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RNA-seq

RNA-seq

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.
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while microarray-based...

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Updated: Jun 12, 2026

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

Published on: June 24, 2021

A probabilistic framework for aligning paired-end RNA-seq data.

Yin Hu¹, Kai Wang, Xiaping He

¹Department of Computer Science, University of Kentucky, Lexington, KY, USA.

Bioinformatics (Oxford, England)

|June 26, 2010

Summary

This summary is machine-generated.

This study introduces a novel method for reconstructing full transcript fragments from paired-end reads (PER) in RNA sequencing, significantly improving transcriptome coverage and splice detection accuracy.

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Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

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Published on: June 24, 2021

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Single Read and Paired End mRNA-Seq Illumina Libraries from 10 Nanograms Total RNA

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Published on: October 27, 2011

Area of Science:

Bioinformatics
Genomics
Molecular Biology

Background:

Paired-end read (PER) protocols in RNA sequencing capture only transcript fragment ends, limiting analysis of longer transcripts.
Reconstructing unsequenced portions of transcript fragments is possible by leveraging overlapping PER data and expected fragment lengths.

Purpose of the Study:

To develop a probabilistic framework for aligning PER transcript fragments to the genome.
To reconstruct the full transcript sequence from paired-end sequencing data.

Main Methods:

A probabilistic framework was developed to align PER transcript fragments.
It constructs potential splicing paths connecting paired ends using exonic and spliced alignments.
An expectation maximization (EM) algorithm assigns likelihoods to splice junctions and determines the most probable transcript fragment alignment.

Main Results:

Applied to PER datasets (2 x 35 bp) from MCF-7 and SUM-102 cancer cell lines.
PER fragment alignment increased transcriptome coverage threefold compared to end reads alone.
Enhanced accuracy in splice detection and confirmed 8 out of 10 fusion events.

Conclusions:

The developed method effectively reconstructs full transcript fragments from PER data.
This approach significantly improves transcriptome analysis by increasing coverage and splice detection accuracy.
Validated by qRT-PCR, demonstrating its reliability for identifying alternative splicing and fusion events.