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Updated: Feb 5, 2026

Hybrid De Novo Genome Assembly for the Generation of Complete Genomes of Urinary Bacteria using Short- and Long-read Sequencing Technologies

Hybrid De Novo Genome Assembly for the Generation of Complete Genomes of Urinary Bacteria using Short- and Long-read Sequencing Technologies

Published on: August 20, 2021

TraRECo: a greedy approach based de novo transcriptome assembler with read error correction using consensus matrix.

Seokhyun Yoon¹, Daeseung Kim², Keunsoo Kang³

¹Department of Electronics Eng., College of Engineering, Dankook University, Yongin-si, Korea.

|September 6, 2018

Summary

This summary is machine-generated.

TraRECo improves de novo transcriptome assembly by constructing error-aware graphs using consensus matrices. This approach enhances graph reliability and read usability, leading to better sensitivity and precision in isoform discovery.

Keywords:

RNA-Seq consensus matrix de novo transcriptome assembly greedy approach read error correction

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Last Updated: Feb 5, 2026

Hybrid De Novo Genome Assembly for the Generation of Complete Genomes of Urinary Bacteria using Short- and Long-read Sequencing Technologies

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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

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

De novo transcriptome assembly faces challenges with read errors and sequence repeats.
Traditional de Bruijn graphs struggle with complexity and errors, hindering accurate assembly.
Improving graph reliability is crucial for robust de novo assembly.

Purpose of the Study:

To develop an error-aware graph construction method for de novo assembly.
To enhance the reliability of graphs used in transcriptome assembly.
To improve the usability of reads containing errors.

Main Methods:

Introduced TraRECo, a greedy approach for de novo assembly.
Employed direct read alignment and junction search for splicing graph construction.
Utilized consensus matrices to represent contigs and incorporate error tolerance.

Main Results:

TraRECo demonstrated considerable improvement in sensitivity and moderate gains in precision.
Error-aware graph construction made previously unusable reads viable for assembly.
Improved coverage depth information enhanced subsequent path search and graph reliability.

Conclusions:

TraRECo offers a potential alternative for enhancing graph reliability in de novo assembly.
The method effectively represents a large number of reads for isoform exploration.
While computationally intensive, TraRECo provides a valuable approach for improving assembly quality.