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Motivation: Accurate identification of genetic variants such as single-nucleotide polymorphisms (SNPs) or RNA editing sites from RNA-Seq reads is important, yet challenging, because it necessitates a very low false-positive rate in read mapping. Although many read aligners are available, no single aligner was specifically developed or tested as an effective tool for SNP and RNA editing prediction. Results: We present RASER, an accurate read aligner with novel mapping schemes and index tree structure that aims to reduce false-positive mappings due to existence of highly similar regions. We demonstrate that RASER shows the best mapping accuracy compared with other popular algorithms and highest sensitivity in identifying multiply mapped reads. As a result, RASER displays superb efficacy in unbiased mapping of the alternative alleles of SNPs and in identification of RNA editing sites. [ABSTRACT FROM AUTHOR]

Pre-mRNA splicing is regulated through the combinatorial activity of RNA motifs, including splice sites and splicing regulatory elements. Here we show that the activity of the G-run (polyguanine sequence) class of splicing enhancer elements is ∼4-fold higher when adjacent to intermediate strength 5′ splice sites (ss) than when adjacent to weak 5′ ss, and ∼1.3-fold higher relative to strong 5′ ss. We observed this dependence on 5′ ss strength in both splicing reporters and in global microarray and mRNA-Seq analyses of splicing changes following RNA interference against heterogeneous nuclear ribonucleoprotein (hnRNP) H, which cross-linked to G-runs adjacent to many regulated exons. An exon's responsiveness to changes in hnRNP H levels therefore depends in a complex way on G-run abundance and 5′ ss strength. This pattern of activity enables G-runs and hnRNP H to buffer the effects of 5′ ss mutations, augmenting both the frequency of 5′ ss polymorphism and the evolution of new splicing patterns. Certain other splicing factors may function similarly. [ABSTRACT FROM AUTHOR]

Accurate and efficient splicing of eukaryotic pre-mRNA5 requires recognition by transacting factors of a complex array of cis-acting RNA elements. Here, we developed a generalized Bayesian network to model the coevolution of splicing cis elements in diverse eukaryotic taxa. Cross-exon but not cross-intron compensatory interactions between the 5′ splice site (5'ss) and 3′ splice site (3'ss) were observed in human/mouse, indicating that the exon is the primary evolutionary unit in mammals. Studied plants, fungi, and invertebrates exhibited exclusively cross-intron interactions, suggesting that intron definition drives evolution in these organisms. In mammals, 5'ss strength and the strength of several classes of exonic splicing silencers (ESS5) evolved in a correlated way, whereas specific exonic splicing enhancers (ESEs), including motifs associated with hTra2, SRp55, and SRp20, evolved in a compensatory manner relative to the 5'ss and 3'ss. Interactions between specific ESS or ESE motifs were not observed, suggesting that elements bound by different factors are not commonly interchangeable. Thus, the splicing elements defining exons coevolve in a way that preserves overall exon strength, allowing specific elements to substitute for loss or weakening of others. [ABSTRACT FROM AUTHOR]

We introduce a system identification method based on weighted-principal component regression (WPCR). This approach aims to identify the dynamics in a linear time-invariant (LTI) model which may represent a resting physiologic system. It tackles the time-domain system identification problem by considering, asymptotically, frequency information inherent in the given data. By including in the model only dominant frequency components of the input signal(s), this method enables construction of candidate models that are specific to the data and facilitates a reduction in parameter estimation error when the signals are colored (as are most physiologic signals). Additionally, this method allows incorporation of preknowledge about the system through a weighting scheme. We present the method in the context of single-input and multi-input single-output systems operating in open-loop and closed-loop. In each scenario, we compare the WPCR method with conventional approaches and approaches that also build data-specific candidate models. Through both simulated and experimental data, we show that the WPCR method enables more accurate identification of the system impulse response function than the other methods when the input signal(s) is colored. [ABSTRACT FROM AUTHOR]

We introduce a model order selection criterion called signal prediction error (SPE) for the identification of a linear regression model, which can be an adequate representation of a resting physiologic system. SPE is an estimate of the prediction error variance due only to model estimation error and not unobserved noise, which distinguishes it from the widely used final prediction error (FPE). We then present a theoretical analysis of SPE, which predicts that its ability to select correctly the model order is more dependent on the signal-to-noise ratio (SNR) and less dependent on the number of data samples available for analysis. We next propose a heuristic procedure based on SPE (called SPED) to improve its robustness to SNR levels. We then demonstrate, through simulated physiologic data at high SNR levels, that SPE will be equivalent to consistent model order selection criteria for long data records but will become superior to FPE and other model order selection criteria as the size of the data record decreases. The simulated data results also show that SPED is indeed a significant improvement over SPE in terms of robustness to SNR. Finally; we demonstrate the applicability of SPE and SPED to actual cardio-respiratory-renal data. [ABSTRACT FROM AUTHOR]

A long-term goal of our research is to understand the RNA splicing code: how exons and splice sites are identified in primary transcripts, and how this code is altered in cell- and condition-specific alternative splicing. We are focusing on understanding the context-dependent activities and interactions among splicing regulatory elements. We have found that most members of a class of elements known as exonic splicing silencers share a consistent pattern of context-dependent activity, silencing splicing when present in exons, activating splicing when present in introns, and altering splice site choice when present between competing splice sites. Splicing regulatory activity may also depend on splice site strength; we are exploring a pattern in which some intronic splicing enhancers enhance splicing of exons more strongly when 5' splice site (5'ss) strength is weak/moderate than when the 5'ss is either stronger or weaker. We are also using computational approaches to identify pairs of preferentially co-occurring motifs, and have used this approach to identify a pair of intronic motifs that can function cooperatively to silence the splicing of intervening exons. [ABSTRACT FROM AUTHOR]

Human speech is one of the few examples of vocal learning among mammals yet ~half of avian species exhibit this ability. Its neurogenetic basis is largely unknown beyond a shared requirement for FoxP2 in both humans and zebra finches. We manipulated FoxP2 isoforms in Area X, a song-specific region of the avian striatopallidum analogous to human anterior striatum, during a critical period for song development. We delineate, for the first time, unique contributions of each isoform to vocal learning. Weighted gene coexpression network analysis of RNA-seq data revealed gene modules correlated to singing, learning, or vocal variability. Coexpression related to singing was found in juvenile and adult Area X whereas coexpression correlated to learning was unique to juveniles. The confluence of learning and singing coexpression in juvenile Area X may underscore molecular processes that drive vocal learning in young zebra finches and, by analogy, humans. [ABSTRACT FROM AUTHOR]

Previous studies had shown that the integration of genome wide expression profiles, in metabolic tissues, with genetic and phenotypic variance, provided valuable insight into the underlying molecular mechanisms. We used RNA-Seq to characterize hypothalamic transcriptome in 99 inbred strains of mice from the Hybrid Mouse Diversity Panel (HMDP), a reference resource population for cardiovascular and metabolic traits. We report numerous novel transcripts supported by proteomic analyses, as well as novel non coding RNAs. High resolution genetic mapping of transcript levels in HMDP, reveals both local and trans expression Quantitative Trait Loci (eQTLs) demonstrating 2 trans eQTL 'hotspots' associated with expression of hundreds of genes. We also report thousands of alternative splicing events regulated by genetic variants. Finally, comparison with about 150 metabolic and cardiovascular traits revealed many highly significant associations. Our data provide a rich resource for understanding the many physiologic functions mediated by the hypothalamus and their genetic regulation. [ABSTRACT FROM AUTHOR]

Background: Although metabolic reprogramming is critical in the pathogenesis of heart failure, studies to date have focused principally on fatty acid and glucose metabolism. Contribution of amino acid metabolic regulation in the disease remains understudied.Methods and Results: Transcriptomic and metabolomic analyses were performed in mouse failing heart induced by pressure overload. Suppression of branched-chain amino acid (BCAA) catabolic gene expression along with concomitant tissue accumulation of branched-chain α-keto acids was identified as a significant signature of metabolic reprogramming in mouse failing hearts and validated to be shared in human cardiomyopathy hearts. Molecular and genetic evidence identified the transcription factor Krüppel-like factor 15 as a key upstream regulator of the BCAA catabolic regulation in the heart. Studies using a genetic mouse model revealed that BCAA catabolic defect promoted heart failure associated with induced oxidative stress and metabolic disturbance in response to mechanical overload. Mechanistically, elevated branched-chain α-keto acids directly suppressed respiration and induced superoxide production in isolated mitochondria. Finally, pharmacological enhancement of branched-chain α-keto acid dehydrogenase activity significantly blunted cardiac dysfunction after pressure overload.Conclusions: BCAA catabolic defect is a metabolic hallmark of failing heart resulting from Krüppel-like factor 15-mediated transcriptional reprogramming. BCAA catabolic defect imposes a previously unappreciated significant contribution to heart failure. [ABSTRACT FROM AUTHOR]

RNA splicing is a major contributor to total transcriptome complexity; however, the functional role and regulation of splicing in heart failure remain poorly understood. Here, we used a total transcriptome profiling and bioinformatic analysis approach and identified a muscle-specific isoform of an RNA splicing regulator, RBFox1 (also known as A2BP1), as a prominent regulator of alternative RNA splicing during heart failure. Evaluation of developing murine and zebrafish hearts revealed that RBFox1 is induced during postnatal cardiac maturation. However, we found that RBFox1 is markedly diminished in failing human and mouse hearts. In a mouse model, RBFox1 deficiency in the heart promoted pressure overload-induced heart failure. We determined that RBFox1 is a potent regulator of RNA splicing and is required for a conserved splicing process of transcription factor MEF2 family members that yields different MEF2 isoforms with differential effects on cardiac hypertrophic gene expression. Finally, induction of RBFox1 expression in murine pressure overload models substantially attenuated cardiac hypertrophy and pathological manifestations. Together, this study identifies regulation of RNA splicing by RBFox1 as an important player in transcriptome reprogramming during heart failure that influence pathogenesis of the disease. [ABSTRACT FROM AUTHOR]

Tumor metastasis remains the major cause of cancer-related death, but its molecular basis is still not well understood. Here we uncovered a splicing-mediated pathway that is essential for breast cancer metastasis. We show that the RNA-binding protein heterogeneous nuclear ribonucleoprotein M (hnRNPM) promotes breast cancer metastasis by activating the switch of alternative splicing that occurs during epithelial-mesenchymal transition (EMT). Genome-wide deep sequencing analysis suggests that hnRNPM potentiates TGFβ signaling and identifies CD44 as a key downstream target of hnRNPM. hnRNPM ablation prevents TGFβ-induced EMT and inhibits breast cancer metastasis in mice, whereas enforced expression of the specific CD44 standard (CD44s) splice isoform overrides the loss of hnRNPM and permits EMT and metastasis. Mechanistically, we demonstrate that the ubiquitously expressed hnRNPM acts in a mesenchymal-specific manner to precisely control CD44 splice isoform switching during EMT. This restricted cell-type activity of hnRNPM is achieved by competition with ESRP1, an epithelial splicing regulator that binds to the same cis-regulatory RNA elements as hnRNPM and is repressed during EMT. Importantly, hnRNPM is associated with aggressive breast cancer and correlates with increased CD44s in patient specimens. These findings demonstrate a novel molecular mechanism through which tumor metastasis is endowed by the hnRNPM-mediated splicing program. [ABSTRACT FROM AUTHOR]

Sequence-specific recognition of nucleic-acid motifs is critical to many cellular processes. We have developed a new and general method called Neighborhood Inference (NI) that predicts sequences with activity in regulating a biochemical process based on the local density of known sites in sequence space. Applied to the problem of RNA splicing regulation, NI was used to predict hundreds of new exonic splicing enhancer (ESE) and silencer (ESS) hexanucleotides from known human ESEs and ESSs. These predictions were supported by cross-validation analysis, by analysis of published splicing regulatory activity data, by sequence-conservation analysis, and by measurement of the splicing regulatory activity of 24 novel predicted ESEs, ESSs, and neutral sequences using an in vivo splicing reporter assay. These results demonstrate the ability of NI to accurately predict splicing regulatory activity and show that the scope of exonic splicing regulatory elements is substantially larger than previously anticipated. Analysis of orthologous exons in four mammals showed that the NI score of ESEs, a measure of function, is much more highly conserved above background than ESE primary sequence. This observation indicates a high degree of selection for ESE activity in mammalian exons, with surprisingly frequent interchangeability between ESE sequences. INSET: Synopsis. [ABSTRACT FROM AUTHOR]

RNA editing enhances the diversity of gene products at the post-transcriptional level. Approaches for genome-wide identification of RNA editing face two main challenges: separating true editing sites from false discoveries and accurate estimation of editing levels. We developed an approach to analyze transcriptome sequencing data (RNA-seq) for global identification of RNA editing in cells for which whole-genome sequencing data are available. We applied the method to analyze RNA-seq data of a human glioblastoma cell line, U87MG. Around 10,000 DNA"RNA differences were identified, the majority being putative A-to-I editing sites. These predicted Ato- I events were associated with a low false-discovery rate (1/45%). Moreover, the estimated editing levels from RNA-seq correlated well with those based on traditional clonal sequencing. Our results further facilitated unbiased characterization of the sequence and evolutionary features flanking predicted A-to-I editing sites and discovery of a conserved RNA structural motif that may be functionally relevant to editing. Genes with predicted A-to-I editing were significantly enriched with those known to be involved in cancer, supporting the potential importance of cancer-specific RNA editing. A similar profile of DNA"RNA differences as in U87MG was predicted for another RNA-seq data set obtained from primary breast cancer samples. Remarkably, significant overlap exists between the putative editing sites of the two transcriptomes despite their difference in cell type, cancer type, and genomic backgrounds. Our approach enabled de novo identification of the RNA editome, which sets the stage for further mechanistic studies of this important step of post-transcriptional regulation. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]