Biology subjects

Saferali, A.

Publications and source records attributed to Saferali, A..

4 recordsLinked to original sources

bioRxiv2026

A framework for designing splice-junction experiments in deep 3' single-cell RNA sequencing

Alternative splicing is cell-type-specific and disease-relevant, but single-cell RNA sequencing is optimized for gene-level quantification, and how sequencing depth governs splice-junction recovery in the dominant 3'-biased chemistries has not been quantified, so experiments cannot be designed to a depth target and datasets cannot be interpreted against expected recovery. We generated deep single-cell RNA sequencing of human airway epithelial cells (12 samples, 102 to 803 million uniquely mapped reads per sample) and benchmarked junction recovery in five annotated samples against matched bulk RNA sequencing from a 190-donor cohort. Junction detection did not saturate with depth, and validity was governed by read support rather than annotation status. Recovering 90% of the observed full-depth junction complement required approximately 52,250 reads per cell, several-fold more than gene-level analysis. Quantifying splicing was more depth-limited than detecting it, and rare cell types were limited by cell number rather than depth. Against matched bulk, single-cell recall plateaued at a practical ceiling set by sample size and sparse sampling, and was independent of distance from the 3' end. These results establish a framework for designing splicing experiments in deep 3'-biased single-cell RNA sequencing, and provide an airway-epithelial dataset with matched bulk for benchmarking splicing methods.

bioinformatics↗

bioRxiv2026

Improving splice site usage prediction with SPLAIRE

BackgroundAlternative splicing, the mechanism by which intronic sequences are excised from pre-mRNAs to produce mature mRNA, affects >95% of human protein-coding genes and is a major driver of human disease states. The spliceosome, a protein-RNA complex responsible for splicing pre-mRNA, identifies candidate splice sites partly through the recognition of characteristic sequence motifs at exon-intron junctions. Deep learning models that predict the presence of splice sites from pre-mRNA sequence have achieved breakthrough performance relative to previous machine-learning techniques, and these models have improved our ability to identify pathogenic genetic variants that alter splicing. ResultsWe show that, while overall performance measures from these models suggest near-perfect performance, substantial gaps in prediction remain, including the identification of splice sites with low usage rates and tissue-specific splice sites. We leverage one of the largest paired RNA and genotyping datasets used to date to train a novel splicing model optimized for a specific cell type, human airway epithelial cells. We trained a dilated convolutional neural network on data from cultured airway epithelial cells from 100 donors, and showed that this model outperforms current state-of-the-art models on splice site identification and splice site usage quantification, including on multiple tissues not included in the model training data. ConclusionsWe present the most comprehensive evaluation of state-of-the-art splicing models published to date, revealing reasonable performance across models for genetic variant effect prediction along with important performance gaps and insights into directions for future model development.

genomics↗

bioRxiv2023

Genetic control of mRNA splicing as a potential mechanism for incomplete penetrance of rare coding variants

Exonic variants present some of the strongest links between genotype and phenotype. However, these variants can have significant inter-individual pathogenicity differences, known as variable penetrance. In this study, we propose a model where genetically controlled mRNA splicing modulates the pathogenicity of exonic variants. By first cataloging exonic inclusion from RNA-seq data in GTEx v8, we find that pathogenic alleles are depleted on highly included exons. Using a large-scale phased WGS data from the TOPMed consortium, we observe that this effect may be driven by common splice-regulatory genetic variants, and that natural selection acts on haplotype configurations that reduce the transcript inclusion of putatively pathogenic variants, especially when limiting to haploinsufficient genes. Finally, we test if this effect may be relevant for autism risk using families from the Simons Simplex Collection, but find that splicing of pathogenic alleles has a penetrance reducing effect here as well. Overall, our results indicate that common splice-regulatory variants may play a role in reducing the damaging effects of rare exonic variants.

genetics↗

bioRxiv2019

Analysis of genetically driven alternative splicing identifies FBXO38 as a novel COPD susceptibility gene

While many disease-associated single nucleotide polymorphisms (SNPs) are associated with gene expression (expression quantitative trait loci, eQTLs), a large proportion of complex disease genome-wide association study (GWAS) variants are of unknown function. Some of these SNPs may contribute to disease by regulating gene splicing. Here, we investigate whether SNPs that are associated with alternative splicing (splice QTL or sQTL) can identify novel functions for existing GWAS variants or suggest new associated variants in chronic obstructive pulmonary disease (COPD). RNA sequencing was performed on whole blood from 376 subjects from the COPDGene Study. Using linear models, we identified 561,060 unique sQTL SNPs associated with 30,333 splice sites corresponding to 6,419 unique genes. Similarly, 708,928 unique eQTL SNPs involving 15,913 genes were detected at 10% FDR. While there is overlap between sQTLs and eQTLs, 60% of sQTLs are not eQTLs. Co-localization analysis revealed that 7 out of 21 loci associated with COPD (p<1x10-6) in a published GWAS have at least one shared causal variant between the GWAS and sQTL studies. Among the genes identified to have splice sites associated with top GWAS SNPs was FBXO38, in which a novel exon was discovered to be protective against COPD. Importantly, the sQTL in this locus was validated by qPCR in both blood and lung tissue, demonstrating that splice variants relevant to lung tissue can be identified in blood. Other identified genes included CDK11A and SULT1A2. Overall, these data indicate that analysis of alternative splicing can provide novel insights into disease mechanisms. In particular, we demonstrated that SNPs in a known COPD GWAS locus on chromosome 5q32 influence alternative splicing in the gene FBXO38. Author SummaryWhile it is known that chronic obstructive pulmonary disease (COPD) is caused in part by genetic factors, few studies have identified specific causative genes. Genetic variants that alter the expression levels of genes have explained part of the genetic component of COPD, however, there are additional genetic variants with unknown function. In some genes the protein coding sequence can be altered by a mechanism known as RNA splicing. We hypothesized that some genetic variants that are associated with risk of COPD contribute to the disease by altering RNA splicing. In this study, we identified genetic variants that are associated both with COPD risk and RNA splicing. In particular, we found that a COPD associated variant of previously unknown function may contribute to the inclusion of a new exon in the FBXO38 gene. These finding are significant because they indicate that analysis of RNA splicing can help identify genes that contribute to disease.

genetics↗