Bellerophon
Bellerophon identifies and removes chimeric sequences from de novo transcriptome assemblies to improve the accuracy of RNA-Seq–based transcriptomic analyses.
Key Features:
- Chimera Detection and Removal: Identifies and eliminates chimeric contigs in de novo assembled transcriptomes.
- Quality Assessment Integration: Incorporates TransRate to evaluate initial assembly quality.
- Expression-Based Filtering: Applies a transcripts per million (TPM) filter to remove lowly expressed contigs.
- Redundancy Reduction: Uses CD-HIT-EST to remove highly identical contigs and reduce redundancy.
- Chimera Validation and Benchmarking: Validated via computational chimera creation, identification in existing assemblies, and simulated RNA-Seq using known reference transcriptomes, reporting chimera removal rates between 40% and 91.9%.
Scientific Applications:
- De novo RNA-Seq assembly quality control: Improves assembly fidelity when a reference genome is unavailable.
- Comparative genomics: Enhances the reliability of cross-species transcript comparisons by reducing assembly artifacts.
- Evolutionary biology: Supports studies that require accurate transcript sequences for evolutionary inference.
- Functional genomics and gene expression analysis: Provides higher-fidelity transcriptomes for downstream expression and functional analyses.
Methodology:
Initial assembly quality is assessed with TransRate; lowly expressed contigs are removed using a TPM filter; redundancy is reduced with CD-HIT-EST; validation is performed via computational chimera creation, identification in existing assemblies, and simulated RNA-Seq using known reference transcriptomes.
Topics
Details
- Programming Languages:
- Ruby, Perl
- Added:
- 1/9/2020
- Last Updated:
- 12/5/2020
Operations
Publications
Kerkvliet J, de Fouchier A, van Wijk M, Groot AT. The Bellerophon pipeline, improving de novo transcriptomes and removing chimeras. Ecology and Evolution. 2019;9(18):10513-10521. doi:10.1002/ece3.5571. PMID:31624564. PMCID:PMC6787812.
DOI: 10.1002/ECE3.5571
PMID: 31624564
PMCID: PMC6787812
Funding: - National Science Foundation: IOS-1052238, IOS-1456973