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Gene set-based signal detection analyses are used to detect an association between a trait and a set of genes by accumulating signals across the genes in the gene set. Since signal detection is concerned with identifying whether any of the genes in the gene set are non-null, a goodness-of-fit (GOF) test can be used to compare whether the observed distribution of gene-level tests within the gene set agrees with the theoretical null distribution. Here, we present a flexible gene set-based signal detection framework based on tail-focused GOF statistics. We show that the power of the various statistics in this framework depends critically on two parameters: the proportion of genes within the gene set that are non-null and the degree of separation between the null and alternative distributions of the gene-level tests. We give guidance on which statistic to choose for a given situation and implement the methods in a fast and user-friendly R package, wHC (https://github.com/mqzhanglab/wHC). Finally, we apply these methods to a whole exome sequencing study of amyotrophic lateral sclerosis. [ABSTRACT FROM AUTHOR]

Here we present an open-source R package ‘meaRtools’ that provides a platform for analyzing neuronal networks recorded on Microelectrode Arrays (MEAs). Cultured neuronal networks monitored with MEAs are now being widely used to characterize in vitro models of neurological disorders and to evaluate pharmaceutical compounds. meaRtools provides core algorithms for MEA spike train analysis, feature extraction, statistical analysis and plotting of multiple MEA recordings with multiple genotypes and treatments. meaRtools functionality covers novel solutions for spike train analysis, including algorithms to assess electrode cross-correlation using the spike train tiling coefficient (STTC), mutual information, synchronized bursts and entropy within cultured wells. Also integrated is a solution to account for bursts variability originating from mixed-cell neuronal cultures. The package provides a statistical platform built specifically for MEA data that can combine multiple MEA recordings and compare extracted features between different genetic models or treatments. We demonstrate the utilization of meaRtools to successfully identify epilepsy-like phenotypes in neuronal networks from Celf4 knockout mice. The package is freely available under the GPL license (GPL> = 3) and is updated frequently on the CRAN web-server repository. The package, along with full documentation can be downloaded from: . [ABSTRACT FROM AUTHOR]

Despite evidence that at the interspecific scale, exonic splicing silencers (ESSs) are under negative selection in constitutive exons, little is known about the effects of slightly deleterious polymorphisms on these splicing regulators. Through the application of a modified version of the McDonald-Kreitman test, we compared the normalized proportions of human polymorphisms and human/rhesus substitutions affecting exonic splicing regulators (ESRs) on sequences of constitutive and alternative exons. Our results show a depletion of substitutions and an enrichment of SNPs associated with ESS gain in constitutive exons. Moreover, we show that this evolutionary pattern is also present in a set of ESRs previously involved in the transition from constitutive to skipped exons in the mammalian lineage. The similarity between these two sets of ESRs suggests that the transition from constitutive to skipped exons in mammals is more frequently associated with the inhibition than with the promotion of splicing signals. This is in accordance with the hypothesis of a constitutive origin of exon skipping and corroborates previous findings about the antagonistic role of certain exonic splicing enhancers. [ABSTRACT FROM AUTHOR]

Microglia are the resident immune cells of the central nervous system and are involved in brain development, homeostasis, and disease. New imaging and genomics technologies are revealing microglial complexity across developmental and functional states, brain regions, and diseases. We curated a set of publicly available gene expression datasets from human microglia spanning disease and health to identify sets of genes reflecting physiological and pathological microglial states. We also integrated multiple human microglial single-cell RNA-seq datasets in Alzheimer’s disease (AD), multiple sclerosis (MS), and Parkinson’s disease, and identified a distinct microglial transcriptional signature shared across diseases. Analysis of germ-line DNA identified genes with variants associated with AD and MS that are overrepresented in microglial gene sets, including the disease-associated transcriptional signature. This work points to genes that are dysregulated in disease states and provides a resource for the analysis of diseases in which microglia are implicated by genetic evidence.Analysis of human microglia across health and disease identifies key gene sets in neurodegeneration, offering insights into their role in CNS disorders. [ABSTRACT FROM AUTHOR]

Background A central aim of precision medicine is to target treatments to the underlying causes of disease. To accurately target treatments we must be able to recognize pathogenic genetic variants. Current methods prioritize variants that directly alter protein sequence (missense and loss of function) but not variants that may cause disease by changing the processing of final transcripts. The difficulty in capturing this effect results in overlooking synonymous and intronic variants when searching for disease risk in sequenced genomes. Methods The TraP score was constructed using three main components: 1) Information acquisition – details of the harboring gene and it’s transcripts are gathered for each variant. 2) Feature calculation - possible changes to sequence motifs are evaluated, including changes to exon-intron boundaries, creation of cryptic splice sites, creations and disruptions of cis-acting binding sites for splicing regulatory proteins, interactions between selected features such as original and new splice sites and others. Overall, 42 features and 14 general properties (chromosome, strand, coordinate, etc.) are collected for each variant. 3) Modeling – the incorporation of selected features into a random forest model. The model is trained on a set of 75 pathogenic synonymous variants and 402 benign variants. Pathogenic variants are strongly associated with rare disease, whereas the 402 benign variants are de novo mutations identified from healthy individuals. Results The Transcript-inferred Pathogenicity score (TraP) presented here was constructed to reliably identify non-coding mutations that cause disease. Trap is strongly negatively correlated with allele frequency in both synonymous and intronic regions, suggesting that the higher the TraP score the stronger the selection against these variants in the population. Moreover, synonymous variants with high TraP scores have significantly lower minor allele frequencies than even missense variants, indicating that Trap identifies a subset of synonymous variants under stronger purifying selection. TraP identifies known pathogenic variants in synonymous and intronic ClinVar datasets (AUC = 0.88 and 0.83, respectively), dismissing benign variants with extremely high specificity of above 99%. Applied to exomes of 281 epilepsy family trios, TraP pinpoints synonymous de novo variants in known epilepsy genes. TraP’s high performance and specificity clearly outperforms existing methods and allows the prioritization of synonymous and intronic variants for use in gene discovery and the interpretation of personal genomes. Discussion Exome sequencing studies consider rare non-synonymous variants as disease candidates, while other variant types are mostly ignored. Some existing methods are able to prioritize synonymous and intronic variants, yet lack the specificity required for detection of causal variants. TraP discards over 99% of non-coding variants as benign while strongly identifying true pathogenic variants. TraP identifies pathogenic variants that are not conserved, yet have rare population frequencies. Doing so without prior population frequency information and in contrast to the GERP++ and CADD scores, suggests that TraP identifies pathogenic events that were not selected against during vertebrate evolution, but are selected against in human population. This conclusion is supported by the highest complexity of alternative splicing found in primates and by the species-specific nature of splicing regulation. [ABSTRACT FROM AUTHOR]

DNA methylation is known to regulate transcription and was recently found to be involved in exon recognition via cotranscriptional splicing. We recently observed that exon--intron architectures can be grouped into two classes: one with higher GC content in exons compared to the flanking introns, and the other with similar GC content in exons and introns. The first group has higher nucleosome occupancy on exons than introns, whereas the second group exhibits weak nucleosome marking of exons, suggesting another type of epigenetic marker distinguishes exons from introns when GC content is similar. We find different and specific patterns of DNA methylation in each of the GC architectures; yet in both groups, DNA methylation clearly marks the exons. Exons of the leveled GC architecture exhibit a significantly stronger DNA methylation signal in relation to their flanking introns compared to exons of the differential GC architecture. This is accentuated by a reduction of the DNA methylation level in the intronic sequences in proximity to the splice sites and shows that different epigenetic modifications mark the location of exons already at the DNA level. Also, lower levels of methylated CpGs on alternative exons can successfully distinguish alternative exons from constitutive ones. Three positions at the splice sites show high CpG abundance and accompany elevated nucleosome occupancy in a leveled GC architecture. Overall, these results suggest that DNA methylation affects exon recognition and is influenced by the GC architecture of the exon and flanking introns. [ABSTRACT FROM AUTHOR]

Exon"intron architecture is one of the major features directing the splicing machinery to the short exons that are located within long flanking introns. However, the evolutionary dynamics of exon"intron architecture and its impact on splicing is largely unknown. Using a comparative genomic approach, we analyzed 17 vertebrate genomes and reconstructed the ancestral motifs of both 3' and 5' splice sites, as also the ancestral length of exons and introns. Our analyses suggest that vertebrate introns increased in length from the shortest ancestral introns to the longest primate introns. An evolutionary analysis of splice sites revealed that weak splice sites act as a restrictive force keeping introns short. In contrast, strong splice sites allow recognition of exons flanked by long introns. Reconstruction of the ancestral state suggests these phenomena were not prevalent in the vertebrate ancestor, but appeared during vertebrate evolution. By calculating evolutionary rate shifts in exons, we identified cis-acting regulatory sequences that became fixed during the transition from early vertebrates to mammals. Experimental validations performed on a selection of these hexamers confirmed their regulatory function. We additionally revealed many features of exons that can discriminate alternative from constitutive exons. These features were integrated into a machine-learning approach to predict whether an exon is alternative. Our algorithm obtains very high predictive power (AUC of 0.91), and using these predictions we have identified and successfully validated novel alternatively spliced exons. Overall, we provide novel insights regarding the evolutionary constraints acting upon exons and their recognition by the splicing machinery. [ABSTRACT FROM AUTHOR]