MOTIVATION: Gene set enrichment analysis has been shown to be effective in identifying relevant biological pathways underlying complex diseases. Existing approaches lack the ability to quantify the enrichment levels accurately, hence preventing the enrichment information to be further utilized in both upstream and downstream analyses. A modernized and rigorous approach for gene set enrichment analysis that emphasizes both hypothesis testing and enrichment estimation is much needed. RESULTS: We propose a novel computational method, Bayesian Analysis of Gene Set Enrichment (BAGSE), for gene set enrichment analysis. BAGSE is built on a Bayesian hierarchical model and fully accounts for the uncertainty embedded in the association evidence of individual genes. We adopt an empirical Bayes inference framework to fit the proposed hierarchical model by implementing an efficient EM algorithm. Through simulation studies, we illustrate that BAGSE yields accurate enrichment quantification while achieving similar power as the state-of-the-art methods. Further simulation studies show that BAGSE can effectively utilize the enrichment information to improve the power in gene discovery. Finally, we demonstrate the application of BAGSE in analyzing real data from a differential expression experiment and a transcriptome-wide association study. Our results indicate that the proposed statistical framework is effective in aiding the discovery of potentially causal pathways and gene networks. AVAILABILITY AND IMPLEMENTATION: BAGSE is implemented using the C++ programing language and is freely available from https://github.com/xqwen/bagse/. Simulated and real data used in this paper are also available at the Github repository for reproducibility purposes. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Motivation: The majority of the human genome is composed of non-coding regions containing regulatory elements such as enhancers, which are crucial for controlling gene expression. Many variants associated with complex traits are in these regions, and may disrupt gene regulatory sequences. Consequently, it is important to not only identify true enhancers but also to test if a variant within an enhancer affects gene regulation. Recently, allele-specific analysis in high-throughput reporter assays, such as massively parallel reporter assays (MPRAs), have been used to functionally validate non-coding variants. However, we are still missing high-quality and robust data analysis tools for these datasets. Results: We have further developed our method for allele-specific analysis QuASAR (quantitative allele-specific analysis of reads) to analyze allele-specific signals in barcoded read counts data from MPRA. Using this approach, we can take into account the uncertainty on the original plasmid proportions, over-dispersion, and sequencing errors. The provided allelic skew estimate and its standard error also simplifies meta-analysis of replicate experiments. Additionally, we show that a beta-binomial distribution better models the variability present in the allelic imbalance of these synthetic reporters and results in a test that is statistically well calibrated under the null. Applying this approach to the MPRA data, we found 602 SNPs with significant (false discovery rate 10%) allele-specific regulatory function in LCLs. We also show that we can combine MPRA with QuASAR estimates to validate existing experimental and computational annotations of regulatory variants. Our study shows that with appropriate data analysis tools, we can improve the power to detect allelic effects in high-throughput reporter assays. Availability and implementation: http://github.com/piquelab/QuASAR/tree/master/mpra. Contact: [email protected] or [email protected]. Supplementary information: Supplementary data are available online at Bioinformatics.

MOTIVATION: Expression quantitative trait loci (eQTL) studies have discovered thousands of genetic variants that regulate gene expression, enabling a better understanding of the functional role of non-coding sequences. However, eQTL studies are costly, requiring large sample sizes and genome-wide genotyping of each sample. In contrast, analysis of allele-specific expression (ASE) is becoming a popular approach to detect the effect of genetic variation on gene expression, even within a single individual. This is typically achieved by counting the number of RNA-seq reads matching each allele at heterozygous sites and testing the null hypothesis of a 1:1 allelic ratio. In principle, when genotype information is not readily available, it could be inferred from the RNA-seq reads directly. However, there are currently no existing methods that jointly infer genotypes and conduct ASE inference, while considering uncertainty in the genotype calls. RESULTS: We present QuASAR, quantitative allele-specific analysis of reads, a novel statistical learning method for jointly detecting heterozygous genotypes and inferring ASE. The proposed ASE inference step takes into consideration the uncertainty in the genotype calls, while including parameters that model base-call errors in sequencing and allelic over-dispersion. We validated our method with experimental data for which high-quality genotypes are available. Results for an additional dataset with multiple replicates at different sequencing depths demonstrate that QuASAR is a powerful tool for ASE analysis when genotypes are not available. AVAILABILITY AND IMPLEMENTATION: http://github.com/piquelab/QuASAR. CONTACT: [email protected] or [email protected] SUPPLEMENTARY INFORMATION: Supplementary Material is available at Bioinformatics online.