detectIS
detectIS identifies precise exogenous DNA integration sites in host genomes from paired-end DNA or RNA sequencing data to characterize recombinant and viral integrations.
Key Features:
- Integration site identification: Pinpoints precise genomic locations of exogenous DNA integration events.
- Input data: Operates on paired-end sequencing data derived from DNA or RNA, including RNA-seq.
- Recombinant context support: Targets integrations arising from viral transduction and plasmid transfection in engineered cell lines.
- Workflow management: Implements a Nextflow workflow to orchestrate computational tasks.
- Containerization: Encapsulates required software components within a Singularity image.
- Validation: Tested on simulated datasets and on real human RNA-seq data infected with Hepatitis B virus.
- Comparative performance: Reported to outperform state-of-the-art tools by providing more precise results while reducing computational demands and execution times.
Scientific Applications:
- Recombinant cell line characterization: Defining integration loci for characterization of recombinant cell lines used in biology, medicine, and biotechnology.
- Viral integration analysis: Mapping integrations of Hepatitis B virus in human RNA-seq samples.
- Transduction and transfection assessment: Assessing genomic integration outcomes following viral transduction and plasmid transfection in engineered cell lines.
Methodology:
Analyzes paired-end DNA or RNA sequencing data to detect integration sites using a Nextflow-managed workflow packaged in a Singularity image; validation was performed on simulated datasets and human RNA-seq infected with Hepatitis B virus.
Topics
Details
- License:
- Apache-2.0
- Tool Type:
- workflow
- Programming Languages:
- Perl, Shell
- Added:
- 9/8/2021
- Last Updated:
- 11/24/2024
Operations
Publications
Grassi L, Harris C, Zhu J, Hardman C, Hatton D. DetectIS: a pipeline to rapidly detect exogenous DNA integration sites using DNA or RNA paired-end sequencing data. Bioinformatics. 2021;37(22):4230-4232. doi:10.1093/bioinformatics/btab366. PMID:33978747. PMCID:PMC9502153.
Links
Issue tracker
https://github.com/AstraZeneca/detectIS/issues