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Related Concept Videos

RNA-seq03:21

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RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
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Alternative RNA splicing is the regulated splicing of exons and introns to produce different mature mRNAs from a single pre-mRNA. Unlike in constitutive splicing where a single gene produces a single type of mRNA, alternative splicing allows an organism to produce multiple proteins from a single gene and plays an important role in protein diversity.
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Splicing is the process by which eukaryotic RNA is edited before its translation into protein. The RNA strand transcribed from eukaryotic DNA is called the primary transcript. The primary transcripts that become mRNAs are called precursor messenger RNAs (pre-mRNAs). Eukaryotic pre-mRNA contains alternating sequences of exons and introns. Exons are nucleotide sequences that code for proteins, whereas introns are the non-coding regions. In RNA splicing, introns are removed and exons are bonded...
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Updated: Sep 14, 2025

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data
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Single cell and spatial alternative splicing analysis with Nanopore long read sequencing.

Yuntian Fu1, Heonseok Kim2,3,4,5, Sharmili Roy2

  • 1Graduate Program in Genomics and Computational Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. yuntianf@pennmedicine.upenn.edu.

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|July 19, 2025
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Summary

Longcell enhances long-read sequencing for single-cell and spatial transcriptomics by improving isoform quantification and analyzing splicing variation. This computational pipeline overcomes Nanopore sequencing challenges to reveal novel insights into gene expression.

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Sequencing of mRNA from Whole Blood using Nanopore Sequencing
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Area of Science:

  • Single-cell genomics
  • Spatial transcriptomics
  • Computational biology
  • RNA sequencing analysis

Background:

  • Long-read sequencing offers advantages for alternative splicing analysis but faces technical hurdles in single-cell and spatial applications.
  • High error rates in Nanopore sequencing impact cell barcode and UMI recovery, while read truncation and misalignment affect isoform quantification.
  • A lack of robust statistical frameworks hinders the assessment of splicing variation within and between single cells or spatial locations.

Purpose of the Study:

  • To introduce Longcell, a computational pipeline designed for isoform quantification from single-cell and spatially barcoded Nanopore long reads.
  • To address technical and computational barriers in analyzing alternative splicing from long-read sequencing data in complex biological samples.
  • To enable the statistical assessment of splicing variation at the single-cell and spatial levels.

Main Methods:

  • Development of a statistical and computational pipeline (Longcell) for processing Nanopore long-read data.
  • Implementation of algorithms for efficient recovery of cell barcodes and UMIs (Unique Molecular Identifiers).
  • Integration of error correction methods for Nanopore reads and modeling of splicing diversity.
  • Application of Longcell to multiple datasets, including perturbation experiments targeting splicing factors.

Main Results:

  • Longcell successfully recovers cell barcodes and UMIs, corrects sequencing errors, and quantifies isoforms from single-cell and spatial long-read data.
  • The pipeline accurately identifies spatial isoform switching events across different datasets.
  • Analysis revealed widespread high intra-cell isoform heterogeneity for highly expressed genes.
  • Perturbation experiments identified regulatory targets of splicing factors, validated by targeted sequencing.

Conclusions:

  • Longcell provides a robust solution for isoform quantification and splicing variation analysis using Nanopore long-read sequencing in single-cell and spatial contexts.
  • The findings highlight the utility of Longcell in uncovering complex splicing dynamics and regulatory relationships.
  • This work advances the application of long-read sequencing technologies in transcriptomics research.