Does conservation account for splicing patterns?

Splicing accuracy varies across human introns, tissues, age and disease

Alternative splicing impacts most multi-exonic human genes. Inaccuracies during this process may have an important role in ageing and disease. Here, we investigate splicing accuracy using RNA-sequencing data from >14k control samples and 40 human body sites, focusing on split reads partially mapping to known transcripts in annotation. We show that splicing inaccuracies occur at different rates across introns and tissues and are affected by the abundance of core components of the spliceosome assembly and its regulators. We find that age is positively correlated with a global decline in splicing fidelity, mostly affecting genes implicated in neurodegenerative diseases. We find support for the latter by observing a genome-wide increase in splicing inaccuracies in samples affected with Alzheimer's disease as compared to neurologically normal individuals. In this work, we provide an in-depth characterisation of splicing accuracy, with implications for our understanding of the role of inaccuracies in ageing and neurodegenerative disorders.

Improved modeling of RNA-binding protein motifs in an interpretable neural model of RNA splicing

Sequence-specific RNA-binding proteins (RBPs) play central roles in splicing decisions. Here, we describe a modular splicing architecture that leverages in vitro-derived RNA affinity models for 79 human RBPs and the annotated human genome to produce improved models of RBP binding and activity. Binding and activity are modeled by separate Motif and Aggregator components that can be mixed and matched, enforcing sparsity to improve interpretability. Training a new Adjusted Motif (AM) architecture on the splicing task not only yields better splicing predictions but also improves prediction of RBP-binding sites in vivo and of splicing activity, assessed using independent data.

Large-scale comparative evaluation of user-friendly tools for predicting variant-induced alterations of splicing regulatory elements

Discriminating which nucleotide variants cause disease or contribute to phenotypic traits remains a major challenge in human genetics. In theory, any intragenic variant can potentially affect RNA splicing by altering splicing regulatory elements (SREs). However, these alterations are often ignored mainly because pioneer SRE predictors have proved inefficient. Here, we report the first large-scale comparative evaluation of four user-friendly SRE-dedicated algorithms (QUEPASA, HEXplorer, SPANR, and HAL) tested both as standalone tools and in multiple combined ways based on two independent benchmark datasets adding up to >1,300 exonic variants studied at the messenger RNA level and mapping to 89 different disease-causing genes. These methods display good predictive power, based on decision thresholds derived from the receiver operating characteristics curve analyses, with QUEPASA and HAL having the best accuracies either as standalone or in combination. Still, overall there was a tight race between the four predictors, suggesting that all methods may be of use. Additionally, QUEPASA and HEXplorer may be beneficial as well for predicting variant-induced creation of pseudoexons deep within introns. Our study highlights the potential of SRE predictors as filtering tools for identifying disease-causing candidates among the plethora of variants detected by high-throughput DNA sequencing and provides guidance for their use in genomic medicine settings.

Combined single-cell profiling of expression and DNA methylation reveals splicing regulation and heterogeneity

BACKGROUND

Alternative splicing is a key regulatory mechanism in eukaryotic cells and increases the effective number of functionally distinct gene products. Using bulk RNA sequencing, splicing variation has been studied across human tissues and in genetically diverse populations. This has identified disease-relevant splicing events, as well as associations between splicing and genomic features, including sequence composition and conservation. However, variability in splicing between single cells from the same tissue or cell type and its determinants remains poorly understood.

RESULTS

We applied parallel DNA methylation and transcriptome sequencing to differentiating human induced pluripotent stem cells to characterize splicing variation (exon skipping) and its determinants. Our results show that variation in single-cell splicing can be accurately predicted based on local sequence composition and genomic features. We observe moderate but consistent contributions from local DNA methylation profiles to splicing variation across cells. A combined model that is built based on genomic features as well as DNA methylation information accurately predicts different splicing modes of individual cassette exons. These categories include the conventional inclusion and exclusion patterns, but also more subtle modes of cell-to-cell variation in splicing. Finally, we identified and characterized associations between DNA methylation and splicing changes during cell differentiation.

CONCLUSIONS

Our study yields new insights into alternative splicing at the single-cell level and reveals a previously underappreciated link between DNA methylation variation and splicing.

Vex-seq: high-throughput identification of the impact of genetic variation on pre-mRNA splicing efficiency

Understanding the functional impact of genomic variants is a major goal of modern genetics and personalized medicine. Although many synonymous and non-coding variants act through altering the efficiency of pre-mRNA splicing, it is difficult to predict how these variants impact pre-mRNA splicing. Here, we describe a massively parallel approach we use to test the impact on pre-mRNA splicing of 2059 human genetic variants spanning 110 alternative exons. This method, called variant exon sequencing (Vex-seq), yields data that reinforce known mechanisms of pre-mRNA splicing, identifies variants that impact pre-mRNA splicing, and will be useful for increasing our understanding of genome function.

Tracing the evolution of the heterotrimeric G protein α subunit in Metazoa

BACKGROUND

Heterotrimeric G proteins are fundamental signaling proteins composed of three subunits, Gα and a Gβγ dimer. The role of Gα as a molecular switch is critical for transmitting and amplifying intracellular signaling cascades initiated by an activated G protein Coupled Receptor (GPCR). Despite their biochemical and therapeutic importance, the study of G protein evolution has been limited to the scope of a few model organisms. Furthermore, of the five primary Gα subfamilies, the underlying gene structure of only two families has been thoroughly investigated outside of Mammalia evolution. Therefore our understanding of Gα emergence and evolution across phylogeny remains incomplete.

RESULTS

We have computationally identified the presence and absence of every Gα gene (GNA-) across all major branches of Deuterostomia and evaluated the conservation of the underlying exon-intron structures across these phylogenetic groups. We provide evidence of mutually exclusive exon inclusion through alternative splicing in specific lineages. Variations of splice site conservation and isoforms were found for several paralogs which coincide with conserved, putative motifs of DNA-/RNA-binding proteins. In addition to our curated gene annotations, within Primates, we identified 15 retrotranspositions, many of which have undergone pseudogenization. Most importantly, we find numerous deviations from previous findings regarding the presence and absence of individual GNA- genes, nuanced differences in phyla-specific gene copy numbers, novel paralog duplications and subsequent intron gain and loss events.

CONCLUSIONS

Our curated annotations allow us to draw more accurate inferences regarding the emergence of all Gα family members across Metazoa and to present a new, updated theory of Gα evolution. Leveraging this, our results are critical for gaining new insights into the co-evolution of the Gα subunit and its many protein binding partners, especially therapeutically relevant G protein - GPCR signaling pathways which radiated in Vertebrata evolution.

Classifiers Accuracy Improvement Based on Missing Data Imputation

In this paper we investigate further and extend our previous work on radar signal identification and classification based on a data set which comprises continuous, discrete and categorical data that represent radar pulse train characteristics such as signal frequencies, pulse repetition, type of modulation, intervals, scan period, scanning type, etc. As the most of the real world datasets, it also contains high percentage of missing values and to deal with this problem we investigate three imputation techniques: Multiple Imputation (MI); K-Nearest Neighbour Imputation (KNNI); and Bagged Tree Imputation (BTI). We apply these methods to data samples with up to 60% missingness, this way doubling the number of instances with complete values in the resulting dataset. The imputation models performance is assessed with Wilcoxon’s test for statistical significance and Cohen’s effect size metrics. To solve the classification task, we employ three intelligent approaches: Neural Networks (NN); Support Vector Machines (SVM); and Random Forests (RF). Subsequently, we critically analyse which imputation method influences most the classifiers’ performance, using a multiclass classification accuracy metric, based on the area under the ROC curves. We consider two superclasses (‘military’ and ‘civil’), each containing several ‘subclasses’, and introduce and propose two new metrics: inner class accuracy (IA); and outer class accuracy (OA), in addition to the overall classification accuracy (OCA) metric. We conclude that they can be used as complementary to the OCA when choosing the best classifier for the problem at hand.

High-throughput sequencing reveals novel lincRNA in age-related cataract

Age-related cataract (ARC) is a major cause of blindness. Long non-coding RNAs (lncRNAs) are a heterogeneous class of RNAs that are non-protein-coding transcripts >200 nucleotides in length. LncRNAs are involved in various critical biological processes, such as chromatin remodeling, gene transcription, and protein transport and trafficking. Furthermore, the dysregulation of lncRNAs causes a number of complex human diseases, including coronary artery diseases, autoimmune diseases, neurological disorders and various cancers. However, the role of lncRNA in cataract remains unclear. Therefore, in the present study, lens anterior capsular membrane was collected from normal subjects and patients with ARC and total RNA was extracted. High-throughput sequencing was applied to detect differentially expressed lncRNAs and mRNAs. The analysis identified a total of 42,556 candidate differentially expressed mRNAs (27,447 +15,109) and a total of 7,041 candidate differentially expressed lncRNAs (4,146 + 2,895). Through bioinformatics analysis, the significant differential expression of novel lincRNA was observed and its possible molecular mechanism was explored. Reverse transcription-quantitative polymerase chain reaction was used to validate the different expression levels of selected lncRNAs. These findings may lead to the development of novel strategies for genetic diagnosis and gene therapy.