Metadata-Driven Project-Level Analysis and Noise Modeling for Amplicon Sequencing Data Using CRISPResso2
- 1Research Centre for Medical Genetics, Moscow 115522, Russia
Abstract
Here we present a lightweight workflow designed to improve the robustness and scalability of high-throughput amplicon sequencing analysis while leveraging CRISPResso2. To handle large batch processing without sensitive runtime interruptions, our pipeline processes samples sequentially, allowing the analysis to resume from the last successfully completed sample if an error occurs. The workflow implements a fallback mechanism for failed runs, automated extraction and separate processing of control samples, and a background noise model derived from control libraries within defined editing windows. Validation using spike-in datasets across Cas9, base editing, and prime editing demonstrated that our noise-adjusted estimates deviated from the ground truth by only 0.1–0.6%, whereas native CRISPResso2 caused a 1.5–3.7% overestimation. Unlike existing tools such as CRISPECTOR, which focus primarily on CRISPR-Cas9, our approach supports multiple editing modalities and enables project-level organization of large datasets. Input complexity is reduced through configurable flags and modular metadata tables, minimizing manual parameter entry. Together, these features provide a flexible, fault-tolerant, and highly accurate framework for analyzing large genome editing datasets.
Related
- IndelQuant: A Workflow for Cost-Efficient Amplicon Sequencing Analysis of Genome-Edited Chickens
- Targeted long-read eukaryotic profiling of human stool by broad-range 18S–28S rRNA gene amplicon sequencing on Oxford Nanopore Technologies: a proof-of-concept study
- Multidisciplinary Delphi consensus statement on minimal standards for clinical metadata and end points in microbiome studies
- Development and expert appraisal of a lifestyle questionnaire for microbiome host metadata
- HiSpoT: a novel one-tube dual-barcode PCR method for NGS spoligotyping amplicon library preparation in Mycobacterium tuberculosis complex
BibTeX
@article{volodina2026metadatadriven,
title = {{Metadata-Driven Project-Level Analysis and Noise Modeling for Amplicon Sequencing Data Using CRISPResso2}},
author = {Olga Volodina and Svetlana Smirnikhina},
journal = {International Journal of Molecular Sciences},
year = {2026},
month = nov,
doi = {10.3390/ijms27219810},
url = {https://www.mdpi.com/1422-0067/27/21/9810},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Metadata-Driven Project-Level Analysis and Noise Modeling for Amplicon Sequencing Data Using CRISPResso2
AU - Olga Volodina
AU - Svetlana Smirnikhina
PY - 2026
DA - 2026/11/01
JO - International Journal of Molecular Sciences
DO - 10.3390/ijms27219810
UR - https://www.mdpi.com/1422-0067/27/21/9810
AB - Here we present a lightweight workflow designed to improve the robustness and scalability of high-throughput amplicon sequencing analysis while leveraging CRISPResso2. To handle large batch processing without sensitive runtime interruptions, our pipeline processes samples sequentially, allowing the analysis to resume from the last successfully completed sample if an error occurs. The workflow implements a fallback mechanism for failed runs, automated extraction and separate processing of control samples, and a background noise model derived from control libraries within defined editing windows. Validation using spike-in datasets across Cas9, base editing, and prime editing demonstrated that our noise-adjusted estimates deviated from the ground truth by only 0.1–0.6%, whereas native CRISPResso2 caused a 1.5–3.7% overestimation. Unlike existing tools such as CRISPECTOR, which focus primarily on CRISPR-Cas9, our approach supports multiple editing modalities and enables project-level organization of large datasets. Input complexity is reduced through configurable flags and modular metadata tables, minimizing manual parameter entry. Together, these features provide a flexible, fault-tolerant, and highly accurate framework for analyzing large genome editing datasets.
SN - 1422-0067
ER -