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CStone: A de novo transcriptome assembler for short-read data that identifies non-chimeric contigs based on

Raquel Linheiro¹, John Archer¹

¹CIBIO/InBIO, Centro de Investigação em Biodiversidade e Recursos Genéticos, Universidade do Porto, Vairão, Portugal.

Plos Computational Biology

|November 23, 2021

Summary

CStone, a new de novo assembler for RNA-Seq data, accurately quantifies chimeric sequences and classifies contig complexity. This tool enhances transcriptomic analysis by providing crucial information on gene families and potential expression biases.

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Last Updated: Oct 12, 2025

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Published on: May 9, 2017

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High-throughput Identification of Gene Regulatory Sequences Using Next-generation Sequencing of Circular Chromosome Conformation Capture 4C-seq

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Published on: October 5, 2018

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Exponential growth in sequence data necessitates advanced assembly methods.
Chimeric sequences in transcriptomics can obscure biological patterns and complicate analysis.
Existing de novo assemblers may not adequately address chimera quantification and complexity classification.

Purpose of the Study:

To develop a de novo assembler for RNA-Seq data that quantifies chimeric sequences.
To implement a classification system for contig complexity based on graph structure.
To evaluate the performance of the new assembler against established tools.

Main Methods:

Developed CStone, a de Bruijn graph-based de novo assembler for RNA-Seq.
Implemented a classification system to label contigs based on graph path ambiguity (three levels).
Validated CStone using simulated and real RNA-Seq data from multiple species (Drosophila melanogaster, Panthera pardus, Rattus norvegicus, Serinus canaria).

Main Results:

CStone produced contigs comparable in quality (length, sequence identity) to Trinity and rnaSPAdes.
The assembler successfully provided additional information on chimerism and gene family complexity.
A side study revealed the impact of chimeric sequences in reference sets on differential gene expression analysis.

Conclusions:

CStone offers high-quality de novo assembly for RNA-Seq data with enhanced chimera detection and complexity classification.
The proposed classification system can be integrated into other de novo assembly tools.
Accurate chimera quantification is vital for reliable transcriptomic and gene expression studies.