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