Long-read sequencing disentangles isoform complexity at allele-specific loci
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ABSTRACT:
PROVIDER: PRJNA1035002 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR26639095_subreads.fastq.gz | Fastqsanger.gz |
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Project description:In recent years, long-read sequencing technologies have detected transcript isoforms with unprecedented accuracy and resolution. However, it remains unclear whether long-read sequencing can effectively disentangle the isoform landscape of complex allele-specific loci that arise from genetic or epigenetic differences between alleles. Here, we combine the PacBio Iso-Seq workflow with the established phasing approach WhatsHap to assign long reads to the corresponding allele in polymorphic F1 mouse hybrids. Upon comparing the long-read sequencing results with matched short reads, we observed general consistency in the allele-specific information and were able to confirm the imprinting status of known imprinted genes. We then explored the complex imprinted Gnas locus known for allele-specific non-coding and coding isoforms and were able to benchmark historical observations. This approach also allowed us to detect isoforms from both the active and inactive X chromosomes of genes that escape X chromosome inactivation. The described workflow offers a promising framework and demonstrates the power of long-read transcriptomic data to provide mechanistic insight into complex allele-specific loci.
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Project description:Accurate quantification of transcript isoforms is crucial for understanding gene regulation, functional diversity, and cellular behavior. Existing methods using either short-read (SR) or long-read (LR) RNA sequencing have significant limitations: SR sequencing provides high depth but struggles with isoform deconvolution, while LR sequencing offers isoform resolution at the cost of lower depth, higher noise, and technical biases. Addressing this gap, we introduce Multi-Platform Aggregation and Quantification of Transcripts (MPAQT), a generative model that combines the complementary strengths of different sequencing platforms to achieve state-of-the-art isoform-resolved transcript quantification, as demonstrated by extensive simulations and experimental benchmarks. Applying MPAQT to an in vitro model of human embryonic stem cell differentiation into cortical neurons, followed by machine learning-based modeling of mRNA abundance determinants, reveals the role of untranslated regions (UTRs) in isoform regulation through isoform-specific interactions with RNA-binding proteins that modulate mRNA stability. These findings highlight MPAQT's potential to enhance our understanding of transcriptomic complexity and underline the role of splicing-independent post-transcriptional mechanisms in shaping the isoform and exon usage landscape of the cell.
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Project description:Accurate quantification of transcript isoforms is crucial for understanding gene regulation, functional diversity, and cellular behavior. Existing methods using either short-read (SR) or long-read (LR) RNA sequencing have significant limitations: SR sequencing provides high depth but struggles with isoform deconvolution, while LR sequencing offers isoform resolution at the cost of lower depth, higher noise, and technical biases. Addressing this gap, we introduce Multi-Platform Aggregation and Quantification of Transcripts (MPAQT), a generative model that combines the complementary strengths of different sequencing platforms to achieve state-of-the-art isoform-resolved transcript quantification, as demonstrated by extensive simulations and experimental benchmarks. Applying MPAQT to an in vitro model of human embryonic stem cell differentiation into cortical neurons, followed by machine learning-based modeling of mRNA abundance determinants, reveals the role of untranslated regions (UTRs) in isoform regulation through isoform-specific interactions with RNA-binding proteins that modulate mRNA stability. These findings highlight MPAQT's potential to enhance our understanding of transcriptomic complexity and underline the role of splicing-independent post-transcriptional mechanisms in shaping the isoform and exon usage landscape of the cell.
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Project description:In this study, we used a barcoding-based synthetic long read (SLR) isoform sequencing approach (LoopSeq) to generate sequencing reads sufficiently long and accurate to identify isoforms using standard short read Illumina sequencers.
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