study of AS events in human diseases, displaying the analysis results with easily interpretable and interactive tables and graphics.

JETTA: junction and exon toolkits for transcriptome analysis

SUMMARY

High-throughput genome-wide studies of alternatively spliced mRNA transcripts have become increasingly important in clinical research. Consequently, easy-to-use software tools are required to process data from these studies, for example, using exon and junction arrays. Here, we introduce JETTA, an integrated software package for the calculation of gene expression indices as well as the identification and visualization of alternative splicing events. We demonstrate the software using data of human liver and muscle samples hybridized on an exon-junction array.

AVAILABILITY

JETTA and its demonstrations are freely available at http://igenomed.stanford.edu/~junhee/JETTA/index.html

Deciphering the plant splicing code: experimental and computational approaches for predicting alternative splicing and splicing regulatory elements

Extensive alternative splicing (AS) of precursor mRNAs (pre-mRNAs) in multicellular eukaryotes increases the protein-coding capacity of a genome and allows novel ways to regulate gene expression. In flowering plants, up to 48% of intron-containing genes exhibit AS. However, the full extent of AS in plants is not yet known, as only a few high-throughput RNA-Seq studies have been performed. As the cost of obtaining RNA-Seq reads continues to fall, it is anticipated that huge amounts of plant sequence data will accumulate and help in obtaining a more complete picture of AS in plants. Although it is not an onerous task to obtain hundreds of millions of reads using high-throughput sequencing technologies, computational tools to accurately predict and visualize AS are still being developed and refined. This review will discuss the tools to predict and visualize transcriptome-wide AS in plants using short-reads and highlight their limitations. Comparative studies of AS events between plants and animals have revealed that there are major differences in the most prevalent types of AS events, suggesting that plants and animals differ in the way they recognize exons and introns. Extensive studies have been performed in animals to identify cis-elements involved in regulating AS, especially in exon skipping. However, few such studies have been carried out in plants. Here, we review the current state of research on splicing regulatory elements (SREs) and briefly discuss emerging experimental and computational tools to identify cis-elements involved in regulation of AS in plants. The availability of curated alternative splice forms in plants makes it possible to use computational tools to predict SREs involved in AS regulation, which can then be verified experimentally. Such studies will permit identification of plant-specific features involved in AS regulation and contribute to deciphering the splicing code in plants.

The Role of Polypyrimidine Tract-Binding Proteins and Other hnRNP Proteins in Plant Splicing Regulation

Alternative precursor mRNA splicing is a widespread phenomenon in multicellular eukaryotes and represents a major means for functional expansion of the transcriptome. While several recent studies have revealed an important link between splicing regulation and fundamental biological processes in plants, many important aspects, such as the underlying splicing regulatory mechanisms, are so far not well understood. Splicing decisions are in general based on a splicing code that is determined by the dynamic interplay of splicing-controlling factors and cis-regulatory elements. Several members of the group of heterogeneous nuclear ribonucleoprotein (hnRNP) proteins are well known regulators of splicing in animals and the comparatively few reports on some of their plant homologs revealed similar functions. This also applies to polypyrimidine tract-binding proteins, a thoroughly investigated class of hnRNP proteins with splicing regulatory functions in both animals and plants. Further examples from plants are auto- and cross-regulatory splicing circuits of glycine-rich RNA binding proteins and splicing enhancement by oligouridylate binding proteins. Besides their role in defining splice site choice, hnRNP proteins are also involved in multiple other steps of nucleic acid metabolism, highlighting the functional versatility of this group of proteins in higher eukaryotes.

Chronic alcohol exposure alters gene expression in HepG2 cells

BACKGROUND

The liver is the primary site of alcohol metabolism and is highly vulnerable to injuries due to chronic alcohol abuse. Several molecular mechanisms, including oxidative stress and altered cellular metabolism, have been implicated in the development and progression of alcoholic liver disease. We sought to gain further insight into the molecular pathogenesis by studying the effects of ethanol exposure on the global gene expression in HepG2 cells.

METHODS

HepG2 cells were cultured in the presence or absence of 75 mM ethanol for 9 days, with fresh media daily. Global gene expression changes were studied using Affymetrix GeneChip(®) Human Exon 1.0 ST Arrays. Gene expression differences were validated for 13 genes by quantitative real-time RT-PCR. To identify biological pathways affected by ethanol treatment, differentially expressed genes were analyzed by Ingenuity Pathway Analysis software.

RESULTS

Long-term ethanol exposure altered the expression of 1,093 genes (false discovery rate ≤ 3%); many of these changes were modest. Long-term ethanol exposure affected several pathways, including acute phase response, amino acid metabolism, carbohydrate metabolism, and lipid metabolism.

CONCLUSIONS

Global measurements of gene expression show that a large number of genes are affected by chronic ethanol, although most show modest effect. These data provide insight into the molecular pathology resulting from extended alcohol exposure.

Transcriptional regulation of the human Raver2 ribonucleoprotein gene

Raver2 is a putative modulator of the activity of the polypyrimidine-tract binding protein (PTB), one of the most intensively studied splicing repressors. Little is known about Raver2 expression, and all current data is from mice where it shows tissue specificity. In the present study, by comparing Raver2 transcript expression in human and mouse tissues, we found that human Raver2 is ubiquitously expressed in adult tissues. In order to investigate human Raver2 transcription regulation, we identified and characterized a putative promoter region in a 1000bp region upstream of the transcription starting site of the gene. Dual luciferase reporter assays demonstrated that this region had promoter activity conferred by the first 160bp. By mutagenic analyses of putative cis-acting regulatory sequences, we identified an individual site that decreased the promoter activity by up to 40% when mutated. Together, our results suggest that regulation of human Raver2 expression involves TATA-less transcriptional activity.

Genetic variation of pre-mRNA alternative splicing in human populations

The precise splicing outcome of a transcribed gene is controlled by complex interactions between cis regulatory splicing signals and trans-acting regulators. In higher eukaryotes, alternative splicing is a prevalent mechanism for generating transcriptome and proteome diversity. Alternative splicing can modulate gene function, affect organismal phenotype and cause disease. Common genetic variation that affects splicing regulation can lead to differences in alternative splicing between human individuals and consequently impact expression level or protein function. In several well-documented examples, such natural variation of alternative splicing has indeed been shown to influence disease susceptibility and drug response. With new microarray and sequencing-based genomic technologies that can analyze eukaryotic transcriptomes at the exon or nucleotide level, it has become possible to globally compare the alternative splicing profiles across human individuals in any tissue or cell type of interest. Recent large-scale transcriptome studies using high-density splicing-sensitive microarray and deep RNA sequencing (RNA-Seq) have revealed widespread genetic variation of alternative splicing in humans. In the future, an extensive catalog of alternative splicing variation in human populations will help elucidate the molecular underpinnings of complex traits and human diseases, and shed light on the mechanisms of splicing regulation in human cells.

Comprehensive exon array data processing method for quantitative analysis of alternative spliced variants

Alternative splicing of pre-mRNA generates protein diversity. Dysfunction of splicing machinery and expression of specific transcripts has been linked to cancer progression and drug response. Exon microarray technology enables genome-wide quantification of expression levels of the majority of exons and facilitates the discovery of alternative splicing events. Analysis of exon array data is more challenging than the analysis of gene expression data and there is a need for reliable quantification of exons and alternatively spliced variants. We introduce a novel, computationally efficient methodology, Multiple Exon Array Preprocessing (MEAP), for exon array data pre-processing, analysis and visualization. We compared MEAP with existing pre-processing methods, and validation of six exons and two alternatively spliced variants with qPCR corroborated MEAP expression estimates. Analysis of exon array data from head and neck squamous cell carcinoma (HNSCC) cell lines revealed several transcripts associated with 11q13 amplification, which is related with decreased survival and metastasis in HNSCC patients. Our results demonstrate that MEAP produces reliable expression values at exon, alternatively spliced variant and gene levels, which allows generating novel experimentally testable predictions.

Identification of Tat-SF1 cellular targets by exon array analysis reveals dual roles in transcription and splicing

Tat specific factor 1 (Tat-SF1) interacts with components of both the transcription and splicing machineries and has been classified as a transcription-splicing factor. Although its function as an HIV-1 dependency factor has been investigated, relatively little is known about the cellular functions of Tat-SF1. To identify target genes of Tat-SF1, we utilized a combination of RNAi and exon-specific microarrays. These arrays, which survey genome-wide changes in transcript and individual exon levels, revealed 450 genes with transcript level changes upon Tat-SF1 depletion. Strikingly, 98% of these target genes were down-regulated upon depletion, indicating that Tat-SF1 generally activates gene expression. We also identified 89 genes that showed differential exon level changes after Tat-SF1 depletion. The 89 genes showed evidence of many different types of alternative exon use consistent with the regulation of transcription initiation sites and RNA processing. Minimal overlap between genes with transcript-level and exon-level changes suggests that Tat-SF1 does not functionally couple transcription and splicing. Biological processes significantly enriched with transcript- and exon-level targets include the cell cycle and nucleic acid metabolism; the insulin signaling pathway was enriched with Tat-SF1 transcript-level targets but not exon-level targets. Additionally, a hexamer, ATGCCG, was over-represented in the promoter region of genes showing changes in transcription initiation upon Tat-SF1 depletion. This may represent a novel motif that Tat-SF1 recognizes during transcription. Together, these findings suggest that Tat-SF1 functions independently in transcription and splicing of cellular genes.

Comprehensive analysis of alternative splicing and functionality in neuronal differentiation of P19 cells

BACKGROUND

Alternative splicing, which produces multiple mRNAs from a single gene, occurs in most human genes and contributes to protein diversity. Many alternative isoforms are expressed in a spatio-temporal manner, and function in diverse processes, including in the neural system.

METHODOLOGY/PRINCIPAL FINDINGS

The purpose of the present study was to comprehensively investigate neural-splicing using P19 cells. GeneChip Exon Array analysis was performed using total RNAs purified from cells during neuronal cell differentiation. To efficiently and readily extract the alternative exon candidates, 9 filtering conditions were prepared, yielding 262 candidate exons (236 genes). Semiquantitative RT-PCR results in 30 randomly selected candidates suggested that 87% of the candidates were differentially alternatively spliced in neuronal cells compared to undifferentiated cells. Gene ontology and pathway analyses suggested that many of the candidate genes were associated with neural events. Together with 66 genes whose functions in neural cells or organs were reported previously, 47 candidate genes were found to be linked to 189 events in the gene-level profile of neural differentiation. By text-mining for the alternative isoform, distinct functions of the isoforms of 9 candidate genes indicated by the result of Exon Array were confirmed.

CONCLUSIONS/SIGNIFICANCE

Alternative exons were successfully extracted. Results from the informatics analyses suggested that neural events were primarily governed by genes whose expression was increased and whose transcripts were differentially alternatively spliced in the neuronal cells. In addition to known functions in neural cells or organs, the uninvestigated alternative splicing events of 11 genes among 47 candidate genes suggested that cell cycle events are also potentially important. These genes may help researchers to differentiate the roles of alternative splicing in cell differentiation and cell proliferation.

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.

Insights into global mechanisms and disease by gene expression profiling

Transcriptomics has played an essential role as proof of concept in the development of experimental and bioinformatics approaches for the generation and analysis of Omics data. We are giving an introduction on how large-scale technologies for gene expression profiling, especially microarrays, have changed the view from studying single molecular events to a systems level view of global mechanisms in a cell, the biological processes, and their pathological mutations. The main platforms available for gene expression profiling (from microarrays to RNA-seq) are presented and the general concepts that need to be taken into account for proper data analysis in order to extract objective and general conclusions from transcriptomics experiments are introduced. We also describe the available main bioinformatics resources used for this purpose.

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.

Exon array analysis using re-defined probe sets results in reliable identification of alternatively spliced genes in non-small cell lung cancer

Background

Treatment of non-small cell lung cancer with novel targeted therapies is a major unmet clinical need. Alternative splicing is a mechanism which generates diverse protein products and is of functional relevance in cancer.

Results

In this study, a genome-wide analysis of the alteration of splicing patterns between lung cancer and normal lung tissue was performed. We generated an exon array data set derived from matched pairs of lung cancer and normal lung tissue including both the adenocarcinoma and the squamous cell carcinoma subtypes. An enhanced workflow was developed to reliably detect differential splicing in an exon array data set. In total, 330 genes were found to be differentially spliced in non-small cell lung cancer compared to normal lung tissue. Microarray findings were validated with independent laboratory methods for CLSTN1, FN1, KIAA1217, MYO18A, NCOR2, NUMB, SLK, SYNE2, TPM1, (in total, 10 events) and ADD3, which was analysed in depth. We achieved a high validation rate of 69%. Evidence was found that the activity of FOX2, the splicing factor shown to cause cancer-specific splicing patterns in breast and ovarian cancer, is not altered at the transcript level in several cancer types including lung cancer.

Conclusions

This study demonstrates how alternatively spliced genes can reliably be identified in a cancer data set. Our findings underline that key processes of cancer progression in NSCLC are affected by alternative splicing, which can be exploited in the search for novel targeted therapies.

Improvements to previous algorithms to predict gene structure and isoform concentrations using Affymetrix Exon arrays

BACKGROUND

Exon arrays provide a way to measure the expression of different isoforms of genes in an organism. Most of the procedures to deal with these arrays are focused on gene expression or on exon expression. Although the only biological analytes that can be properly assigned a concentration are transcripts, there are very few algorithms that focus on them. The reason is that previously developed summarization methods do not work well if applied to transcripts. In addition, gene structure prediction, i.e., the correspondence between probes and novel isoforms, is a field which is still unexplored.

RESULTS

We have modified and adapted a previous algorithm to take advantage of the special characteristics of the Affymetrix exon arrays. The structure and concentration of transcripts -some of them possibly unknown- in microarray experiments were predicted using this algorithm. Simulations showed that the suggested modifications improved both specificity (SP) and sensitivity (ST) of the predictions. The algorithm was also applied to different real datasets showing its effectiveness and the concordance with PCR validated results.

CONCLUSIONS

The proposed algorithm shows a substantial improvement in the performance over the previous version. This improvement is mainly due to the exploitation of the redundancy of the Affymetrix exon arrays. An R-Package of SPACE with the updated algorithms have been developed and is freely available.

Position-dependent alternative splicing activity revealed by global profiling of alternative splicing events regulated by PTB

To gain global insights into the role of the well-known repressive splicing regulator PTB, we analyzed the consequences of PTB knockdown in HeLa cells using high-density oligonucleotide splice-sensitive microarrays. The major class of identified PTB-regulated splicing event was PTB-repressed cassette exons, but there was also a substantial number of PTB-activated splicing events. PTB-repressed and PTB-activated exons showed a distinct arrangement of motifs with pyrimidine-rich motif enrichment within and upstream of repressed exons but downstream of activated exons. The N-terminal half of PTB was sufficient to activate splicing when recruited downstream of a PTB-activated exon. Moreover, insertion of an upstream pyrimidine tract was sufficient to convert a PTB-activated exon to a PTB-repressed exon. Our results show that PTB, an archetypal splicing repressor, has variable splicing activity that predictably depends upon its binding location with respect to target exons.

HITS-CLIP: panoramic views of protein-RNA regulation in living cells

The study of gene regulation in cells has recently begun to shift from a period dominated by the study of transcription factor-DNA interactions to a new focus on RNA regulation. This was sparked by the still-emerging recognition of the central role for RNA in cellular complexity emanating from the RNA World hypothesis, and has been facilitated by technologic advances, in particular high throughput RNA sequencing and crosslinking methods (RNA-Seq, CLIP, and HITS-CLIP). This study will place these advances in context, and, focusing on CLIP, will explain the method, what it can be used for, and how to approach using it. Examples of the successes, limitations, and future of the technique will be discussed.