RNA Sequencing Reads Research Articles

Contrasting and combining transcriptome complexity captured by short and long RNA sequencing reads.

Mapping transcriptomic variations using either short- or long-reads RNA sequencing is a staple of genomic research. Long reads are able to capture entire isoforms and overcome repetitive regions, while short reads still provide improved coverage and error rates. Yet how to quantitatively compare the technologies, can we combine those, and what may be the benefit of such a combined view remain open questions. We tackle these questions by first creating a pipeline to assess matched long and short reads data using a variety of transcriptome statistics. We find that across datasets, algorithms, and technologies, matched short reads data detects roughly 30% more splice junctions such that 10-30% of the splice junctions included at 20% or more by short reads are missed by long reads. In contrast, long reads detect many more intron retention events and can detect full isoforms, pointing to the benefit of combining the technologies. We introduce MAJIQ-L, an extension of the MAJIQ software to enable a unified view of transcriptome variations from both technologies and demonstrate its benefits. Our software can be used to assess any future long-read technology or algorithm, and combine it with short reads data for improved transcriptome analysis.

Deciphering transcript architectural complexity in bacteria and archaea.

RNA transcripts are potential therapeutic targets, yet bacterial transcripts have uncharacterized biodiversity. We developed an algorithm for transcript prediction called tp.py using it to predict transcripts (mRNA and other RNAs) in Escherichia coli K12 and E2348/69 strains (Bacteria:gamma-Proteobacteria), Listeria monocytogenes strains Scott A and RO15 (Bacteria:Firmicute), Pseudomonas aeruginosa strains SG17M and NN2 strains (Bacteria:gamma-Proteobacteria), and Haloferax volcanii (Archaea:Halobacteria). From >5 million E. coli K12 and >3 million E. coli E2348/69 newly generated Oxford Nanopore Technologies direct RNA sequencing reads, 2,487 K12 mRNAs and 1,844 E2348/69 mRNAs were predicted, with the K12 mRNAs containing more than half of the predicted E. coli K12 proteins. While the number of predicted transcripts varied by strain based on the amount of sequence data used, across all strains examined, the predicted average size of the mRNAs was 1.6-1.7 kbp, while the median size of the 5'- and 3'-untranslated regions (UTRs) were 30-90 bp. Given the lack of bacterial and archaeal transcript annotation, most predictions were of novel transcripts, but we also predicted many previously characterized mRNAs and ncRNAs, including post-transcriptionally generated transcripts and small RNAs associated with pathogenesis in the E. coli E2348/69 LEE pathogenicity islands. We predicted small transcripts in the 100-200 bp range as well as >10 kbp transcripts for all strains, with the longest transcript for two of the seven strains being the nuo operon transcript, and for another two strains it was a phage/prophage transcript. This quick, easy, and reproducible method will facilitate the presentation of transcripts, and UTR predictions alongside coding sequences and protein predictions in bacterial genome annotation as important resources for the research community.IMPORTANCEOur understanding of bacterial and archaeal genes and genomes is largely focused on proteins since there have only been limited efforts to describe bacterial/archaeal RNA diversity. This contrasts with studies on the human genome, where transcripts were sequenced prior to the release of the human genome over two decades ago. We developed software for the quick, easy, and reproducible prediction of bacterial and archaeal transcripts from Oxford Nanopore Technologies direct RNA sequencing data. These predictions are urgently needed for more accurate studies examining bacterial/archaeal gene regulation, including regulation of virulence factors, and for the development of novel RNA-based therapeutics and diagnostics to combat bacterial pathogens, like those with extreme antimicrobial resistance.

Enhancing transcriptome expression quantification through accurate assignment of long RNA sequencing reads with TranSigner.

Recently developed long-read RNA sequencing technologies promise to provide a more accurate and comprehensive view of transcriptomes compared to short-read sequencers, primarily due to their capability to achieve full-length sequencing of transcripts. However, realizing this potential requires computational tools tailored to process long reads, which exhibit a higher error rate than short reads. Existing methods for assembling and quantifying long-read data often disagree on expressed transcripts and their abundance levels, leading researchers to lack confidence in the transcriptomes produced using this data. One approach to address the uncertainties in transcriptome assembly and quantification is by assigning the long reads to transcripts, enabling a more detailed characterization of transcript support at the read level. Here, we introduce TranSigner, a versatile tool that assigns long reads to any input transcriptome. TranSigner consists of three consecutive modules performing: read alignment to the given transcripts, computation of read-to-transcript compatibility based on alignment scores and positions, and execution of an expectation-maximization algorithm to probabilistically assign reads to transcripts and estimate transcript abundances. Using simulated data and experimental datasets from three well-studied organisms - Homo sapiens, Arabidopsis thaliana, and Mus musculus - we demonstrate that TranSigner achieves accurate read assignments, obtaining higher accuracy in transcript abundance estimation compared to existing tools.

Transcriptome analysis reveals mechanisms of metabolic detoxification and immune responses following farnesyl acetate treatment in Metisa plana

Metisa plana is a widespread insect pest infesting oil palm plantations in Malaysia. Farnesyl acetate (FA), a juvenile hormone analogue, has been reported to exert in vitro and in vivo insecticidal activity against other insect pests. However, the insecticidal mechanism of FA on M. plana remains unclear. Therefore, this study aims to elucidate responsive genes in M. plana in response to FA treatment. The RNA-sequencing reads of FA-treated M. plana were de novo-assembled with existing raw reads from non-treated third instar larvae, and 55,807 transcripts were functionally annotated to multiple protein databases. Several insecticide detoxification-related genes were differentially regulated among the 321 differentially expressed transcripts. Cytochrome P450 monooxygenase, carboxylesterase, and ATP-binding cassette protein were upregulated, while peptidoglycan recognition protein was downregulated. Innate immune response genes, such as glutathione S-transferases, acetylcholinesterase, and heat shock protein, were also identified in the transcriptome. The findings signify that changes occurred in the insect’s receptor and signaling, metabolic detoxification of insecticides, and immune responses upon FA treatment on M. plana. This valuable information on FA toxicity may be used to formulate more effective biorational insecticides for better M. plana pest management strategies in oil palm plantations.

Integrative analysis of nanopore direct RNA sequencing data reveals a role of PUS7-dependent pseudouridylation in regulation of m 6 A and m 5 C modifications.

Understanding the interactions between different RNA modifications is essential for unraveling their biological functions. Here, we report NanoPsiPy, a computational pipeline that employs nanopore direct RNA sequencing to identify pseudouridine (Ψ) sites and quantify their levels at single-nucleotide resolution. We validated NanoPsiPy by transcriptome-wide profiling of PUS7-dependent Ψ sites in poly-A RNA and rRNA. NanoPsiPy leverages Ψ-induced U-to-C basecalling errors in nanopore sequencing data, allowing detection of both low and high stoichiometric Ψ sites. We identified 8,624 PUS7-dependent Ψ sites in 1,246 mRNAs encoding proteins associated with ribosome biogenesis, translation, and energy metabolism. Importantly, integrative analysis revealed that PUS7 knockdown increases global mRNA N 6 -methyladenosine (m 6 A) and 5-methylcytosine (m 5 C) levels, suggesting an antagonistic relationship between Ψ and these modifications. Our study underscores the potential of nanopore direct RNA sequencing in revealing the co-regulation of RNA modifications and the capacity of NanoPsiPy in analyzing pseudouridylation and its impact on other RNA modifications.

Ornaments for efficient allele-specific expression estimation with bias correction

Allele-specific expression plays a crucial role in unraveling various biological mechanisms, including genomic imprinting and gene expression controlled by cis-regulatory variants. However, existing methods for quantification from RNA-sequencing (RNA-seq) reads do not adequately and efficiently remove various allele-specific read mapping biases, such as reference bias arising from reads containing the alternative allele that do not map to the reference transcriptome or ambiguous mapping bias caused by reads containing the reference allele that map differently from reads containing the alternative allele. We present Ornaments, a computational tool for rapid and accurate estimation of allele-specific transcript expression at unphased heterozygous loci from RNA-seq reads while correcting for allele-specific read mapping biases. Ornaments removes reference bias by mapping reads to a personalized transcriptome and ambiguous mapping bias by probabilistically assigning reads to multiple transcripts and variant loci they map to. Ornaments is a lightweight extension of kallisto, a popular tool for fast RNA-seq quantification, that improves the efficiency and accuracy of WASP, a popular tool for bias correction in allele-specific read mapping. In experiments with simulated and human lymphoblastoid cell-line RNA-seq reads with the genomes of the 1000 Genomes Project, we demonstrate that Ornaments improves the accuracy of WASP and kallisto, is nearly as efficient as kallisto, and is an order of magnitude faster than WASP per sample, with the additional cost of constructing a personalized index for multiple samples. Additionally, we show that Ornaments finds imprinted transcripts with higher sensitivity than WASP, which detects imprinted signals only at gene level.

Applications of Nanopore sequencing in precision cancer medicine.

Oxford Nanopore Technologies sequencing, also referred to as Nanopore sequencing, stands at the forefront of a revolution in clinical genetics, offering the potential for rapid, long read, and real-time DNA and RNA sequencing. This technology is currently making sequencing more accessible and affordable. In this comprehensive review, we explore its potential regarding precision cancer diagnostics and treatment. We encompass a critical analysis of clinical cases where Nanopore sequencing was successfully applied to identify point mutations, splice variants, gene fusions, epigenetic modifications, non-coding RNAs, and other pivotal biomarkers that defined subsequent treatment strategies. Additionally, we address the challenges of clinical applications of Nanopore sequencing and discuss the current efforts to overcome them.

Whole-genome sequencing of 13 Arctic plants and draft genomes of Oxyria digyna and Cochlearia groenlandica

To understand the genomic characteristics of Arctic plants, we generated 28–44 Gb of short-read sequencing data from 13 Arctic plants collected from the High Arctic Svalbard. We successfully estimated the genome sizes of eight species by using the k-mer-based method (180–894 Mb). Among these plants, the mountain sorrel (Oxyria digyna) and Greenland scurvy grass (Cochlearia groenlandica) had relatively small genome sizes and chromosome numbers. We obtained 45 × and 121 × high-fidelity long-read sequencing data. We assembled their reads into high-quality draft genomes (genome size: 561 and 250 Mb; contig N50 length: 36.9 and 14.8 Mb, respectively), and correspondingly annotated 43,105 and 29,675 genes using ~46 and ~85 million RNA sequencing reads. We identified 765,012 and 88,959 single-nucleotide variants, and 18,082 and 7,698 structural variants (variant size ≥ 50 bp). This study provided high-quality genome assemblies of O. digyna and C. groenlandica, which are valuable resources for the population and molecular genetic studies of these plants.

MiRglmm: a generalized linear mixed model of isomiR-level counts improves estimation of miRNA-level differential expression and uncovers variable differential expression between isomiRs.

MicroRNA-seq data is produced by aligning small RNA sequencing reads of different miRNA transcript isoforms, called isomiRs, to known microRNAs. Aggregation to microRNA-level counts discards information and violates core assumptions of differential expression (DE) methods developed for mRNA-seq data. We establish miRglmm, a DE method for microRNA-seq data, that uses a generalized linear mixed model of isomiR-level counts, facilitating detection of miRNA with differential expression or differential isomiR usage. We demonstrate that miRglmm outperforms current DE methods in estimating DE for miRNA, whether or not there is significant isomiR variability, and simultaneously provides estimates of isomiR-level DE.

The annotation of GBA1 has been concealed by its protein-coding pseudogene GBAP1.

Mutations in GBA1 cause Gaucher disease and are the most important genetic risk factor for Parkinson's disease. However, analysis of transcription at this locus is complicated by its highly homologous pseudogene, GBAP1. We show that >50% of short RNA-sequencing reads mapping to GBA1 also map to GBAP1. Thus, we used long-read RNA sequencing in the human brain, which allowed us to accurately quantify expression from both GBA1 and GBAP1. We discovered significant differences in expression compared to short-read data and identify currently unannotated transcripts of both GBA1 and GBAP1. These included protein-coding transcripts from both genes that were translated in human brain, but without the known lysosomal function-yet accounting for almost a third of transcription. Analyzing brain-specific cell types using long-read and single-nucleus RNA sequencing revealed region-specific variations in transcript expression. Overall, these findings suggest nonlysosomal roles for GBA1 and GBAP1 with implications for our understanding of the role of GBA1 in health and disease.

First report of white muscle disease caused by Photobacterium damselae subsp. damselae in Kuruma shrimp (Penaeus japonicus)

In August and September 2022, two disease outbreaks were observed in Kuruma shrimp (Penaeus japonicus) farms in Okinawa, Japan. Diseased animals exhibited clinical signs of whitened musculature extending from the abdominal to the distal region. Histopathology revealed degeneration of fibers due to liquified muscle necrosis and haemocytic infiltration. Analysis of nonhost contigs from the assembled unmapped RNA and DNA shotgun sequence reads showed significant taxonomic alignment to bacterial species. This led to the investigation of a bacterium as a possible causative agent by characterizing its population in the diseased shrimp muscle. The majority of the 16S rRNA sequence recombinant clones had their highest homology with Photobacterium sp. (> 99%). The three bacterial isolates from the whitened muscle tissue were identified as Photobacterium damselae subsp. damselae (WMD-P1, WMD-P2 and WMD-P3) by biochemical and molecular assays and were further characterized. The Pdd genomes consisted of two circular chromosomes with varying numbers of plasmid. Its size ranges from 4.47 Mb to 4.60 Mb with an average GC content of 40.8%, with predicted number of coding sequences (CDs) ranging from 3816 to 4031. hlyA and pldA genes encoding for leukocidin pore-forming toxin and phospholipase were identified. Putative virulence genes are involved in adherence, antiphagocytosis, chemotaxis and motility, iron uptake, quorum sensing, secretion system, and immune evasion. The presence of prophages, genomic islands and antimicrobial resistant genes in the Pdd genomes suggests episodes of horizontal gene transfer. Average nucleotide identity (ANI) and pangenome analyses revealed a high genetic relationship of the Pdd strains (>98%) to isolates from other sources. PCR assays validated the presence of two bacterial virulence genes encoding the pore-forming toxin and phospholipase respectively in all isolates. Also, the strains had chitinase, phospholipase, and hemolytic activities. Intramuscular injection at 1 × 108 CFU/ml bacterial concentration produced pathological signs similar to those in naturally infected shrimp after 24 hpi. Lower concentration of 1 × 103 CFU/ml resulted in morbidity after 10 dpi. These results show that P. damselae subsp. damselae (Pdd) is associated with the occurrence of white muscle disease in Kuruma shrimp.

RNA m6A detection using raw current signals and basecalling errors from Nanopore direct RNA sequencing reads.

Nanopore direct RNA sequencing (DRS) enables the detection of RNA N6-methyladenosine (m6A) without extra laboratory techniques. A number of supervised or comparative approaches have been developed to identify m6A from Nanopore DRS reads. However, existing methods typically utilize either statistical features of the current signals or basecalling-error features, ignoring the richer information of the raw signals of DRS reads. Here, we propose RedNano, a deep-learning method designed to detect m6A from Nanopore DRS reads by utilizing both raw signals and basecalling errors. RedNano processes the raw-signal feature and basecalling-error feature through residual networks. We validated the effectiveness of RedNano using synthesized, Arabidopsis, and human DRS data. The results demonstrate that RedNano surpasses existing methods by achieving higher area under the ROC curve (AUC) and area under the precision-recall curve (AUPRs) in all three datasets. Furthermore, RedNano performs better in cross-species validation, demonstrating its robustness. Additionally, when detecting m6A from an independent dataset of Populus trichocarpa, RedNano achieves the highest AUC and AUPR, which are 3.8%-9.9% and 5.5%-13.8% higher than other methods, respectively. The source code of RedNano is freely available at https://github.com/Derryxu/RedNano.

Analyzing RNA-Seq Data from Chlamydia with Super Broad Transcriptomic Activation: Challenges, Solutions, and Implications for Other Systems.

RNA sequencing (RNA-Seq) offers profound insights into the complex transcriptomes of diverse biological systems. However, standard differential expression analysis pipelines based on DESeq2 and edgeR encounter challenges when applied to the immediate early transcriptomes of Chlamydia spp., obligate intracellular bacteria. These challenges arise from their reliance on assumptions that do not hold in scenarios characterized by extensive transcriptomic activation and limited repression. Standard analyses using unique chlamydial RNA-Seq reads alone identify nearly 300 upregulated and about 300 downregulated genes, significantly deviating from actual RNA-Seq read trends. By incorporating both chlamydial and host reads or adjusting for total sequencing depth, the revised normalization methods each detected over 700 upregulated genes and 30 or fewer downregulated genes, closely aligned with observed RNA-Seq data. Further validation through qRT-PCR analysis confirmed the effectiveness of these adjusted approaches in capturing the true extent of transcriptomic activation during the immediate early phase of chlamydial infection. While the strategies employed are developed in the context of Chlamydia, the principles of flexible and context-aware normalization may inform adjustments in other systems with unbalanced gene expression dynamics, such as bacterial spore germination. The code for reproducing the presented bioinformatic analysis is available at https://zenodo.org/records/11201379.

Nm-Nano: a machine learning framework for transcriptome-wide single-molecule mapping of 2´-O-methylation (Nm) sites in nanopore direct RNA sequencing datasets

ABSTRACT 2´-O-methylation (Nm) is one of the most abundant modifications found in both mRNAs and noncoding RNAs. It contributes to many biological processes, such as the normal functioning of tRNA, the protection of mRNA against degradation by the decapping and exoribonuclease (DXO) protein, and the biogenesis and specificity of rRNA. Recent advancements in single-molecule sequencing techniques for long read RNA sequencing data offered by Oxford Nanopore technologies have enabled the direct detection of RNA modifications from sequencing data. In this study, we propose a bio-computational framework, Nm-Nano, for predicting the presence of Nm sites in direct RNA sequencing data generated from two human cell lines. The Nm-Nano framework integrates two supervised machine learning (ML) models for predicting Nm sites: Extreme Gradient Boosting (XGBoost) and Random Forest (RF) with K-mer embedding. Evaluation on benchmark datasets from direct RNA sequecing of HeLa and HEK293 cell lines, demonstrates high accuracy (99% with XGBoost and 92% with RF) in identifying Nm sites. Deploying Nm-Nano on HeLa and HEK293 cell lines reveals genes that are frequently modified with Nm. In HeLa cell lines, 125 genes are identified as frequently Nm-modified, showing enrichment in 30 ontologies related to immune response and cellular processes. In HEK293 cell lines, 61 genes are identified as frequently Nm-modified, with enrichment in processes like glycolysis and protein localization. These findings underscore the diverse regulatory roles of Nm modifications in metabolic pathways, protein degradation, and cellular processes. The source code of Nm-Nano can be freely accessed at https://github.com/Janga-Lab/Nm-Nano.

Comprehensive profiling of cancer neoantigens from aberrant RNA splicing

BackgroundCancer neoantigens arise from protein-altering somatic mutations in tumor and rank among the most promising next-generation immuno-oncology agents when used in combination with immune checkpoint inhibitors. We previously developed a...

Disentangling genetic effects on transcriptional and post-transcriptional gene regulation through integrating exon and intron expression QTLs

Expression quantitative trait loci (eQTL) studies typically consider exon expression of genes and discard intronic RNA sequencing reads despite their information on RNA metabolism. Here, we quantify genetic effects on exon and intron levels of genes and their ratio in lymphoblastoid cell lines, revealing thousands of cis-QTLs of each type. While genetic effects are often shared between cis-QTL types, 7814 (47%) are not detected as top cis-QTLs at exon levels. We show that exon levels preferentially capture genetic effects on transcriptional regulation, while exon-intron-ratios better detect those on co- and post-transcriptional processes. Considering all cis-QTL types substantially increases (by 71%) the number of colocalizing variants identified by genome-wide association studies (GWAS). It further allows dissecting the potential gene regulatory processes underlying GWAS associations, suggesting comparable contributions by transcriptional (50%) and co- and post-transcriptional regulation (46%) to complex traits. Overall, integrating intronic RNA sequencing reads in eQTL studies expands our understanding of genetic effects on gene regulatory processes.

Accelerating spliced alignment of long RNA sequencing reads using parallel maximal exact match retrieval

The genomics landscape has undergone a revolutionary transformation with the emergence of third-generation sequencing technologies. Fueled by the exponential surge in sequencing data, there is an urgent demand for accurate and rapid algorithms to effectively handle this burgeoning influx. Under such circumstances, we developed a parallelized, yet accuracy-lossless algorithm for maximal exact match (MEM) retrieval to strategically address the computational bottleneck of uLTRA, a leading spliced alignment algorithm known for its precision in handling long RNA sequencing (RNA-seq) reads. The design of the algorithm incorporates a multi-threaded strategy, enabling the concurrent processing of multiple reads simultaneously. Additionally, we implemented the serialization of index required for MEM retrieval to facilitate its reuse, resulting in accelerated startup for practical tasks. Extensive experiments demonstrate that our parallel algorithm achieves significant improvements in runtime, speedup, throughput, and memory usage. When applied to the largest human dataset, the algorithm achieves an impressive speedup of 10.78 × , significantly improving throughput on a large scale. Moreover, the integration of the parallel MEM retrieval algorithm into the uLTRA pipeline introduces a dual-layered parallel capability, consistently yielding a speedup of 4.99 × compared to the multi-process and single-threaded execution of uLTRA. The thorough analysis of experimental results underscores the adept utilization of parallel processing capabilities and its advantageous performance in handling large datasets. This study provides a showcase of parallelized strategies for MEM retrieval within the context of spliced alignment algorithm, effectively facilitating the process of RNA-seq data analysis. The code is available at https://github.com/RongxingWong/AcceleratingSplicedAlignment.

Deciphering Bacterial and Archaeal Transcriptional Dark Matter and Its Architectural Complexity.

Transcripts are potential therapeutic targets, yet bacterial transcripts remain biological dark matter with uncharacterized biodiversity. We developed and applied an algorithm to predict transcripts for Escherichia coli K12 and E2348/69 strains (Bacteria:gamma-Proteobacteria) with newly generated ONT direct RNA sequencing data while predicting transcripts for Listeria monocytogenes strains Scott A and RO15 (Bacteria:Firmicute), Pseudomonas aeruginosa strains SG17M and NN2 strains (Bacteria:gamma-Proteobacteria), and Haloferax volcanii (Archaea:Halobacteria) using publicly available data. From >5 million E. coli K12 ONT direct RNA sequencing reads, 2,484 mRNAs are predicted and contain more than half of the predicted E. coli proteins. While the number of predicted transcripts varied by strain based on the amount of sequence data used for the predictions, across all strains examined, the average size of the predicted mRNAs is 1.6-1.7 kbp while the median size of the predicted bacterial 5'- and 3'- UTRs are 30-90 bp. Given the lack of bacterial and archaeal transcript annotation, most predictions are of novel transcripts, but we also predicted many previously characterized mRNAs and ncRNAs, including post-transcriptionally generated transcripts and small RNAs associated with pathogenesis in the E. coli E2348/69 LEE pathogenicity islands. We predicted small transcripts in the 100-200 bp range as well as >10 kbp transcripts for all strains, with the longest transcript for two of the seven strains being the nuo operon transcript, and for another two strains it was a phage/prophage transcript. This quick, easy, inexpensive, and reproducible method will facilitate the presentation of operons, transcripts, and UTR predictions alongside CDS and protein predictions in bacterial genome annotation as important resources for the research community.

Epigenetic Factor MicroRNAs Likely Mediate Vaccine Protection Efficacy against Lymphomas in Response to Tumor Virus Infection in Chickens through Target Gene Involved Signaling Pathways.

Epigenetic factors, including microRNAs (miRNAs), play an important role in affecting gene expression and, therefore, are involved in various biological processes including immunity protection against tumors. Marek's disease (MD) is a highly contagious disease of chickens caused by the MD virus (MDV). MD has been primarily controlled by vaccinations. MD vaccine efficacy might, in part, be dependent on modulations of a complex set of factors including host epigenetic factors. This study was designed to identify differentially expressed miRNAs in the primary lymphoid organ, bursae of Fabricius, in response to MD vaccination followed by MDV challenge in two genetically divergent inbred lines of White Leghorns. Small RNA sequencing and bioinformatic analyses of the small RNA sequence reads identified hundreds of miRNAs among all the treatment groups. A small portion of the identified miRNAs was differentially expressed within each of the four treatment groups, which were HVT or CVI988/Rispens vaccinated line 63-resistant birds and line 72-susceptible birds. A direct comparison between the resistant line 63 and susceptible line 72 groups vaccinated with HVT followed by MDV challenge identified five differentially expressed miRNAs. Gene Ontology analysis of the target genes of those five miRNAs revealed that those target genes, in addition to various GO terms, are involved in multiple signaling pathways including MAPK, TGF-β, ErbB, and EGFR1 signaling pathways. The general functions of those pathways reportedly play important roles in oncogenesis, anti-cancer immunity, cancer cell migration, and metastatic progression. Therefore, it is highly likely that those miRNAs may, in part, influence vaccine protection through the pathways.

The impact of genetically controlled splicing on exon inclusion and protein structure.

Common variants affecting mRNA splicing are typically identified though splicing quantitative trait locus (sQTL) mapping and have been shown to be enriched for GWAS signals by a similar degree to eQTLs. However, the specific splicing changes induced by these variants have been difficult to characterize, making it more complicated to analyze the effect size and direction of sQTLs, and to determine downstream splicing effects on protein structure. In this study, we catalogue sQTLs using exon percent spliced in (PSI) scores as a quantitative phenotype. PSI is an interpretable metric for identifying exon skipping events and has some advantages over other methods for quantifying splicing from short read RNA sequencing. In our set of sQTL variants, we find evidence of selective effects based on splicing effect size and effect direction, as well as exon symmetry. Additionally, we utilize AlphaFold2 to predict changes in protein structure associated with sQTLs overlapping GWAS traits, highlighting a potential new use-case for this technology for interpreting genetic effects on traits and disorders.