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Updated: Apr 10, 2026

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Published on: August 3, 2018

Misassembly detection using paired-end sequence reads and optical mapping data.

Martin D Muggli¹, Simon J Puglisi¹, Roy Ronen¹

¹Department of Computer Science, Colorado State University, Fort Collins, CO 80526, USA, Department of Computer Science, University of Helsinki, Finland and Bioinformatics Graduate Program, University of California, San Diego, La Jolla, CA 92093, USA.

Bioinformatics (Oxford, England)

|June 15, 2015

Summary

This summary is machine-generated.

We developed misSEQuel, an open-source tool that uses paired-end reads and optical mapping to identify and correct genome misassembly errors, significantly improving draft genome quality.

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Genome assembly is challenged by misassembly errors in draft genomes.
Accurate genome sequences are crucial for biological research and applications.
Existing methods for detecting misassemblies can be limited.

Purpose of the Study:

To develop and validate misSEQuel, a novel computational method for detecting and correcting genome misassembly errors.
To provide an open-source tool for analyzing optical mapping data in genome assembly.
To enhance the quality of draft genomes using a combination of sequence reads and optical mapping.

Main Methods:

misSEQuel integrates paired-end sequence reads and optical mapping data.
The method identifies misassembly errors and their breakpoints.
It functions as a post-processing step compatible with any genome assembler.

Main Results:

misSEQuel detected over 54% of extensively and 60% of locally misassembled contigs in *F. tularensis*.
In loblolly pine assemblies, it identified 31-100% of extensively and 57-73% of locally misassembled contigs.
Using real optical mapping data, misSEQuel achieved 75-77% detection of extensively misassembled contigs and 100% detection of locally misassembled contigs in rice and budgerigar.

Conclusions:

misSEQuel effectively enhances draft genome quality by identifying misassembly errors.
The tool provides a valuable open-source resource for genomic data analysis.
This method improves the accuracy and reliability of genome assemblies across diverse species.