{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"submitter":["Yang K"],"funding":["NCI NIH HHS","NLM NIH HHS","NIGMS NIH HHS"],"pubmed_abstract":["RNA-sequencing (RNA-seq) is widely adopted for transcriptome analysis but has inherent biases which hinder the comprehensive detection and quantification of alternative splicing. To address this, we present an efficient targeted RNA-seq method that greatly enriches for splicing-informative junction-spanning reads. Local Splicing Variation sequencing (LSV-seq) utilizes multiplexed reverse transcription from highly scalable pools of primers anchored near splicing events of interest. Primers are designed using Optimal Prime, a novel machine learning algorithm trained on the performance of thousands of primer sequences. In experimental benchmarks, LSV-seq achieves high on-target capture rates and concordance with RNA-seq, while requiring significantly lower sequencing depth. Leveraging deep learning splicing code predictions, we used LSV-seq to target events with low coverage in GTEx RNA-seq data and newly discover hundreds of tissue-specific splicing events. Our results demonstrate the ability of LSV-seq to quantify splicing of events of interest at high-throughput and with exceptional sensitivity."],"journal":["bioRxiv : the preprint server for biology"],"pagination":["2024.09.20.614162"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11463589"],"repository":["biostudies-literature"],"pubmed_title":["Machine learning-optimized targeted detection of alternative splicing."],"pmcid":["PMC11463589"],"funding_grant_id":["R01 LM013437","R00 CA208028","R01 GM128096","DP2 GM146251"],"pubmed_authors":["Islas N","Choi PS","Jewell S","Barash Y","Yang K","Lynch KW","Jha A","Pleiss JA","Radens CM"],"additional_accession":[]},"is_claimable":false,"name":"Machine learning-optimized targeted detection of alternative splicing.","description":"RNA-sequencing (RNA-seq) is widely adopted for transcriptome analysis but has inherent biases which hinder the comprehensive detection and quantification of alternative splicing. To address this, we present an efficient targeted RNA-seq method that greatly enriches for splicing-informative junction-spanning reads. Local Splicing Variation sequencing (LSV-seq) utilizes multiplexed reverse transcription from highly scalable pools of primers anchored near splicing events of interest. Primers are designed using Optimal Prime, a novel machine learning algorithm trained on the performance of thousands of primer sequences. In experimental benchmarks, LSV-seq achieves high on-target capture rates and concordance with RNA-seq, while requiring significantly lower sequencing depth. Leveraging deep learning splicing code predictions, we used LSV-seq to target events with low coverage in GTEx RNA-seq data and newly discover hundreds of tissue-specific splicing events. Our results demonstrate the ability of LSV-seq to quantify splicing of events of interest at high-throughput and with exceptional sensitivity.","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Sep","modification":"2025-04-04T02:35:58.783Z","creation":"2025-04-04T02:35:58.783Z"},"accession":"S-EPMC11463589","cross_references":{"pubmed":["39386495"],"doi":["10.1101/2024.09.20.614162"]}}