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Related Experiment Video

Updated: Feb 20, 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

Evaluation of tools for long read RNA-seq splice-aware alignment.

Krešimir Križanovic¹, Amina Echchiki^2,3, Julien Roux^2,3

¹Department of Electronic Systems and Information Processing, Faculty of Electrical Engineering and Computing, University of Zagreb, 10000 Zagreb, Croatia.

Bioinformatics (Oxford, England)

|October 26, 2017

Summary

RNA sequencing (RNA-seq) tools struggle with long, error-prone reads from new sequencing technologies. Error correction significantly improves alignment accuracy for these long reads, enhancing RNA-seq analysis.

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Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations

Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations

Published on: August 4, 2016

Area of Science:

Bioinformatics
Genomics
Computational Biology

Background:

High-throughput sequencing, particularly RNA-seq, is crucial for studying gene expression.
Third-generation sequencing technologies offer longer reads but present bioinformatics challenges due to high error rates.
Existing RNA-seq alignment tools, developed for short reads, face difficulties with long, error-prone data.

Purpose of the Study:

To evaluate the performance of current RNA-seq splice-aware alignment tools with long reads from Pacific Biosciences (PacBio) and Oxford Nanopore Technologies (ONT).
To assess the impact of error correction on the alignment accuracy of long RNA-seq reads.
To develop and utilize a tool for evaluating RNA-seq alignment results.

Main Methods:

Testing RNA-seq alignment tools on synthetic and real datasets generated by PacBio and ONT.
Comparing alignment quality and resource consumption across different aligners.
Investigating the effect of error correction (self-correction and external short-read correction) on long reads.
Developing a dedicated tool, RNAseqEval, for assessing alignment accuracy against genomic origins or annotated transcripts.

Main Results:

Some RNA-seq aligners performed poorly with long, error-prone reads, while others showed acceptable results.
Alignment accuracy was demonstrably improved when using error-corrected long reads.
The developed RNAseqEval tool provides a robust method for evaluating alignment performance.

Conclusions:

Current RNA-seq aligners have limitations when processing long, error-prone reads from third-generation sequencing technologies.
Error correction is a critical step to enhance the reliability of RNA-seq analysis with long reads.
The study provides insights into tool selection and highlights the importance of read correction for accurate gene expression studies.